Spatial Statistics

Papers
(The H4-Index of Spatial Statistics is 17. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-11-01 to 2024-11-01.)
ArticleCitations
Analysing point patterns on networks — A review46
The SPDE approach for Gaussian and non-Gaussian fields: 10 years and still running44
The Spillover Effects of Institutional Quality and Economic Openness on Economic Growth for the Belt and Road Initiative (BRI) countries43
Using multiple linear regression and random forests to identify spatial poverty determinants in rural China42
Spatial statistics and soil mapping: A blossoming partnership under pressure41
Mapping road traffic crash hotspots using GIS-based methods: A case study of Muscat Governorate in the Sultanate of Oman36
Maximum likelihood estimation of spatially varying coefficient models for large data with an application to real estate price prediction31
Population-weighted exposure to air pollution and COVID-19 incidence in Germany28
Prediction of intensity and location of seismic events using deep learning26
Accounting for spatial varying sampling effort due to accessibility in Citizen Science data: A case study of moose in Norway25
Point-process based Bayesian modeling of space–time structures of forest fire occurrences in Mediterranean France24
Application of improved Moran’s I in the evaluation of urban spatial development24
Demography and Crime: A Spatial analysis of geographical patterns and risk factors of Crimes in Nigeria22
Spatio-temporal modelling of COVID-19 incident cases using Richards’ curve: An application to the Italian regions20
Bayesian disease mapping: Past, present, and future20
Higher-dimensional spatial extremes via single-site conditioning19
Stochastic local interaction model with sparse precision matrix for space–time interpolation18
Bayesian spatio-temporal joint disease mapping of Covid-19 cases and deaths in local authorities of England17
Endemic–epidemic models to understand COVID-19 spatio-temporal evolution17
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