Monday, Tuesday, Wednesday, Thursday, Friday View Readings List
Day 1. Introduction to Spatial Data Analysis:
Anselin, Luc. 2010. “
Thirty Years of Spatial Econometrics.” Papers in Regional Science 89(1):3-25. [A broad, sweeping overview of the development of the field over the past 3 decades by, unquestionably, the premier contributor to that development] (288kb)Loftin, Colin, and Sally K. Ward. 1983. “
A Spatial Autocorrelation Model of the Effects of Population Density on Fertility.” American Sociological Review, 48(1):121-128. [Together with the following reading, a classic motivational example] (1.2MB)Galle, Omer R., Walter R. Gove, & J. Miller McPherson. 1972. “
Population Density and Pathology: What Are the Relations for Man?” Science (new series) 176:23-30. (2.1MB)Anselin, Luc. 1989. “
What Is Special about Spatial Data? Alternative Perspectives on Spatial Data Analysis.” Conference Proceedings, Spatial Statistics: Past, Present, and Future. Institute of Mathematical Geography, Syracuse University. [Now somewhat dated, but a nice overview of why spatial data require special attention] (77kb)Day 1. Lab:
Anselin, Luc. 2005.
Exploring Spatial Data with GeoDa: A Workbook. [Relevant chapters: 2, 3 and 7-12] (5.1MB)Venables, W. N. & D. M. Smith and the R Development Core Team. 2010.
An Introduction to R. [Perhaps the most widely cited introduction to R; there are many!] (624kb)Anselin, Luc. 2005.
Spatial Regression Analysis in R: A Workbook. [Relevant chapters: 1 & 2] (629kb)Voss, Paul R., David D. Long, Roger B. Hammer, and Samantha Friedman. 2006 “
County Child Poverty Rates in the U.S.: A Spatial Regression Approach. ”Population Research and Policy Review 25:369-391. [An introduction to the example used throughout the week] (488kb)Day 2. Spatial Autocorrelation:
Anselin, Luc. 1996. “
The Moran Scatterplot as an ESDA Tool to Assess Local Instability in Spatial Association.” Pp. 111-125 in Fischer, Manfred, Henk J. Scholten, and David Unwin (eds.) Spatial Analytical Perspectives on GIS: GISDATA 4 (London: Taylor & Francis). [Introduction to a key diagnostic tool in spatial data analysis] (2MB)Tolnay, Stewart E., Glenn Deane, & E.M. Beck. 1996. “
Vicarious Violence: Spatial Effects on Southern Lynchings, 1890-1919.” American Journal of Sociology 102(3):788-815. [An interesting example of negative spatial autocorrelation arising in a social process] (2.7MB)Tobler, Waldo R. 1970. “
A Computer Movie Simulating Urban Growth in the Detroit Region.” Economic Geography 46(June):234-240. [The classic on the concept of positive spatial autocorrelation] (1.4MB)Getis, Arthur. 2007. “
Reflections on Spatial Autocorrelation.” Regional Science and Urban Economics 37:491-496. [A brief essay by a quantitative geographer who has contributed much to the spatial autocorrelation literature] (117kb)Getis, Arthur. 2008. “
A History of the Concept of Spatial Autocorrelation: A Geographer’s Perspective.” Geographical Analysis 40:297-309. (98kb)Day 2. Lab:
Anselin, Luc. 2005.
Exploring Spatial Data with GeoDa: A Workbook. [Relevant chapters: 15-18] (5.1MB)Anselin, Luc. 2005.
