Leveraging temporal changes of spatial accessibility measurements for better policy implications: a case study of electric vehicle (EV) charging stations in Seoul, South Korea

ABSTRACT

The implementation of temporal dynamic variables improves the accuracy of spatial accessibility measurements. However, in previous studies, the temporal dynamics were partially incorporated, and a few snapshots were subjectively selected to demonstrate temporal changes of spatial accessibility, which may not represent the entire variation. In this study, we proposed a conceptual framework to leverage spatial accessibility temporal fluctuation, facilitating decision-making. Not only was the full implementation of time-dependent inputs, but the framework also employed the Gaussian two-step floating catchment area (G2SFCA) method and measured hourly spatial accessibility over 24 hours. Then, a temporal clustering with K-means and hierarchical clustering methods was performed, detecting a few distinctive temporal changes. Lastly, the significance of dynamic accessibility measurement and temporal clustering was validated using Pearson’s correlations. We took electric vehicle (EV) charging stations in Seoul, South Korea, as a case study. The results presented that neglecting temporal dynamics could fail to predict accessibility during the daytime. Additionally, temporal clustering summarized the 24-hour changes of accessibility into five temporal phases and showed the need for additional resources for insufficient accessibility locations in the afternoon. Consequently, our framework elicited the temporal changes of accessibility measures and identified a specific space and time for supplementary infrastructure.

KEYWORDS:

• Spatial analysis
• spatial accessibility
• temporal clustering
• decision-making
• electric vehicle charging station

Acknowledgements

The authors would like to thank Samantha Ray from the Department of Computer Science & Engineering at Texas A&M University for her thoughtful comments and suggestions that helped improve the quality of this paper.

Data and codes availability statement

The data and codes that support the findings of this study are published with a DOI at https://doi.org/10.6084/m9.figshare.12981023.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Jinwoo Park

Jinwoo Park is a Ph.D. Candidate in the Department of Geography at Texas A&M University, under the supervision of Dr. Daniel W. Goldberg. He holds a B.S. in Geography and an M.S. in Geography from Kyung Hee University in Seoul, South Korea. His research focuses on advancing spatial accessibility measurements of urban infrastructure by uncovering temporal dynamics and uncertainty in recent data-rich environments.

Jeon-Young Kang

Jeon-Young Kang is an Assistant Professor in the Department of Geography Education at Kongju National University, South Korea. He holds a Ph.D. in Geography from the State University of New York at Buffalo. His research interests include spatial data science, agent-based modeling, CyberGIS, and health geography.

Daniel W. Goldberg

Daniel W. Goldberg is an Associate Professor of Geography and Computer Science & Engineering at Texas A&M University, where he is also the Director of the TAMU Center for Geospatial Science, Applications & Technology (GeoSAT) and the TAMU GeoInnovation Service Center. His research encompasses various topics, from spatial databases and geospatial health to geographic education to geocomputational approaches, including geocoding techniques. He has served in geospatial leadership positions at the national level for UCGIS and USGIF and has helped facilitate the largest GIS Day in the world, held annually at Texas A&M.

Tracy A. Hammond

Tracy A. Hammond is the Director of the Sketch Recognition Lab and Professor in the Department of Computer Science & Engineering at Texas A&M University. She is an international leader in activity recognition (focusing on eye, body, and sketch motions), haptics, intelligent fabrics, smartphone development, and computer-human interaction research.