作者:Marcos Oliveira, Eraldo Ribeiro, Carmelo Bastos-Filho and Ronaldo Menezes 来源:EPJ Data Science 发布时间:2018/11/19 13:53:47
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听诊城市心跳,把握犯罪规律 | Springer Open

论文标题:Spatio-temporal variations in the urban rhythm: the travelling waves of crime

期刊:EPJ Data Science

作者:Marcos Oliveira, Eraldo Ribeiro, Carmelo Bastos-Filho and Ronaldo Menezes

发表时间:2018/09/26

数字识别码:10.1140/epjds/s13688-018-0158-4

原文链接:https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0158-4?utm_source=wechat&utm_medium=social&utm_content=organic&utm_campaign=JRCN_1_CelZH_WeChat_DailyPost

微信链接:https://mp.weixin.qq.com/s/zeglDdlvuehV_cCWHcstxw

随着人口的增长,城市不断发展重组。这一不断发展的过程使城市具备了强大的弹性和适应性,同时也为分析城市现象提出了挑战。长久以来的证据表明,犯罪具有时间和空间规律,然而迄今为止对这方面的研究皆基于城市是静态的这一假设。发表在EPJ Data Science上的一项新研究将时空规律纳入考量范围,分析了犯罪事件的时空变化。

城市是人类文明的标志,它以积极的方式将人们聚集在一起,促进创新中心的形成和文化的多样性发展。但与此同时,挑战也随之而来,如犯罪率和贫困率的增长。更好地了解城市的复杂动态变化可以尽可能降低风险,实现效益最大化。

在过去的几十年里,科学家将城市视为不断发展的系统,主要呈现由地方层面决策构建的种种新兴现象。具体而言,证据表明随着人口增长,城市多项指标也迅速发展,这一发现促使研究人员将城市作为一个复杂体系进行探索。

城市,一个复杂的体系

复杂体系是指一个体系内部有许多相互关联的部分,这些部分组合之后展现的特性并非单个部分特性的简单相加。就城市而言,我们可以从收入、暴力犯罪或专利等方面理解这一点。假定我们选取了几个城市,将这些城市的某一指标进行汇总,汇总结果与人口等于这些城市人口总和的单个城市的这一指标结果并不相同。

城市发展与城市指标之间的关系表明城市化背后有其通用机制,且城市也存在与城市特征无关的固有规律。

从这个角度来看,城市看似混乱,却在不同层面都呈现出了规律性。这一观点说明,城市处于不断变化之中,创造了一个富有弹性和适应性的环境,但也为分析城市现象提出了挑战。

城市中的暴力犯罪

在最近的研究中,我们在上述视角下分析了城市的时空规律。我们的方法不仅关注规律,同时关注这些规律随时间和城市不同区域而发生的变化。我们研究了城市中一个非常严重的问题——暴力犯罪。

事实上,关于犯罪时间规律的证据可追溯到十九世纪。比利时数学家Adolphe Quetelet是现代社会学和心理学统计学之父,他首先描述了犯罪活动的季节性规律。此后,对于类似规律的探索都基于城市是静态的这一基本假设。大多数研究都假设时间是静态固定的,且经常忽略城市中不同区域的时空差异。

“犯罪狂潮”不断移动

通过我们的方法,我们发现了犯罪动态的部分特质。可以确认的是,犯罪现象存在周期,这种周期性在整个城市中分布并不均匀。我们发现,这些“犯罪狂潮”在城市中不断移动。

我们的研究结果表明,城市确实在地方层面不断变化,对城市现象产生了某些影响。

随着城市不断变化,政策制定需要不断更新方法并对城市进行持续评估以跟上城市变化的步伐。在这种情况下,我们的方法有助于跟踪城市随时间的变化。通过使用适当的工具,我们可以更好地倾听城市的心跳,从而改善人们的生活。

摘要:

In the last decades, the notion that cities are in a state of equilibrium with a centralised organisation has given place to the viewpoint of cities in disequilibrium and organised from bottom to up. In this perspective, cities are evolving systems that exhibit emergent phenomena built from local decisions. While urban evolution promotes the emergence of positive social phenomena such as the formation of innovation hubs and the increase in cultural diversity, it also yields negative phenomena such as increases in criminal activity. Yet, we are still far from understanding the driving mechanisms of these phenomena. In particular, approaches to analyse urban phenomena are limited in scope by neglecting both temporal non-stationarity and spatial heterogeneity. In the case of criminal activity, we know for more than one century that crime peaks during specific times of the year, but the literature still fails to characterise the mobility of crime. Here we develop an approach to describe the spatial, temporal, and periodic variations in urban quantities. With crime data from 12 cities, we characterise how the periodicity of crime varies spatially across the city over time. We confirm one-year criminal cycles and show that this periodicity occurs unevenly across the city. These ‘waves of crime’ keep travelling across the city: while cities have a stable number of regions with a circannual period, the regions exhibit non-stationary series. Our findings support the concept of cities in a constant change, influencing urban phenomena—in agreement with the notion of cities not in equilibrium.

阅读论文全文请访问:

https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0158-4?utm_source=wechat&utm_medium=social&utm_content=organic&utm_campaign=JRCN_1_CelZH_WeChat_DailyPost

期刊介绍:

EPJ Data Science(https://epjdatascience.springeropen.com/, 2.982 - 2-year Impact Factor, 3.042 - 5-year Impact Factor) covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.

(来源:科学网)

 
 
 
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