摘要
通过Python技术从互联网获取自杀死亡案例数据,采用数理统计分析方法和地理空间分析方法,对2000—2018年中国自杀死亡案例的时空特征及案例自杀率与经济发展的关系进行研究,结果表明:1)中国自杀死亡案例数总体呈上升趋势;一年之中,夏季5—6月为自杀死亡高发期,冬季2—3月为自杀死亡低发期;一月之中,1、10、20日为自杀死亡高发日;一天之中,77.2%的自杀死亡案例发生在T 06:00—19:00,T 09:00和T 15:00为自杀死亡高峰时点。2)中国90.98%的自杀死亡案例数分布在人口稠密的东南半壁;案例自杀率东南半壁高于西北半壁,南方地区高于北方地区,东部、中部、西部呈梯度下降;大兴安岭至云贵高原、秦巴山区至大别山区、苏北海岸至海南岛,案例自杀率相对较高。3)中国绝大部分地区属于自杀率低等级区,但低等级区有向高等级区转变的趋势;研究期内自杀热点区有由东向西扩散趋势,京津唐、长三角、珠三角地区始终都是自杀热点区。4)中国自杀死亡的时空特征与经济发展的关系十分密切。市域尺度上,案例自杀率与人均GDP、城镇化率都呈显著的正相关性,经济因素对自杀率的影响在东南沿海要大于西北内陆。文章得到的重要启示是:贫富差距是导致人心理失衡发生自杀事件的重要因素,只有共同富裕的新发展道路才是人民的幸福之路和健康之路。
Suicide is a serious negative social phenomenon.In this study,we used Python technology to obtain suicide death data from a network and applied mathematical statistical and geographic spatial analyses to study the spatial-temporal characteristics of suicide deaths and the relationship between suicide rate and economic development in China from 2000 to 2018.Following conclusions were drawn from the results.(1)The number of suicide deaths in China is on the rise.Within a year,the high-incidence period of suicide deaths is from May to June,whereas the low-incidence period is from February to March.Within a month,the 1st,10th,and 20th days have the highest incidences of suicide deaths.Within a day,77.2%of the suicide deaths occur from 06:00 to 19:00,and 09:00 and 15:00 were the peak times in which suicide deaths take place.(2)A total of 90.98%of the suicide deaths occur in southeast China.The suicide rate is higher in the southeast than in the northwest,higher in the south than in the north,and decreases gradually from east to west.At county level,a relatively high suicide rate is seen in regions spanning from Great Khingan Mountains to Yunnan Guizhou Plateau,from Qinling-Dabashan Mountains to Dabie Mountains,and from the coast of northern Jiangsu to Hainan Island.(3)Most areas in China present a low-grade suicide rate.However,low-grade areas appeared to change to high-grade areas during the period 2000–2018.The hotspots of suicide deaths spread from east to west,except for the Beijing–Tianjin–Tangshan area,Yangtze River Delta,and Pearl River Delta,which have always been suicide hotspots.(4)The spatial and temporal characteristics of suicide deaths in China are closely related to economic development,and on a city scale,the suicide rate has a significant positive correlation with the per capita GDP and urbanization rate.The impact of economic factors on suicide rate is greater on the southeast coast than on the northwest inland.An important conclusion from this study is that the gap between the rich and poor is a key factor,leading to psychological imbalance and suicidal behavior in the poor;therefore,only the new development path based on common prosperity is the road for people to reach happiness and health.In addition,in this study,we prove that network suicide data,obtained using the web-crawler technology(Python),not only have the same consistency and credibility as sampling statistics but also have a better spatiotemporal resolution,with a temporal resolution of one hour and spatial resolution of a county.Therefore,by analyzing this spatiotemporal dataset,we can scientifically extract the time differences in suicide deaths at quarterly,monthly,daily,and hourly scales and the spatial differences in suicide deaths at regional,provincial,and county scales.In the future,network suicide data may become an important data source for suicide research,and the use of the Internet to monitor suicidal behavior may become an important method of suicide intervention.
作者
龚胜生
李春明
肖克梅
Gong Shengsheng;Li Chunming;Xiao Kemei(College of City and Environment Science,Central China Normal University,Wuhan 430079,China;Key Laboratory for Geographical Process Analysis and Simulation of Hubei Province,Wuhan 430079,China;Anning Shijiang School,Kunming 650300,China)
出处
《热带地理》
CSCD
北大核心
2023年第9期1760-1776,共17页
Tropical Geography
基金
国家自然科学基金项目(42371265)
湖北省自然科学基金项目(2016CFA026)。
关键词
自杀死亡案例
案例自杀率
网络自杀数据
时空特征
经济发展
中国
suicide death cases
case-suicide-rate
suicide data from network
spatial-temporal characteristics
economic development
China
