Socio-economic, built environment, and mobility conditions associated with crime: a study of multiple cities

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.

Understanding peace through the world news

“…Many researchers use mobility data, such as mobile phone records and GPS traces [42][43][44][45][46] in combination with traditional data, to predict and prevent crime [47][48][49][50][51], compare how the different factors correlate with crime in various cities [52]. Moreover, researchers combine social media data with phone records to infer migration events [53][54][55][56][57] and use GPS data, combined with subjective and objective data, to study perceived safety [58].…”

Understanding peacefulness through the world news

“…Besides social media data, many researchers use mobility data, such as mobile phone records and GPS traces, usually in combination with traditional data, to predict and eventually prevent crime [6,11,26,70,92], to compare how the different factors correlate with crime in various cities [23], and to estimate deprivation and objective well-being [24,61,62,78]. In addition, researchers, combine social media data with mobile phone records to infer human migration and evaluate migration events [18,75] and use GPS data, combined with subjective and objective data, to study perceived safety [21].…”

A network spatial analysis simulating response time to calls for service at variable staffing levels

“…There exist some efforts in the literature to create similar tools with regards to other public safety risks. In [7], the authors relate these reported incident events with "socio-economic factors, built environment and mobility characteristic of the neighborhoods", providing us information regarding the calls for service data we used in our study. The work in [8] focuses on the response time of "Emergency Medical Services" and uses the Uber movement dataset to roughly construct Greater London city, and create "nodes/regions" as building blocks which are connected with other nodes by creating edges between 2 regions.…”