Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality
Open Access
- 18 June 2021
- Vol. 10 (6), 648
- https://doi.org/10.3390/land10060648
Abstract
Enriching Asian perspectives on the rapid identification of urban poverty and its implications for housing inequality, this paper contributes empirical evidence about the utility of image features derived from high-resolution satellite imagery and machine learning approaches for identifying urban poverty in China at the community level. For the case of the Jiangxia District and Huangpi District of Wuhan, image features, including perimeter, line segment detector (LSD), Hough transform, gray-level cooccurrence matrix (GLCM), histogram of oriented gradients (HoG), and local binary patterns (LBP), are calculated, and four machine learning approaches and 25 variables are applied to identify urban poverty and relatively important variables. The results show that image features and machine learning approaches can be used to identify urban poverty with the best model performance with a coefficient of determination, R2, of 0.5341 and 0.5324 for Jiangxia and Huangpi, respectively, although some differences exist among the approaches and study areas. The importance of each variable differs for each approach and study area; however, the relatively important variables are similar. In particular, four variables achieved relatively satisfactory prediction results for all models and presented obvious differences in varying communities with different poverty levels. Housing inequality within low-income neighborhoods, which is a response to gaps in wealth, income, and housing affordability among social groups, is an important manifestation of urban poverty. Policy makers can implement these findings to rapidly identify urban poverty, and the findings have potential applications for addressing housing inequality and proving the rationality of urban planning for building a sustainable society.Funding Information
- Fundamental Research Funds for the Central Universities (2021-11088)
This publication has 45 references indexed in Scilit:
- The geography of poverty: Review and research prospectsJournal of Rural Studies, 2022
- Urbanization and income inequality in Sub-Saharan AfricaSustainable Cities and Society, 2019
- A comparison of machine learning approaches for identifying high-poverty counties: robust features of DMSP/OLS night-time light imageryInternational Journal of Remote Sensing, 2019
- Urbanization patterns and poverty reduction: A new perspective to explore the countries along the Belt and RoadHabitat International, 2019
- Urban–rural income change: Influences of landscape pattern and administrative spatial spillover effectApplied Geography, 2018
- Urbanization for rural sustainability – Rethinking China's urbanization strategyJournal of Cleaner Production, 2018
- The residential resettlement in suburbs of Chinese cities: A case study of ChangshaCities, 2017
- Labor out-migration and agricultural change in rural China: A systematic review and meta-analysisJournal of Rural Studies, 2016
- Land conversion and urban settlement intentions of the rural population in China: A case study of suburban NanjingHabitat International, 2016
- Poverty Reduction During the Rural–Urban Transformation – The Role of the Missing MiddleWorld Development, 2014