Social Inequalities in Neighborhood-Level Streetscape Perceptions in Shanghai: The Coherence and Divergence between the Objective and Subjective Measurements

Song, Qiwei; Li, Wenjing; Li, Meikang; Qiu, Waishan

doi:10.2139/ssrn.4179127

Cited by 5 publications

(10 citation statements)

References 23 publications

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“…Another major part which has been less discussed is the micro-level subjective perceptions. Subjective measures are derived from the perception surveys and questionaries and have been integrated with ML algorithms to predict and map citywide perceptions in emerging urban analytic studies [23,[38][39][40][41]. Kang et al [22] studied how subjective perceptions (lively, safe, etc.)…”

Section: Housing Prices Hpm Macro-and Micro-level Variablesmentioning

confidence: 99%

“…Xu et al [39] measured the street qualities in both subjective and objective measures for the same perception concepts, and the subjective measures were stronger in explaining housing prices. Song et al [38] spatially mapped the pairwise perceptions using both measures in Shanghai and identified huge differences regarding the within-perception heterogeneity patterns when compared with their counterparts. However, to what extent the subjective perceptions complement the objectively measured street features is less investigated.…”

Section: Housing Prices Hpm Macro-and Micro-level Variablesmentioning

confidence: 99%

“…First, the existing HPM literature has relied on macro-level indicators, while the impact of fine-grained and micro-level neighborhood environment perceptions needs to be discussed more. We provide a high-throughput framework [38] that measures five subjective perceptions and ten key objectively perceived street features using SVI data. The study sheds light on whether micro-level neighborhood attributes influence housing prices compared to macro-level attributes.…”

Section: Research Questions Research Gaps and Contributionmentioning

confidence: 99%

“…Downloading SVI Data and Extracting Physical Features SVI can represent the human-centric perspectives of pedestrians and becomes a great data source for urban studies [44]. We followed methods from previous studies [38,39] and sampled SVIs at 50 m intervals [45] along the road segments within 1 km radius of each property's coordinates in QGIS software, requested SVI data from Baidu Street View Static API, and retrieved 25,276 valid SVIs for our study. Previous studies indicate that neighborhood-level perceptions should be proxied in areas rather than points, and a 1 km radius is the approximate walking distance of a 15 min neighborhood, which is a widely suggested concept in urban design [13].…”

Section: Selection Of Micro-scale Perceived Variablesmentioning

confidence: 99%

“…View index denotes the ratio of the feature's pixels to the entire scene and can be calculated effectively using a pyramid scene parsing network (PSPNet) algorithm [46], which has been very popular in emerging studies [38,47]. Specifically, the PSPNet is pretrained on the ADE20K dataset which includes 150 object types tailed for cityscapes and can achieve around 80% accuracy [48].…”

Section: Selection Of Micro-scale Perceived Variablesmentioning

confidence: 99%

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Investigating the Impact of Perceived Micro-Level Neighborhood Characteristics on Housing Prices in Shanghai

Song

Liu

Qiu

et al. 2022

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It is widely accepted that houses in better-designed neighborhoods are found to enjoy a price premium. Prior studies have mainly examined the impact of macro-level neighborhood attributes (e.g., park accessibility using land use data) on housing prices. More recently, research has investigated the micro-level features using street view imagery (SVI) data, though scholars limited the scope to objective indicators such as the green view index and sky view index. The role of subjectively measured street qualities is less discussed due to the lack of large-scale perception data. To provide better explanations of whether and how the micro-level neighborhood environment affects housing prices, this article introduces a framework to collect designers’ perceptions on five subjective urban design perceptions from pairwise SVI rankings in Shanghai with an online visual survey and further predicted through machine learning (ML) algorithms. We also extracted ten important objective features from the scenes. The predictive power of micro-level neighborhood street perceptions (subjective perceptions and objective features) on housing prices was investigated using the hedonic price model (HPM) through ordinary least squares (OLS) and spatial regression, which considers spatial dependence. The findings prove the significance of the value of perceived qualities of the neighborhoods. It reveals that both objective perceived features and subjective perceptions significantly contribute to housing prices; while the objective features show more collective strengths, individual subjective perceptions have more explanatory power, and we argue that these two measures can complement each other. This study provides an important reference for decision makers when selecting street quality indicators to inform city planning, urban design, and community and housing development plans.

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