Sociology Spatial Analysis
by
Stephen Matthews, Ellis Logan, Rachel Bacon
  • LAST MODIFIED: 31 August 2015
  • DOI: 10.1093/obo/9780199756384-0058

Introduction

Recent years have seen a rapid growth in interest in the addition of a spatial perspective, especially in the social and health sciences, and in part this growth has been driven by the ready availability of georeferenced or geospatial data, and the tools to analyze them: geographic information systems (GIS), spatial analysis, and spatial statistics. Indeed, research on race/ethnic segregation and other forms of social stratification as well as research on human health and behavior problems, such as obesity, mental health, risk-taking behaviors, and crime, depend on the collection and analysis of individual- and contextual-level (geographic area) data across a wide range of spatial and temporal scales. Given all of these considerations, researchers are continuously developing new ways to harness and analyze geo-referenced data. Indeed, a prerequisite for spatial analysis is the availability of information on locations (i.e., places) and the attributes of those locations (e.g., poverty rates, educational attainment, religious participation, or disease prevalence). Spatial analysis is a general term to describe an array of statistical techniques that utilize locational information to better understand the pattern of observed attribute values and the processes that generated the observed pattern. The best-known early example of spatial analysis is John Snow’s 1854 cholera map of London, but the origins of spatial analysis can be traced back to France during the 1820s and 1830s and the period of morale statistique, specifically the work of Guerry, D’Angeville, Duplin, and Quetelet. The foundation for current spatial statistical analysis practice is built on methodological development in both statistics and ecology during the 1950s and quantitative geography during the 1960s and 1970s and it is a field that has been greatly enhanced by improvements in computer and information technologies relevant to the collection, and visualization and analysis of geographic or geospatial data. Today four main methodological approaches to spatial analysis can be identified in the literature: exploratory spatial data analysis (ESDA), spatial statistics, spatial econometrics, and geostatistics. The diversity of spatial-analytical methods available to researchers is wide and growing; a function of the different types of analytical units and data types used in formal spatial analysis—specifically, point data (e.g., crime events, disease cases), line data (e.g., networks, routes), spatial continuous or field data (e.g., accessibility surfaces), and area or lattice data (e.g., unemployment and mortality rates). Applications of geospatial data and/or spatial analysis are increasingly found in sociological research, especially in studies of spatial inequality, residential segregation, demography, education, religion, neighborhoods and health, and in criminology.

General Overviews

Many problems faced by society and by social scientists require analysis of complex patterns of interrelated social, behavioral, economic, and environmental phenomena. In addressing these problems, it has been argued that both spatial thinking and spatial analytical perspectives play an important role. The role of spatial thinking and analysis is clearly articulated in Goodchild, et al. 2000, a comprehensive review of the emerging interest in space and place in recent social science literatures. Indeed, the authors don’t just provide a review; they develop a vision for a spatially integrated social science, whereby the spatial perspective becomes an incubator for interdisciplinary research. Examples of how spatial analysis informs interdisciplinary research can be found in the Goodchild and Janelle 2004 “best practices” book. This edited collection includes state-of-the-art research on a diverse set of substantive topics, including crime incidence in urban environments, migration, population and environment research, and the diffusion of fertility decline in third world settings. Two recent overview articles by Logan (Logan, et al. 2010, Logan 2012) both provide concise introductions to the challenges and opportunities of spatial analysis in sociology and especially in the areas of community health, population and environment, residential segregation, land use, fertility, and migration research. Logan argues for substantive questions to be linked to more clear spatial thinking and choice of analytical method. The rediscovery of spatial thinking and spatial perspectives within sociology has been promoted in an excellent article, Gieryn 2000, while Lobao, et al. 2007 focuses explicitly on the need to incorporate spatial perspectives in sociological research, broadening the study of (spatial) inequality. Porter and Howell 2012 provides an important account of the historical roots of spatial thinking in sociology as well as introduces the application of spatial regression models and hierarchical or multilevel models. In the field of sociology, several substantive areas have embraced spatial thinking and analytical methods; see Spatial Inequality, Residential Segregation, Demography, and Crime.

  • Gieryn, Thomas F. 2000. A space for place in sociology. Annual Review of Sociology 26:463–495.

    DOI: 10.1146/annurev.soc.26.1.463Save Citation »Export Citation »E-mail Citation »

    This often-cited article argues that sociologists “have a stake in place no matter what they analyze, or how.” Gieryn provides exemplars of place-sensitive sociology in studies of inequality, power, politics, social movements, community, deviance, crime, identity, memory, and history.

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    • Goodchild, M., L. Anselin, R. Appelbaum, and B. Harthorn. 2000. Towards spatially integrated social science. International Regional Science Review 23:139–159.

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      This paper synthesizes the vision behind the Center for Spatially Integrated Social Science at UC Santa Barbara, an effort to advance and disseminate geographic tools and concepts—spatial analysis, geographic information systems, geolibraries—as integrating themes that cut across the traditional disciplinary boundaries of the social and behavioral sciences.

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      • Goodchild, Michael F., and Donald G. Janelle, eds. 2004. Spatially integrated social science. Spatial Information Systems. New York: Oxford Univ. Press.

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        This is an outstanding collection of applications of spatial analysis and spatial thinking in the social sciences colloquially referred to as the “Best Practices” book. Chapters are grouped on the basis of four levels of analysis: individual/household, neighborhood, region, and multiscale. Contributors are leading scholars with sociologists, criminologists, and demographers well represented.

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        • Lobao, Linda M., Gregory Hooks, and Ann R. Tickamyer, eds. 2007. The sociology of spatial inequality. Albany: State Univ. of New York Press.

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          Based on an ASA/NSF workshop, this book reasserts spatial thinking/analysis in inequality research. The book is organized around conceptual and methodological issues, studies of spatial inequality, and future directions in spatial sociology. Sociologists are encouraged to pay attention to the scale of geographic levels at which social processes occur.

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          • Logan, John R. 2012. Making a place for space: Spatial thinking in social science. Annual Review of Sociology 38:507–524.

            DOI: 10.1146/annurev-soc-071811-145531Save Citation »Export Citation »E-mail Citation »

            This is a clear articulation of basic concepts, measures, and methods of spatial thinking and spatial analysis. Throughout Logan argues for more emphasis on spatial thinking and that theoretical and substantive concerns ought to guide the use of spatial analytic methods (e.g., greater attention to what a spatial effect represents).

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            • Logan, John R., Weiwei Zhang, and Hongwei Xu. 2010. Applying spatial thinking in social science research. GeoJournal 75.1: 15–27.

              DOI: 10.1007/s10708-010-9343-0Save Citation »Export Citation »E-mail Citation »

              The theme of this paper is that the appropriate use of spatial tools requires careful thinking about spatial concepts. Key concepts are reviewed and methodological innovations are discussed with exemplars taken from applications of spatial models in community health, population and environment, residential segregation, land use, fertility, and migration research.

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              • Porter, Jeremy R., and Frank M. Howell. 2012. Geographical sociology: Theoretical foundations and methodological applications in the sociology of location. New York: Springer.

                DOI: 10.1007/978-94-007-3849-2Save Citation »Export Citation »E-mail Citation »

                Geo-sociology is presented as a synergy between ecologically centered macro theory and the application of spatially centered research methods in the examination of sociological questions. Strengths of this book include chapters on the historical roots of spatial thinking in sociology. The book closes with chapters on hierarchical linear modeling and spatial regression.

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                Advanced Textbooks

                There are many useful spatial analysis textbooks, but few explicitly target social sciences, the field of sociology, or even related fields such as demography. Even comprehensive quantitative methods textbooks within the field ignore discussion of the challenges and opportunities that arise in handling spatial data (or what is now commonly labeled as “geospatial” data). The social scientists exploring the spatial analysis textbook market will encounter a bifurcated literature—specialized and high-end texts or maddeningly simple and even naïve workbooks—with little in the middle (for a discussion of excellent introductory textbooks and workbooks, see Introductory Textbooks and Workbooks). Many excellent specialized or advanced textbooks do exist, of which several are highlighted here. Andy Cliff and Keith Ord are among the leaders in the development of spatial analytical methods, their diffusion, and their application (especially as applied to economic models and disease processes). Cliff and Ord wrote several texts, but a synthesis of their work can be found in Cliff and Ord 1981. Noel Cressie’s text Statistics for Spatial Data (Cressie 1993) is regarded as one of, if not the, classic(s) in the field. Geographer Robert Haining has authored numerous papers on spatial analysis applied to economics, health outcomes, and crime, and his books on spatial data analysis were among the first geared more toward the social scientist (Haining 2003). A classic text on spatial econometrics is Anselin 1988. Other notable, and more recent, advanced textbooks include general overviews such as Schabenberger and Gotway 2005 and those focused on specific methodologies, such as Banajee, et al. 2004 on hierarchical modeling and LeSage and Pace 2009 on spatial econometrics. Advanced textbooks linked to spatial analytic software include the original text on geographically weighted regression, Fotheringham, et al. 2002 (cited under Introductory Textbooks and Workbooks) and more recently, reflecting trends in the use of open-source software, Bivand, et al. 2008, an excellent text on R.

                • Anselin, Luc. 1988. Spatial econometrics: Methods and models. Studies in Operational Regional Science 4. Boston: Kluwer Academic.

                  DOI: 10.1007/978-94-015-7799-1Save Citation »Export Citation »E-mail Citation »

                  Luc Anselin is a pioneer in the development of spatial econometric methods and software (SpaceStat, GeoDa). His first book has received over 4,000 citations (according to Google Scholar). Anselin emphasizes a model-driven approach (not data-driven) combining a rigorous econometric perspective with comprehensive treatment of methodological issues in spatial analysis.

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                  • Banajee, Sudipto, Bradley P. Carlin, and Alan E. Gelfand. 2004. Hierarchical modeling and analysis for spatial data. Monographs on Statistics and Applied Probability 101. Boca Raton, FL: Chapman and Hall.

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                    This well-received text covers hierarchical methods, modeling, and data analysis for spatial and spatio-temporal data. Content leans toward public health but has broad appeal to social scientists. Learning is facilitated by exercises, tutorials, and worked examples with supporting web resources that include sample data and code for R and WinBugs.

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                    • Bivand, Roger S., Edzer J. Pebesma, and Virgilio Gómez-Rubio. 2008. Applied spatial data analysis with R. New York: Springer.

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                      Bivand and his colleagues are among the leading developers of software for spatial analysis with R. This book is organized in two parts: handling spatial data and analyzing spatial data. Additional material including data sets and code for running examples in WinBUGS, GRASS, and ArcGIS is available online.

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                      • Cliff, Andrew D., and John K. Ord. 1981. Spatial processes: Models & applications. London: Pion.

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                        This is an updated and extended version of Spatial Autocorrelation (London: Pion, 1973) that includes material on the estimation of models of spatial processes, the determination of spatial scales at which processes are operating, the use of simultaneous and conditional autoregressive and moving average models, and the analysis of spatial point patterns.

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                        • Cressie, Noel. 1993. Statistics for spatial data. Rev. ed. New York: Wiley.

                          DOI: 10.1002/9781119115151Save Citation »Export Citation »E-mail Citation »

                          One of the classic advanced texts on spatial data analysis. The revised edition provides comprehensive coverage of modeling techniques for analyzing point pattern and geostatistical and lattice data. This book runs to more than nine hundred pages, including many detailed examples and seventy pages of references. Reading requires solid mathematical knowledge.

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                          • Haining, Robert. 2003. Spatial data analysis: Theory and practice. Cambridge, UK: Cambridge Univ. Press.

                            DOI: 10.1017/CBO9780511754944Save Citation »Export Citation »E-mail Citation »

                            Haining provides an accessible overview of spatial data analysis for social scientists. The book has five sections: (1) context for spatial data analysis, (2) obtaining data and quality issues, (3) exploratory analysis of spatial data, (4) hypothesis testing in the presence of spatial autocorrelation, and (5) modeling.

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                            • LeSage, James, and R. Kelley Pace. 2009. Introduction to spatial econometrics. Boca Raton, FL: CRC.

                              DOI: 10.1201/9781420064254Save Citation »Export Citation »E-mail Citation »

                              This text covers the gamut of spatial econometric modeling techniques and topics including maximum likelihood and Bayesian estimation, different spatial regression model specifications, and a variety of applied modeling situations. MATLAB code is available from the authors’ websites for the Spatial Econometrics Toolbox and Spatial Statistics Toolbox.

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                              • Schabenberger, Oliver, and Carol A. Gotway. 2005. Statistical methods for spatial data analysis. Texts in Statistical Science. Boca Raton, FL: Chapman and Hall/CRC.

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                                Billed as a graduate-level text and professional reference, this textbook provides comprehensive coverage of basic statistical theory and methods for spatial data analysis (for point, lattice (area), and geostatistical data). The book includes an overview of the theoretical framework of random fields and an excellent chapter on spatial regression models.

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                                Introductory Textbooks and Workbooks

                                There are several recent textbooks that provide outstanding, substance-driven introductions to both spatial analytic theories and methods. One of the best examples of a highly accessible introduction is Cromley and McLafferty 2011. Ellen Cromley and Sara McLafferty are medical geographers and have written an outstanding introduction to geospatial data, geographic information systems, and spatial analysis in public health. O’Sullivan and Unwin 2010, another second edition, is arguably one of the single best introductory overviews of geographic information analysis. Aimed at final-year undergraduates and early-graduate-level students, this is simultaneously both easy to read and comprehensive. One of the most useful introductions of spatial analytic data, methods, and software is the de Smith, et al. 2015 web textbook. This text, written by leaders in the field, can be acquired in hardcopy form, but the web version, as one would expect, is an evolving document with numerous useful external links embedded. For those seeking an easy entry into spatial regression modeling, a good place to start is Ward and Gleditsch 2008 although the best practical introduction remains Anselin 2005, a GeoDa workbook; and the recent Anselin and Rey 2014. Fotheringham, et al. 2002 provides the first coverage of geographically weighted regression, while Lloyd 2011 offers an overview of local spatial analysis. Brunsdon and Comber 2015 is the most recent guide to using R for spatial analysis (see Bivand, et al. 2008, cited under Advanced Textbooks).

