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

This paper reviewed major remote sensing image classification techniques, including pixel-
wise, sub-pixel-wise, and object-based image classification methods, and highlighted the …

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

During the past decades, multiple remote sensing data sources, including nighttime light
images, high spatial resolution multispectral satellite images, unmanned drone images, and …

Housing values are heavily influenced by urban facilities. However, the amenity effects of
urban facilities have not been fully assessed, and their spatial heterogeneity has largely …

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

Modelling spatial heterogeneity (SH) is a controversial subject in real estate economics.
Single-family-home prices in Austria are explored to investigate the capability of global and …

In the era of big data, advances in relevant technologies are profoundly impacting the field of
real estate appraisal. Many scholars regard the integration of big data technology as an …

Negative amenities, such as air pollutions, have plagued many rapidly growing cities.
However, researchers have not comprehensively examined their effects on housing values …

Abstract Analysis of changes over time in the spatial structure of housing prices provides
reliable information to infer latent patterns and processes of urban growth. Although the …