Examining the data at the census tract level covering years between 2009 and 2020, which enables us to include the effects of socio-demographic factors during periods of heightened public attention to SLR around 2013, this study assesses the impact of climate change on the local residential market in Miami-Dade County, one of the riskiest regions due to environmental changes. Unlike previous research, this study accesses both prices as an indicator of sellers’ reactions and transaction volume as a measure of buyers’ reactions to SLR-associated risks. (Kim, D., & Tepe, E. (2025)).

This study is to analyze the character of activity change of city actors over time. through dynamically visualizing big data by employing mobile phone data and new tool. Kim, D (2017), To assist decision-makers to render intuitive judgmen by providing spatio-temporal activity patterns.

This study is to analyze the character of activity change of city actors over time. through dynamically visualizing big data by employing mobile phone data and new tool. Kim, D (2017), To assist decision-makers to render intuitive judgmen by providing spatio-temporal activity patterns.

This ongoing study explores graph neural network–based community detection for identifying functional living areas using large-scale mobility and POI data.

Spatial Netwrok Based Metropolitan: Through this pilot study, I explored a more network-based and scientifically grounded approach to delineating metropolitan regions in Korea. In particular, I applied the concept of structural holes to examine inter-regional connectivity. Compared to existing metropolitan boundary definitions, the analysis revealed meaningful differences in actual mobility and usage patterns. This approach suggests potential applications for evidence-based urban and regional policy in the future.

My year of experience in Malawi showed me how well appropriate spatial data analysis can support a public decision. As a GIS analysist, I participated in the Gumulira Millennium Villages Project (Millennium Villages), an aid demonstration project run by the United Nations Development Program (UNDP). I witnessed how aid proliferation without adequate spatial monitoring data led to policy ineffectiveness. In the case of a healthcare scheme, even though a number of projects were conducted, no decision could be made regarding the placement of new donations due to the lack of knowledge on the current spatial distribution of both disease and aid supplies. By educating village youth on the use of GPS and by visiting every single household, I created a village health map using ArcGIS. When I showed the geospatial map presenting the distribution of mosquito nets, AIDS and malaria patients, and health facilities, the leaders of villages could reach an agreement on the allocation of new mosquito nets and health centers. This experience made me consider the role of geospatial information in the planning field.

I participated in the 5th OECD Roundtable on Smart Cities and Inclusive Growth held in Paris at the OECD headquarters. At this roundtable, I introduced the current status of AI-based urban planning in Korea and engaged in discussions with policymakers and researchers from different countries.

The discussions went beyond technological adoption and focused on governance, ethics, and equity issues related to integrating AI into urban systems. Listening to international cases and policy perspectives provided valuable insights into how AI can be responsibly embedded within planning institutions.

This ongoing paper examines how sea level rise (SLR) may affect housing prices in coastal cities using AI-based methods. To forecast near-future market responses, it integrates diverse spatial and physical variables with socioeconomic information and micro-level big data, and applies ensemble learning techniques.