Introduction to Spatial Regression Analysis ICPSR 2014

Transcription

1 Introduction to Spatial Regression Analysis ICPSR 2014 Elisabeth Dowling Root University of Colorado, Boulder Department of Geography and Institute of Behavioral Science th Street, 483 UCB Boulder, CO Course Description Spatially-referenced data add important contextual and locational information to the social and behavioral sciences, such as sociology, anthropology, political science, economics and public health. A geographic or spatial-analytic framework, which will be taught in this workshop, can be used to explore the importance of spatial relationships in a variety of social and behavioral processes. However, spatial data and spatial relationships necessitate the use of analytic tools beyond those provided by standard statistical methods. This 5-day course introduces the spatial-analytic framework, and explores the range of issues that generally must be dealt with when analyzing spatial data. The role of spatial autocorrelation in spatial data sets is a central focus of this course. Throughout the course we will address the following questions: how and why does spatial autocorrelation arise; how is it measured and understood; how does it relate to issues of spatial heterogeneity and spatial dependence; and how should it inform the specification and estimation of regression models. Course Organization Each day will begin with a lecture, where we discuss theoretical and conceptual material. After lunch we will spend time in the computer lab, where you will participate in hands-on application of methods and techniques. In the computer lab, we will almost exclusively use R, an open source software that has become one of the mainstays in spatial analysis in the last 10 years. We will provide prepared R scripts, and any accompanying datasets, and we will work through these during the lab sessions. slides, recommended readings and lab exercises will be made available via the IBS intranet. We plan to leave time on Friday afternoon for participants to present their own work in order to: a) showcase techniques acquired in the workshop or b) solicit assistance from peers in brainstorming how to apply techniques to current research. Participants may bring their own data to work with in the computing lab sessions. We will set aside time throughout the week for one-on-one meetings with the instructors (typically after lunch and at the end of the day) to work through your data and any questions you may have about applying techniques to your own research. Since this is not a GIS course (though instructors have significant GIS training), participants who wish to bring their own data should have their data in shapefile format. Daily Schedule 9:00-12:30 lecture 12:30-1:30 lunch and one-on-one meetings 1:30-4:30 lab 4:30-5:00 one-on-one meetings (if necessary) Advanced Preparation Although we do not assume you will have any R programming skills, we recommend you at least experiment with the software prior to attending class in August. If you wish to use your own laptop, 1

2 rather than the computers provided in the IBS lab, please load R/RStudio prior to arriving at CU. We have provided instructions for loading R on your own computer below. We also encourage you to look at the book Introductory Statistics with R by Daalgard, at least the first 3-4 chapters, before you arrive. Participants should be comfortable with multivariate regression analysis. Software The course will use the spatial analysis package OpenGeoDa as well as the open source programming application R. Experience with either of these software applications is not required, although those participants with some exposure to R will likely find the afternoon computing lab sessions easier than will those without R experience. Suggested Books There are two books we recommend for this course. One (Dalgaard) is a great introduction to R, the other (Ward & Gleditsch) is a wonderful introduction to spatial regression. Dalgaard may be available to many of you as an ebook through your University library. We will assign several chapters of Ward & Gleditsch during the 5-day course, so I recommend purchasing that. - Dalgaard, P Introductory Statistics with R. 2nd edition. New York: Springer. - Ward, M.D. & K.S. Gleditsch Spatial Regression Models. Thousand Oaks, CA: Sage. COURSE SCHEDULE Day 1 What is a spatial analytic framework? What constitutes a spatial question? Why do we need spatial regression? - Introduction to the spatial analytic framework; introducing spatial data; why spatial is special and why it matters; review of OLS regression including diagnostics; exploratory spatial data analysis Introduction R reading R scripts, importing data, running programs; exploratory data analysis; introduction to GeoDa; univariate and bivariate data exploration; variable transformations and data visualization Readings, The Spatial Analytic Framework: 1. Goodchild, M.F., L. Anselin, R.P. Appelbaum & B.H. Harthorn Toward Spatially Integrated Social Science. International Regional Science Review, 23(2): Anselin, L 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. 3. Loftin, C. & S.K. Ward A Spatial Autocorrelation Model of the Effects of Population Density on Fertility. American Sociological Review, 48(1):

3 4. Galle, O.R., W.R. Gove, & J.M. McPherson Population Density and Pathology: What Are the Relations for Man? Science (new series) 176: Anselin, L Exploring Spatial Data with GeoDa: A Workbook. [Relevant chapters: 2, 3 and 7-12] 2. Dalgaard, P Introductory Statistics with R. 2nd edition. New York: Springer. [My favorite introduction to R book, but there are many.] 3. Anselin, L Spatial Regression Analysis in R: A Workbook. [Relevant chapters: 1 & 2] Day 2 What is spatial autocorrelation? How does it arise? How do we examine it? - Introduction to spatial autocorrelation; language relating to spatial autocorrelation; thinking about neighborhood influences; spatial weights matrices; spatial lag term Examining global & local spatial autocorrelation (mostly in R); visual representations of spatial autocorrelation; constructing spatial weights matrices (GeoDa & R) Readings, Spatial Autocorrelation: 1. Ward, M.D. and K.S. Gleditsch (2008). Spatial Regression Models. [Relevant chapters: 1 & 2]. 2. Anselin, L., S. Sridharan & S. Gholston Using Exploratory Spatial Data Analysis to Leverage Social Indicator Databases: The Discovery of Interesting Patterns. Social Indicators Research 82: Holt, J.B The Topography of Poverty in the United States: A Spatial Analysis Using County-Level Data from the Community Health Status Indicators Project. Preventing Chronic Disease 4(4). 4. Tolnay, S.E., Deane, G. & E.M. Beck Vicarious Violence: Spatial Effects on Southern Lynchings, American Journal of Sociology 102(3): Getis, A A History of the Concept of Spatial Autocorrelation: A Geographer s Perspective. Geographical Analysis 40: Gallo, J.L. & C. Ertur Exploratory spatial data analysis of the distribution of regional per capital GDP in Europe, Papers in regional science 82(2): Anselin, Luc Exploring Spatial Data with GeoDa: A Workbook. [Relevant chapters: 15-18] 2. Anselin, Luc Spatial Regression Analysis in R: A Workbook. [Relevant chapter: 3] 3

