Nonparametric spatiotemporal analysis of violent crime. A case study in the Rio de Janeiro metropolitan area

Fuentes-Santos, I.; González–Manteiga, Wenceslao; Zubelli, Jorge P.

doi:10.1016/j.spasta.2020.100431

Cited by 7 publications

(3 citation statements)

References 72 publications

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“…First, we estimated the intensity (𝜆) of the deforestation events using the kernel smoothed estimator of intensity, and the edge correction described by Jones (1993) and Diggle (2010) (Equation 1). The kernel estimator allows the spatial distribution characterization of the events under study (Fuentes-Santos et al 2020).…”

Section: Ripley's K Function Analysismentioning

confidence: 99%

Spatial pattern analysis of deforestation in the northeast of Minas Gerais State, Brazil

DINIZ,

REIS,

ACERBI JUNIOR

2023

An. Acad. Bras. Ciênc.

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Understanding the spatial pattern of a particular geographic phenomenon such as deforestation is a key issue to establish monitoring programs to prevent the depletion of natural resources. Thus, the goal of this study was to assess the spatial pattern of deforested areas in the Pardo and Jequitinhonha River basins using Ripley's K function.First, we mapped all deforested areas in these basins using Landsat multispectral imagery from 2007 to 2015. Then, we used the Ripley's K function to test for spatial interactions between deforestation events. Our results showed that deforestations predominantly occur in a clustering spatial pattern in these basins. Spatial statistical analyses as Ripley's K function may provide a baseline for deforestation monitoring, as well as allowing us to understand the spatial pattern of deforestation in different natural ecosystems, especially in countries like Brazil, where the territorial dimension presents a great difficulty for the effectiveness of deforestation monitoring.

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Section: Ripley's K Function Analysismentioning

confidence: 99%

Spatial pattern analysis of deforestation in the northeast of Minas Gerais State, Brazil

DINIZ,

REIS,

ACERBI JUNIOR

2023

An. Acad. Bras. Ciênc.

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“…A cross-validation threshold and statistical tests were used to identify false positives and negatives. Fuentes-Santos [19] also applied kernel density for the analysis of spatial-temporal patterns of shots in Rio de Janeiro City, Brazil. They applied first-and second-order non-parametric inference tools to the reported events and compared them with crime prediction hotspot models, identifying chronic critical points.…”

Section: Related Workmentioning

confidence: 99%

Smart Patrolling Based on Spatial-Temporal Information Using Machine Learning

Guevara

Santos

2022

Mathematics

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With the aim of improving security in cities and reducing the number of crimes, this research proposes an algorithm that combines artificial intelligence (AI) and machine learning (ML) techniques to generate police patrol routes. Real data on crimes reported in Quito City, Ecuador, during 2017 are used. The algorithm, which consists of four stages, combines spatial and temporal information. First, crimes are grouped around the points with the highest concentration of felonies, and future hotspots are predicted. Then, the probability of crimes committed in any of those areas at a time slot is studied. This information is combined with the spatial way-points to obtain real surveillance routes through a fuzzy decision system, that considers distance and time (computed with the OpenStreetMap API), and probability. Computing time has been analized and routes have been compared with those proposed by an expert. The results prove that using spatial–temporal information allows the design of patrolling routes in an effective way and thus, improves citizen security and decreases spending on police resources.

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“…Rachdi, Laksaci and Al‐Awadhi (2021) proposed conditional quantile estimate by using parametric and nonparametric approach for spatiofunctional data. We can also refer to others nonparametric context as in Weller and Hoeting (2020), Xu and Bai (2020), Gupta and Hidalgo (2020), Meilán‐Vila, Fernández‐Casal and Francisco‐Fernández (2020), Kurisu (2020), Fuentes‐Santos, González‐Manteiga and Zubelli (2020).…”

Section: Introductionmentioning

confidence: 99%

Asymptotic properties of nonparametric quantile estimation with spatial dependency

Kanga

Hili

Dabo‐Niang

et al. 2022

Statistica Neerlandica

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The purpose of this work is to nonparametrically estimate the conditional quantile for a locally stationary multivariate spatial process. The new kernel quantile estimate derived from the one of conditional distribution function (CDF). The originality in the paper is based on the ability to take into account some local spatial dependency in estimate CDF form. Consistency and asymptotic normality of the estimates are obtained under α$$ \alpha $$‐mixing condition. Numerical study and application to real data are given in order to illustrate the performance of our methodology.

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Nonparametric spatiotemporal analysis of violent crime. A case study in the Rio de Janeiro metropolitan area

Cited by 7 publications

References 72 publications

Spatial pattern analysis of deforestation in the northeast of Minas Gerais State, Brazil

Spatial pattern analysis of deforestation in the northeast of Minas Gerais State, Brazil

Smart Patrolling Based on Spatial-Temporal Information Using Machine Learning

Asymptotic properties of nonparametric quantile estimation with spatial dependency

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