地理学报 ›› 2021, Vol. 76 ›› Issue (2): 310-325.doi: 10.11821/dlxb202102005

• 人口与城市研究 • 上一篇    下一篇

中国失踪人口的时空格局演变与形成机制

李钢1,2(), 薛淑艳1,2, 马雪瑶1,2, 周俊俊1,2, 徐婷婷1,2, 王皎贝1,2   

  1. 1.西北大学城市与环境学院,西安 710127
    2.陕西省地表系统与环境承载力重点实验室,西安 710127
  • 收稿日期:2019-08-15 修回日期:2020-08-23 出版日期:2021-02-25 发布日期:2021-04-25
  • 作者简介:李钢(1979-), 男, 四川成都人, 教授, 博士生导师, 中国地理学会会员(S110009694M), 研究方向为人地关系与空间安全,灾害地理与犯罪地理,时空大数据与数字人文。E-mail: lig@nwu.edu.cn
  • 基金资助:
    国家自然科学基金项目(41871144);教育部人文社会科学研究规划基金项目(16YJAZH028);西北大学“仲英青年学者”计划(2016)

Spatio-temporal pattern evolution and formation mechanism of missing person incidents in China

LI Gang1,2(), XUE Shuyan1,2, MA Xueyao1,2, ZHOU Junjun1,2, XU Tingting1,2, WANG Jiaobei1,2   

  1. 1. College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China
    2. Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi'an 710127, China
  • Received:2019-08-15 Revised:2020-08-23 Published:2021-02-25 Online:2021-04-25
  • Supported by:
    National Natural Science Foundation of China(41871144);Humanities and Social Science Foundation of Chinese Ministry of Education(16YJAZH028);Tang Scholar Program of Northwest University(2016)

摘要:

人口失踪作为一种复杂的社会问题,给家庭和社会造成了严重危害。在尊重生命与保障人权的现实背景下,对失踪人口开展深入研究具有重要意义。利用“中国儿童失踪预警平台(CCSER)”数据,综合运用文本分析、数理统计、空间分析等方法,管窥了2015—2019年中国失踪人口的基本特征、时空格局演变与形成机制。结果表明:① 失踪人口中男性多于女性,高发年龄段由高到低依次为8~16岁、2~7岁、0~1岁和60~65岁;失踪类型由高到低依次为无意识失踪、主动失踪与被动失踪,失踪亚类由高到低依次为离家出走、走失、被拐卖、身患疾病、联系中断与家庭不和。② 时间上,失踪人口总数、性别与年龄变化均呈“驼峰”状,并以2017年为轴于两侧对称分布。空间上,总体为“低—高”和“高—低”集聚,广东、浙江和四川是人口失踪的高发省份。③ 人口失踪省内流动远高于跨省流动,省内流动以广东、四川、河南和江苏为最,跨省流动以“安徽—江苏”和“广西—广东”为主要路径。④ 人口失踪可用社会网络理论中的“强弱连接理论”来解释,其主要受人与社会网络关系的强度变化的影响。

关键词: 失踪人口, 时空格局, 形成机制, 中国

Abstract:

The problem of missing persons brings about serious harm to their families and the society. An in-depth investigation of this issue is of great importance to protecting human lives and human rights. In this research, we collect the missing persons data during the period from 2015 to 2019 from the "China's Child Safety Emergency Response (CCSER)" platform. We use a series of techniques including text analysis, mathematical statistics, and spatial analysis to analyze the socio-demographic characteristics, evolution and formation mechanism of spatio-temporal patterns of missing persons in China. Major findings include: (1) The number of missing males is larger than that of missing females. The highest missing rate is found in people aged 8-16, followed by aged 2-7, aged 0-1, and aged 60-65. Three categories of missing persons are observed in the data, which are (in order of decreasing frequency): unconscious disappearance, active disappearance, and passive disappearance. Six sub-types of missing persons in a descending order by frequency are: running away from home, wandering away, abduction, physical or mental illness, losing track, and family dissension. (2) Hump-shaped curves are observed for temporal variations of the number, gender and age of missing persons, and the curves are symmetric about the year of 2017. The local spatial autocorrelation tests indicate that incidents of missing persons generally exhibit "low-high" and "high-low" clustering patterns. Provinces with a high incidence of missing persons are Guangdong, Zhejiang and Sichuan. (3) With respect to the spatial mobility of missing persons, intra-provincial mobility is more prevalent than inter-provincial mobility. Guangdong, Sichuan, Henan, and Jiangsu experience the highest intra-provincial mobility rate. Dominant paths of inter-provincial mobility are "Anhui-Jiangsu" route and "Guangxi-Guangdong" route. (4) The underlying mechanism of missing person incidents can be understood from the perspective of "strong and weak ties in social network". That is, the strentgth of people's social ties can impact the occurrence of missing persons.

Key words: missing persons, spatio-temporal pattern, formation mechanism, China