Key research themes
1. How can spatial analysis tools and software effectively integrate GIS and advanced statistical methods for comprehensive spatial data exploration and modeling?
This research area investigates the development and integration of spatial data analysis (SDA) functionalities within Geographic Information Systems (GIS) to enable efficient visualization, exploration, and statistical modeling of spatial data. The focus is on overcoming limitations posed by isolated or loosely coupled SDA and GIS software, enhancing user accessibility, supporting extensible architectures, and providing advanced analytical features such as spatial autocorrelation and spatial regression.
2. What are the challenges and methodological advances in quantifying, visualizing, and assessing spatial uncertainty and sensitivity in spatial cluster and multiple criteria evaluations?
This theme focuses on handling uncertainty and sensitivity in spatial data and spatial decision-making. It covers approaches for quantifying uncertainty in spatial cluster boundaries detected by spatial scan statistics, frameworks for sensitivity analysis in spatial multiple criteria evaluation (S-MCE), and conceptual modeling of uncertainty types in spatial information. The goal is to improve reliability, robustness, and interpretability of spatial analytical results for better decision support.
3. How can spatial analysis elucidate urban spatial patterns and support decision-making in urban expansion, walkability, and segregation using quantitative and visualization methods?
This area examines spatial analysis applications to understand and quantify urban phenomena including urban growth dynamics, walking experience related to urban form, and residential segregation patterns. It leverages spatial statistical measures, GIS visualization, and modeling to support urban planning and policy by revealing spatial patterns, temporal changes, and social interactions within urban environments.