Background Obesity rates are recognized to be at epidemic levels throughout much of the
world, posing significant threats to both the health and financial security of many nations …

Assessing the effect of street built environment on crime occurrence is a hot research subject
in environmental criminology, which also plays an important role in crime prevention or even …

This paper is a methodological guide to using machine learning in the spatial context. It
provides an overview of the existing spatial toolbox proposed in the literature: unsupervised …

Comprehensive and accurate grasp of land-use carbon emissions (LCE) level and its
driving mechanism is key to success in China's pursuit of low-carbon development, and it is …

The concept of 'spatial scale', or simply 'scale'is implicit in any discussion of global versus
local models. The raison d'etre of local models is that a global scale (where here …

Nighttime light (NTL) intensity is highly associated with the unique footprint of human
activities, reflecting the development of socioeconomic and urbanization. Therefore, better …

Abstract Models designed to capture spatially varying processes are now employed
extensively in the social and environmental sciences. The main strength of such models is …

This paper proposes a novel spatially varying coefficient (SVC) regression through a
Geographical Gaussian Process GAM (GGP-GAM): a Generalized Additive Model (GAM) …

Abstract We employ a Multiscale Geographically Weighted Regression (MGWR) model to
examine the spatial variation of researchers in China in 2015 and its determinants. It is …

Spatial models are effective in obtaining local details on grassland biomass, and their
accuracy has important practical significance for the stable management of grasses and …