                                • Anselin, Luc. 2005. Exploring spatial data with GeodaTM: A workbook. Urbana-Champaign: Spatial Analysis Laboratory Department of Geography, Univ. of Illinois.

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                                  This is a 244-page workbook on GeoDa, a freeware statistical package that supports a mapping, exploratory spatial data analysis and spatial regression modeling. An extensive set of supporting materials for GeoDa and other software is freely available from the GeoDa Center. The GeoDa Workbook is available online.

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                                  • Anselin, Luc, and Sergio J. Rey. 2014. Modern spatial econometrics in practice. Chicago: GeoDa.

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                                    This is both a guide to and overview of spatial econometrics methods using GeoDa, GeoDaSpace, and the spreg module of PySAL (software, January 2014). Main sections cover nonspatial regression, models for handling spatial dependence and spatial heterogeneity. Practical applications are based on point (house price) and area (homicide) data.

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                                    • Brunsdon, Chris, and Lex Comber. 2015. An introduction to R for spatial analysis and Mapping. Los Angeles: SAGE.

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                                      This new text assumes no prior knowledge of R or spatial analysis (and provides numerous resources including data, code, and sample exercises). Early chapters are introductory, followed by more advanced topics (point pattern analysis, autocorrelation, basic spatial regression, and local indicators of spatial association). The book has an accompanying website.

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                                      • Cromley, Ellen K., and Sara L. McLafferty. 2011. GIS and public health. 2d ed. New York: Guilford.

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                                        While targeted at a public health audience, social scientists using geospatial data can benefit from this book as an introduction to GIS technologies, data, and methods. This book balances the applied and the academic, the conceptual, and the technical. The second edition provides expanded coverage of neighborhoods and health research.

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                                        • de Smith, Michael J., Michael F. Goodchild, and Paul A. Longley. 2015. Geospatial analysis. 3d ed. Leicester, UK: Troubador.

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                                          This is a comprehensive textbook on geospatial analytical concepts and methods. The material is geared toward beginning and intermediate users as an introduction to basic and advanced spatial analysis topics. The book can be purchased in hardcopy form but is also an evolving online resource. Hyperlinks to excellent resources (e.g., software lists) can be found online. A PDF e-book is also available.

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                                          • Fotheringham, A. Stewart, Chris Brunsdon, and Martin Charlton. 2002. Geographically weighted regression: The analysis of spatially varying relationships. Chichester, UK: Wiley.

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                                            This is the primer on geographically weighted regression (GWR), an exploratory modeling technique for local spatial analysis. This technique allows local (as opposed to global) spatial models to be calibrated and interesting variations in relationships to be measured and mapped.

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                                            • Lloyd, Christopher. 2011. Local models for spatial analysis. 2d ed. Boca Raton, FL: CRC.

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                                              Provides a clear introduction to local models and methods; approaches that focus on subsets of a complete data set. Many examples and case studies reinforce the understanding of basic concepts (e.g., scale, nonstationarity) and the application of local modeling techniques. The book has an accompanying website.

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                                              • O’Sullivan, David, and David J. Unwin. 2010. Geographic information analysis. 2d ed. Hoboken, NJ: Wiley.

                                                DOI: 10.1002/9780470549094Save Citation »Export Citation »E-mail Citation »

                                                The first edition was a well-received overview of geographic information analysis. It provided a strong conceptual and theoretical introduction to a wide array of spatial methods. The second edition includes new material on mapping, geovisualization, local statistics, and geographically weighted regression. The book includes excellent examples and a useful reference list.

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                                                • Ward, Michael D., and Kristian Skrede Gleditsch. 2008. Spatial regression models. Quantitative Applications in the Social Sciences 155. Thousand Oaks, CA: SAGE:

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                                                  This is a short SAGE primer on spatial regression modeling based on examples drawn mainly from political science. This book is a good starting point for someone new to spatial econometrics, providing detailed discussion of easy-to-follow examples and a brief introduction to statistical software for spatial regression modeling.

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                                                  Handbooks

                                                  Over the past twenty-five years, many edited collections on spatial analysis and advanced spatial methods (spatial econometrics, pattern analysis, and geostatistics) have been published. These edited collections tended to be high-end and targeted at those already using spatial analytical methods. In recent years, the growing popularity of spatial analysis across social, behavioral, and health fields has contributed to the emergence of several handbooks. Among the first handbooks focusing on spatial analysis was Fotheringham and Rogerson 2009. This edited collection includes twenty-five chapters written by the leading scholars in the field, focusing on debates and issues in spatial analysis as well as the main analytical techniques. Fischer and Getis 2010 is an impressive eight-hundred-page handbook and, as suggested by the subtitle of the collection, one that covers software, method, and applications. Anselin and Rey 2010 provides both a look back and a look forward at the field of spatial analysis focusing on the contribution and influence of Art Getis. All of these texts provide excellent collections summarizing the progress, prospect, and state of the art of spatial analysis and, importantly, have been written not just for experts or experienced users but for a broader audience of scholars.

                                                  • Anselin, Luc, and Sergio Rey. 2010. Perspectives on spatial data analysis. Berlin: Springer.

                                                    DOI: 10.1007/978-3-642-01976-0Save Citation »Export Citation »E-mail Citation »

                                                    This book takes both a retrospective and prospective view of the field of spatial analysis by combining selected articles by one of its leaders, Arthur Getis, with contributions from leading experts. The book is organized in four sections: spatial analysis, pattern analysis, local statistics, and empirical applications.

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                                                    • Fischer, Manfred, M., and Arthur Getis, eds. 2010. Handbook of applied spatial analysis: Software tools, methods and applications. Berlin: Springer.

                                                      DOI: 10.1007/978-3-642-03647-7Save Citation »Export Citation »E-mail Citation »

                                                      This is a comprehensive handbook for both the novice and the expert. The book includes ten chapters on software for spatial analysis; thirteen chapters on spatial statistics, geostatistics, and spatial econometric methods; and another three specific to remote sensing. The handbook closes with nine chapters on the application of spatial analysis in the economic, environmental, and health sciences.

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                                                      • Fotheringham, A. Stewart, and Peter A. Rogerson, eds. 2009. The SAGE handbook of spatial analysis. Thousand Oaks, CA: SAGE.

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                                                        The handbook includes twenty-five chapters written by leading scholars. The chapters include discussion of advances, debates, problems that still exist, and anticipated future directions. Contributing authors were asked to maintain a balance between concepts, theories, and methods, describe applications across different disciplines, and provide extensive references.

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                                                        Classic Works

                                                        Sociologists have known about some of the pitfalls associated with the analysis of spatial data, ecological data, and correlations in sociology since the mid-1930s (see several short papers that appeared in Journal of the American Statistical Association 29, especially Gehlke and Biehl 1934). Like many other social scientists, however, few sociologists explicitly considered issues such as spatial autocorrelation in spatially distributed data (e.g., census tracts within a metropolitan area, counties within a state) in part because of the lack of software to facilitate such analysis (see also Methodological Issues). In the 1960s, a classic but relatively unknown text, Otis Dudley Duncan and colleagues’ Statistical Geography, introduced sociologists to many of the same concerns that are relevant to 21st-century users of geospatial data. In the early 1980s, several quantitative sociologists were motivated by a growing concern that incorrect inferences could be drawn from simple linear relationships that failed to incorporate an underlying spatial process. Leaders at this time included Patrick Doreian, Colin Loftin, and Sally Ward, who began to alert the field to the emergence of new statistical methods based on maximum likelihood estimation (MLE) procedures for handling spatial data and, more importantly, applied these methods directly to sociological research questions. Doreian 1980 and Doreian 1981 applied spatial models to political insurgency and presidential voting patterns while Loftin and Ward 1983 considered the effects of population density on fertility. All three spatial methodology papers appeared in leading sociology journals, all three highlight the potential for inflated parameter estimates and standard errors under ordinary least squares (OLS) as well as the potential for varying inferential results from OLS compared to spatial models, and all demonstrate that spatial effects are important and they need to be treated with appropriate analytic methods when present. Thirty years after the pioneering work of Doreian, and Loftin and Ward, sociologists are increasingly employing more critically and analytically sophisticated approaches to spatial data.

                                                        • Doreian, Patrick. 1980. Linear models with spatially distributed data: Spatial disturbances or spatial effects? Sociological Methods & Research 9:29–60.

                                                          DOI: 10.1177/004912418000900102Save Citation »Export Citation »E-mail Citation »

                                                          This article introduces maximum likelihood estimation (MLE) procedures for spatial models. Empirical applications focus on political insurgency and presidential voting and contrast MLE results with conventional ordinary least squares regression (which ignore spatial relations between observations). In the presence of spatial autocorrelation, the MLE procedure is preferable.

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                                                          • Doreian, Patrick. 1981. Estimating linear models with spatially distributed data. Sociological Methodology 12:359–388.

                                                            DOI: 10.2307/270747Save Citation »Export Citation »E-mail Citation »

                                                            Doreian suggests that geographical characteristics of social phenomena are overlooked, especially when data analyses are performed. His objective is not to claim that geographical space must be included, but rather to claim that when it is appropriate to include geographical space, conceptual and methodological issues need to be addressed.

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                                                            • Duncan, Otis Dudley, Ray P. Cuzzort, and Beverly Duncan. 1961. Statistical geography: Problems in analyzing areal data. Westport, CT: Greenwood.

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                                                              This is probably the first book-length treatment by sociologists of methodological issues relevant to the study of geographic data, specifically areal data. The book is divided in to three sections: preliminaries, the way areal data are generated and their quality, and the statistical analysis of areal data.

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                                                              • Gehlke, C. E., and Biehl K. 1934. Certain effects of grouping upon the size of the correlation coefficient in census tract material. Journal of the American Statistical Association 29.185: 169–170.

                                                                DOI: 10.2307/2277827Save Citation »Export Citation »E-mail Citation »

                                                                This was perhaps the first paper to consider scale and aggregation effects in the analysis of census tract data. When data on census tracts in Cleveland, Ohio, were grouped successively into larger units, the authors observed that the size of the correlation coefficient between male juvenile delinquency and median monthly income increased.

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                                                                • Loftin, Colin, and Sally K. Ward. 1983. A spatial autocorrelation model of the effects of population density on fertility. American Journal of Sociology 48.1: 121–128.

                                                                  DOI: 10.2307/2095150Save Citation »Export Citation »E-mail Citation »

                                                                  This article is a critique and a reanalysis of a study published in Science on the effects of density on fertility. The authors argue, and demonstrate, that research that excludes consideration of spatial processes is incomplete and parameter estimates are misleading.

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                                                                  Data Sources and Resources

                                                                  Enormous amounts of freely available geospatial data are readily availability on the Internet, much of it made available via the US Federal Government. Universities also play multiple roles with respect to facilitating access to, and providing instructional resources on, geospatial data, spatial analysis, and software (see University-Based resources.), Spatial analysis—both basic data manipulation as well as spatial statistical analysis—depends on accurate and precise information on an object’s geographic location or spatial reference, but these can vary widely. Users are encouraged to check any technical documentation that accompanies digital spatial data files as well as existing metadata (data about the data).

                                                                  US Federal Government

                                                                  The best starting points for learning about the availability and utilization of geospatial data from the federal government is via Data.gov. The US Bureau of the Census makes available the most commonly used geospatial boundary data files for all levels of census geography from the block up to the nation-state. These data files are essential to any user seeking to map and analyze attribute data aggregated to any of the census geographies. These boundary files represent an organizing framework for data not collected by the US Census but made available for census units (e.g., county-level data on crime, labor market conditions, health expenditures). The National Center for Education Statistics houses attribute data on schools (points) and school districts (areas) collected over time and across the country. These attribute data can be easily extracted, geocoded, or mapped and analyzed. Similarly, the National Center for Health Statistics together with the Health Resources and Service Administration (HRSA) house and facilitate access to a wealth of geographically aggregated data on vital statistics, health services, and health service utilization as well as a comprehensive array of morbidity and mortality data. Several federal agencies provide access to geospatial data on the physical environment and the interface between environment and human conditions/human health. Among the most commonly used sites for environmental data for academics, journalists, and the public are those housed at the Environmental Protection Agency Data Home Page and the National Aeronautics and Space Administration’s Global Change Master Directory.

                                                                  University-Based

                                                                  While the federal government is one source of geospatial data, several university-based websites house resources that facilitate searching for, and decision making on geospatial data products, and some house important geospatial data products. Among those that facilitate the finding and utility of geospatial data, the most useful early in the development of the field were the Alexandria Digital Library Project at the University of California at Santa Barbara and geospatial data resources housed at the University of Arkansas While these two sites are now somewhat dated, they were pioneer sites and of tremendous value to the GIS community in the United States. Several university-based sites are worthy of special mention for the data they make available. These include the National Historic Geographic Information System (NCGIS) Project at the University of Minnesota, Brown University’s Spatial Structures in Social Science (S4) initiative, and Columbia University’s Center for International Earth Science Information Network (CIESIN). The NHGIS Project is helping to create and maintain a series of fundamental historic boundary files (and attribute data) dating back to 1790, and the Minnesota Population Center is also home to many other geospatial data projects (e.g., Terra Populus and Integrated Demographic and Health Surveys). The S4 initiative at Brown creates and maintains unique data on segregation (among others), while CIESIN provides access to a host of valuable social and environmental data for the United States and the rest of the world. Other university-based websites are important to academic researchers because of the access they provide to training materials and resources on spatial analysis and/or software. The most noteworthy of this group include the excellent training resources housed at the Center for Spatially Integrated Social Science (CSISS) at the University of California at Santa Barbara, the GeoDa Center at the University of Arizona, and the resources at GISPopSci (Penn State and UCSB). An impressive, non-US site is the Centre for Advanced Spatial Analysis (CASA) at University College London.

