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

In this study, we aimed to examine spatial inequalities of COVID-19 mortality rate in relation
to spatial inequalities of socioeconomic and environmental factors across England …

This paper considers a filter for smoothing spatial data. It can be used to smooth data on the
vertices of arbitrary undirected graphs with arbitrary non-negative spatial weights. It consists …

ABSTRACT Geographically Weighted Regression (GWR) has been broadly used in various
fields to model spatially non-stationary relationships. Multi-scale Geographically Weighted …

Background Accessibility and utilization of antenatal care (ANC) service varies depending
on different geographical locations, sociodemographic characteristics, political and other …

Analyzing ecological data often requires modeling the autocorrelation created by spatial and
temporal processes. Many seemingly disparate statistical methods used to account for …

Recent work in local spatial modeling has affirmed and broadened interest in multivariate
local spatial analysis. Two broad approaches have emerged: Geographically Weighted …

This study develops a spatially varying coefficient model by extending the random effects
eigenvector spatial filtering model. The developed model has the following properties: its …

As an emerging mobility service, bike-sharing has become increasingly popular around the
world. A critical question in planning and designing bike-sharing services is to know how …

Spatial interaction and spatial structure are foundational geographical abstractions, though
there is often variation in how they are conceptualized and deployed in quantitative models …