The role of transport systems in housing insecurity: a mobility-based analysis

IF 3 2区 计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Nandini Iyer, Ronaldo Menezes, Hugo Barbosa
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Abstract

With trends of urbanisation on the rise, providing adequate housing to individuals remains a complex issue to be addressed. Often, the slow output of relevant housing policies, coupled with quickly increasing housing costs, leaves individuals with the burden of finding housing that is affordable and in a safe location. In this paper, we unveil how transit service to employment hubs, not just housing policies, can prevent individuals from improving their housing conditions. We approach this question in three steps, applying the workflow to 20 cities in the United States of America. First, we propose a comprehensive framework to quantify housing insecurity and assign a housing demographic to each neighbourhood. Second, we use transit-pedestrian networks and public transit timetables (GTFS feeds) to estimate the time it takes to travel between two neighbourhoods using public transportation. Third, we apply geospatial autocorrelation to identify employment hotspots for each housing demographic. Finally, we use stochastic modelling to highlight how commuting to areas associated with better housing conditions results in transit commute times of over an hour in 15 cities. Ultimately, we consider the compounded burdens that come with housing insecurity, by having poor transit access to employment areas. In doing so, we highlight the importance of understanding how negative outcomes of housing insecurity coincide with various urban mechanisms, particularly emphasising the role that public transportation plays in locking vulnerable demographics into a cycle of poverty.

Abstract Image

交通系统在住房不安全中的作用:基于流动性的分析
随着城市化趋势的加剧,为个人提供适当的住房仍然是一个需要解决的复杂问题。通常情况下,由于相关住房政策出台缓慢,加上住房成本快速增长,个人不得不承担寻找负担得起且位置安全的住房的重担。在本文中,我们将揭示通往就业中心的交通服务,而不仅仅是住房政策,是如何阻碍个人改善住房条件的。我们分三步解决这一问题,并将工作流程应用于美国的 20 个城市。首先,我们提出了一个量化住房不安全的综合框架,并为每个街区分配了一个住房人口统计。其次,我们利用公交行人网络和公共交通时刻表(GTFS feeds)来估算使用公共交通往返于两个街区所需的时间。第三,我们利用地理空间自相关性来确定每个住房人口的就业热点。最后,我们利用随机建模来强调在 15 个城市中,通勤到与较好住房条件相关的地区如何导致公交通勤时间超过一小时。最后,我们考虑了住房不安全所带来的复合负担,即通往就业地区的交通不便。在此过程中,我们强调了理解住房无保障的负面结果如何与各种城市机制相吻合的重要性,特别强调了公共交通在将弱势人口锁定在贫困循环中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
EPJ Data Science
EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -
CiteScore
6.10
自引率
5.60%
发文量
53
审稿时长
13 weeks
期刊介绍: EPJ Data Science 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.
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