4 Day 3 What is a spatial regression and how is it different from OLS? What s an error model? A lag model? A spatial Durban model? How are they different and when do I use them? - Introduction to spatial regression modeling; spatial lag model; spatial error model; spatial Durbin model; higher-order spatial regression models. Specifying and estimating spatial models (R & GeoDa, but mostly R); spatial lag model; spatial error model; spatial Durbin model; understanding spatial regression model diagnostics. Readings, Spatial Regression Models: 1. Anselin, Luc, & Anil Bera Spatial Dependence in Linear Regression Models with An Introduction to Spatial Econometrics. Chapter 7 (pp ) in Aman Ullah & David Giles (eds.) Handbook of Applied Economic Statistics (New York: Marcel Dekker). 2. Anselin, Luc Under the Hood: Issues in the Specification and Interpretation of Spatial Regression Models. Agricultural Economics 27(3): Ward, M.D. and K.S. Gleditsch (2008). Spatial Regression Models. [Relevant chapters: 3 & 4] 4. Voss, Paul R., David D. Long, Roger B. Hammer, and Samantha Friedman County Child Poverty Rates in the U.S.: A Spatial Regression Approach. Population Research and Policy Review 25: Emch, M., M. Ali, J.K. Park, M. Yunus, D.A. Sack & J.D. Clemens Relationship between neighborhood-level killed oral cholera vaccine coverage and protective efficacy: evidence for herd immunity. International Journal of Epidemiology 35: Sparks, P.J., & C.S. Sparks An Application of Spatially Autoregressive Models to the Study of US County Mortality Rates. Population, Space and Place 16: Burnett, J.W. & D.J. Lacombe Accounting for Spatial Autocorrelation in the 2004 Presidential Popular Vote: A Reassessment of the Evidence. The Review of Regional Studies 42: Root, E.D., M. Lucero, H. Nohynek, P. Anthamatten, D.S.K. Thomas, V. Tallo, A. Tansskanen, B. Quiambao, T. Puumalainen, S. Lupisan, P. Ruutu, E. Ladesma, G. Williams, I. Riley, E.A.S. Simoes Distance to health services affects local-level vaccine efficacy for pneumococcal conjugate vaccine (PVC) among rural Filipino children. Proceedings of the National Academy of Sciences 111(9): Anselin, Luc Exploring Spatial Data with GeoDa: A Workbook. [Relevant chapters: 22-25] 2. Anselin, Luc Spatial Regression Analysis in R: A Workbook. [Relevant chapters: 6 & 7] 4

5 Day 4 What is spatial nonstationarity and why is it important? What is the difference between global and local spatial regression models? - Simpson s paradox; measures of local spatial autocorrelation and spatial clustering; local Moran s I statistic, G and G* statistics; introduction to geographically weighted regression (GWR); criticisms and critiques of GWR. Local measures of spatial autocorrelation and clustering; GWR in R. Readings, Spatial Nonstationarity: 1. Fotheringham, A.S. & C. Brunsdon Local forms of Spatial Analysis. Geographical Analysis 31(4): Wheeler, D. & M. Tiefelsdorf Multicollinearity and Correlation among Local Regression Coefficients in Geographically Weighted Regression. Journal of Geographical Systems 7: O Loughlin, J. & F. Witmer The Localized Geographies of Violence in the North Caucasus of Russia, Annals of the Association of American Geographers 101(1): Grillet, M-E., R. Barrera, J-E Martinez, J. Berti & M-J Fortin Disentangling the Effects of Local and Global Spatial Variation on a Mosquito-Borne Infection in a Neotropical Heterogeneous Environment. American Journal of Tropical Medicine and Hygiene 82(2): LeSage, James P. & R. Kelley Pace Pitfalls in Higher Order Model Extensions of Basic Spatial Regression Methodology. The Review of Regional Studies 41: Burnett, J.W. & D.J. Lacombe Accounting for Spatial Autocorrelation in the 2004 Presidential Popular Vote: A Reassessment of the Evidence. The Review of Regional Studies 42: Gros, D., C. Brunsdon & R. Harris. Introduction to Geographically Weighted Regression (GWR) and to Grid Enabled GWR. [Relevant chapters: 4 & 5] 2. Anselin, L Discrete Spatial Heterogeneity & Continuous Spatial Heterogeneity. [Relevant chapters: 8 & 9] Day 5 What other types of spatial models do researchers use? What are the frontiers in spatial analysis? - Bayesian perspectives; spatial logit models; spatial multilevel models; spatio-temporal modeling; spatial panel models 5

6 Participant presentations. The spatial logit model; other topics depending on number of participant presentations. Readings: 1. Morenoff, J.D Neighborhood Mechanisms and the Spatial Dynamics of Birth Weight. American Journal of Sociology 108(5): Chaix, B., J. Merlo & P. Chauvin 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): TBD paper on Bayesian application 4. TBD paper on spatio-temporal modeling 6