GESIS Training Courses

Wiss. Koordination

Reinhard Schunck
Tel: +49 221 47694-160

Administrative Koordination

Angelika Ruf
Tel: +49 221 47694-162

Week 2: Spatial Analysis and Spatial Econometrics

Prof. Jude C. Hays, PhD, Asst. Prof. Scott J. Cook, PhD

Datum: 26.02 - 02.03.2018 ics-Datei


This course focuses on detecting, estimating, and analyzing models of spatially dependent data. Spatial interdependence - that the actions, outcomes, behaviors of some units are affected by those of other units - is ubiquitous throughout the social sciences. It includes not simply geographic space, but any means by which we can conceive of units being linked (e.g., cultural ties, political affiliations, economic relationships). As such, many of the most interesting phenomena in political science have a theoretically meaningfully spatial component: contextual or network effects on individual voting behaviors and opinions; strategic decision making amongst two or more actors (e.g., countries in a conflict, parties in an election, votes in a legislature); the diffusion of demonstrations, riots, coups, and wars, etc. This course demonstrates how to effectively model such dependence using spatial and spatiotemporal econometric models.
Spatial Econometrics, Interdependent Outcomes, Maximum Likelihood Estimation


Participants will find the course useful if they
  • Use cross-sectional or time-series-cross-sectional (or longitudinal-network) data in their research
  • Are interested in ensuring inferences are robust to possible spatial autocorrelation  (i.e., space as nuisance)
  • Are interested in testing theories on how relationships among actors affect outcomes  (i.e., space as substance)


By the end of the course participants will
  • Test for spatial dependence in outcomes & residuals
  • Estimate a variety of explicitly spatial models, including: SAR, SEM, SLX, and combinations thereof.    
  • Select the appropriate model for their data & theory
  • Calculate a present spatial and spatio-temporal effects


Participants should have some familiarity with concepts from  
  • Time Series Analysis   
  • Maximum Likelihood Estimation
  • Matrix Algebra
However, necessary concepts will be (re)introduced throughout the course



Referenteninformationen - Prof. Jude C. Hays, PhD

Referenteninformationen - Asst. Prof. Scott J. Cook, PhD