Exploring multi-scale variability in hotspot mapping: A case study on housing prices and crime occurrences in Heerlen

Kortas, Fabiënne; Grigoriev, Alexander; Piccillo, Giulia

doi:10.1016/j.cities.2022.103814

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…To assess and evaluate temporal changes, emerging hot spot analysis relies on a Mann-Kendall statistic that compares temporal trends in each bin to the null hypothesis of no temporal change and then also combines the Getis-Ord Gi* and Mann-Kendall results together to designate patterns into ordinal categories. These novel techniques have been used in recent crime studies (Kortas et al 2022;Nepomuceno and Costa 2019;Sweet et al 2020;Vadlamani and Hashemi 2020) and in other fields (see, for example, Ashok et al 2022;Fatima et al 2021;Mamiit et al 2021;Morckel and Durst 2021;Van Der Zee et al 2020;Zerbe et al 2022).…”

Section: Spatiotemporal Analysesmentioning

confidence: 99%

Hot Spots of Gun Violence in the Era of Focused Deterrence: A Space-Time Analysis of Shootings in South Philadelphia

Boschan,

Roman

2024

Social Sciences

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Gun and street group violence remains a serious problem in cities across the United States and the focused deterrence strategy has been a widely applied law enforcement intervention to reduce it. Although two meta-analytical studies concluded that the intervention had a significant effect on violence, questions remain about how violence changes across space and time during and after the intervention. This study applies novel geospatial analyses to assess spatiotemporal changes in gun violence before, during, and after the implementation of Philadelphia Focused Deterrence. Emerging hot spot analysis employing space–time cubes of ten annual time bins (2009–2018) at the Thiessen polygon level was used to detect and categorize patterns. The analyses revealed a non-significant decreasing trend across the ten-year period. Furthermore, there were ninety-three statistically significant hot spots categorized into four hot spot patterns: fourteen new hot spots; twenty-three consecutive; one persistent; and fifty-three sporadic. There was no evidence showing statistically significant hot spots for the “diminishing” pattern. Knowledge of these patterns that emerge across micro-locations can be used by law enforcement practitioners to complement data-driven problem solving and fine tune these strategies and other place-based programming. Policymakers can use findings to prioritize resources when developing complementary prevention and intervention efforts by tailoring those efforts to the different emergent patterns.

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Section: Spatiotemporal Analysesmentioning

confidence: 99%

Hot Spots of Gun Violence in the Era of Focused Deterrence: A Space-Time Analysis of Shootings in South Philadelphia

Boschan,

Roman

2024

Social Sciences

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“…The crime-prone spaces overlap offenders' daily activity spaces. For example, the areas where the burglars, robbers, or thieves once lived, the residence of their siblings and other places they may have visited, and places with low housing prices are often their targeted areas [7][8][9]. Activity nodes such as bus stops, bars, and Internet cafes are also hot spots with high crime rates as robbers frequently visit or pass by [10].…”

Section: Routine Activities and Activity Nodesmentioning

confidence: 99%

Explaining Theft Using Offenders’ Activity Space Inferred from Residents’ Mobile Phone Data

Liu,

Li,

Xiao

et al. 2023

IJGI

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Both an offender’s home area and their daily activity area can impact the spatial distribution of crime. However, existing studies are generally limited to the influence of the offender’s home area and its immediate surrounding areas, while ignoring other activity spaces. Recent studies have reported that the routine activities of an offender are similar to those of the residents living in the same vicinity. Based on this finding, our study proposed a flow-based method to measure how offenders are distributed in space according to the spatial mobility of the residents. The study area consists of 2643 communities in ZG City in southeast China; resident flows between every two communities were calculated based on mobile phone data. Offenders’ activity locations were inferred from the mobility flows of residents living in the same community. The estimated count of offenders in each community included both the offenders living there and offenders visiting there. Negative binomial regression models were constructed to test the explanatory power of this estimated offender count. Results showed that the flow-based offender count outperformed the home-based offender count. It also outperformed a spatial-lagged count that considers offenders from the immediate neighboring communities. This approach improved the estimation of the spatial distribution of offenders, which is helpful for crime analysis and police practice.

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“…In fact, there is no shortage of research on the correlation between housing prices and the urban built environment, both domestically and internationally. In terms of the spatial scale, most of the settings for the impact range of housing prices rely on buffer distance [36,37] and grid size execution [38,39]. However, this kind of spatial range delineation based on regular shapes easily blurs the accurate expression of crowd travel behavior and the real path associated with pedestrian streets.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Hierarchical Effects of Housing Prices and Built Environment Based on Multiscale Life Circle—A Case Study of Chengdu

Song,

Zhang,

Deng

2023

IJGI

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Determining the optimal planning scale for urban life circles and analyzing the associated built environment factors are crucial for comprehending and regulating residential differentiation. This study aims to bridge the current research void concerning the nonlinear hierarchical relationships between the built environment and residential differentiation under the multiscale effect. Specifically, six indicators were derived from urban crowdsourcing data: diversity of built environment function (DBEF1), density of built environment function (DBEF2), blue–green environment (BGE), traffic accessibility (TA), population vitality (PV), and shopping vitality (SV). Then, a gradient boosting decision tree (GBDT) was applied to derive the analysis of these indicators. Finally, the interpretability of machine learning was leveraged to quantify the relative importance and nonlinear relationships between built environment indicators and housing prices. The results indicate a hierarchical structure and inflection point effect of the built environment on residential premiums. Notably, the impact trend of the built environment on housing prices within a 15 min life circle remains stable. The effect of crowd behavior, as depicted by PV and SV, on housing prices emerges as the most significant factor. Furthermore, this study also categorizes housing into common and high-end residences, thereby unveiling that distinct residential neighborhoods exhibit varying degrees of dependence on the built environment. The built environment exerts a scale effect on the formation of residential differentiation, with housing prices exhibiting increased sensitivity to the built environment at a smaller life circle scale. Conversely, the effect of the built environment on housing prices is amplified at a larger life circle scale. Under the dual influence of the scale and hierarchical effect, this framework can dynamically adapt to the uncertainty of changes in life circle planning policies and residential markets. This provides strong theoretical support for exploring the optimal life circle scale, alleviating residential differentiation, and promoting group fairness.

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Exploring multi-scale variability in hotspot mapping: A case study on housing prices and crime occurrences in Heerlen

Cited by 11 publications

References 19 publications

Hot Spots of Gun Violence in the Era of Focused Deterrence: A Space-Time Analysis of Shootings in South Philadelphia

Hot Spots of Gun Violence in the Era of Focused Deterrence: A Space-Time Analysis of Shootings in South Philadelphia

Explaining Theft Using Offenders’ Activity Space Inferred from Residents’ Mobile Phone Data

Nonlinear Hierarchical Effects of Housing Prices and Built Environment Based on Multiscale Life Circle—A Case Study of Chengdu

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