                                                                  Other Useful Geospatial Data Websites

                                                                  Interactive web-based geospatial data sites are increasingly common. Some of these sites provide direct access to aggregate geospatial data and/or tools that are especially useful for basic spatial queries and comparisons. The examples below include a well-established site, Social Explorer, that provides easy access to historic US Census data across multiple census geographies (parts of the site are open access, but the bulk of the materials requires a license; potential users should check with their local institution). The Net Migration site, hosted by the University of Wisconsin, also maintains historic data with a more specific emphasis on county-level net migration post-1950 in the United States. Although demographic data often dominate geospatial websites, another increasingly common topical area is health. A relatively new health-related website, County Health Rankings and Roadmaps, is supported by the Robert Wood Johnson Foundation and provides easy access to county-level health rankings for US states and counties, with the dual focus on health factors (determinants of health) and health outcomes. The availability of international data is changing quickly, and one of the most innovative websites is the WorldPop Project, which provides access to high-resolution and the latest data on human population distributions for many parts of the developing world.

                                                                  • County Health Rankings and Roadmaps.

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                                                                    This is a useful site supported by Robert Wood Johnson Foundation for finding health indicator data (at the county level) based on recent national, state, and local data resources. Users can examine and map health factors related to health outcomes, including the ability to compare counties.

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                                                                    • Net Migration.

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                                                                      This site includes estimates of net migration for US counties by five-year age group, sex, and race each decade from 1950 to 2010. The 1990s and 2000s also include estimates by Hispanic origin. The data do not include flows of in-migrants or out-migrants, but only the net balance.

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                                                                      • Social Explorer.

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                                                                        Provides easy access to a wealth of US census data, both current and historic. A useful feature of the website is the interface that facilitates the creation of maps and reports. This is a licensed product, and so access is dependent on the institutional home.

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                                                                        • WorldPop Project.

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                                                                          The WorldPop Project combines three previous population mapping projects (AfriPop, AsiaPop, and AmeriPop) and provides open access to archives of spatial data for these three areas of the world. Data sets, documentation on methodology behind the creation of WorldPop, and publications by the research team are available at this site.

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                                                                          Journals

                                                                          There are several specialized journals that focus on spatial analytic methods (e.g., Geographical Analysis, GeoJournal, International Journal of Health Geographics, Transactions in GIS); and in recent years several new journals have emerged (e.g., Applied Spatial Analysis and Policy, Letters in Spatial and Resource Sciences, Spatial Demography, and Spatial Economic Analysis). The interest in spatial analysis and applications of geographic information systems (GIS) has grown rapidly in many fields in the past decade. One consequence of this growth has been that several discipline-specific journals have included special issues on the use of GIS, geospatial data, and/or spatial analysis. These disciplines have included sociology, education, public health, and general social science journals. Many sociology and social science journals carry occasional papers that use geospatial data, GIS approaches, and/or advanced spatial analysis methods.

                                                                          Methodological Issues

                                                                          Spatial analysis can take many forms, but underlying spatial data analysis are several important methodological issues. These include spatial autocorrelation (spatial dependence) and the modifiable areal unit problem (MAUP). To be sure, there are other methodological issues (see the references cited under Advanced Textbooks or Handbooks), but these two are fundamental issues and warrant special attention. A useful and accessible overview of spatial autocorrelation is provided by Goodchild 1986. Introductions to more recent developments in measuring spatial autocorrelation, specifically local measures, can be found in the original and classic papers in Anselin 1995 on local indicators of spatial association (LISA) and in Getis and Ord 1992 on the use of distance-based statistics (G statistic). The modifiable areal unit problem (MAUP) is concerned with the variability in findings that can exist when either the scale of analysis is changed or units of analysis are aggregated into different zones or areas. The classic overview of the MAUP, its history, the analytical implications (including empirical examples), and potential solutions is the short monograph Openshaw 1984. An excellent extension of MAUP into multivariate analysis is the cautionary and somewhat somber but enlightening paper Fotheringham and Wong 1991. Flowerdew, et al. 2008 examines the effects of using different boundaries on analytical results pertaining to health outcomes. Hipp 2007 has demonstrated how different definitions of neighborhood context (and thus MAUP issues) can influence results in multilevel models of crime. A few papers have tried to integrate spatial and multilevel models; among the most creative has been Chaix, et al. 2005, on health care utilization in France.

                                                                          • Anselin, Luc. 1995. Local indicators of spatial association—LISA. Geographical Analysis 27.2: 93–115.

                                                                            DOI: 10.1111/j.1538-4632.1995.tb00338.xSave Citation »Export Citation »E-mail Citation »

                                                                            Anselin outlines a new general class of local indicators of spatial association (LISA). The LISA for each observation provides an indication of the extent of significant spatial clustering of similar values around the observation. The sum of all LISAs is proportional to a global indicator of spatial association (Moran’s I).

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                                                                            • Chaix, Basile, Juan Merlo, and Pierre Chauvin. 2005. Comparison of a spatial approach with the multilevel approach for investigating place effects on health: The example of healthcare utilization in France. Journal of Epidemiology and Community Health 59.6: 517–526.

                                                                              DOI: 10.1136/jech.2004.025478Save Citation »Export Citation »E-mail Citation »

                                                                              Rather than using arbitrary areas to define neighborhood contexts, the authors of this original paper operationalize neighborhood context as a “continuous” surface surrounding individual residences. The findings indicate that place indicators better explained variations in health care utilization when measured across continuous space, rather than within an administrative area.

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                                                                              • Flowerdew, Robin, David J. Manley, and Clive E. Sabel. 2008. Neighborhood effects on health: Does it matter where you draw the boundaries? Social Science and Medicine 66:1241–1255.

                                                                                DOI: 10.1016/j.socscimed.2007.11.042Save Citation »Export Citation »E-mail Citation »

                                                                                The authors compared existing neighborhood boundaries to five realistically defined pseudo-areas (i.e., based on criteria such as total population, compact shape, and internal homogeneity). Their findings reinforce the need to think about the geography of “effective” neighborhoods prior to analysis and also to experiment with different scales and aggregations.

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                                                                                • Fotheringham, A. Stewart, and David W. S. Wong. 1991. The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning A 23.7: 1025–1044.

                                                                                  DOI: 10.1068/a231025Save Citation »Export Citation »E-mail Citation »

                                                                                  A must read for those interested in understanding MAUP and how it can influence results in multivariate regression. As stated in the paper, “the results of the analysis are rather depressing in that they provide strong evidence of the unreliability of any multivariate analysis undertaken with data from areal units.”

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                                                                                  • Getis, Arthur, and J. Keith Ord. 1992. The analysis of spatial association by the use of distance statistics. Geographical Analysis 24.3: 189–206.

                                                                                    DOI: 10.1111/j.1538-4632.1992.tb00261.xSave Citation »Export Citation »E-mail Citation »

                                                                                    This paper, which introduced the G statistic, helped promote interest in measures of local spatial association in the field of spatial analysis. G measures spatial association in a spatially distributed variable within a specified distance of a single point/observation. The paper compares G with Moran’s I and includes two empirical examples.

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                                                                                    • Goodchild, Michael F. 1986. Spatial autocorrelation. Concepts and Techniques in Modern Geography (CATMOG) series 47. Norwich, UK: Geo.

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                                                                                      The Concepts and Techniques in Modern Geography (CATMOG) series (launched in 1975) provides useful short, accessible summaries of emergent techniques in quantitative geography. Goodchild provides an overview of spatial autocorrelation focusing on the use of Moran I and Geary C for interval data and join-counts for nominal data. The references to software for spatial autocorrelation are dated.

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                                                                                      • Hipp, John P. 2007. Block, tract, and levels of aggregation: Neighborhood structure and crime and disorder as a case in point. American Sociological Review 72.5: 659–680.

                                                                                        DOI: 10.1177/000312240707200501Save Citation »Export Citation »E-mail Citation »

                                                                                        Decisions about the level of aggregation and the scale at which processes operate are important for researchers who estimate neighborhood effects. Most papers rarely discuss MAUP, but Hipp provides an excellent empirical example in a study of crime and disorder using data at both census block and census tract levels.

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                                                                                        • Openshaw, Stanley. 1984. The modifiable areal unit problem. Concepts and Techniques in Modern Geography (CATMOG) series 38. Norwich, UK: Geo.

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                                                                                          Openshaw’s short monograph on MAUP is often cited. He provides a background on modifiable areal units (e.g., census areas, ZIP codes), defines the scale and aggregation problems associated with MAUP, summarizes the historical work in early correlation studies (by sociologists), and illustrates MAUP via empirical experiments (using data on Iowa).

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                                                                                          Qualitative Methods

                                                                                          Geographic information systems (GIS) applications have not only focused on quantitative data. Several social scientists have begun to explore and develop new methods for integrating GIS with qualitative methods. The advent of tracking technologies—such as global positioning systems (GPS)—has led to the reemergence of time geography with studies integrating qualitative and quantitative data in a GIS framework. The applications of geo-ethnography (Matthews, et al. 2005) and geo-narrative (Kwan and Ding 2008) methods are not only descriptive but also may push social science research into new territory—territory that questions the conceptualization and measurement of neighborhood as used in sociology and other social sciences and leads to investigations of questions regarding exposure to neighborhood by looking at the dependence of individuals and families on extralocal resources, opportunities, and social support networks. Indeed, new tracking technologies such as GPS and innovative research designs have great—as yet unrealized—potential to permit better functional understandings of spatial behavior and the development of measures of exposure to place-based resources and risks. Cope and Elwood 2009 is a useful edited collection that summarizes much of the work in qualitative GIS prior to 2008.

                                                                                          • Cope, Meghan, and Sarah Elwood, eds. 2009. Qualitative GIS: A mixed methods approach. Thousand Oaks, CA: SAGE.

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                                                                                            This is the first book on qualitative methods and GIS. It is organized into three sections: representations, analytical interventions and innovations, and conceptual engagements. While the book is written by geographers, it should have wide appeal to other social scientists, specifically sociologists and anthropologists, engaged in mixed methods research.

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                                                                                            • Kwan, Mei-Po, and Guoxiang Ding. 2008. Geo-narrative: Extending geographic information systems for narrative analysis in qualitative and mixed-method research. Professional Geographer 60.4: 443–465.

                                                                                              DOI: 10.1080/00330120802211752Save Citation »Export Citation »E-mail Citation »

                                                                                              Kwan is a leading expert on the integration of GIS and qualitative methods. Kwan and Ding extend current GIS capabilities for the analysis and interpretation of narrative materials such as oral histories, life histories, and biographies. A case study of Muslim women in Columbus, Ohio, after 9/11 illustrates the approach.

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                                                                                              • Matthews, Stephen A., James Detwiler, and Linda M. Burton. 2005. Geoethnography: Coupling geographic information analysis techniques with ethnography methods in urban research. Cartographica 40.4: 75–90.

                                                                                                DOI: 10.3138/2288-1450-W061-R664Save Citation »Export Citation »E-mail Citation »

                                                                                                This article appeared in a special issue of Cartographica on “Critical GIS.” It describes the process of integrating data from multiple sources and the potential use of spatial activity data combined with ethnographic fieldwork. Discussions cover conceptual and methodological issues and strategies for combining qualitative and quantitative research.

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                                                                                                Ethical Issues

                                                                                                The increasing availability of precise, accurate spatial data on individuals and their activity paths, and the ease with which these data can be integrated with other contextual databases and health data mean that the need for expertise in handling confidentiality and privacy issues has never been greater. This was summarized in a short but important National Research Council report (Gutmann and Stern 2007) on confidentiality and spatial data. The inclusion of VanWey, et al. 2005 in the special issue of the Proceedings of the National Academy of Sciences on spatial demography was a strong indication of the seriousness of this emergent issue. As reported by Gutmann, et al. 2008, there are many challenges to be faced by those collecting, archiving, and sharing data. Indeed, echoing others, Boulos, et al. 2009 argues that social science researchers, including sociologists and demographers and health scientists, need to become better informed regarding privacy and confidentiality issues. Armstrong 2002, in a nice short commentary, and Curtis, et al. 2006, in a real-world case based on Hurricane Katrina, demonstrate why researchers need to better understand acceptable practices for disseminating mapped data. Marc Armstrong is a recognized leader in this field, and Armstrong, et al. 1999 provides a solid review of how geographic identifiers are handled within geographic information systems and available strategies for the masking of data.

                                                                                                • Armstrong, Marc P. 2002. Geographic information technologies and their potentially erosive effects on personal privacy. Studies in the Social Sciences 27.1: 19–28.

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                                                                                                  A short essay on the potential impacts of geospatial technologies (including GPS and cell phone) on the surveillance of day-to-day activities and the risks to personal privacy, as well as how we think about privacy. The paper includes a simple example of the dangers of naïve dot-mapping and reverse geocoding.

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                                                                                                  • Armstrong, Marc P., Gerald Rushton, and Dale L. Zimmerman. 1999. Geographically masking health data to preserve confidentiality. Statistics in Medicine 18:497–525.

                                                                                                    DOI: 10.1002/(SICI)1097-0258(19990315)18:5<497::AID-SIM45>3.0.CO;2-#Save Citation »Export Citation »E-mail Citation »

                                                                                                    This paper describes and evaluates a comprehensive list of alternative approaches to encoding the geography of health records—geographic masking—that protect the confidentiality of individuals but also ensure the possibility of valid geographical analyses of the data.

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                                                                                                    • Boulos, Maged N. K., Andrew J. Curtis, and Philip AbdelMalik. 2009. Musings on privacy issues in health research involving disaggregate geographic data about individuals. International Journal of Health Geographics 8:46.

