Working with Spatial Data for Policy Research using R
The Arizona State University (ASU) Center on Technology, Data, and Society (CTDS) will host a series of educational lectures on the topic “working with spatial data for policy research using R” via Zoom (Virtual Workshop). The lecture will focus on demonstrating the integration, cleanup, and processing of data with spatial components using R. The lecture may include (1) how to use R to perform the necessary data work related to spatial components, (2) the challenges and problems that may arise in spatial data analysis and how to deal with them, and (3) the resources on the subject of the lecture. This series provides a unique learning opportunity for PhD/graduate students who are interested in working with spatial data for policy research using R. The invitation is open to students at ASU and other institutions. It is assumed that participants have minimal experience with R (e.g., settings, libraries). If you are interested in the series, please register using the registration link below. You can register once and join all or any of the lectures. The link for lectures will be sent to those who are registered. For questions, email to Dr. Yushim Kim (ykim@asu.edu).
Register in advance for this meeting:
https://asu.zoom.us/meeting/register/tZwsdu6przIsEtYNjc3YJfZ5Q0qgVjgimc_3
October 7, 2022 (Friday), 11:00 AM – 12:30 PM (Phoenix Time)
- Jesse Lecy, Associate Professor, School of Public Affairs, Arizona State University. Dr. Lecy’s research focuses on evidence-based management in government and the nonprofit sector, the economics of the nonprofit sector including organizational life cycles and industry change, and managing social enterprise for sustainability and impact.
- How to add spatial attributes to administrative data: Useful tools for data preparation and description: The presentation includes parsing and standardization of raw address data, geocoding options, spatial joins of points to shapefiles, dorling cartograms, calculation of point-distance matrices, calculation of travel time between points as a better measure of distance.
October 14, 2022 (Friday), 11:00 AM – 12:30 PM (Phoenix Time)
- Anthony Howell, School of Public Affairs, Arizona State University. Dr. Howell’s research areas of interest include program or policy evaluations -- industrial and place-based, social, and environmental -- applied to thematic topics related to local economic development, economic geography, and rural/urban/regional economics.
- Intro to R packages for mapping census data: The presentation includes both data visualization and mapping. It will mainly introduce everyone to spatial data types/components, popular (new) packages in R, and the necessary coding semantics for combining socioeconomic census data and spatial data to generate maps (choropleths).
- Research examples
- Impact of Anti-Foreign stamp duty on Local Housing Market Outcomes in New South Wales (R&R at Journal of Economic Geography) (Link to the paper)
October 21, 2022 (Friday), 11:00 AM – 12:30 PM (Phoenix Time)
- Michelle Stuhlmacher, Assistant Professor, Department of Geography, DePaul University. Dr. Stuhlmacher’s research interests center around urban green space and its social and environmental impacts on urban systems.
- Processing census data for the mapping of Chicago’s gentrified neighborhoods: This presentation will build on the previous lectures and demonstrate the spatial data analysis process specifically for green gentrification in Chicago as an example.
- Research examples
- Stuhlmacher, M., Kim, Y., & Kim, J.E. (2022). The role of green space in Chicago’s gentrification. Urban Forestry & Urban Greening. doi.org/10.1016/j.ufug.2022.127569 (Link to the paper)
October 28, 2022 (Friday), 11:00 AM – 12:30 PM (Phoenix Time)
- Youngwan Chun, Professor, School of Economic, Political and Policy Sciences, University of Texas-Dallas. Dr. Chun’s research interests include quantitative modeling (especially, spatial statistical approaches), geographic data uncertainty, and geographic flow data modeling (i.e., population migration) among others.
- Working with spatial weight matrices for spatial autoregressive models: This presentation includes the demonstration of configurations of spatial weight matrices and their use in regression models.
- Slides, Scripts, Data files: https://www.dropbox.com/sh/t1dqbdoor54t5t3/AABiVxiMkfLIVZMxv9o2GuBKa?dl…
- Research examples
- Hu, Lan, Yongwan Chun, and Daniel A. Griffith, 2020, Uncovering a positive and negative spatial autocorrelation mixture pattern: a spatial analysis of breast cancer incidences in Broward County, Florida, 2000-2010, Journal of Geographical Systems, 22(3), pp. 291–308. (Link to the paper)
- Additional resources
- Bivand, R., Millo, G., & Piras, G. (2021). A review of software for spatial econometrics in R. Mathematics, 9(11), 1276. (Link to the paper)