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墨西哥蒙特雷市 8 年杀人案件演变:网络方法。

Eight years of homicide evolution in Monterrey, Mexico: a network approach.

机构信息

Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, 14610, Mexico.

El Colegio de México (COLMEX), Mexico City, Mexico.

出版信息

Sci Rep. 2020 Dec 9;10(1):21564. doi: 10.1038/s41598-020-78352-9.

DOI:10.1038/s41598-020-78352-9
PMID:33299031
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7726575/
Abstract

Homicide is without doubt one of Mexico's most important security problems, with data showing that this dismal kind of violence sky-rocketed shortly after the war on drugs was declared in 2007. Since then, violent war-like zones have appeared and disappeared throughout Mexico, causing unfathomable human, social and economic losses. One of the most emblematic of these zones is the Monterrey metropolitan area (MMA), a central scenario in the narco-war. Being an important metropolitan area in Mexico and a business hub, MMA has counted hundreds to thousands of casualties. In spite of several approaches being developed to understand and analyze crime in general, and homicide in particular, the lack of accurate spatio-temporal homicide data results in incomplete descriptions. In order to describe the manner in which violence has evolved and spread in time and space through the city, here we propose a network-based approach. For this purpose, we define a homicide network where nodes are geographical entities that are connected through spatial and temporal relationships. We analyzed the time series of homicides in different municipalities and neighborhoods of the MMA, to observe whether or not a global correlation appeared. We studied the spatial correlation between neighborhoods where homicides took place, to observe whether distance is a factor of influence in the frequency of homicides. We constructed yearly co-occurrence networks, by correlating neighborhoods with homicides happening within a same week, and counting the co-occurrences of these neighborhood pairs in 1 year. We also constructed a crime network by aggregating all data of homicides, eliminating the temporal correlation, in order to observe whether homicide clusters appeared, and what those clusters were distributed geographically. Finally, we correlated the location and frequency of homicides with roads, freeways and highways, to observe if a trend in the homicidal violence appeared. Our network approach in the homicide evolution of MMA allows us to identify that (1) analyzing the whole 86-month period, we observed a correlation between close cities, which decreases in distant places. (2) at neighborhood level, correlations are not distance-dependent, on the contrary, highest co-occurrences appeared between distant neighborhoods and a polygon formed by close neighborhoods in downtown Monterrey. Moreover, (3) An elevated number of homicides occur close to the 85th freeway, which connects MMA with the US border. (4) Some socioeconomic barriers determine the presence of homicide violence. Finally, (5) we show a relation between homicidal crime and the urban landscape by studying the distance of safe and violent neighborhoods to the closest highway and by studying the evolution of highway and crime distance over the cartel-related years and the following period. With this approach, we are able to describe the spatial and temporal evolution of homicidal crime in a metropolitan area.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/5d6f48f231ee/41598_2020_78352_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/0b5f5eeaf22e/41598_2020_78352_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/2572f9bff6a7/41598_2020_78352_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/6eaa7a18ea4f/41598_2020_78352_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/00791762434b/41598_2020_78352_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/f784c5adc9a6/41598_2020_78352_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/9e5e7cb8e869/41598_2020_78352_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/e6d1a58d7a6c/41598_2020_78352_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/995fcf5a915f/41598_2020_78352_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/fb26e7998e43/41598_2020_78352_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/cc4098e0ec0c/41598_2020_78352_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/5d6f48f231ee/41598_2020_78352_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/0b5f5eeaf22e/41598_2020_78352_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/2572f9bff6a7/41598_2020_78352_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/6eaa7a18ea4f/41598_2020_78352_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/00791762434b/41598_2020_78352_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/f784c5adc9a6/41598_2020_78352_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/9e5e7cb8e869/41598_2020_78352_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/e6d1a58d7a6c/41598_2020_78352_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/995fcf5a915f/41598_2020_78352_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/fb26e7998e43/41598_2020_78352_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/cc4098e0ec0c/41598_2020_78352_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a71a/7726575/5d6f48f231ee/41598_2020_78352_Fig11_HTML.jpg
摘要

凶杀无疑是墨西哥最重要的安全问题之一,数据显示,自 2007 年宣布开展打击毒品战争以来,这种可怕的暴力事件急剧上升。从那时起,墨西哥各地出现了类似战区的暴力地区,造成了难以估量的人员、社会和经济损失。这些地区中最具代表性的是蒙特雷大都市区(MMA),这是毒品战争的一个重要战场。作为墨西哥的一个重要大都市区和商业中心,MMA 已经有数以百计甚至数千人伤亡。尽管已经提出了几种方法来理解和分析一般犯罪,特别是凶杀案,但由于缺乏准确的时空凶杀数据,导致描述不完整。为了描述暴力在时间和空间上通过城市的演变和传播方式,我们在这里提出了一种基于网络的方法。为此,我们定义了一个凶杀网络,其中节点是通过空间和时间关系连接的地理实体。我们分析了 MMA 不同市镇和社区的凶杀时间序列,以观察是否出现了全局相关性。我们研究了发生凶杀案的社区之间的空间相关性,以观察距离是否是凶杀案频率的影响因素。我们通过将同一周内发生的凶杀案与相关社区进行关联,构建了每年的共同发生网络,并计算了这些社区对在一年内的共同发生次数。我们还通过聚合所有凶杀数据构建了一个犯罪网络,消除了时间相关性,以观察是否出现了凶杀集群,以及这些集群在地理上的分布情况。最后,我们将凶杀案的位置和频率与道路、高速公路和高速公路进行了关联,以观察是否出现了凶杀暴力的趋势。我们在 MMA 凶杀演变中的网络方法使我们能够识别出:(1)在分析整个 86 个月的时间段时,我们观察到临近城市之间存在相关性,而在偏远地区这种相关性则降低。(2)在社区层面上,相关性不受距离的影响,相反,在市中心蒙特雷附近的社区和一个由临近社区形成的多边形之间出现了最高的共同发生次数。此外,(3)靠近 85 号高速公路的地方发生了大量的凶杀案,这条高速公路连接了 MMA 与美国边境。(4)一些社会经济障碍决定了凶杀暴力的存在。最后,(5)我们通过研究距离最近的高速公路的安全和暴力社区,以及研究高速公路和犯罪距离在卡特尔相关年份和随后的时期的演变,展示了凶杀犯罪与城市景观之间的关系。通过这种方法,我们能够描述大都市地区凶杀犯罪的时空演变。

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本文引用的文献

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Unveiling relationships between crime and property in England and Wales via density scale-adjusted metrics and network tools.通过密度尺度调整指标和网络工具揭示英格兰和威尔士犯罪与财产之间的关系。
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