                                                                                                      DOI: 10.1186/1476-072X-8-46Save Citation »Export Citation »E-mail Citation »

                                                                                                      This is an overview of privacy, confidentiality, and security issues encountered in research involving disaggregate geographic data about individuals. The paper includes a review of location privacy/confidentiality concerns and on privacy-preserving solutions as well as summarizing a 2009 workshop titled “Protecting Privacy and Confidentiality of Geographic Data in Health Research.”

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                                                                                                      • Curtis, Andrew J., Jacqueline W. Mills, and Michael Leitner. 2006. Spatial confidentiality and GIS: Re-engineering mortality locations from published maps about Hurricane Katrina. International Journal of Health Geographics 5:44

                                                                                                        DOI: 10.1186/1476-072X-5-44Save Citation »Export Citation »E-mail Citation »

                                                                                                        An excellent, and disturbing, example of reverse geocoding based on published map data on mortality locations from Hurricane Katrina.

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                                                                                                        • Gutmann, Myron P., and Paul C. Stern, eds. 2007. Putting people on the map: Protecting confidentiality with linked social-spatial data. Washington, DC: National Academies Press.

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                                                                                                          This is an important report on the opportunities and challenges that arise when precise spatial data on research participants (e.g., home or workplace) are linked to personal information provided under promises of confidentiality. Recommendations are offered for educators, researchers, professional societies, federal agencies, institutional review boards, and data stewards.

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                                                                                                          • Gutmann, Myron P., Kristine Witkowski, Corey Colyer, JoAnne McFarland O’Rourke, and James McNally. 2008. Providing spatial data for secondary analysis: Issues and current practices relating to confidentiality. In Special Issue: Spatial Demography. Population Research and Policy Review 27.6: 639–665.

                                                                                                            DOI: 10.1007/s11113-008-9095-4Save Citation »Export Citation »E-mail Citation »

                                                                                                            This paper, which appeared in a special issue of PRPR on spatial demography, focuses on the challenges involved in producing, archiving, and sharing social science data that have spatially explicit information embedded within them, all while avoiding the risk of disclosing private information about individuals.

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                                                                                                            • VanWey, Leah K., Ronald R. Rindfuss, Myron P. Gutmann, Barbara Entwisle, and Deborah L. Balk. 2005. Confidentiality and spatially explicit data: Concerns and challenges. In Special Issue: Spatial Demography. Proceedings of the National Academy of Science 102.43: 15337–15342.

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                                                                                                              This paper appeared in a special issue of PNAS on spatial demography. The paper focuses on four sometimes-conflicting principles for the conduct of ethical and high-quality science that involves spatially explicit data: protection of confidentiality, the social-spatial linkage, data sharing, and data preservation. Further basic research is needed.

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                                                                                                              Spatial Inequality

                                                                                                              Within social science research, there is a return to matters pertaining to spatial patterns of inequality, the persistence of inequality, the importance of context or neighborhood effects, and macroscocial determinants of health. Two important edited collections focusing on these issues are Kawachi and Berkman 2003 and Galea 2008. Tickamyer 2000 argues that the “where” questions of social inequality are important, and that conventional approaches to social inequality would benefit from an explicit spatial perspective. While urban and cross-national research dominate the field, Tickamyer points out that spatial inequality and social processes operate at a variety of geographic scales from micro-level units (e.g., residential neighborhoods) to macro-level units (nation-states). Three subsections include articles on multiple dimensions of spatial inequality (e.g., birth outcomes, poverty and economic crises, access to food, racial inequality, income inequality, crime and lynching, and welfare policy) divided up according to the scales of analysis: Local, State and Regional, and National levels.

                                                                                                              • Galea, Sandro, ed. 2008. Macrosocial determinants of population health. New York: Springer.

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                                                                                                                This is a comprehensive text on macrosocial determinants of population health. Of particular relevance to this bibliography are contributions on methods (second section). While this section does not focus on spatial analysis per se, there are useful chapters on ecological studies (Curtis and Cummins) and scale (Osypuk and Galea).

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                                                                                                                • Kawachi, Ichiro, and Lisa F. Berkman, eds. 2003. Neighborhoods and health. New York: Oxford Univ. Press.

                                                                                                                  DOI: 10.1093/acprof:oso/9780195138382.001.0001Save Citation »Export Citation »E-mail Citation »

                                                                                                                  An excellent collection of chapters on theoretical, conceptual, and methodological issues relevant to the study of neighborhood effects on health; several empirical studies; and cross-cutting themes. Spatial analysis is not a focus but spatial thinking, the creation and use of geospatial databases, and ecological analysis and scale issues are evident throughout.

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                                                                                                                  • Tickamyer, Ann R. 2000. Space matters! Spatial inequality in future sociology. Contemporary Sociology 29.6: 805–813.

                                                                                                                    DOI: 10.2307/2654088Save Citation »Export Citation »E-mail Citation »

                                                                                                                    This is an important paper on the state of spatial perspectives and spatial analysis in sociology, specifically focusing on the integration of space and place in the sociology of inequality. An agenda for bringing spatial inequality back into the study of social inequality is proposed (expanded on Lobao, et al. 2007, cited under General Overviews)).

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                                                                                                                    Local

                                                                                                                    Research on local spatial inequality tends to be urban focused and is dominated by a handful of key substantive themes. A well-established theme is that of environmental justice and racial inequality, and useful methodological contributions and empirical examples can be found in the work of Downey (Downey 2003, Downey 2006) and Mennis and Jordan 2005. An emerging topic (across many social science and public health disciplines) is access to health food and the overall quality of the neighborhood food environment. As noted, this is a vibrant field, and some of the exemplary studies include Austin, et al. 2005; Block, et al. 2004; and Zenk, et al. 2005. Several important studies of local spatial inequality have emerged from the Project on Human Development in Chicago Neighborhoods (PHDCN), an interdisciplinary study of how families, schools, and neighborhoods affect child and adolescent development. Morenoff 2003, which uses PHDCN, is a fascinating study of local variability in birth weight. A recent international perspective on spatial inequality is found in Weeks, et al. 2014, which is a collection of papers from multiple integrated research projects on social and health inequalities in the rapidly growing African city of Accra, Ghana.

                                                                                                                    • Austin, S. Bryn, Steven J. Melly, Brisa N. Sanchez, Aarti Patel, Stephen Buka, and Steven L. Gortmaker. 2005. Clustering of fast-food restaurants around schools: A novel application of spatial statistics to the study of food environments. American Journal of Public Health 95.9: 1575–1581.

                                                                                                                      DOI: 10.2105/AJPH.2004.056341Save Citation »Export Citation »E-mail Citation »

                                                                                                                      This study of food environments used restaurant and school addresses to examine locational patterns of fast-food restaurants and schools (kindergartens, primary, and secondary) in Chicago. The analysis uses bivariate K function statistical methods to quantify the degree of clustering (spatial dependence) of fast-food restaurants around school locations.

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                                                                                                                      • Block, Jason P., Richard A. Scribner, and Karen B. DeSalvo. 2004. Fast food, race/ethnicity, and income: A geographical analysis. American Journal of Preventive Medicine 27.3: 211–217.

                                                                                                                        DOI: 10.1016/S0749-3797(04)00139-4Save Citation »Export Citation »E-mail Citation »

                                                                                                                        The paper explores the distribution of fast-food restaurants relative to neighborhood sociodemographics in New Orleans. Spatial buffer techniques define neighborhoods, and fast food locations are used to derive outlet densities. Multiple regression was used to assess the geographic association between fast-food restaurant density and black and low-income neighborhoods.

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                                                                                                                        • Downey, Liam. 2003. Spatial measurement, geography, and urban racial inequality. Social Forces 81.3: 937–952.

                                                                                                                          DOI: 10.1353/sof.2003.0031Save Citation »Export Citation »E-mail Citation »

                                                                                                                          This article introduces a variable construction technique (based on use of a GIS) that allows researchers to measure the distance between social groups and risks/resources more precisely than is otherwise possible. The paper demonstrates the power of maps to examine the distribution of social groups and resources/risks (in Detroit).

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                                                                                                                          • Downey, Liam. 2006. Using geographic information systems to reconceptualize spatial relationships and ecological contexts. American Journal of Sociology 112.2: 567–612.

                                                                                                                            DOI: 10.1086/506418Save Citation »Export Citation »E-mail Citation »

                                                                                                                            The use of GIS and geospatial data facilitate alternative conceptualization of physical space, allowing sociologists to develop new ways to think about and measure spatial relationships, ecological context, and place-based social inequality. The paper focuses on environmental inequality in Detroit and includes an overview of the Modifiable Areal Unit Problem.

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                                                                                                                            • Mennis, Jeremy L., and Lisa M. Jordan. 2005. The distribution of environmental equity: Exploring spatial nonstationarity in multivariate models of air toxic releases. Annals of the Association of American Geographers 95.2: 249–268.

                                                                                                                              DOI: 10.1111/j.1467-8306.2005.00459.xSave Citation »Export Citation »E-mail Citation »

                                                                                                                              A number of recent publications have demonstrated the analytical utility of geographically weighted regression (GWR) in social science research. In this paper, GWR analysis shows that the relationships among race, class, employment, urban concentration, and land use with air toxic-release density in New Jersey vary significantly over space.

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                                                                                                                              • Morenoff, Jeffrey D. 2003. Neighborhood mechanisms and the spatial dynamics of birth weight. American Journal of Sociology 108.5: 976–1017.

                                                                                                                                DOI: 10.1086/374405Save Citation »Export Citation »E-mail Citation »

                                                                                                                                Morenoff uses both spatial and multilevel models in his research. This paper demonstrates that contextual effects on birth weight extend to the social environment beyond the immediate neighborhood. Morenoff uses Moran’s I and spatial regression models (i.e., models take into account spatial interdependencies among neighborhoods).

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                                                                                                                                • Weeks, John R., Allan G. Hill, and Stoler Justin, eds. 2014. Spatial inequalities: Health, poverty, and place in Accra, Ghana. New York: Springer.

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                                                                                                                                  This edited collection offers a detailed look at spatial inequalities in Accra, Ghana. The collection includes sections on conceptual and measurement concerns (e.g., defining neighborhoods), as well as empirical chapters on health inequality across the city and within slums (e.g., adolescent health, fertility, and access to water, food, and health care).

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                                                                                                                                  • Zenk, Shannon N., Amy J. Schulz, Barbara A. Israel, Sherman A. James, Shuming Bao, and Mark L. Wilson. 2005. Neighborhood racial composition, neighborhood poverty, and the spatial access of supermarkets in metropolitan Detroit. American Journal of Public Health 95.4: 660–667.

                                                                                                                                    DOI: 10.2105/AJPH.2004.042150Save Citation »Export Citation »E-mail Citation »

                                                                                                                                    This study focuses on accessibility to large-chain supermarkets in relation to neighborhood race/ethnic composition and poverty. Spatial accessibility was measured via road network distances between residences and supermarkets using a GIS, and analytic methods included spatial regression models to adjust for spatial autocorrelation.

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                                                                                                                                    State and Regional

                                                                                                                                    The studies of spatial inequality and the state and regional level listed in this section all draw on advanced spatial methods, specifically but not limited to spatial econometrics. The substantive applications vary widely, as does the geographical coverage and the geographic unit of analysis. Perhaps the most commonly studied area of inequality (if not spatial inequality) is income inequality. Rey 2004 provides a useful synthesis of several methodological concerns in income inequality research and an empirical application of spatial econometrics using state and regional data. An interesting spatially informed study of changes in state-level welfare policy is provided by DeJong, et al. 2006. Changing the unit of analysis, Voss, et al. 2006 focuses on US child poverty levels using county-level data. The Voss, et al. paper is an excellent example of the reanalysis of data using conventional nonspatial approaches that clearly demonstrate how results can change when explicitly spatial modeling techniques are used. Many other regional studies draw on county-level data. Brasier 2005, focusing on the Great Plains states, looks at the predictors of local economic change during the farm crisis of the 1980s while Tolnay, et al. 1996, in a relatively early paper, looks at the pattern of lynching in the South. An innovative study of spatial inequality in economic recovery, drawing on census tract–level data in different regions of the United States is provided by Pais and Elliott 2008. Baller and Richardson 2002 uses data from both France and the United States to examine that classic area of sociological inquiry, suicide.

                                                                                                                                    • Baller, Robert. D., and K. K. Richardson. 2002. Social integration, imitation, and the geographic patterning of suicide. American Sociological Review 67.6: 873–888.

                                                                                                                                      DOI: 10.2307/3088974Save Citation »Export Citation »E-mail Citation »

                                                                                                                                      This paper tests two competing hypotheses regarding the spatial clustering of suicide based on the work of Durkheim (integration/regulation) and Tarde (imitation hypothesis). French and US data sets are used, along with global Moran’s I (clustering of the dependent variable and residual analysis) and spatial regression modeling techniques.

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                                                                                                                                      • Brasier, Kathryn J. 2005. Spatial analysis of changes in the number of farms during the Farm Crisis. Rural Sociology 70.4: 540–560.

                                                                                                                                        DOI: 10.1526/003601105775012732Save Citation »Export Citation »E-mail Citation »

                                                                                                                                        This reanalysis of county-level data to study factors affecting farm change during the farm crisis of the 1980s uses a spatially explicit framework. Brasier reports the Moran’s I for all variables, revealing spatial clustering, indicating the potential for a significant spatial effect, and thus the need to use spatial regression models.

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                                                                                                                                        • DeJong, Gordon F., Deborah Roempke Graefe, Shelley K. Irving, and Tanja St. Pierre. 2006. Measuring state TANF policy variations and change after reform. Social Science Quarterly 87.4: 755–781.

                                                                                                                                          DOI: 10.1111/j.1540-6237.2006.00432.xSave Citation »Export Citation »E-mail Citation »

                                                                                                                                          This paper focuses on the variability in state-level policies adopted after “Welfare Reform” (1996–2003). It is included here as an example of spatially informed sociological research, as the analytic plan included the use of exploratory spatial data analysis tools for exploring global spatial clustering and spatial econometric models.

