Investigating Temporal and Spatial Effects of Urban Planning Variables on Crime Rate: A GWR and Ols Based Approach

Irandegani, Z.; Mohammadi, Reza Karami; Taleai, Mohammad

doi:10.5194/isprs-archives-xlii-4-w18-559-2019

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…These analyses can be developed through several types of tools, ranging from Exploratory Spatial Data Analysis (ESDA) techniques [32][33][34], to other procedures that allow the identification of correlations between variables, such as the Ordinary Least Squares Regression (OLS) [35][36][37] or the Spatial Autoregressive models (SAR) [38], as well as the geographically weighted regression (GWR), which enables the detection of the heterogeneity and spatial variation of variables [39][40][41].…”

Section: Mapping and Spatial Analysis Of Crimementioning

confidence: 99%

Spatial Modeling for Homicide Rates Estimation in Pernambuco State-Brazil

Silva

Melo

Santos

et al. 2020

IJGI

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Homicide rates have been increasing worldwide, especially in Latin America, where it is considered one of the most lethal of the continents. Despite that, the occurrence of homicides are not homogeneous in time and space on the continent or in the Brazilian cities. Therefore, the main objective of this study is to present a spatial analysis of homicides in the state of Pernambuco, Brazil, between the years of 2016 and 2019, by the use of an exploratory analysis of spatial homicide data with five variables that could explain its occurrence. In addition to that, it was applied the Global and Local Moran’s Index, Ordinary Least Squares (OLS) regression, and Geographically Weighted Regression (GWR), all implemented in the Geographic Information System (GIS) software. Thus, the distribution of clusters revealed a spatial autocorrelation for homicide rates, confirming a spatial dependence. This data also showed the polarization of the rate between the coast and the interior of the state of Pernambuco.

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Section: Mapping and Spatial Analysis Of Crimementioning

confidence: 99%

Spatial Modeling for Homicide Rates Estimation in Pernambuco State-Brazil

Silva

Melo

Santos

et al. 2020

IJGI

View full text Add to dashboard Cite

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“…These groups are used to identify interesting areas (e.g. zones with high crime [99] or natural resource potential [100]) and timespans (e.g. areas of disease transmission during specific times [101]).…”

Section: Spatial Clusteringmentioning

confidence: 99%

Spatial Decision Support Systems with Automated Machine Learning: A Review

Wen¹,

Li²

2022

Preprint

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Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness were discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems were also discussed to address these challenges. This paper argues that integrating spatial decision support systems with automated machine learning can not only encourage user adoption, but also mutually benefit research in both fields — bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning.

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