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Workshop Readings

Readings Monday, June 20

Day 1. Introduction to Spatial Data Analysis:

1. 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)

2. 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)

3. 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)

4. 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:

1. Anselin, Luc. 2005.

   Exploring Spatial Data with GeoDa: A Workbook. [Relevant chapters: 2, 3 and 7‐12]   (5.1MB)

2. 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)

3. Anselin, Luc. 2005.

   Spatial Regression Analysis in R: A Workbook. [Relevant chapters: 1 & 2]   (629kb)

4. 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)

Readings for Tuesday, June 21

Day 2. Spatial Autocorrelation:

1. 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)

2. 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)

3. 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)

4. 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)

5. Getis, Arthur. 2008. “

   A History of the Concept of Spatial Autocorrelation: A Geographer’s Perspective.” Geographical Analysis 40:297-309.   (98kb)

Day 2. Lab:

1. Anselin, Luc. 2005.

   Exploring Spatial Data with GeoDa: A Workbook. [Relevant chapters: 15-18]   (5.1MB)

2. Anselin, Luc. 2005.

   Spatial Regression Analysis in R: A Workbook. [Relevant chapter: 3]   (629kb)

3. 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)

Readings for Wednesday, June 22

Day 3. Spatial Regression Models:

1. 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)

2. 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)

3. 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)

4. 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)

5. 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:

1. Anselin, Luc. 2005.

   Exploring Spatial Data with GeoDa: A Workbook. [Relevant chapters: 22-25]   (5.1MB)

2. Anselin, Luc. 2005.

   Spatial Regression Analysis in R: A Workbook. [Relevant chapter: 6]   (629kb)

Readings for Thursday, June 23

Day 4. Spatial Heterogeneity in Effects:

1. Fotheringham, A. Stewart, & Chris Brunsdon. 1999. “

   Local forms of Spatial Analysis.” Geographical Analysis 31(4):340-358. [Understanding GWR]

2. 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)

3. 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)

4. 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:

1. 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)

2. 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)

3. 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)

Readings for Friday, June 24

Day 5. Bayesian Approaches to Spatial Data Analysis:

1. Besag, Julian, Jeremy York, & Annie Mollié. 1991. “

Day 5. Lab:

1. Package ‘

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   Description |