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                                                                                                                                          • Pais, Jeremy F., and James R. Elliott. 2008. Places as recovery machines: Vulnerability and neighborhood change after major hurricanes. Social Forces 86.4: 1415–1453.

                                                                                                                                            DOI: 10.1353/sof.0.0047Save Citation »Export Citation »E-mail Citation »

                                                                                                                                            This is an innovative paper that combines census data and biophysical data on wind speed in models of the spatial variation in hurricane damage and recovery in four regions of the United States during the early 1990s. Analysis of census tract–level data is based on spatial error models.

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                                                                                                                                            • Rey, Sergio J. 2004. Spatial analysis of regional income inequality. In Spatially integrated social science. Edited by Michael F. Goodchild and Donald G. Janelle, 280–299. New York: Oxford Univ. Press.

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                                                                                                                                              Rey provides a methodological overview of often ignored spatial effects in studies of regional-scale income inequality (dependence, scale, and inferential issues). An empirical example demonstrates the importance of these three issues using state, division, and regional data on income inequality in the United States between 1929 and 2000.

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                                                                                                                                              • Tolnay, Stewart E., Glenn Deane, and E. M. Beck. 1996. Vicarious violence: Spatial effects on Southern lynchings, 1890–1919. American Journal of Sociology 102.3: 788–815.

                                                                                                                                                DOI: 10.1086/230997Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                This paper explicitly looks at the spatial relationship between lynchings in neighboring places. Analysis suggests there is a negative spatial effect—the probability of lynchings in a given locale decline when lynchings occurred elsewhere—consistent with the deterrence model. This is a rare example of negative spatial dependence.

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                                                                                                                                                • Voss, Paul, David D. Long, Roger B. Hammer, and Samantha Friedman. 2006. County child poverty rates in the US: A spatial regression approach. Population Research and Policy Review 25.4: 369–391.

                                                                                                                                                  DOI: 10.1007/s11113-006-9007-4Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                  The authors reanalyze data from a published study of child poverty to demonstrate the value added of a spatial regression framework. The reanalysis improved the model, correcting original inaccurate inferences about predictors (shifting of “wrong-sign” parameters), reduced the residual squared error, and eliminated spatial autocorrelation in model residuals.

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                                                                                                                                                  National

                                                                                                                                                  National and international studies of inequality are common, but relatively few focus on spatial econometric methods or other approaches to the study of inequality. A recent spatially informed paper on regional income and regional investment is provided by Dall’erba 2005. Dall’erba’s paper is exploratory but useful in that it compares findings using multiple ways to specify neighboring relations between regions (i.e., the spatial weights matrix), and it offers a sound demonstration of the use of global and local measures of spatial autocorrelation. Extending the definition of relationships (weights matrices) further is an excellent and innovative paper, Beck, et al. 2006, that draws on data capturing flows and shared dyadic relationships between countries to measure functional ties between each other rather than from a distance metric.

                                                                                                                                                  • Beck, Nathaniel, K. S. Gleditsch, and K. Beardsley. 2006. Space is more than geography: Using spatial econometrics in the study of political economy. International Studies Quarterly 50.1: 27–44.

                                                                                                                                                    DOI: 10.1111/j.1468-2478.2006.00391.xSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                    A fascinating paper using spatial econometric models and based on political economy notions of distance (such as trade relations between countries and dyadic relations based on common membership) rather than on conventional geographic distance. The empirical examples of spatial econometric analysis focus on trade and democracy.

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                                                                                                                                                    • Dall’erba, Sandy. 2005. Distribution of regional income and regional funds in Europe 1989–1999: An exploratory spatial data analysis. Annals of Regional Science 39:121–148.

                                                                                                                                                      DOI: 10.1007/s00168-004-0199-4Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                      Persistent regional income disparities are of increasing concern. While inherently geographical, such issues are rarely explored by means of spatial methods. Global and local spatial autocorrelation reveals the persistence of rich and poor regions. Exploratory analysis suggests that future research should include spatial effects and spatial econometric estimation of spatial inequality.

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                                                                                                                                                      Residential Segregation

                                                                                                                                                      Urban sociologists have long been interested in the race and ethnic structure of American neighborhoods and communities. The main methodological issues confronting researchers analyzing race/ethnic segregation revolve around the definition of racial and ethnic categories, geographic boundaries, and segregation measures. A focus on these methodological issues has led to new ways to explore and analyze segregation, focusing on spatially derived measures and/or techniques. The literature is synthesized in two subsections. The first and largest subsection, Spatial Segregation, focuses on how race/ethnic residential segregation has been measured and recent spatial extensions to this. The second subsection, Exploratory Spatial Data Analysis, focuses on recent examples to incorporate exploratory spatial data analysis (ESDA) and spatial autocorrelation techniques, specifically measures of local spatial association, into the study of race/ethnic residential segregation.

                                                                                                                                                      Spatial Segregation

                                                                                                                                                      Reliable and meaningful measurement of residential segregation is essential to the study of the causes, patterns, and consequences of racial and socioeconomic segregation. Massey and Denton 1988 describes twenty different indexes in a comprehensive review of measures of segregation and identifies five key dimensions of segregation: evenness, exposure, concentration, centralization, and clustering. Today the most common conceptualization of residential segregation is based on the dimension of evenness (Reardon and O’Sullivan 2004), and the most popular measure of residential segregation, in general, is the index of dissimilarity, D. This measure is computationally straightforward to calculate from census data. While the index of dissimilarity was originally applied to a comparison of two different population groups, most often whites and blacks, recent papers have extended this measure to the multigroup context (see Reardon and Firebaugh 2002 for an overview). Geographers, most notably David Wong, have extended the index of dissimilarity in a different way by explicitly incorporating the spatial dimension (Wong 1993, Wong 2002, Wong 2004). White 1983 was among the first in sociology to consider and develop a spatially informed measure of segregation; White’s work included the use of proximity measures. An emerging preference in the race/ethnic segregation literature is another evenness measure, the entropy index (or the information theory index). The Entropy index, referred to as Theil’s H, measures the weighted average deviation of difference between an areal unit’s group proportions and that of a larger area (e.g., metropolitan area). Entropy or race/ethnic diversity is greatest when each group is equally represented in the area. Two recent evaluations of segregation indices find both the spatial and aspatial versions of H to be conceptually and mathematically superior to the more popular index of dissimilarity D (Reardon and Firebaugh 2002, Reardon and O’Sullivan 2004). The spatial H has been defined and applied in Reardon, et al. 2008. A recent paper on the scale of segregation is Osth, et al. 2014, and the most recent paper on the implementation of spatial segregation measures in R can be found in Hong, et al. 2014.

                                                                                                                                                      • Hong, Seong-Yun, David O’Sullivan, and Yukio Sadahiro. 2014. Implementing spatial segregation measures in R. PLOS One 9.11: e113767.

                                                                                                                                                        DOI: 10.1371/journal.pone.0113767Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                        Discusses the implementation of spatial segregation measures in R, package seg, including measures not available elsewhere (e.g., decomposable segregation measure). Seg offers advantages over other computationally demanding alternatives, and the output can be converted into general R classes for other statistical analysis and post-processing.

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                                                                                                                                                        • Massey, Douglas S., and Nancy A. Denton. 1988. The dimensions of residential segregation. Social Forces 67.2: 281–315.

                                                                                                                                                          DOI: 10.2307/2579183Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                          This is the classic paper in the field, providing a comprehensive review and grouping of twenty different measures of racial residential segregation. Measures are grouped into one of five dimensions of segregation: evenness, exposure, concentration, centralization, and clustering. The paper includes the formulas used for all indexes.

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                                                                                                                                                          • Osth, John, William A. V. Clark, and Bo Malmberg. 2014. Measuring the scale of segregation using k-nearest neighbor analysis. Geographical Analysis 47.1: 34–49.

                                                                                                                                                            DOI: 10.1111/gean.12053Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                            The authors propose an individually based spatial segregation measure (k-nearest neighbor spatial isolation) that uses population-count-based definitions of neighborhood scale (rather than administrative units). The approach is illustrated with applications for Los Angeles, California, and metropolitan areas in Sweden that show how scale influences segregation outcomes.

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                                                                                                                                                            • Reardon, Sean F., and Glenn Firebaugh. 2002. Measures of multigroup segregation. Sociological Methodology 32:33–67.

                                                                                                                                                              DOI: 10.1111/1467-9531.00110Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                              This paper develops methods for deriving and evaluating measures of multigroup segregation. The conceptual focus is on measures that capture the evenness dimension of segregation. In cases when evenness is the conceptual dimension of segregation of interest, the Thiel information theory index H is superior to all other measures.

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                                                                                                                                                              • Reardon, Sean F., Stephen A. Matthews, David O’Sullivan, et al. 2008. The geographic scale of metropolitan racial segregation. Demography 45.3: 489–514.

                                                                                                                                                                DOI: 10.1353/dem.0.0019Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                This paper addresses an aspect of racial residential segregation that has been largely ignored in prior work: the issue of geographical scale. The paper develops an approach—featuring the segregation profile and the corresponding macro/micro segregation ratio—that offers a scale-sensitive alternative to standard methodological practice for understanding segregation.

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                                                                                                                                                                • Reardon, Sean F., and David O’Sullivan. 2004. Measures of spatial segregation. Sociological Methodology 34:121–162.

                                                                                                                                                                  DOI: 10.1111/j.0081-1750.2004.00150.xSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                                  This paper defines a general approach to measuring spatial segregation among multiple population groups, one that allows researchers to specify any theoretically based definition of spatial proximity desired in computing segregation measures. The spatial information theory index is the most conceptually and mathematically satisfactory of the proposed spatial indices.

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                                                                                                                                                                  • White, Michael J. 1983. The measurement of spatial segregation. American Journal of Sociology 88.5: 1008–1018.

                                                                                                                                                                    DOI: 10.1086/227768Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                    White was one of the first to derive a measure of segregation that explicitly incorporates the spatial relationships (proximity) among geographic units (census tracts). With reference to 1970 census data, the proximity statistic is compared with other indices and is shown to be valuable because of its capacity to distinguish between single-cluster and multiple-cluster residential settlement patterns.

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                                                                                                                                                                    • Wong, David W. S. 1993. Spatial indices of segregation. Urban Studies 30.3: 559–572.

                                                                                                                                                                      DOI: 10.1080/00420989320080551Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                      Wong developed a family of segregation indices based on the index of dissimilarity that incorporates spatial components of the subunits (e.g., census tracts) used in the calculation; these spatial components include the length of the common boundary between two areal units and the size and shape (i.e., compactness) of areal units.

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                                                                                                                                                                      • Wong, David W. S. 2002. Spatial measures of segregation and GIS. Urban Geography 23.1: 85–92.

                                                                                                                                                                        DOI: 10.2747/0272-3638.23.1.85Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                        This is a short research note on Wong’s work to incorporate spatial measures of segregation into easy-to-use GIS (ArcView). One goal of the project was to provide easy access to these measures and thus to promote their use. The software calculates traditional and several different spatial dissimilarity indexes.

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                                                                                                                                                                        • Wong, David W. S. 2004. Comparing traditional and spatial segregation measures: A spatial scale perspective. Urban Geography 25.1: 66–82.

                                                                                                                                                                          DOI: 10.2747/0272-3638.25.1.66Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                          This is one of the first papers to explore whether segregation measures are sensitive to changes in spatial scale. Based on 1990 data for thirty US metro areas, Wong finds that spatial measures, like nonspatial measures, generate higher levels of segregation when smaller areal units are used in analysis.

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                                                                                                                                                                          Exploratory Spatial Data Analysis

                                                                                                                                                                          Exploratory spatial data analysis tools such as global and local measures of spatial autocorrelation (Moran’s I and G) also have been used in studies of race/ethnic segregation. In a detailed local study, Brown and Chung 2006 utilize Luc Anselin’s local Moran’s I. See work cited under Methodological Issues (Anselin 1995 and other works), and more recently in Johnston, et al. 2010, an example based on data from Auckland, New Zealand, that makes innovative use of the G statistic introduced in Getis and Ord 1992 (again, see work cited under Methodological Issues).

                                                                                                                                                                          • Brown, Lawrence A., and Su-Yeul Chung. 2006. Spatial segregation, segregation indices and the geographical perspective. Population, Space and Place 12.2: 125–143.

                                                                                                                                                                            DOI: 10.1002/psp.403Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                            Segregation is inherently geographical, but this dimension is seldom addressed in the dominant genre of empirical research. The authors critique global nonspatial approaches and apply local measures (the location quotient and local Moran’s I) to Franklin County (Columbus), Ohio. Analysis is based on local knowledge, fieldwork, and a mixed-method framework.

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                                                                                                                                                                            • Johnston, Ron, Michael Poulson, and James Forrest. 2010. Using spatial statistics to identify and characterise ethnoburbs: Establishing a methodology using the example of Auckland, New Zealand. GeoJournal (29 June).

                                                                                                                                                                              DOI: 10.1007/s10708-010-9366-6Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                              This is primarily methodological, focusing on one aspect of ethnoburbs only: their residential location and characteristics. It presents a procedure for examining the emergence and characteristics of ethnoburbs within an urban area, based on a well-established local statistics technique, G statistic (see Getis and Ord 1992, cited under Methodological Issues).

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                                                                                                                                                                              Demography

                                                                                                                                                                              Demography is an interdisciplinary field, but one in which sociologists are well represented. Indeed, sociologists who are demographers have been some of the leaders in the application and use of geospatial data and methods in the social sciences. Below there are two subsections. The first includes descriptions of several special issues in the field of “spatial demography” that span research over much of the past decade. The second section includes exemplar papers in spatial demography covering applications in both US and international research.

