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A Proposed Weighting Scheme for Spatial Moving Average Model in an Irregular Lattice
Spatial Moving average models are used to analyze aggregated data on a spatial regular or irregular lattice. The models are available with various...
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Spatial Wildfire Risk Modeling Using a Tree-Based Multivariate Generalized Pareto Mixture Model
Wildfires pose a severe threat to the ecosystem and economy, and risk assessment is typically based on fire danger indices such as the McArthur...
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GPS data on tourists: a spatial analysis on road networks
This paper proposes a spatial point process model on a linear network to analyse cruise passengers’ stop activities. It identifies and models...
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Prediction in non-sampled areas under spatial small area models
In this article we study the prediction problem in small geographic areas in the situation where the survey data does not cover a substantial...
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Adjusted Inference for the Spatial Scan Statistic
A modification is proposed to the usual inference test of the Kulldorff’s spatial scan statistic, incorporating additional information about the size...
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Accommodating False Positives Within Acoustic Spatial Capture–Recapture, with Variable Source Levels, Noisy Bearings and an Inhomogeneous Spatial Density
Passive acoustic monitoring is a promising method for surveying wildlife populations that are easier to detect acoustically than visually. When...
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Bayesian Nonparametric Generative Modeling of Large Multivariate Non-Gaussian Spatial Fields
Multivariate spatial fields are of interest in many applications, including climate model emulation. Not only can the marginal spatial fields be...
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A spatial semiparametric M-quantile regression for hedonic price modelling
This paper proposes an M-quantile regression approach to address the heterogeneity of the housing market in a modern European city. We show how...
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Minimum contrast for the first-order intensity estimation of spatial and spatio-temporal point processes
In this paper, we harness a result in point process theory, specifically the expectation of the weighted K -function, where the weighting is done by...
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Geographically Weighted Comedian method for spatial outlier detection
A spatial outlier is defined as an object whose non-spatial attributes are different from the other objects in its spatial neighborhood. In this...
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A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes
Motivated by the Extreme Value Analysis 2021 (EVA 2021) data challenge, we propose a method based on statistics and machine learning for the spatial...
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COVID-19 Hotspot Map** and Prediction in Aizawl District of Mizoram: a Hotspot and SEIR Model-Based Analysis
The COVID-19 virus rapidly expanded worldwide and infected people from most of the countries (215) within a span of three months. The virus did not...
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Evaluating the spatial heterogeneity of innovation drivers: a comparison between GWR and GWPR
In studies focusing on innovation activities, the potential spatial heterogeneity in the relationships between innovation and its triggering factors...
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Geographically Weighted Regression-Based Model Calibration Estimation of Finite Population Total Under Geo-referenced Complex Surveys
In sample surveys, the model calibration approach is an improvement over the usual calibration approach, where the concept of the calibration...
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Local spatial log-Gaussian Cox processes for seismic data
In this paper, we propose the use of advanced and flexible statistical models to describe the spatial displacement of earthquake data. The paper aims...
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A spatio-temporal model for binary data and its application in analyzing the direction of COVID-19 spread
It is often of primary interest to analyze and forecast the levels of a continuous phenomenon as a categorical variable. In this paper, we propose a...
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Automatic estimation of spatial spectra via smoothing splines
Spectra are frequently used to depict the dependence features of a second-order stationary process. In this paper, the spatial log-spectral density...
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An association measure for spatio-temporal time series
Spatial association measures for univariate static spatial data are widely used. Suppose the data is in the form of a collection of spatial vectors,...
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Tail and Quantile Estimation for Real-Valued \(\boldsymbol{\beta}\)-Mixing Spatial Data
AbstractThis paper deals with extreme-value index estimation of a heavy-tailed distribution of a spatial dependent process. We are particularly...
