Methodological advancements in road safety research reveal an increasing inclination
toward integrating spatial approaches in hot spot identification, spatial pattern analysis, and …

Interprovincial disparities in carbon emissions from the construction industry (CECI) are an
important challenge for future emissions reductions. Based on the CECI data of 30 provinces …

Moran's I is a spatial autocorrelation measure of univariate spatial data. Therefore, even if p
spatial data exist, we can only obtain p values for Moran's I. In other words, Moran's I cannot …

Abstract Deep Neural Networks (DNNs) have garnered significant attention across various
research domains due to their impressive performance, particularly Convolutional Neural …

In recent decades, farmland markets have risen sharply due to their attractiveness as safe
investment and sav-ings allocation instruments. This growth has occurred globally at …

Spatial time series (STS), which refers to time-series data collected at fixed spatial locations,
is crucial for understanding the spatiotemporal dynamics of geographical phenomena …

The rapid advancement of spatial transcriptomics (ST) sequencing technology has made it
possible to capture gene expression with spatial coordinate information at the cellular level …

The black soil region of northeastern China, one of the world's major black soil belts, is
China's main grain-producing area, producing a quarter of China's commercial grain …

Geary'sc is a prominent measure of spatial autocorrelation in univariate spatial data. It uses
a weighted sum of squared differences. This paper develops Geary'sc for multivariate spatial …

ABSTRACT Sentinel-2 MultiSpectral Instrument (MSI) data exhibits the great potential of
enhanced spatial and temporal coverage for monitoring biomass burning which could …