                                                                                                                                                                              Special Issues on Spatial Demography

                                                                                                                                                                              Focusing on demographic applications, special issues of several leading journals emphasize spatially informed research on demographic processes and outcomes. Watcher 2005 is an outstanding special issue of the Proceedings of the National Academy of Sciences on spatial demography, while Voss 2007 is a similarly titled special issue of Population Research and Policy Review. In the opening paper of the latter, Voss demonstrates that both sociology and demography had a strong spatial heritage dating back one hundred years. The increasing interest in spatial econometrics, local spatial modeling, and the bridging of micro (individual) and macro (geographically defined contexts) levels via the integration of new forms of geospatial data with multilevel models implies a solid future for spatial sociology and spatial demography. Matthews and Parker 2013 is the most recent overview of spatial analysis in demography in a leading demography journal, Demographic Research. Early-career scholars were the lead authors of all of the papers published in Demographic Research.

                                                                                                                                                                              • Matthews, Stephen A., and Daniel M. Parker. 2013. Progress in spatial demography. Demographic Research 28.10: 271–312.

                                                                                                                                                                                DOI: 10.4054/DemRes.2013.28.10Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                Serves as the introduction on spatial demography assembled for Demographic Research. The article is organized around four analytic methods: spatial econometrics, geographically weighted regression, multilevel modeling, and spatial pattern analysis. Each methodological area is reviewed and six papers in the Special Collection are introduced.

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                                                                                                                                                                                • Voss, Paul. 2007. Demography as a spatial social science. In Special issue: Spatial demography, part 1. Population Research and Policy Review 26.5: 457–476.

                                                                                                                                                                                  DOI: 10.1007/s11113-007-9047-4Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                  In the opening paper of two special issues of PRPR on spatial demography, Voss discusses the role of geographic space in quantitative demography and provides a review of spatial demography since the early 20th century. The growing interest in spatial econometrics suggests an exciting future for quantitative spatial demographers. The preceding two pages (introduction to the special issue of PRPR on spatial demography, pp. 455–456) are also recommended reading.

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                                                                                                                                                                                  • Watcher, Kenneth W. 2005. Spatial demography. In Special issue: Spatial demography. Proceedings of the National Academy of Sciences 102.43: 15299–15300.

                                                                                                                                                                                    DOI: 10.1073/pnas.0508155102Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                    Watcher provides a two-page introduction to a special issue of PNAS on spatial demography that includes papers by Hanson; Clark; Plane, et al.; Rogerson and Kim; Ellis and Wright; Forest; and VanWey, et al. As Watcher writes, they provide “a taste of current work in spatial demography.”

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                                                                                                                                                                                    Demographic Studies

                                                                                                                                                                                    Demographic projects are beginning to pay attention to the spatial characteristics of the phenomena being studied, and some are applying spatial statistical techniques that explicitly incorporate spatial relationships between geographic objects. Weeks 2004 and Castro 2007 have both provided detailed reviews of how demography has incorporated spatial thinking and analysis (see also both Voss 2007 and Watcher 2005, cited under Special Issues on Spatial Demography). Weeks’s chapter, though one of the first general overviews of spatial demography, still offers a useful synthesis of methods and a framework for the field. Castro’s paper is denser in content and provides a detailed review of spatial perspectives in core subfields of demography and, as its title suggests, is heavier on the implications of spatial analysis for population policy. While overviews have been useful in raising awareness of the advantage of spatial thinking, the real traction in disseminating a spatial perspective lies in the development and empirical application of spatial methods or the innovative use of geospatial data. Demographers have been active in promoting spatial analysis tools to their colleagues (see Voss, et al. 2006, cited under Spatial Inequality: State and Regional). A recent article-length primer on spatial econometrics was supplied in Chi and Zhu 2008. Innovation in data collected and methods used was illustrated in Entwisle, et al. 1997, a classic paper on contraceptive choice in Nang Rong, Thailand. In fertility research, Guilmoto and Rajan 2001 provides a rare illustration of spatial correlograms and was perhaps among the first demography papers to utilize kriging methods. Castro, et al. 2006 shows how spatial pattern analysis tools can be used to study malaria outbreaks and inform public policy. Environmental justice in the United States is a vibrant research area that has seen numerous applications of geographic information systems (GIS) and spatial analysis, and research that is increasingly more sophisticated in terms of data and method (see Mennis and Jordan 2005, Downey 2003, and Downey 2006, all cited under Spatial Inequality: Local). An early and important study of environmental justice is Anderton, et al. 1994. Liverman, et al. 1998 and Fox, et al. 2003 are excellent edited collections that discuss the challenges and opportunities associated with the use of remote sensing, GIS, and spatial econometrics and demonstrate how these tools have been used effectively to analyze the relationship between human activities and local environmental change.

                                                                                                                                                                                    • Anderton, Douglas L., Andrew B. Anderson, John M. Oakes, and Michael R. Fraser. 1994. Environmental equity: The demographics of dumping. Demography 31.2: 229–248.

                                                                                                                                                                                      DOI: 10.2307/2061884Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                      An insightful paper, on the emotionally charged subject of environmental equity, revealing how findings may vary by the geographic units of analysis one chooses. Tract-level results in US metro areas do not support earlier research on environmental racism or inequity in location of facilities for treatment, storage, or disposal of hazardous waste.

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                                                                                                                                                                                      • Castro, Marcia Caldas de. 2007. Spatial demography: An opportunity to improve policy making at diverse decision levels. Population Research and Policy Review 26.5–6: 477–509.

                                                                                                                                                                                        DOI: 10.1007/s11113-007-9041-xSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                        This is a review of demographic studies that specifically addressed space with spatial statistical models, and that focused on core areas of demography: fertility, mortality, migration, and population models. Castro argues that spatial demography can make a significant contribution to the monitoring, evaluation, and implementation of population policies.

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                                                                                                                                                                                        • Castro, Marcia Cadas de, R. L. Monte-Mór, D. O. Sawyer, and Burton H. Singer. 2006. Malaria risk on the Amazon frontier. Proceedings of the National Academy of Sciences 103.7: 2452–2457.

                                                                                                                                                                                          DOI: 10.1073/pnas.0510576103Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                          Spatial statistical analysis revealed that the early stages of frontier settlement are dominated by environmental risks, consequential to ecosystem transformations that promote larval habitats of Anopheles darling. Risks of new malaria infection are largely driven by human behavioral factors. This type of research is significant in crafting policies for malaria mitigation.

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                                                                                                                                                                                          • Chi, Guangqing, and Jun Zhu. 2008. Spatial regression models for demographic analysis. In Special Issue: Spatial Demography. Population Research and Policy Review 27.1: 17–42.

                                                                                                                                                                                            DOI: 10.1007/s11113-007-9051-8Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                            This paper appeared in a special issue of PRPR on spatial demography. It is a short primer on both exploratory spatial data analysis and spatial regression models with an application to population change in Wisconsin. The paper closes with a discussion of future opportunities and directions in spatial demographic research.

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                                                                                                                                                                                            • Entwisle, Barbara, Ronald R. Rindfuss, Stephen J. Walsh, Tom P. Evans, and Sara R. Curran. 1997. Geographic information systems, spatial network analysis, and contraceptive choice. Demography 34.2: 171–187.

                                                                                                                                                                                              DOI: 10.2307/2061697Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                              One of the first papers to appear in Demography that explicitly used geospatial data (GIS and GPS) and spatial methods (spatial network analysis). GPS data were integrated with survey and administrative records for Nang Rong, Thailand, permitting a more nuanced analysis of contraceptive choice in the study area.

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                                                                                                                                                                                              • Fox, Jefferson, Ronald R. Rindfuss, Stephen. J. Walsh, and Vinod Mishra, eds. 2003. People and the environment: Approaches for linking household and community surveys to remote sensing and GIS. Boston: Kluwer.

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                                                                                                                                                                                                Empirical research on the reciprocal relations between population dynamics and the natural environment at the local level have been quite rare, but as shown in this edited collection, the research environment is changing fast as population scientists begin to integrate GIS, remote sensing, and spatial analysis methods.

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                                                                                                                                                                                                • Guilmoto, Christophe Z., and S. Irudaya Rajan. 2001. Spatial patterns of fertility transition in Indian districts. Population and Development Review 27.4: 713–738.

                                                                                                                                                                                                  DOI: 10.1111/j.1728-4457.2001.00713.xSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                  Regional variation in fertility is explored by means of a variety of spatial and statistical methods. Geostatistical analysis (kriging) and global autocorrelation statistic (Moran’s I and spatial correlograms) reveal that spatial variations in fertility across districts are not random, and that the spatial structure of fertility decline has intensified over time.

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                                                                                                                                                                                                  • Liverman, Diana, Emilo F. Moran, Ronald R. Rindfuss, and Paul C. Stern, eds. 1998. People and pixels: Linking remote sensing and social science. Washington, DC: National Academy Press.

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                                                                                                                                                                                                    This report summarizes the successes and failures that have emerged from efforts to link remote sensing and social science research. The opening chapter (Rindfuss and Stern) addresses how the gap can be bridged between remote sensing (not a new technology) and social science. Leading sociologists/demographers contributed to the report.

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                                                                                                                                                                                                    • Weeks, John R. 2004. The role of spatial analysis in demographic research. In Spatially integrated social science. Edited by Michael F. Goodchild and Donald G. Janelle, 381–399. New York: Oxford Univ. Press.

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                                                                                                                                                                                                      Weeks provides a general framework for the application of spatial analysis to demographic research, concentrating on spatial analysis based on (1) environmental contexts and (2) networks and connections between locations. Data requirements for spatial analysis are reviewed and followed by an application of spatial analysis to fertility change in Egypt.

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                                                                                                                                                                                                      Crime

                                                                                                                                                                                                      The mapping of crime and crime-related outcomes has an important place in the early history of cartography, specifically the “morale statistique” period of the 1820s and 1830s in France. While there have been many important contributions to the geographical study of crime, the past decade in particular has seen a reemergence of an explicitly spatial analytical perspective in studies of crime (Goldsmith, et al. 2000). There have been several important overviews of spatial analysis applied to crime (Anselin, et al. 2000), and there is growing utilization of both exploratory spatial data analysis (Messner, et al. 1999) and spatial regression–based methods by criminologists and sociologists (Baller, et al. 2001; Morenoff, et al. 2001). Indeed, as conceptual and theoretical developments as well as the growth in “spatial thinking” have coalesced, the sophistication and creative use of spatial methods in the field has grown (e.g., local indicators of spatial association by Cohen and Tita 1999 and Tita and Cohen 2004 and geographically weighted regression by Graif and Sampson 2009).

                                                                                                                                                                                                      • Anselin, Luc., Jacqueline Cohen, David Cook, Wilpen Gorr, and George Tita. 2000. Spatial analyses of crime. In Measurement and analysis of crime and justice. Edited by David Duffee, 213–262. Criminal Justice 2000 4. Washington, DC: National Institute of Justice.

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                                                                                                                                                                                                        This article provides both an illustrative theoretical overview of crime and place (social ecology and place-based theories of crime) and an introduction to spatial data analysis tools and spatial modeling. Summaries and discussions of salient methodological concerns related to the analysis of spatial and space/time data are also provided.

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                                                                                                                                                                                                        • Baller, Robert. D., Luc Anselin, Steven F. Messner, Glenn Deane, and Darnell F. Hawkins. 2001. Structural covariates of U.S. county homicide rates: Incorporating spatial effects. Criminology 39.3: 561–590.

                                                                                                                                                                                                          DOI: 10.1111/j.1745-9125.2001.tb00933.xSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                          This paper provides a clear and concise overview of conceptual and theoretical frameworks for spatial analysis. Methodologically the starting point draws on exploratory spatial data analysis (ESDA) methods. ESDA helps identify spatial clusters and outliers and can diagnose possible model misspecification and inform the spatial econometric modeling that follows.

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                                                                                                                                                                                                          • Cohen, Jaqueline, and George Tita. 1999. Diffusion in homicide: Exploring a general method for detecting spatial diffusion processes. Journal of Quantitative Criminology 15.4: 451–493.

                                                                                                                                                                                                            DOI: 10.1023/A:1007596225550Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                            Cohen and Tita illustrate the integration of spatial diffusion modeling in the analysis of homicide patterns in Pittsburgh. They extend the use of the Moran scatterplot to explore dynamic features of change over time in spatial dependencies and show how the method can differentiate between contagious and hierarchical diffusion processes.

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                                                                                                                                                                                                            • Goldsmith, Victor, Philip G. McGuire, John H. Mollenkopf, and Timothy A. Ross. 2000. Analyzing crime patterns: Frontiers of practice. Thousand Oaks, CA: SAGE.

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                                                                                                                                                                                                              Not the first book but an early edited collection published at a time of renewed interest in geographic information systems and spatial analytical methods for the study of crime. The chapters are written by both practitioners and academics and cover a wide mix of analytical methods.

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                                                                                                                                                                                                              • Graif, Corina, and Robert J. Sampson. 2009. Spatial heterogeneity in the effects of immigration and diversity on neighborhood homicide rates. Homicide Studies 13.3: 242–260.

                                                                                                                                                                                                                DOI: 10.1177/1088767909336728Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                This is one of the first studies to utilize geographically weighted regression (GWR) methods in a study of crime. GWR can help identify nonstationarity in the relationship between outcome and predictors across a study site. Findings suggest that the same neighborhood characteristics differentially predict homicide rates in different parts of Chicago.

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                                                                                                                                                                                                                • Messner, Steven F., Luc Anselin, Robert D. Baller, Darnell F. Hawkins, Glenn Deane, and Stewart E. Tolnay. 1999. The spatial patterning of county homicide rates: An application of exploratory spatial analysis. Journal of Quantitative Criminology 15.4: 423–450.

                                                                                                                                                                                                                  DOI: 10.1023/A:1007544208712Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                  This paper is an early application of exploratory spatial data analysis, focusing on homicide rates in counties in and around the St. Louis metro area. Evidence that there is a strong positive spatial autocorrelation of homicide rates and that some of the relations between homicide and its covariates are not stable across space is observed.

