Urban neighborhood socioeconomic status (SES) inference: A machine learning approach based on semantic and sentimental analysis of online housing advertisements

Wang, Lingqi; He, Shenjing; Su, Shiliang; Liu, Yu; Hu, Lirong; Li, Guie

doi:10.1016/j.habitatint.2022.102572

Cited by 11 publications

(17 citation statements)

References 77 publications

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Section: Study Area and Research Designmentioning

confidence: 87%

“…It should be further elaborated that the temporal span of three years is relatively limited due to the scarcity of neighborhood deprivation data in China. Fortunately for our study, earlier studies have demonstrated that comparisons between 2016 and 2018 could provide sufficient insights into the rapid changes of built environment and sociodemographic characteristics in the Hangzhou metropolitan area [4,25,28,29]. Thus, such a spatiotemporal scale can adequately facilitate a contribution to the current literature about methodology and further provide refreshed empirical knowledge.…”

Section: Study Area and Research Designmentioning

confidence: 91%

“…Such an approach has been criticized for potentially incorporating objective bias, and most importantly, this approach has no compatible sociodemographic statistical data. In accordance with prior related studies [4,25,28,29], the neighborhood in this study refers to the residential quarter (juzhu xiaoqu in Chinese), which is the finest A neighborhood refers to a geographical unit which has sociodemographic characteristics that are relatively homogeneous, or which exhibits identical built environment features. No uniform criteria exist as to the geographical delineation of neighborhoods.…”

Section: Study Area and Research Designmentioning

confidence: 95%

“…At the same time, socio-spatial segregation gradually evolved along with the rapid urban construction and development, which served as a mirror of modern cities in China. Researchers have observed the inequitable distribution of public facilities, open spaces, and mobility patterns across socioeconomic groups [4,25,28,29]. In this respect, how to make a walkable environment compatible with an equal society becomes an urgent issue for the Hangzhou metropolitan area.…”

Section: Study Area and Research Designmentioning

confidence: 99%

“…Machine learning algorithms have recently mushroomed in a plethora of land use research fields [23][24][25][26]. They present a strong capability for untangling nonlinearity in associations and handling structural interdependences among variables [22,27], thereby offering a new impetus for unpacking the dynamic relationship between neighborhood deprivation and walkability over time.…”

Section: Introductionmentioning

confidence: 99%

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Unraveling the Dynamic Relationship between Neighborhood Deprivation and Walkability over Time: A Machine Learning Approach

Wang,

Li,

Weng

2024

Land

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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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Section: Study Area and Research Designmentioning

confidence: 87%