Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of
social and environmental data. It allows spatial heterogeneities in processes and …

Geographically weighted regression (GWR) is a local spatial statistical technique for
exploring spatial nonstationarity. This chapter reviews previous approaches to mapping the …

Recent advancements in spatial transcriptomic technologies have enabled the
measurement of whole transcriptome profiles with preserved spatial context. However …

Both space and time are fundamental in human activities as well as in various physical
processes. Spatiotemporal analysis and modeling has long been a major concern of …

Cities are home to around half of the global population but face intensified and unevenly
distributed heat stresses. Trees are utilized to adapt to urban heat; however, most tree …

Spatial statistics is a growing discipline providing important analytical techniques in a wide
range of disciplines in the natural and social sciences. In the R package GWmodel, we …

Integrating cultural dimensions into the ecosystem service framework is essential for
appraising non-material benefits stemming from different human–environment interactions …

Spatial epidemiology is the description and analysis of the geographical distribution of
disease. It is more important now than ever, with modern threats such as bio-terrorism …

The Definitive Volume on Cutting-Edge Exploratory Analysis of Massive Spatial and
Spatiotemporal DatabasesSince the publication of the first edition of Geographic Data …

The widely adopted techniques for regional crash modeling include the negative binomial
model (NB) and Bayesian negative binomial model with conditional autoregressive prior …