ArticleScientific reports2020
Socio-economic, built environment, and mobility conditions associated with crime: a study of multiple cities.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Review
- Understanding the Influence of Income Inequality and Violent Crime on Police Fatal Shootings in U.S. Counties: 2015-2022.Journal of racial and ethnic health disparities · 2026Article
- No Place Like Home? Local Crime and Older Adults' Time at Home.Social problems · 2026Article
- Disentangling the impacts of collective mobility of residents and non-residents on burglary levels.Computational urban science · 2026Article
- Analyzing the stability of gun violence patterns during the COVID-19 pandemic in Syracuse, New York.International journal of health geographics · 2025Article
- Examining associations between street vegetation and child firearm violence at microgeographic locations in 3 US cities: a case-control study.BMC pediatrics · 2025Article
- Comprehensive walkability assessment of urban pedestrian environments using big data and deep learning techniques.Scientific reports · 2024Article
- Bayesian analysis of urban theft crime in 674 Chinese cities.Scientific reports · 2024Article
- Using Google Street View for virtual observations of neighborhoods and dwelling units: A feasibility study.PloS one · 2024Article
- Describing the geographical linkage between the UK Millennium Cohort Study and crime incidents in England and Wales.International journal of population data science · 2024Article
- Does Neighborhood Crime Mediate the Relationship Between Neighborhood Socioeconomic Status and Birth Outcomes? An Application of the Mediational G-Formula.American journal of epidemiology · 2023Article
- Crime, inequality and public health: a survey of emerging trends in urban data science.Frontiers in big data · 2023Review
- Investigating the impact of the COVID-19 pandemic on crime incidents number in different cities.Journal of safety science and resilience = An quan ke xue yu ren xing (Ying wen) · 2022Article
- How Neighborhood Characteristics Influence Neighborhood Crimes: A Bayesian Hierarchical Spatial Analysis.International journal of environmental research and public health · 2022Article
- Mobility, Nonstationary Density, and Robbery Distribution in the Tourist Metropolis.European journal on criminal policy and research · 2022Article
- An integrated cognitive framework for understanding modern cities.Computational urban science · 2022Article
- Enhancing short-term crime prediction with human mobility flows and deep learning architectures.EPJ data science · 2022Article
- Understanding peace through the world news.EPJ data science · 2022Article
- A Deep Gravity model for mobility flows generation.Nature communications · 2021Article
- City consumption profile: a city perspective on the spending behavior of citizens.Applied network science · 2021Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Nowadays, 23% of the world population lives in multi-million cities. In these metropolises, criminal activity is much higher and violent than in either small cities or rural areas. Thus, understanding what factors influence urban crime in big cities is a pressing need. Seminal studies analyse crime records through historical panel data or analysis of historical patterns combined with ecological factor and exploratory mapping. More recently, machine learning methods have provided informed crime prediction over time. However, previous studies have focused on a single city at a time, considering only a limited number of factors (such as socio-economical characteristics) and often at large in a single city. Hence, our understanding of the factors influencing crime across cultures and cities is very limited. Here we propose a Bayesian model to explore how violent and property crimes are related not only to socio-economic factors but also to the built environmental (e.g. land use) and mobility characteristics of neighbourhoods. To that end, we analyse crime at small areas and integrate multiple open data sources with mobile phone traces to compare how the different factors correlate with crime in diverse cities, namely Boston, Bogotá, Los Angeles and Chicago. We find that the combined use of socio-economic conditions, mobility information and physical characteristics of the neighbourhood effectively explain the emergence of crime, and improve the performance of the traditional approaches. However, we show that the socio-ecological factors of neighbourhoods relate to crime very differently from one city to another. Thus there is clearly no "one fits all" model.
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