Scientific Coordination

Alisa Remizova
Tel: +49 221 47694-475

Administrative Coordination

Noemi Hartung
Tel: +49 621 1246-211

Geodata and Spatial Regression Analysis

About

Date:
01.07 - 03.07.2024

Location:
Mannheim B6, 4-5

General Topics:

Data Analysis

Course Level:

Advanced

Format:

Workshop

Software used:

Duration:

3 days

Language:

English

Fees:

Students: 330 €

Academics: 495 €

Commercial: 990 €

Keywords

Geographic Information System, Geospatial Data, Spatial Dependence, Spatial Regression

Additional links

Terms and Conditions
FAQs

Lecturer(s): Tobias Rüttenauer

About the lecturer - Tobias Rüttenauer

Course description

In recent years, more and more spatial data has become available, providing the possibility to combine otherwise unrelated data, such as survey data with contextual information, and to analyze spatial patterns and processes (e.g., spillover effects or diffusion).

Many social science research questions are spatially dependent such as voting outcomes, housing prices, protest behavior, or migration decisions. Observing an event in one region or neighborhood increases the likelihood that we observe similar processes in proximate areas. As Tobler's first law of geography puts it: “Everything is related to everything else, but near things are more related than distant things”. This dependence can stem from spatial contagion, spatial spillovers, or common confounders. Therefore, basic assumptions of standard regression models are violated while analyzing spatial data. Spatial regression models can be used to detect this spatial dependence and explicitly model spatial relations, identifying spatial spillovers or diffusion.

The main objective of the course is the theoretical understanding and practical application of spatial regression models. This course will first give an overview on how to perform common spatial operations using spatial information, such as aggregating spatial units, calculating distances, merging spatial data as well as visualizing them. The course will further focus on the analysis of geographic data and the application of spatial regression techniques to model and analyze spatial processes, and furthermore, the course addresses several methods for defining spatial relationships. Hereby, the detection and diagnostic of spatial dependence as well as autocorrelation are demonstrated. Finally, we will discuss various spatial regression techniques to model processes, clarify the assumptions of these models, and show how they differ in their applications and interpretations.

Organizational structure of the course

Aim of course: 3 hours of lecture + 3 hours of lab exercise every day

Lab time: work on hands-on exercises with real-world data, discuss unclear topics of lecture

Support: Support exercises during lab, discussion and consultations of own projects

Target group

Participants will find the course useful if:

Working with spatial data

Analyzing spatial research questions

Using spatial econometrics

Combining different data sources with geo-coordinates

Learning objectives

By the end of the course participants will:

Manipulate and combine spatial data sources

Detect and visualize spatial patterns and relationships

Analyze spatial processes

Construct spatial weights matrices

Apply and interpret spatial econometric models

Prerequisites

Profound knowledge of standard regression estimators (OLS and Maximum Likelihood)

Advanced knowledge of R

Basic knowledge of matrix algebra and statistics

Basic knowledge of GIS in R would be preferable

Software and hardware requirements

R with spatial software packages (sf, gstat, mapview, spdep, spatialreg) should be installed. These require some spatial libraries: GDAL, GEOS, PROJ, S2.

Schedule

Monday, 1st July - Working with Spatial Data
10:00 - 11:30	Refresher on R as GIS
	Coffee break
11:45 - 13:00	Spatial Data Manipulation & Visualization
	Lunch break
14:00 - 15:30	Defining Spatial Relationships (W)
	Coffee break
15:45 - 17:15	Detecting Spatial Dependence

Literature:

Anselin, L., Bera, A. K., Florax, R., & Yoon, M. J. (1996). Simple Diagnostic Tests for Spatial Dependence. Regional Science and Urban Economics, 26(1), 77-104. https://doi.org/10.1016/0166-0462(95)02111-6.
Bivand, R. (2022). R Packages for Analyzing Spatial Data: A Comparative Case Study with Areal Data. Geographical Analysis, 54(3), 488-518. https://doi.org/10.1111/gean.12319.
Brunsdon, C., & Comber, L. (2018). An introduction to R for spatial analysis and mapping (2nd edition). SAGE Publications.
Bivand, R., Pebesma, E., & Gómez-Rubio, V. (2008). Creating Neighbours. https://r-spatial.github.io/spdep/articles/nb.html.
LeSage, J. P., & Pace, R. K. (2014). The Biggest Myth in Spatial Econometrics. Econometrics, 2(4), 217-249. https://doi.org/10.3390/econometrics2040217.
Neumayer, E., & Plümper, T. (2016). W. Political Science Research and Methods, 4(01), 175-193. https://doi.org/10.1017/psrm.2014.40
Pebesma, E., & Bivand, R. (2023). Spatial Data Science: With Applications in R (1st ed.). Chapman and Hall/CRC. Version online: https://r-spatial.org/book/

