Shifting patterns in data sources and techniques used in geographic and spatially-enabled research

Keywords: Spatial analysis, Urban analytics, Private-collected data, Governmentally-collected data
Session Type: Virtual Paper Abstract
Day: Tuesday
Session Start / End Time: 3/1/2022 05:20 PM (Eastern Time (US & Canada)) - 3/1/2022 06:40 PM (Eastern Time (US & Canada))
Room: Virtual 30

Abstract

Before the wide adoption of the Internet and mobile phones, spatial analyses may have been limited to governmentally-collected data and data collected from fieldwork for their research. Social media data and data collected from GPS and location-based services have provided exciting new data for spatial analysis. This new data supply has diversified the data ecosystem and provided new, large data sources. This changing trend begs many questions: Does spatial analysis research use less governmentally-collected data and more private sector-collected data? Which subfields are ahead or behind this shift? Moreover, do spatial scientists who use private data also use more advanced analytic techniques (such as machine learning)?

To answer these questions, we conduct a desk review and descriptive analysis of articles from flagship journals in GIS, Geography, and Urban Analytics that publish articles on spatial data analysis. We review seven journals: IJGIS, CEUS, TaGIS, GA, EPB, PG, and Annals of the AAG for even years, spanning from 2000 to 2020. We conduct this research by reading articles and recording the data set used and analysis method used. We report the findings of our research questions based on this corpus of data.

The implications of this study are that if spatial analysis studies increasingly rely on private datasets, we should revisit (a) how industry data collection faces fewer regulations on data quality and has different motivations for their collection, (b) how this affects our ability to trust our datasets, and (c) the role of government data collection and dissemination in the future.