Exploring the Impact of Floating Population with Different Household Registration on Theft

Bibliographic Data

ID22031345
AuthorsChong Xu (0000-0002-3956-4925, Guangzhou University), Xi Chen (0000-0002-2058-0351, Guangzhou University), Jianguo Chen (0000-0001-6809-9711, Guangzhou University, corresponding author), Debao Chen (0009-0007-9150-1482, Guangzhou University), D Y Chen (0009-0003-9313-2618, Guangzhou University)
Year2022
Volume11
Issue8
Pages443
Publication date2022-08-07
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi11080443
OpenAlexW4290601168
LanguageEN
Citations received4
References cited42

The floating population is frequently treated as a homogeneous whole to explore its impact on crime in numerous crime studies in China. However, there are different compositions within the floating population and significant differences in the effects on crime. In this study, the floating population was divided into three types based on household registration (i.e., Hukou): the floating population from other districts in the same city (FPFOD), the floating population from other cities in the same province (FPFOC) and the floating population from other provinces (FPFOP). The Moran index was used to analyze their spatial distribution patterns and aggregation, respectively, and several negative binomial regression models were constructed to explore the influence of different types of floating populations on theft. The results show that the three types of floating populations are mainly distributed in different urban areas, implying differences in their impact on theft. Among them, the proportion of the FPFOD shows insignificant negative correlation on theft, while the proportion of the FPFOC and the FPFOP present a significant positive correlation. Meanwhile, the proportion of the FPFOP creates a stronger effect on theft than the proportion of entire floating population. Overall, the model performs best when variables of the proportion of the FPFOC and the FPFOP are included. The research conclusions can provide a meaningful reference for precisely measuring the floating population in crime research

Cartography · China · Econometrics · Floating population · Geography · Homogeneous · Negative binomial distribution · Population · Statistics · Crime Patterns and Interventions · Crime, Illicit Activities, and Governance · Demography · HIV, Drug Use, Sexual Risk · Mathematics

Tools

Open DOIOpen Access