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                                                                                                                                                                                                                  • Morenoff, Jeffery D., Robert J. Sampson, and Stephen W. Raudenbush. 2001. Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology 39.3: 517–560.

                                                                                                                                                                                                                    DOI: 10.1111/j.1745-9125.2001.tb00932.xSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                    Morenoff and colleagues have used a combination of multilevel and spatial models in several of their papers. In this paper the local Moran’s I is used to measure the spatial dependence in homicide, and collective efficacy and spatial lag regression models and spatial regime models are employed.

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                                                                                                                                                                                                                    • Tita, George, and Jaqueline Cohen. 2004. Measuring spatial diffusion of shots fired activity across city neighborhoods. In Spatially integrated social science. Edited by Michael F. Goodchild and Donald G. Janelle, 171–204. New York: Oxford Univ. Press.

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                                                                                                                                                                                                                      Tita and Cohen provide an overview of different diffusion processes and their application to gun violence in Pittsburgh. Their work explores the spatial features (spatial clustering) of gunshots over time using an original application of local indicators of spatial association (LISA) to look at neighborhood transitions in exposure to gunshots.

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                                                                                                                                                                                                                      Spatial Sociology

                                                                                                                                                                                                                      While race/ethnic segregation and demographic studies have seen much attention in the use of spatial data and methods, the emergence of spatial thinking and perspectives in other areas of sociological inquiry is now evident as well. Included below are sections on spatial dimensions in the sociology of religion, the sociology of education, and in neighborhoods and health, with an emphasis on mental health.

                                                                                                                                                                                                                      Spatial Dimensions of the Sociology of Religion

                                                                                                                                                                                                                      Understanding where religions spread over time and their impact on private and public life has captured the attention of many social scientists. In the 1960s and 1970s, descriptive papers by geographers identified distinct religious regions in the United States where groups such as Mainline Protestants, Catholics, and others were the most dominant (revisited by Bauer 2012). Tracking changes in the distribution and size of different groups is still popular in research on religion, but explicitly spatial studies have largely been limited to the United States. This is likely because geographic data on religious groups has been more readily available in the United States, a country that is also regarded as unique for its high levels of religious participation and pluralism. County- and state-level data sets on religious membership collected by the US census in the early 1900s, and distributed by other organizations since 1952 (e.g., the American Religion Data Archive [ARDA]), were used by many scholars during the late 20th century to assess the relationship between religious pluralism and religious participation (Land, et al. 1991; Warf and Winsberg 2008). Other studies have used the county-level data to analyze contextual influences on trends of religious growth and decline (Stump 1998) and to document spatial shifts in religious group dominance (Crawford 2005, Bauer 2012). Among the explicitly spatial papers on religion, the methods used have been primarily descriptive (Newman and Halvorson 2000; Warf and Winsberg 2010; Bauer 2012), but occasional papers have incorporated simple spatial statistics such as mean centering and deviational ellipses (Crawford 2005; Ayhan, et al. 2010) and even spatial regression models (Land, et al. 1991). Recent years have seen a rapid expansion of spatial data on religious sentiment or attitudes in the general population as well as religious congregations and denominations in local places, focusing on the community. Some researchers are investigating new religious forms such as megachurches that are transforming suburbia In the United States (Warf and Winsberg 2010), others are exploring how far people travel for their religious experience (Sinha, et al. 2007) and what the historical religious landscape can tell us about urban development (Ayhan, et al. 2010). As more data become available and interest in the global trends of religion grows, we should expect more articles to assess the spatial aspects of religion outside of the United States and Christianity (Ayhan, et al. 2010).

                                                                                                                                                                                                                      • Association of Religion Data Archives.

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                                                                                                                                                                                                                        This website is the premier source for downloading survey and aggregate data on religion from the local to the national scale. In addition to state and county maps of religious groupings and other social variables, the “GIS tool” can generate community profiles of congregations in the location of your choice.

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                                                                                                                                                                                                                        • Ayhan, Irem K., and Mert Cubukcu. 2010. Explaining historical urban development using the locations of mosques: A GIS/spatial statistics-based approach. Applied Geography 30:229–238.

                                                                                                                                                                                                                          DOI: 10.1016/j.apgeog.2009.05.002Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                          This paper uses spatial statistics (mean center, weighted mean center, and standard deviational ellipse) to derive the historical urban development of Izmir, Turkey, from 1550 to 2008 based on the distribution of 525 mosques. Relationships between sacred places and community building make mosque age and placement an excellent proxy for development.

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                                                                                                                                                                                                                          • Bauer, John T. 2012. U.S religious regions revisited. The Professional Geographer 64:521–539.

                                                                                                                                                                                                                            DOI: 10.1080/00330124.2011.611429Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                            Expands upon descriptive studies from the 1960s and 1970s that first identified religious regions in the United States. Using maps of multivariate cluster analysis on county-level membership, Bauer describes the stability and change of eight religious regions in 1980 and 2010.

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                                                                                                                                                                                                                            • Crawford, Thomas W. 2005. Stability and change on the American religious landscape: A centrographic analysis of major US religious groups. Journal of Cultural Geography 22:51–86.

                                                                                                                                                                                                                              DOI: 10.1080/08873630509478239Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                              Uses centrographic spatial statistics (weighted mean centers and standard deviational ellipses) to analyze patterns of change and stability of the largest Christian denominations at the county level. Catholics, Mormons, and Seventh-day Adventists moved the most from 1980 to 2000, while Southern Baptists were the most spatially stable.

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                                                                                                                                                                                                                              • Land, Kenneth C., Glenn Deane, and Judith R. Blau. 1991. Religious pluralism and church membership: A spatial diffusion model. American Sociological Review 56:237–249.

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                                                                                                                                                                                                                                One of many studies on religious pluralism in early-20th-century America, this paper analyzes the structural determinants of church membership at the county level. More importantly, it introduces the spatial effects regression model to the sociology of religion, describing the calculation of the spatial lag variable for total church membership.

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                                                                                                                                                                                                                                • Newman, William M., and Peter L. Halvorson. 2000. Atlas of American religion: The denominational era, 1776–1990. Lanham, MD: Rowman & Littlefield.

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                                                                                                                                                                                                                                  An indispensable reference, this atlas focuses on thirty-nine major denominations and documents spatial shifts at regional and national scales over the last two centuries. It discusses and utilizes multiple sources of religious data, and the authors present a new classification typology that accounts for the geographic distribution of each denomination.

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                                                                                                                                                                                                                                  • Sinha, Jill W., Amy Hillier, Ram A. Cnaan, and Charlene C. McGrew. 2007. Proximity matters: Exploring relationships among neighborhoods, congregations, and the residential patterns of members. Journal for the Scientific Study of Religion 46:245–260.

                                                                                                                                                                                                                                    DOI: 10.1111/j.1468-5906.2007.00354.xSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                    Using GIS to link the addresses of more than a thousand Philadelphia congregations to their census tracts, this article explores what makes “commuter” congregations different from those whose members live nearby. They find that these congregation types are distinct in terms of race, denomination, size, and neighborhood characteristics.

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                                                                                                                                                                                                                                    • Stump, Roger W. 1998. The effects of geographical variability on Protestant Church membership trends, 1980–1990. Journal for the Scientific Study of Religion 37.4: 636–651.

                                                                                                                                                                                                                                      DOI: 10.2307/1388146Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                      Examines recent trends (mainline decline and conservative growth) by predicting the likelihood of growth and decline for eight denominations. Membership distributions are compared with maps of the probability of growth; mainline denominations are concentrated in counties where local contexts limit growth while conservative groups benefit from favorable contexts.

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                                                                                                                                                                                                                                      • Warf, Barney, and Mort Winsberg. 2008. The geography of religious diversity in the United States. The Professional Geographer 60:413–424.

                                                                                                                                                                                                                                        DOI: 10.1080/00330120802046786Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                        In this article, Warf and Winsberg use Dorling cartograms to show where four different indices (including Shannon’s Index and Simpson’s Index) diverge and converge in identifying regions of American religious diversity in 2000. They find consistently high religious diversity in the northwest and the area between Denver and Pittsburgh.

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                                                                                                                                                                                                                                        • Warf, Barney, and Mort Winsberg. 2010. Geographies of megachurches in the United States. Journal of Cultural Geography 27:33–51.

                                                                                                                                                                                                                                          DOI: 10.1080/08873631003593216Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                          Delves into the contexts for the growth of megachurches, which are explored at the county level and in case studies of Chicago, Dallas-Ft. Worth, and Atlanta. They map the size of congregations in these cities in detail and discuss their spatial placement in relation to race and suburbia.

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                                                                                                                                                                                                                                          Sociology of Education

                                                                                                                                                                                                                                          The extant literature focusing on the ways in which spatial factors shape educational opportunities and outcomes has expanded rapidly over the past decade. Two special issues of key academic journals in the field, specifically Urban Studies and the American Journal of Education, have focused on issues broadly relating to geography and education (Butler and Hamnett 2007 and Lubienski and Dougherty 2009). Further exhibiting the increasing importance of using spatial thinking when studying education, an influential primer, Spatial Theories of Education: Policy and Geography Matters, includes a range of published articles in the field (Gulson and Symes 2007). Recent conceptual articles have pushed spatial thinking in educational research forward and emphasized the hierarchy of scale in which educational spaces are embedded (Taylor 2009 and Collins and Coleman 2008). In addition to theoretical and conceptual advances, the field has witnessed methodological developments, specifically utilizing multilevel modeling techniques (Garner and Raudenbush 1991), spatial econometric methods (Ainsworth 2008 and Saporito and Sohoni 2007), and longitudinal data to assess causal pathways (Luke, et al. 2000). Research on the link between education and place has also focused on a variety of contexts including urban, rural, international, and domestic (Gulson and Symes 2007, Butler and Hamnett 2007, and Lubienski and Dougherty 2009), as well as different types of schools (public, private, charter, etc.) (Eras and Roch 2014).

                                                                                                                                                                                                                                          • Ainsworth, James W. 2008. Why does it take a village? The mediation of neighborhood effects on educational achievement. Social Forces 81.1: 117–152.

                                                                                                                                                                                                                                            DOI: 10.1353/sof.2002.0038Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                            Using nationally representative longitudinal data linked to Census records, Ainsworth investigates variables mediating relationships between neighborhoods and education, linking micro- (individual) and macro- (structural) level processes. High SES residents and residential stability are linked to educational outcomes, and these relationships are mediated by educational/occupational expectations, school atmosphere, and student networks.

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                                                                                                                                                                                                                                            • Butler, Tim, and Chris Hamnett. 2007. The geography of education: Introduction. Urban Studies 44.7: 1161–1174.

                                                                                                                                                                                                                                              DOI: 10.1080/00420980701329174Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                              The authors introduce this special issue of Urban Studies focusing on geographic variation in educational outcomes and delivery. Articles therein situate these phenomena within broader urban problems such as race and class segregation, gentrification, and suburbanization. Generally, the issue investigates school’s contribution to the (re)-production of “concentrated disadvantage” and “social exclusion.”

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                                                                                                                                                                                                                                              • Collins, Damian, and Tara Coleman. 2008. Social geographies of education: Looking within, and beyond, school boundaries. Geography Compass 2.1: 281–299.

                                                                                                                                                                                                                                                DOI: 10.1111/j.1749-8198.2007.00081.xSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                Schools can be conceptualized as both singularly bounded places and places within the larger social landscape. Hence, they are simultaneously vehicles of socialization with many “hidden geographies” and reflections/reproducers of “place-based identities” of the particular geographies in which they are embedded. The relationship between schools and communities is reciprocal.

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                                                                                                                                                                                                                                                • Eras, Nevbahar, and Christine Roch. 2014. Charter schools, equity, and student enrollments: The role of for-profit educational management. Education and Urban Society 46.5: 548–579.

                                                                                                                                                                                                                                                  DOI: 10.1177/0013124512458118Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                  The authors investigate the “locational choices” of both public and two types of charter schools: those funded via a nonprofit organization and those run by educational management organizations for-profit. The authors find variation in the location of these schools by Census tract level characteristics such as urbanity and race.

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                                                                                                                                                                                                                                                  • Garner, Catherine L., and Stephen W. Raudenbush. 1991. Neighborhood effects on educational attainment: A multilevel analysis. Sociology of Education 64.4: 251–262.

                                                                                                                                                                                                                                                    DOI: 10.2307/2112706Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                    This pioneering article in the field utilizes multilevel modeling techniques to investigate neighborhoods’ effects on educational outcomes in Scotland. Neighborhood deprivation and educational achievement are inversely related after controlling for individual and school variables. Students in lower SES neighborhoods are “multiply deprived”; educational disparities are “multivariate and multilevel.”

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                                                                                                                                                                                                                                                    • Gulson, Kalervo N., and Colin Symes. 2007. Spatial theories of education: Policy and geography matters. New York: Routledge.

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                                                                                                                                                                                                                                                      In this collection of fourteen articles focusing on theory and qualitative findings in the education/place literature, the authors situate the emergent spatial focus in education within a broader “spatial turn” in modern social theory. This primer provides a good foundation to the literature and raises important avenues for future research.

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                                                                                                                                                                                                                                                      • Lubienski, Christopher, and Jack Dougherty. 2009. Introduction: Mapping educational opportunity: Spatial analysis and school choices. American Journal of Education 15.4: 485–491.

                                                                                                                                                                                                                                                        DOI: 10.1086/599783Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                        The authors introduce this special issue in AJE on spatial concerns in school choice and review the articles therein. An underused tool in an inherently spatial topic, educational research is beginning to incorporate mapping technologies (GIS) and increasingly refined conceptions of space/place. Movement beyond standard measures of space is imperative.

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                                                                                                                                                                                                                                                        • Luke, Douglas, Emily Esmundo, and Yael Bloom. 2000. Smoke signs: Patterns of tobacco billboard advertising in a metropolitan region. Tobacco Control 9:16–23.

