Implications for spatial non-stationarity and the neighborhood effect averaging problem (NEAP) in green inequality research: evidence from three states in the USA

IF 2.8 3区 地球科学 Q1 GEOGRAPHY
Sophiya Gyanwali, Shashank Karki, Kee Moon Jang, Tom Crawford, Mengxi Zhang, Junghwan Kim
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Abstract

Recent studies on green space exposure have argued that overlooking human mobility could lead to erroneous exposure estimates and their associated inequality. However, these studies are limited as they focused on single cities and did not investigate multiple cities, which could exhibit variations in people’s mobility patterns and the spatial distribution of green spaces. Moreover, previous studies focused mainly on large-sized cities while overlooking other areas, such as small-sized cities and rural neighborhoods. In other words, it remains unclear the potential spatial non-stationarity issues in estimating green space exposure inequality. To fill these significant research gaps, we utilized commute data of 31,862 people from Virginia, West Virginia, and Kentucky. The deep learning technique was used to extract green spaces from street-view images to estimate people’s home-based and mobility-based green exposure levels. The results showed that the overall inequality in exposure levels reduced when people’s mobility was considered compared to the inequality based on home-based exposure levels, implying the neighborhood effect averaging problem (NEAP). Correlation coefficients between individual exposure levels and their social vulnerability indices demonstrated mixed and complex patterns regarding neighborhood type and size, demonstrating the presence of spatial non-stationarity. Our results underscore the crucial role of mobility in exposure assessments and the spatial non-stationarity issue when evaluating exposure inequalities. The results imply that local-specific studies are urgently needed to develop local policies to alleviate inequality in exposure precisely.

Abstract Image

绿色不平等研究中的空间非平稳性和邻里效应平均问题(NEAP)的影响:来自美国三个州的证据
最近关于绿地暴露的研究认为,忽视人的流动性可能会导致错误的暴露估计及其相关的不平等。然而,这些研究存在局限性,因为它们只关注单个城市,而没有调查多个城市,而多个城市的人们流动模式和绿地的空间分布可能存在差异。此外,以往的研究主要集中在大型城市,而忽略了其他地区,如小型城市和农村社区。换句话说,在估算绿地暴露不平等时,潜在的空间非平稳性问题仍不明确。为了填补这些重要的研究空白,我们利用了弗吉尼亚州、西弗吉尼亚州和肯塔基州 31,862 人的通勤数据。我们使用深度学习技术从街景图像中提取绿地,以估算人们基于家庭和流动的绿地暴露水平。结果表明,与基于家庭的暴露水平不平等相比,当考虑到人们的流动性时,暴露水平的总体不平等程度降低了,这意味着邻里效应平均问题(NEAP)。个人暴露水平与其社会脆弱性指数之间的相关系数显示出与邻里类型和规模有关的混合而复杂的模式,表明存在空间非平稳性。我们的研究结果强调了流动性在暴露评估中的关键作用,以及在评估暴露不平等时的空间非平稳性问题。这些结果表明,亟需开展针对地方的研究,以制定地方政策,准确缓解暴露不平等问题。
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来源期刊
CiteScore
5.40
自引率
6.90%
发文量
33
期刊介绍: The Journal of Geographical Systems (JGS) is an interdisciplinary peer-reviewed academic journal that aims to encourage and promote high-quality scholarship on new theoretical or empirical results, models and methods in the social sciences. It solicits original papers with a spatial dimension that can be of interest to social scientists. Coverage includes regional science, economic geography, spatial economics, regional and urban economics, GIScience and GeoComputation, big data and machine learning. Spatial analysis, spatial econometrics and statistics are strongly represented. One of the distinctive features of the journal is its concern for the interface between modeling, statistical techniques and spatial issues in a wide spectrum of related fields. An important goal of the journal is to encourage a spatial perspective in the social sciences that emphasizes geographical space as a relevant dimension to our understanding of socio-economic phenomena. Contributions should be of high-quality, be technically well-crafted, make a substantial contribution to the subject and contain a spatial dimension. The journal also aims to publish, review and survey articles that make recent theoretical and methodological developments more readily accessible to the audience of the journal. All papers of this journal have undergone rigorous double-blind peer-review, based on initial editor screening and with at least two peer reviewers. Officially cited as J Geogr Syst
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