Kernel smoothing is a highly flexible and popular approach for estimation of probability
density and intensity functions of continuous spatial data. In this role, it also forms an integral …

The coordinated development of the economy, society, and the environment is an important
issue for enhancing regional green competitiveness. Therefore, we established a model to …

Predictive hotspot mapping plays a critical role in hotspot policing. Existing methods such as
the popular kernel density estimation (KDE) do not consider the temporal dimension of …

Predicting ambulance demand accurately at fine time and location scales is critical for
ambulance fleet management and dynamic deployment. Large-scale datasets in this setting …

This study explores the clusters of closed restaurants in Seoul in response to the COVID-19
pandemic using the relative risk surface (RRS). The RRS developed based on kernel …

Spatial-temporal kernel density visualization (STKDV) has been extensively used in a wide
range of applications, eg, disease outbreak analysis, traffic accident hotspot detection, and …

Many cities in the United States and Canada offer a 311 helpline to their residents for
submitting requests for non-emergency municipal services. By dialing 311, urban residents …

This textbook integrates GIS, spatial analysis, and computational methods for solving real-
world problems in various policy-relevant social science applications. Thoroughly updated …

In this work, we consider space-time point processes and study their continuous space-time
evolution. We propose an innovative nonparametric methodology to estimate the unknown …

Network kernel density visualization, or NKDV, has been extensively used to visualize
spatial data points in various domains, including traffic accident hotspot detection, crime …