Measurement of Potential Victims of Burglary at the Mesoscale: Comparison of Census, Phone Users, and Social Media Data

Abstract

Since the target of burglars is generally the property of the inhabitant, it is crucial to accurately measure potential victims when analyzing burglaries, especially in small areas. Previous studies on burglary are mostly based on large units such as census tracts or communities. One of the difficulties is the measurement of the potential victims of burglary at the mesoscale. We compare the measuring effects of census population, census households, nighttime mobile phone users, and nighttime social media, such as the Tencent regional heatmap (TRH), on potential victims of burglary on 150 m × 150 m grids. Based on the rational choice theory, and controlling for the potentially confounding effects of risks and cost, we show that the TRH performed best, followed by census households and census population, and phone users performed poorly. The best-performing time period for TRH data was 3:00–5:00am on weekends. These findings could lead to an improved measurement of potential victims of burglary at the mesoscale, and could provide scientific insight for crime prevention.

A study of the influence of sports venues on the intra-city population layout based on multi-source data—Taking Xi’an city and Zhengzhou city as examples

“…The population data were obtained from WorldPop grid data and the resident population data of the 7th National Population Census. Census data are usually aggregated by administrative units on a cascading basis, and they are authoritative, systematic and standardized [ 30 ]. However, the administrative units on which such data rely do not coincide with the boundaries of the natural units in the actual study.…”

Risk Assessment of Crowd-Gathering in Urban Open Public Spaces Supported by Spatio-Temporal Big Data

“…Tencent user density (TUD) data are big data generated by the location of smartphone users who use Tencent applications [24]. TUD data have been widely utilized in research considering urban population distribution mapping [25][26][27], identification of urban functional areas [28], park use measurement [29,30], estimation of potential victims of burglary [31], measuring polycentric urban structures [32], urban land use classification [33], and in other studies [24,34,35].…”