Working with Spatial Data for Policy Research using R

Arizona State University Center on Technology, Data, and Society (CTDS) will host a series of educational lectures on the topic, “Working with spatial data in policy research using R.” The center invited three researchers who have conducted policy-relevant research using spatial data and R. The focus of the lecture will be given on demonstrating data integration, cleanup, and processing involving spatial components using R. The lecture may include (1) how to use R to perform the necessary data works related to spatial components, (2) what challenges and problems may arise in spatial data analysis and how to deal with them, and (3) what resources on the subject of the lecture may be available. This series provides a unique learning opportunity for Ph.D./graduate students who are interested in working with spatial data for policy research. The invitation won’t be limited to ASU students and it is open to students in other institutions. The workshop assumes that participants have minimal experience with R (e.g., settings, libraries). See below for schedules, speakers, and zoom links. If you are interested in the series, please register using the link below. You can register once and join all or any of the lectures. The link for lectures will be sent to those who registered. For any questions, email Dr. Yushim Kim (ykim@asu.edu).

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. Research example: Impact of Anti-Foreign stamp duty on Local Housing Market Outcomes in New South Wales (R&R at Journal of Economic Geography)
● Intro to R packages for mapping census data: The presentation includes both data visualization and mapping, mainly will introduce everyone to spatial data types/components, and popular (new) packages in R, and the necessary coding semantics to combine socio-economic census data and spatial data to generate maps (choropleths).

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. Research example: 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
● Processing census data for the mapping of Chicago’s gentrified neighborhoods: This presentation will build on the first lecture and demonstrate spatial data analysis process specifically for green gentrification in Chicago

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. Research example: 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.
● 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.