揭示邻里贫困与步行能力随时间变化的动态关系:一种机器学习方法

IF 3.2 2区 环境科学与生态学 Q2 ENVIRONMENTAL STUDIES Land Pub Date : 2024-05-12 DOI:10.3390/land13050667
Qian Wang, Guie Li, Min Weng
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引用次数: 0

摘要

创造适宜步行的环境是实现 2030 年可持续发展目标的重要一步。然而,并不是所有人都能享受到适宜步行的环境,不同社会经济地位的社区在适宜步行程度方面也存在很大差异。以往的研究通常从时间静态的角度来揭示社区贫困与步行能力之间的关系,对时间点快照的估算被认为包含了很大的不确定性。居民区步行能力随时间变化与贫困之间的关系尚不清楚。以杭州大都市区为例,我们首先通过计算一组修订后的步行得分,测量了2016年至2018年的街区步行能力。此外,我们还应用了一种机器学习算法,特别是基于核的正则化最小二乘回归,来揭示街区步行能力随着时间的推移与贫困的关系是如何变化的。结果不仅捕捉到了邻里贫困程度与步行能力之间随时间变化的非线性关系,还突出了每个邻里贫困程度指标的边际效应。此外,对机器学习算法和 OLS 回归结果的比较表明,机器学习方法确实讲述了一个不同的故事,应有助于纠正早期研究中相互矛盾的结论。本文将时间动态和结构性相互依存关系的意义凸显出来,相信将有助于人们重新认识步行能力方面的社会不平等现象。
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Unraveling the Dynamic Relationship between Neighborhood Deprivation and Walkability over Time: A Machine Learning Approach
Creating a walkable environment is an essential step toward the 2030 Sustainable Development Goals. Nevertheless, not all people can enjoy a walkable environment, and neighborhoods with different socioeconomic status are found to vary greatly with walkability. Former studies have typically unraveled the relationship between neighborhood deprivation and walkability from a temporally static perspective and the produced estimations to a point-in-time snapshot were believed to incorporate great uncertainties. The ways in which neighborhood walkability changes over time in association with deprivation remain unclear. Using the case of the Hangzhou metropolitan area, we first measured the neighborhood walkability from 2016 to 2018 by calculating a set of revised walk scores. Further, we applied a machine learning algorithm, the kernel-based regularized least squares regression in particular, to unravel how neighborhood walkability changes in relation to deprivation over time. The results not only capture the nonlinearity in the relationship between neighborhood deprivation and walkability over time, but also highlight the marginal effects of each neighborhood deprivation indicator. Additionally, comparisons of the outputs between the machine learning algorithm and OLS regression illustrated that the machine learning approach did tell a different story and should contribute to remedying the contradictory conclusions in earlier studies. This paper is believed to renew the understanding of social inequalities in walkability by bringing the significance of temporal dynamics and structural interdependences to the fore.
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来源期刊
Land
Land ENVIRONMENTAL STUDIES-Nature and Landscape Conservation
CiteScore
4.90
自引率
23.10%
发文量
1927
期刊介绍: Land is an international and cross-disciplinary, peer-reviewed, open access journal of land system science, landscape, soil–sediment–water systems, urban study, land–climate interactions, water–energy–land–food (WELF) nexus, biodiversity research and health nexus, land modelling and data processing, ecosystem services, and multifunctionality and sustainability etc., published monthly online by MDPI. The International Association for Landscape Ecology (IALE), European Land-use Institute (ELI), and Landscape Institute (LI) are affiliated with Land, and their members receive a discount on the article processing charge.
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