The Spillover Effect of Geotagged Tweets as a Measure of Ambient Population for Theft Crime

Lan, Minxuan; Lin, Liu; Hernández, Andrés; Liu, Weiyi; Zhou, Hanlin; Wang, Zengli

doi:10.3390/su11236748

Cited by 30 publications

(30 citation statements)

References 83 publications

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“…Crime research usually takes the number of crimes as a dependent variable, which is discrete, whereas the OLS model assumes that the dependent variable should be continuous. Therefore, the Poisson and negative binomial regression models have been adopted for crime modeling [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model

Chen

Liu

Xiao

et al. 2020

IJGI

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Negative binomial (NB) regression model has been used to analyze crime in previous studies. The disadvantage of the NB model is that it cannot deal with spatial effects. Therefore, spatial regression models, such as the geographically weighted Poisson regression (GWPR) model, were introduced to address spatial heterogeneity in crime analysis. However, GWPR could not account for overdispersion, which is commonly observed in crime data. The geographically weighted negative binomial model (GWNBR) was adopted to address spatial heterogeneity and overdispersion simultaneously in crime analysis, based on a 3-year data set collected from ZG city, China, in this study. The count of residential burglaries was used as the dependent variable to calibrate the above models, and the results revealed that the GWPR and GWNBR models performed better than NB for reducing spatial dependency in the model residuals. GWNBR outperformed GWPR for incorporating overdispersion. Therefore, GWNBR was proven to be a promising tool for crime modeling.

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Section: Introductionmentioning

confidence: 99%

Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model

Chen

Liu

Xiao

et al. 2020

IJGI

Self Cite

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“…This implies that it retrieves all tweets that were publicly available online at the time of data collection, but does not discover tweets that got deleted in the meantime. This method of collecting tweets for scientific analysis has been used, inter alia, byLan et al (2019) who focus on the locations of users and show that twitter data can serve as an alternative to census population data, by Tavazoee et al (2017) who look at popularity of candidates of the US election 2016 in social media or bySong and Miled (2017) who use tweets to monitor flu vaccine rates.5 Whenever we refer to tweets in our sample, this also includes replies.ECB Working Paper Series No 2594 / October 2021…”

mentioning

confidence: 99%

Central bank communication with non-experts – A road to nowhere?

Ehrmann

Wabitsch

2022

Journal of Monetary Economics

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Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use:Documents in EconStor may be saved and copied for your personal and scholarly purposes.You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an OpenContent Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.

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“…Similarly, the crime rates and the expected numbers of crimes for each area are calculated using the registered population, which may not be the best choice. In recent years, ambient populations have been adopted more often in studies and have proven to yield better performance [62,63]. If appropriate data are available, it is worth using the ambient population to estimate the expected number of crimes in the Bayesian model and making a comparison with the results of this study.…”

Section: Discussionmentioning

confidence: 99%

Understanding the Spatiotemporal Pattern of Crimes in Changchun, China: A Bayesian Modeling Approach

et al. 2021

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Chinese cities have been undergoing extraordinary changes in many respects during the process of urbanization, which has caused crime patterns to evolve accordingly. This research applies a Bayesian spatiotemporal model to explore and understand the spatiotemporal patterns of crime risk from 2008 to 2017 in Changchun, China. The overall temporal trend of crime risk, the effects of land use covariates, spatial random effects, and area-specific differential trends are estimated through a Bayesian spatiotemporal model fitted using the Integrated Nested Laplace Approximation (INLA). The analytical results show that the regression coefficient for the overall temporal trend of crime risk changed from significantly positive to negative after the land use variables are incorporated into the Bayesian spatiotemporal model. The covariates of road density, commercial and recreational land per capita, residential land per capita, and industrial land per capita are found to be significantly associated with crime risk, which relates to classic theories in environmental criminology. In addition, some areas still exhibit significantly increasing crime risks compared with the general trend even after controlling for the land use covariates and the spatial random effects, which may provide insights for law enforcement and researchers regarding where more attention is required since there may be some unmeasured factors causing higher crime trend in these areas.

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The Spillover Effect of Geotagged Tweets as a Measure of Ambient Population for Theft Crime

Cited by 30 publications

References 83 publications

Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model

Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model

Central bank communication with non-experts – A road to nowhere?

Understanding the Spatiotemporal Pattern of Crimes in Changchun, China: A Bayesian Modeling Approach

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