Spatial Regression Analysis in R: A Workbook. [Relevant chapter: 3] (629kb)Messner, Steven F., Luc Anselin, Robert D. Baller, Darnell F. Hawkins, Glenn Deane, & Stewart E. Tolnay. 1999. “
The Spatial Patterning of County Homicide Rates: An Application of Exploratory Spatial Data Analysis.” Journal of Quantitative Criminology 15(4):423-450. [A nice example of ESDA] (524kb)Day 3. Spatial Regression Models:
Anselin, Luc, & Anil Bera. 1998. “
Spatial Dependence in Linear Regression Models with An Introduction to Spatial Econometrics.” Chapter 7 (pp. 237-289) in Aman Ullah & David Giles (eds.) Handbook of Applied Economic Statistics (New York: Marcel Dekker). [A strong, foundational reading] (2.9MB)Anselin, Luc. 2002. “
Under the Hood: Issues in the Specification and Interpretation of Spatial Regression Models.” Agricultural Economics 27(3):247-267. [An overview of spatial regression model specifications & interpretation] (168kb)Baller, Robert D., & Kelly K. Richardson. 2002. “
Social Integration, Imitation, and the Geographic Patterning of Suicide.” American Sociological Review 67(6):873-888. [A good example of theoretically grounded spatial data analysis] (777kb)Sparks, Patrice Johnelle, & Corey S. Sparks. 2010. “
An Application of Spatially Autoregressive Models to the Study of US County Mortality Rates.” Population, Space and Place 16:465-481. [A nice example of putting it all together – and sticking with your theory despite diagnostics to the contrary] (78kb)Crowder, Kyle and Scott J. South. 2008. “
Spatial Dynamics of White Flight: The Effects of Local and Extralocal Racial Conditions on Neighborhood Out-Migration.” American Sociological Review 73(5):792-812. [A theoretically motivated study incorporating space as a cross-regressive process] (271kb)Day 3. Lab:
Anselin, Luc. 2005.
Exploring Spatial Data with GeoDa: A Workbook. [Relevant chapters: 22-25] (5.1MB)Anselin, Luc. 2005.
Spatial Regression Analysis in R: A Workbook. [Relevant chapter: 6] (629kb)Day 4. Spatial Heterogeneity in Effects:
Fotheringham, A. Stewart, & Chris Brunsdon. 1999. “
Local forms of Spatial Analysis.” Geographical Analysis 31(4):340-358. [Understanding GWR]Wheeler, David, & Michael Tiefelsdorf. 2005. “
Multicollinearity and Correlation among Local Regression Coefficients in Geographically Weighted Regression.” Journal of Geographical Systems 7:161-187. [GWR has its critics] (734kb)O’Loughlin, John, Colin Flint, & Luc Anselin. 1994. “
The Geography of the Nazi Vote: Context, Confession, and Class in the Reichstag Election of 1930.” Annals of the Association of American Geographers 84(3):351-380. [Excellent example of regime analysis] (2.3MB)Cahill, Meagan, & Gordon Mulligan. 2007. “
Using Geographically Weighted Regression to Explore Local Crime Patterns.” Social Science Computer Review 25(2):174-193. [One of many empirical applications of GWR] (78kb)Day 4. Lab:
Grose, Daniel, Chris Brunsdon & Richard Harris. No date.
Introduction to Geographically Weighted Regression (GWR) and to Grid Enabled GWR. [How to for R] (5.4MB)Harris, Richard, Alex Singleton, Daniel Grose, Chris Brunsdon & Paul Longley. 2010. “
Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England.” Transactions in GIS 14(1):43-61. [GWR for particularly large data sets] (405kb)Anselin, Luc. 2007. “Discrete Spatial Heterogeneity” & “Continuous Spatial Heterogeneity.” Pp. 102-115 & 116-130 in
Spatial Regression Analysis in R: A workbook. (CSISS) [How to for R] (629kb)Day 5. Bayesian Approaches to Spatial Data Analysis:
1. Besag, Julian, Jeremy York, & Annie Mollié. 1991. “
Bayesian Image Restoration with Two Applications in Spatial Statistics.” Annals of the Institute of Statistical Mathematics43(1):1-20. [In the beginning.] (1.1MB)Day 5. Lab:
1. Package ‘
R2WinBUGS: Running WinBUGS and OpenBUGS from R / S-PLUS. Version 2.1-18. March 22, 2011. [Useful acces to R functionality in a Bayesian framework] (166kb)
This website is preserved as an Archive for the NIH-funded GISPopSci / Advanced Spatial Analysis Training Programs (2005–2013). Current resources in support of Spatially Integrated Social Science are now available at the following: www.spatial.ucsb.edu www.gispopsci.org www.teachspatial.org PSU 2011 June 19-June 24, 2011: University Park, PA |