doi:10.16186/j.cnki.1673-9787.2020.3.7

Received:2019/08/11

Revised:2019/10/21

Published:2020/05/15

Analysis of spatial-temporal characteristics and impact factors of child trafficking crime in China based on GIS

LI Guangyi, LI Haiping, BAI Xiaoqiong

School of Environment & Natural Resources， Renmin University of China ，Beijing 100872 ， China

Abstract:The sample data of child trafficking in each province during 1977 —2017 was obtained from the online public platform" baobeihuijia. com" based on Python software. Through the function of GIS spatial analysis， the local spatial autocorrelation of panel data was analyzed， and the spatial agglomeration and heterogeneity of child abduction crime were revealed on the basis of analyzing the basic characteristics of child trafficking crimes. Spatial Markov matrix was used to analyze the spatial-temporal transfer probability of the number of trafficked children in different regions. Based on the geographical weighted regression， it was identified that the three indicators of the educational level， urban-rural income gap and floating population had spatial differences in impact of the child trafficking crimes in different regions. The results showed that ： （ 1 ） In 1977 —2017 ， the area of child trafficking crimes not only increased， but also concentrated in the southwest region. （2 ） There was an obvious spatial positive correlation between the child trafficking crimes in the provinces， and it was a long coexistence of the high agglomeration and low agglomeration areas， in addition to the obvious phenomenon of spatial agglomeration. （3 ） The high-risk and low-risk areas in the next period of time were more inclined to maintain the original state， and the transfer probability between adjacent levels was greater than that between the cross-levels. （4 ） There was a significant positive correlation between the urban-rural income gap， the floating population and the children trafficking crime， in which the impact of the floating population was greater than that of the urban-rural income gap， while the education level was negatively correlated， and the different influence factor had significant spatial differences in different regions.

Key words:GIS;child trafficking crime;spatial-temporal characteristic;spatial agglomeration;spatial Markov matrix;geographically weighted regression

基于GIS的中国拐卖儿童犯罪时空特征及影响因素分析_李光一.pdf