Tuesday, 2nd July - Spatial Regression Models I
10:00 - 11:30	Spatial Regression Models (SLX, Error, lagged DV): Theory
	Coffee break
11:45 - 13:00	Estimating Spatial Regression Models
	Lunch break
14:00 - 15:30	Lab Exercises in R
	Coffee break
15:45 - 17:15	Interpreting Results: Spatial Impacts

Literature:

Anselin, L. (2003). Spatial Externalities, Spatial Multipliers, and Spatial Econometrics. International Regional Science Review, 26(2), 153-166. https://doi.org/10.1177/0160017602250972.
Franzese, R. J., & Hays, J. C. (2007). Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data. Political Analysis, 15(2), 140-164. https://doi.org/10.1093/pan/mpm005.
Halleck Vega, S., & Elhorst, J. P. (2015). The SLX Model. Journal of Regional Science, 55(3), 339-363. https://doi.org/10.1111/jors.12188.
Kelejian, H. H., & Piras, G. (2017). Spatial Econometrics. Elsevier. https://doi.org/10.1016/C2016-0-04332-2.
LeSage, J. P. (2014). What Regional Scientists Need to Know about Spatial Econometrics. The Review of Regional Studies, 44(1), 13-32. https://dx.doi.org/10.2139/ssrn.2420725.
LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press.
LeSage, J. P., & Pace, R. K. (2014). The Biggest Myth in Spatial Econometrics. Econometrics, 2(4), 217-249. https://doi.org/10.3390/econometrics2040217.
Pace, R. K., & LeSage, J. P. (2010). Omitted Variable Biases of OLS and Spatial Lag Models. In A. Páez, J. Gallo, R. N. Buliung, & S. Dall'erba (Eds.), Progress in Spatial Analysis (pp. 17-28). Springer.
Rüttenauer, T. (2022). Spatial Regression Models: A Systematic Comparison of Different Model Specifications Using Monte Carlo Experiments. Sociological Methods & Research, 51(2), 728-759. https://doi.org/10.1177/0049124119882467.
Ward, M. D., & Gleditsch, K. S. (2008). Spatial Regression Models (Vol. 155). Sage.

Wednesday, 3rd July - Spatial Regression Models II
10:00 - 11:30	Lab Exercises in R
	Coffee break
11:45 - 13:00	Comparing and Selecting Models
	Lunch break
14:00 - 15:30	Lab Exercises in R
	Coffee break
15:45 - 17:15	Other Models: GWR & Binary Outcomes, Spatiotemporal Models & GWR

Literature:

Cook, S. J., Hays, J. C., & Franzese, R. J. (2020). Model Specification and Spatial Interdependence. In L. Curini & R. Franzese (Eds.), The Sage handbook of research methods in political science and international relations (1st ed, pp. 730-747). SAGE Inc.
Cook, S. J., Hays, J. C., & Franzese, R. J. (2023). STADL Up! The Spatiotemporal Autoregressive Distributed Lag Model for TSCS Data Analysis. American Political Science Review, 117(1), 59-79. https://doi.org/10.1017/S0003055422000272.
Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. https://doi.org/10.1007/978-3-642-40340-8.
Gibbons, S., & Overman, H. G. (2012). Mostly Pointless Spatial Econometrics? Journal of Regional Science, 52(2), 172-191. https://doi.org/10.1111/j.1467-9787.2012.00760.x.
Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2015). GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models. Journal of Statistical Software, 63(17). https://doi.org/10.18637/jss.v063.i17.
Manski, C. F. (1993). Identification of endogenous social effects: The reflection problem. The Review of Economic Studies, 60(3), 531-542. https://doi.org/10.2307/2298123.
Rüttenauer, T. (2022). Spatial Regression Models: A Systematic Comparison of Different Model Specifications Using Monte Carlo Experiments. Sociological Methods & Research, 51(2), 728-759. https://doi.org/10.1177/0049124119882467.
Rüttenauer, T., & Best, H. (2021). Environmental Inequality and Residential Sorting in Germany: A Spatial Time-Series Analysis of the Demographic Consequences of Industrial Sites. Demography, 58(6), 2243-2263. https://doi.org/10.1215/00703370-9563077.
Wimpy, C., Whitten, G. D., & Williams, L. K. (2021). X Marks the Spot: Unlocking the Treasure of Spatial-X Models. The Journal of Politics, 83(2), 722-739. https://doi.org/10.1086/710089.

Scientific Coordination

Administrative Coordination

Geodata and Spatial Regression Analysis

About the lecturer - Tobias Rüttenauer

About the lecturer - Tobias Rüttenauer

Course description

Target group

Learning objectives

Prerequisites

Schedule

Schedule

Recommended readings

Recommended readings