Using street view images and a geographical detector to understand how street-level built environment is associated with urban poverty: A case study in Guangzhou

Abstract

Understanding and ending poverty has become one of the most important SDG (Sustainable Development Goals) all over the world. The street-level built environment is an important indicator to reflect urban poverty. However, traditional data such as satellite imagery may not provide fine-grained information of built environment at the street level. In recent years, street view image has become promising data for assessing an urban micro environment. This study aimed to use street view data and deep learning technique to examine the association between street-level built environment and urban poverty in Guangzhou, China, from a geographical heterogeneity perspective. First, we measured urban poverty in Guangzhou based on the Index of Multiple Deprivation. Second, we used the Pyramid Scene Parsing Network model for image segmentation and then performed principal component analysis to extract five major street view factors (i.e., vegetation enclosure sense, color complexity sense, road openness sense, sky openness sense, and building enclosure sense) from the street view data. Third, we conducted the geographical detector analysis to examine how street view factors is associated with urban poverty. Results suggested that vegetation enclosure sense, color complexity sense, and road openness sense are significantly related to the spatial heterogeneity of urban poverty. Among all factors, vegetation enclosure sense played a leading role. The results also confirmed the coexistence of different street view factors have association with the spatial heterogeneity of urban poverty. In conclusion, street-level built environment is generally associated with urban poverty, and therefore our proposed method can be considered as an efficiently method for identifying urban poor communities.