This article considers critically how one of the oldest and most widely applied statistical
methods, principal components analysis (PCA), is employed with spatial data. We first …

This paper reviews real estate price estimation in France, a market that has received little
attention. We compare seven popular machine learning techniques by proposing a different …

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 …

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 …

National land use policies and strategies worldwide have attempted to establish a healthy
housing rental market towards urban sustainability. Monitoring fine-scale housing rental …

Spatial data contain locational information as well as attribute information. It is increasingly
recognized that most data sets are spatial in that the attribute being measured is typically …

Geographically weighted regression (GWR) is an important local technique for exploring
spatial heterogeneity in data relationships. In fitting with Tobler's first law of geography, each …

Determining real estate market dynamics has become an important issue in the city
economy for achieving sustainable urban land management and investment planning. This …

While recent developments have extended geographically weighted regression (GWR) in
many directions, it is usually assumed that the relationships between the dependent and the …

Losses from natural hazards are escalating dramatically, with more properties and critical
infrastructure affected each year. Although the magnitude, intensity, and/or frequency of …