Course Material

Introduction

This course material is designed for the Summer Institute in Computational Social Science (SICSS) Oxford.

The material introduces the use of geographical information to connect and analyze different spatial data sources. This introduction is limited to the fundamentals of using geographical information in R. The field has develops very fast over since few years, and R now provides a rich set of packages for various spatial data operations. For a more in-depth introduction into spatial data analysis in R, have a look into the materials references below.

Instead of using exemplary data shipped with the packages, the course relies on real world data sources from the London Datastore.

Why is spatial data linkage and analysis part of SICSS?

The amount of spatial (‘readymade’) data sources is steadily increasing. For instance, the UK open data portal provides many indicators on a spatially agrregated level: https://data.gov.uk/. Moreover, geographical information is increasingly available for traditional (‘custommade’) data sources, such as survey data. Using geographic information allows us to:

  • Link information from different (unrelated) data sources

  • Incorporate a spatial dimension into the analysis

We can combine different sources of social science data, but we can also enrich existing social science data with information on aggregated demographics or other contextual information.

Figure: Spatial data linkage, Source: Jünger (2019)

Some examples

Evolution and epidemic spread of SARS-CoV-2 in Brazil (Candido et al. 2020)

Urban Scaling and the Regional Divide (Keuschnigg, Mutgan, and Hedström 2019)

Local candidates, place-based identities, and electoral success (Schulte-Cloos and Bauer 2021)

Inequality is rising where social network segregation interacts with urban topology (Tóth et al. 2021)

Extreme Weather Events Elevate Climate Change Belief but not Pro-Environmental Behaviour (Rüttenauer 2021)

Some useful packages

By now, R provides a lot of functionalities for GIS applications and spatial econometrics, and further extensions. There are lots of packages providing a huge variety of spatial functionalities and methods (see e.g. Bivand, Millo, and Piras 2021). Important packages for fundamental spatial operations are:

Further materials

Readings

Course materials

References

Appelhans, Tim, Florian Detsch, Chritoph Reudenbach, and Stefan Woellauer. 2021. “mapview: Interactive Viewing of Spatial Data in R.” https://CRAN.R-project.org/package=mapview.
Bivand, Roger, Giovanni Millo, and Gianfranco Piras. 2021. “A Review of Software for Spatial Econometrics in R.” Mathematics 9 (11): 1276. https://doi.org/10.3390/math9111276.
Bivand, Roger, Edzer Pebesma, and Virgilio Gómez-Rubio. 2013. Applied Spatial Data Analysis with R. New York: Springer. https://doi.org/10.1007/978-1-4614-7618-4.
Bivand, Roger, and Gianfranco Piras. 2015. “Comparing Implementations of Estimation Methods for Spatial Econometrics.” Journal of Statistical Software 63 (18): 1–36. https://doi.org/10.18637/jss.v063.i18.
Bivand, Roger, and David W. S. Wong. 2018. “Comparing implementations of global and local indicators of spatial association.” TEST 27 (3): 716–48. https://doi.org/10.1007/s11749-018-0599-x.
Candido, Darlan S., Ingra M. Claro, Jaqueline G. de Jesus, William M. Souza, Filipe R. R. Moreira, Simon Dellicour, Thomas A. Mellan, et al. 2020. “Evolution and epidemic spread of SARS-CoV-2 in Brazil.” Science 369 (6508): 1255–60. https://doi.org/10.1126/science.abd2161.
Gräler, Benedikt, Edzer Pebesma, and Gerard Heuvelink. 2016. “Spatio-Temporal Interpolation using gstat.” The R Journal 8 (1): 204–18.
Halleck Vega, Solmaria, and J. Paul Elhorst. 2015. “The SLX Model.” Journal of Regional Science 55 (3): 339–63. https://doi.org/10.1111/jors.12188.
Jünger, Stefan. 2019. “Using Georeferenced Data in Social Science Survey Research: The Method of Spatial Linking and Its Application with the German General Social Survey and the GESIS Panel.” PhD thesis, SSOAR - GESIS Leibniz Institute for the Social Sciences. https://doi.org/10.21241/ssoar.63688.
Keuschnigg, Marc, Selcan Mutgan, and Peter Hedström. 2019. “Urban Scaling and the Regional Divide.” Science Advances 5 (1): eaav0042. https://doi.org/10.1126/sciadv.aav0042.
LeSage, James P. 2014. “What Regional Scientists Need to Know about Spatial Econometrics.” The Review of Regional Studies 44 (1): 13–32.
LeSage, James P., and R. Kelley Pace. 2009. Introduction to Spatial Econometrics. Statistics, Textbooks and Monographs. Boca Raton: CRC Press.
Lovelace, Robin, Jakub Nowosad, and Jannes Muenchow. 2019. Geocomputation with R. 1st ed. Chapman & Hall/CRC the R series. Boca Raton: Chapman & Hall/CRC.
Pebesma, Edzer. 2018. “Simple features for R: Standardized support for spatial vector data.” The R Journal 10 (1): 439. https://doi.org/10.32614/RJ-2018-009.
———. 2021. “stars: Spatiotemporal Arrays, Raster and Vector Data Cubes. R package version 0.5-3.” https://CRAN.R-project.org/package=stars.
Rüttenauer, Tobias. 2019. “Spatial Regression Models: A Systematic Comparison of Different Model Specifications Using Monte Carlo Experiments.” Sociological Methods & Research OnlineFirst. https://doi.org/10.1177/0049124119882467.
———. 2021. “Extreme Weather Events Elevate Climate Change Belief but not Pro-Environmental Behaviour.” SocArXiv. https://doi.org/10.31235/osf.io/574uf.
Schulte-Cloos, Julia, and Paul C. Bauer. 2021. “Local Candidates, Place-Based Identities, and Electoral Success.” Political Behavior 31 (3): 301. https://doi.org/10.1007/s11109-021-09712-y.
Tennekes, Martijn. 2018. “tmap : Thematic Maps in R.” Journal of Statistical Software 84 (6). https://doi.org/10.18637/jss.v084.i06.
Tóth, Gergő, Johannes Wachs, Riccardo Di Clemente, Ákos Jakobi, Bence Ságvári, János Kertész, and Balázs Lengyel. 2021. “Inequality is rising where social network segregation interacts with urban topology.” Nature Communications 12 (1): 1143. https://doi.org/10.1038/s41467-021-21465-0.