Syllabus for NR 245
Advanced Spatial Methods
Instructor: Austin Troy (firstname.lastname@example.org)
Room: Aiken 101
Time: Wednesday, 8:45-11:30
Austinís office hours: TBD
TA: Felix Wai (email@example.com)
This course teaches various statistical and spatial analysis methods through weekly lab exercises and a final project. Among the methods addressed are advanced overlay analysis with geoprocessing and cross-tabulation, Geographically Weighted Regression, spatial cluster analysis, analysis of variance, logistic regression, multi-model inference, spatially weighted regression, analysis of spatial residuals, and measures of spatial autocorrelation. Students will be introduced to S-Plus (including S-Plus spatial module) and GWR software, and will learn new methods in software they have already worked with, including ArcGIS and Microsoft Access. The course currently uses data from the Baltimore Ecosystem Study, an NSF-funded Long Term Ecological Research Project as the focus for all labs. These exercises build sequentially and thematically on a single question from the case study and methods are introduced in the context of answering this question. For instance, the course currently analyzes the relationships between urban green space and socio-economic factors. In each lab, the instructor gives a short lecture on the analytic tools to be covered, giving the statistical, conceptual and mathematical background. Students then apply these concepts in the lab using instructions given on the website.
∑ Class attendance and participation: 10%
∑ Lab exercises: 40%
∑ Personal Exercise 15%
∑ Class project: 35%
This is a project to be conducted in groups of 2-4 people that focuses on some aspect of spatial analysis, integrated with quantitative or statistical analysis. It should incorporate at least one of the tools that we learned in class, or related tool we did not cover. Students are encouraged to conduct projects related to the Baltimore Ecosystem Study because of the richness and the quality of the dataset (meaning youíll save time you would have otherwise spent acquiring and processing data). However, if students have other projects they are working on outside of the class, they may use those as the basis for the project if needed data is reasonably available. Students are expected to pose some kind of hypothesis that can be tested using spatial data and statistical methods. Looking at significant differences in variables, relationships, and trends, or developing new approaches for categorizing or segmenting places or populations are all recommended. The subject can be biophysical, socio-economic or some mix. The scale can range from local (e.g. a city or county), to watershed, to county, to global, although local scale is preferred. Tentative details
Please make sure to attend every class. Let the instructor know ahead of time if you will have to miss a class. Please also arrive on time.
Online Readings (preliminary):
Anselin, L. and A. Getis (1992). Spatial statistical analysis and geographic information systems. The Annals of regional science 26(1): 19-34.
Anselin, L. and W. K. T. Cho (2000). Spatial Effects and Ecological Inference. Political Analysis 10(3):276-297.
Brunsdon, C, S. Fotheringham, and M. Charlton. 1998. Geographically Weighted Regression-Modeling Spatial Non-stationarity. The Statistician. 47(3):431-443.
Burnham, K and D Anderson. 2002. Model selection and multimodel inference: a practical information theoretic approach. 2nd ed. New York, Springer. Selected pages.
Diniz-Filho, J.A., L.M. Bini and B.A. Hawkins. 2003. Spatial autocorrelation and red herrings in geographical ecology. Global Ecological and Biogeography. (12):53-64.
Fortin, M. and M. Dale. 2005. Spatial Analysis: A guide for ecologists. Cambridge, England: Cambridge University Press.
Goodchild, M. F., L. Anselin, et al. (2000). Toward Spatially Integrated Social Science. International Regional Science Review 23(2): 139-159.
Mitchell, A. GIS Analysis Volume 2: Spatial Measurements and Statistics. Redlands CA: ESRI Press.
Troy, A. 2008. Geodemographic segmentation. In: Shenkar, S. and Xiong, H. (eds.), Encyclopedia of Geographical Information Science. New York: Springer-Verlag. Pp.347-355.
Troy, A. and J.M. Grove. 2008. Property Values, parks, and crime: a hedonic analysis in Baltimore, MD. Landscape and Urban Planning. 87:233-245.
Zorn, C. 2003. Agglomerative Clustering of Rankings Data, with an Application to Prison Rodeo Events. Unpublished Working Paper. Emory Univerity.
∑ Use GWR software to run regression between socio-economic variables and tree cover
∑ Learn to read GWR output: test local model against global model; test individual parameters for spatial non-stationarity; test gains to model fit
∑ 3/7: NO CLASS (Spring break)