                                                                                                                                                                                                                                                          DOI: 10.1136/tc.9.1.16Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                          Maps specific physical characteristics of a city using GIS. The researchers map billboards advertising tobacco in St. Louis and, among other objectives, look at their proximity to schools. Their findings confirm the concentration of tobacco billboards in low-income, ethnic minority neighborhoods and near public schools.

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                                                                                                                                                                                                                                                          • Saporito, Salvatore, and Deenesh Sohoni. 2007. Mapping educational inequality: Concentrations of poverty among poor and minority students in public schools. Social Forces 85.3: 1227–1253.

                                                                                                                                                                                                                                                            DOI: 10.1353/sof.2007.0055Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                            Using data from twenty-one large school districts in the United States and census block data, the authors use a “population weighted geographic interpolation method” to investigate incongruences between school administrative boundaries and “neighborhood” school enrollment data. They find differences in exposure between schools and neighborhoods with respect to racial and socioeconomic composition.

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                                                                                                                                                                                                                                                            • Taylor, Chris. 2009. Towards a geography of education: Taylor. Oxford Review of Education 35.5: 651–669.

                                                                                                                                                                                                                                                              DOI: 10.1080/03054980903216358Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                              Provides an overview of published literature incorporating geography and education, emphasizing the interdisciplinary nature of the field. Varying hierarchical scales and specific linked processes are reviewed culminating with a “geography of education” framework. Taylor discusses recent methodological and theoretical advances and concludes with areas for future academic scholarship.

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                                                                                                                                                                                                                                                              Mental Health and Place

                                                                                                                                                                                                                                                              The breadth of research on the relationship between place and mental health is remarkable. Tracing its roots to the early Chicago human ecology school, the field has seen growth in theoretical frameworks, methodology, and causal conclusions over the past several decades (Curtis 2010). Recent work has seen more fully developed conceptual models (Wandersman and Nation 1998), more refined conceptualization of “neighborhood effects” with respect to mental health (Diez Roux and Mair 2010), and issues relating to spatial scale in the place/mental health relationship (Diez Roux and Mair 2010 and Chaix, et al. 2006). A thorough review of the literature was conducted by March, et al. 2008 and provides a good starting place for reviewing important literature in the field (March, et al. 2008). The field has seen substantial growth in sophisticated methodology, specifically the utilization of twin studies (Caspi, et al. 2000), natural experiments (Leventhal and Brooks-Gunn 2003), multilevel modeling (Vallée, et al. 2011), activity space/mental mapping techniques (Vallée, et al. 2011), and spatial econometric methods (Chaix, et al. 2006). Research on the locational effects on mental health has also incorporated a spatio-temporal approach, linking time and space via longitudinal data (Wheaton and Clarke 2003). Indeed, central pathways and causal models have been developed within the field, pushing both the mental health and the spatial effects literature (in general) further (Latkin and Curry 2003).

                                                                                                                                                                                                                                                              • Caspi, Avshalom, Alan Taylor, Terrie E. Moffitt, and Robert Plomin. 2000. Neighborhood deprivation affects children’s mental health: Environmental risks identified in a genetic design. Psychological Science 11.4: 338–342.

                                                                                                                                                                                                                                                                DOI: 10.1111/1467-9280.00267Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                                Utilizing monozygotic and dizygotic twins and the logic of a natural experiment, the authors used structural equation modeling to investigate the effects of neighborhood deprivation on children’s mental well-being. As one of the many factors affecting children’s mental health, the neighborhood exerts a significant independent effect on children’s behavior.

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                                                                                                                                                                                                                                                                • Chaix, Basile, Alastair H. Leyland, Clive E. Sabel, et al. 2006. Spatial clustering of mental disorders and associated characteristics of the neighbourhood context in Malmö, Sweden, in 2001. Journal of Epidemiology and Community Health 60.5: 427–435.

                                                                                                                                                                                                                                                                  DOI: 10.1136/jech.2005.040360Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                                  The authors use spatial econometric methods to investigate geographic distributions of two varieties of mental disorders. To address issues with scale, they utilized the spatial scan technique. They found differences between neighborhood deprivation/disorganization, and neurotic vs. substance-induced disorders both in the magnitude of the relationship and the accurate spatial scale.

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                                                                                                                                                                                                                                                                  • Curtis, Sarah. 2010. Space, place and mental health. Farnham, UK: Ashgate.

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                                                                                                                                                                                                                                                                    This textbook offers an excellent overview of “emotional geographies” by conceptualizing key terms, introducing theoretical frameworks, and reviewing the extant literature. The author distinguishes between location-based environmental and social processes, and the “relational” and “conventional” understandings of these processes. Further, challenges and opportunities for future research are detailed.

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                                                                                                                                                                                                                                                                    • Diez Roux, Ana V., and Christina Mair. 2010. Neighborhoods and health. Annals of the New York Academy of Science 1186:125–145.

                                                                                                                                                                                                                                                                      DOI: 10.1111/j.1749-6632.2009.05333.xSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                                      Stressing the physical and social attributes of place, the authors differentiate three types of neighborhood effects: “endogenous,” “contextual,” “environmental,” and “synergistic” effects. The authors describe innovative methods (including propensity score matching, quasi-experiments, and marginal structural models) and discuss issues with spatial scale. Prior research and future avenues therein are explored.

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                                                                                                                                                                                                                                                                      • Latkin, Carl A., and Aaron D. Curry. 2003. Stressful neighborhoods and depression: A prospective study of the impact of neighborhood disorder. Journal of Health and Social Behavior 44.1: 34–44.

                                                                                                                                                                                                                                                                        DOI: 10.2307/1519814Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                                        The authors investigate effects of neighborhood perceptions on depression and found that CES-D scores were higher among those who perceived more social disorder in their neighborhoods. Social support, however, did not benefit these deleterious effects. Persisting feedback loops link neighborhood social disorder and depression among those with worse neighborhood perceptions

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                                                                                                                                                                                                                                                                        • Leventhal, Tama, and Jeanne Brooks-Gunn. 2003. Moving to Opportunity: An experimental study of neighborhood effects on mental health. American Journal of Public Health 93.9: 1576–1582.

                                                                                                                                                                                                                                                                          DOI: 10.2105/AJPH.93.9.1576Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                                          The authors discuss the mental health findings from the MTO study, a landmark experimental study in the neighborhood effects literature. Using an instrumental variable approach to estimate TOT effects, the authors found significant mental health improvements for subjects who moved to higher SES neighborhoods, most notably for boys and children.

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                                                                                                                                                                                                                                                                          • March, Dana, Stephani L. Hatch, Craig Morgan, et al. 2008. Psychosis and place. Epidemiological Reviews 30.1: 84–100.

                                                                                                                                                                                                                                                                            DOI: 10.1093/epirev/mxn006Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                                            Tracing the history of research on spatial variation in mental illness, the authors assess the literature. They discuss differences in research design, sampling, and measurement, and the range of disease studied, sample attributes, causal conclusions among forty-four studies. An exhaustive review, this article references many important studies in the field.

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                                                                                                                                                                                                                                                                            • Vallée, Julie, Emmanuelle Cadot, Christine Roustit, Isabelle Parizot, and Pierre Chauvin. 2011. The role of daily mobility in mental health inequalities: The interactive influence of activity apace and neighborhood of residence on depression. Social Science and Medicine 73.8: 1133–1144.

                                                                                                                                                                                                                                                                              DOI: 10.1016/j.socscimed.2011.08.009Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                                              The authors utilize a sample of Parisians and multilevel modeling techniques to illustrate positive effects of neighborhood deprivation on depression. Further, they found that reporting smaller activity spaces confined to perceived neighborhoods magnified this effect. These findings emphasize accounting for the range of places occupied by people across time.

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                                                                                                                                                                                                                                                                              • Wandersman, Abraham, and Maury Nation. 1998. Urban neighborhoods and mental health: Psychological contributions to understanding toxicity, resilience, and interventions. American Psychologist 53.6: 647–656.

                                                                                                                                                                                                                                                                                DOI: 10.1037/0003-066X.53.6.647Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                                                The authors present three conceptual models exploring how neighborhoods affect mental health outcomes. They discuss both direct and indirect effects of the structural, physical, and social aspects of “healthy” and “toxic” places on mental health. The authors present theoretically driven intervention strategies and particular topics requiring future research.

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                                                                                                                                                                                                                                                                                • Wheaton, Blair, and Philippa Clarke. 2003. Space meets time: Integrating temporal and contextual influences on mental health in early adulthood. American Sociological Review 68.5: 680–706.

                                                                                                                                                                                                                                                                                  DOI: 10.2307/1519758Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                                                  Stressing the spatio-temporal interconnection, the authors use a life course approach and a “cross-nested random effects model” to investigate the mental health effects of low SES neighborhoods until adolescence using longitudinal data. The authors found evidence for “compound disadvantage,” lagged effects, and neighborhood effects independent of individual or family attributes.

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                                                                                                                                                                                                                                                                                  Activity Space Research

                                                                                                                                                                                                                                                                                  The development and utilization of data from global positioning system (GPS) technologies and other wireless technologies (cellphones) has given rise to a growing interest in measuring human spatial behavior and in the context of sociological, health, and demographic studies an individual exposure to geographical contexts. The field of activity space research, while it has origins back to the 1950s and 1960s, is growing rapidly due to new technologies and the ability to collect and use activity space data. An overview of the growth of interest in activity space research can be found in Matthews and Yang 2013. The breadth of methods used to collect data necessary for activity space research are revealed in the examples below: conventional address data on key activity locations (Jones and Pebley 2014), recreating journeys through tablet computers (Basta, et al. 2010), GPS tracking (integrated with accelerometers) (Rainham, et al. 2012 and Zenk, et al. 2011), and cellphones (Palmer, et al. 2013). GPS and related studies are common in obesity research (Rainham, et al. 2012 and Zenk, et al. 2011). Activity space studies are of interest to those seeking to better understand contextual effects on health (or exposures) and encourage researchers to focus attention on an individual’s movement throughout the day rather than relying on the residential address and residential neighborhood to define the individual’s exposure to risks (e.g., crime, pollution, fast food) and resources (healthy food choices, parks, health care).

                                                                                                                                                                                                                                                                                  • Basta, L. A., T. S. Richmond, and D. J. Wiebe. 2010. Neighborhoods, daily activities, and measuring health risks experienced in urban environments. Social Science and Medicine 71.11: 1943–1950.

                                                                                                                                                                                                                                                                                    DOI: 10.1016/j.socscimed.2010.09.008Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                                                    Adolescent hand-drawn neighborhoods and reported activity paths did not correspond to census tract boundaries or other administratively defined boundaries (block, blockgroup, tract, ZIP code, or local municipally defined neighborhood). Youth activity paths intersected with numerous tracts and most of the time spent outdoors was outside of the residential census tract.

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                                                                                                                                                                                                                                                                                    • Jones, Malia, and Anne R. Pebley. 2014. Redefining neighborhoods using common definitions: Social characteristics of activity spaces and home census tract compared. Demography 51.3: 727–752.

                                                                                                                                                                                                                                                                                      DOI: 10.1007/s13524-014-0283-zSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                                                      This paper uses data from the Los Angeles Families and Neighborhoods Study to compare the socioeconomic characteristics of residential neighborhoods and adult activity spaces (as defined by the locations they routinely visit). Activity spaces are more heterogeneous than residential neighborhoods, but the characteristics of both are associated with individual characteristics.

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                                                                                                                                                                                                                                                                                      • Matthews, Stephen A., and Tse-Chuan Yang. 2013. Spatial Polygamy and Contextual Exposures (SPACEs): Promoting activity space approaches in research on place and health. American Behavioral Scientist 57.8: 1057–1081.

                                                                                                                                                                                                                                                                                        DOI: 10.1177/0002764213487345Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                                                        The authors introduce the concept of spatial polygamy to demonstrate the need to collect new forms of data on human spatial behavior and contextual exposures across time and space. They discuss the opportunities and challenges offered by focusing on human mobility and the utilization of activity space concepts and data.

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                                                                                                                                                                                                                                                                                        • Palmer, John R., Thomas J. Espenshade, Frederic Bartumeus, Chang Y. Chung, Necati E. Ozgencil, and Kathleen Li. 2013. New approaches to human mobility: using mobile phones for demographic research. Demography 50.3: 1105–1128.

                                                                                                                                                                                                                                                                                          DOI: 10.1007/s13524-012-0175-zSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                                                          Reports on a pilot study that uses cellphones to gather demographic data, location, and respondent mobility data. The paper examines data in the context of activity space, spatial segregation, and subjective well-being. The potential exists to harness new technologies to examine social and demographic outcomes dynamically.

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                                                                                                                                                                                                                                                                                          • Rainham, D. G., C. J. Bates, C. M. Blanchard, T. J. Dummer, S. F. Kirk, and C. L. Shearer. 2012. Spatial classification of youth physical activity patterns. American Journal of Preventive Medicine 42.5: e87–e96.

                                                                                                                                                                                                                                                                                            DOI: 10.1016/j.amepre.2012.02.011Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                                                            Used global positioning systems and accelerometer data to examine physical activity among adolescents. What is particularly interesting about this study is not just the multiplicity of places where some form of physical activity occurs but that the journeys between locations accounted for the majority of physical activity.

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                                                                                                                                                                                                                                                                                            • Zenk, S. N., A. J. Schulz, S. A. Matthews, et al. 2011. Activity space environment and eating and physical activity behaviors: A pilot study. Health and Place 17:1150–1161.

                                                                                                                                                                                                                                                                                              DOI: 10.1016/j.healthplace.2011.05.001Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                                                                                                                              In a study of Detroit women, activity spaces were larger than residential neighborhoods. Importantly, the environmental features of residential neighborhoods and activity spaces were weakly associated, but some activity space environmental features were related to dietary behaviors. Environmental features of the residential neighborhood are a poor proxy for individual exposure.

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