Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USA

Jendryke, Michael; McClure, Stephen C.

doi:10.1080/17538947.2021.1886356

Cited by 11 publications

(3 citation statements)

References 48 publications

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“…To address both questions, this study includes the altitude value in the spatial data structure. This approach differs from previous research (Cliff et al, 1982;Stetzer, 1982;Anselin, 1988;Fotheringham et al, 1997;Getis & Griffith, 2002;Getis & Aldstadt, 2004;LeSage, 2008;Geniaux & Martinetti 2018), partiucularly for lattice space structure (Boots & Dufournaud, 1994;Frazier & Kockelman, 2005;Stakhovych & Bijmolt, 2009;Arbia, 2014;Jendryke & McClure, 2019;Jendryke & McClure, 2021;Harke et al, 2022;Perk & Otto, 2022). In this study, we include the altitude variable embedded for each spatial unit as the third-dimensional data structure for analysis and then we formulate the spatial W matrix.…”

Section: Introductionmentioning

confidence: 79%

Simple Formulation of Spatial Weights Matrix in 3D Lattice Space: Unravelling Regional Interlinkages Beyond the Flat Surface

Fitrianto

2024

Preprint

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A rigorous spatial weights matrix (W) is essential for unraveling neighboring linkages between spatial units. Precedent research only focused on how to construct a two-dimensional space by imposing a fundamental assumption that linkages between flat areas and areas with higher variance of elevation are the same. This study presents a comprehensive yet simple formulation to construct a spatial W matrix in three-dimensional space. Central to this approach is the innovative use of the Kronecker product in combination with shift matrices, which incorporating altitude dimension into the spatial W matrix formulation. This comprehensive framework benefit to captures the complexity of different spatial interactions in topographically diverse environments.

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Section: Introductionmentioning

confidence: 79%

Simple Formulation of Spatial Weights Matrix in 3D Lattice Space: Unravelling Regional Interlinkages Beyond the Flat Surface

Fitrianto

2024

Preprint

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“…This maximizes the likelihood of the provided forecasts. Therefore, efforts that might be useful in terms of the stated needs include: the geo-visualization of forecasts based on spatial clustering to reflect the characteristics of adjacent terrains [32][33][34][35][36]; forecast geo-visualization for sparse data [37][38][39][40]; the geo-visualization of the forecasting of criminal activities using ma-chine learning and deep learning techniques [34][35][36][39][40][41][42][43]; event forecasting using classical, improved classical, machine learning, and deep learning techniques for multivariate time series [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60]; and, finally, multivariate time series forecasting with sparse data [61][62][63][64][65].…”

Section: Work Related To the Concept Of Spatiotemporal Predictive Geo...mentioning

confidence: 99%

Spatiotemporal Predictive Geo-Visualization of Criminal Activity for Application to Real-Time Systems for Crime Deterrence, Prevention and Control

Salcedo-Gonzalez

Suarez-Paez

Esteve

et al. 2023

IJGI

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This article presents the development of a geo-visualization tool, which provides police officers or any other type of law enforcement officer with the ability to conduct the spatiotemporal predictive geo-visualization of criminal activities in short and continuous time horizons, according to the real events that are happening: that is, for those geographical areas, time slots, and dates that are of interest to users, with the ability to consider individual events or groups of events. This work used real data collected by the Colombian National Police (PONAL); it constitutes a tool that is especially effective when applied to Real-Time Systems for crime deterrence, prevention, and control. For its creation, the spatial and temporal correlation of the events is carried out and the following deep learning techniques are employed: CNN-1D (Convolutional Neural Network-1D), MLP (multilayer perceptron), LSTM (long short-term memory), and the classical technique of VAR (vector autoregression), due to its appropriate performance in the multi-step and multi-parallel forecasting of multivariate time series with sparse data. This tool was developed with Open-Source Software (OSS) as it is implemented in the Python programming language with the corresponding machine learning libraries. It can be implemented with any geographic information system (GIS) and used in relation to other types of activities, such as natural disasters or terrorist activities.

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“…✓ La geo-visualización de pronóstico de actividades delictivas usando técnicas de Machine Learning y Deep Learning [64], [65], [66], [70], [69],…”

Section: ) Una Vez Definidas Las Ventanas Deslizantes Temporales Inic...unclassified

Arquitectura de un sistema de geo-visualización espacio-temporal de actividad delictiva, basada en el análisis masivo de datos, aplicada a sistemas de información de comando y control (C2IS)

González¹

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Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USA

Cited by 11 publications

References 48 publications

Simple Formulation of Spatial Weights Matrix in 3D Lattice Space: Unravelling Regional Interlinkages Beyond the Flat Surface

Simple Formulation of Spatial Weights Matrix in 3D Lattice Space: Unravelling Regional Interlinkages Beyond the Flat Surface

Spatiotemporal Predictive Geo-Visualization of Criminal Activity for Application to Real-Time Systems for Crime Deterrence, Prevention and Control

Arquitectura de un sistema de geo-visualización espacio-temporal de actividad delictiva, basada en el análisis masivo de datos, aplicada a sistemas de información de comando y control (C2IS)

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