Abstract:

Taking Guangzhou as an example, Baidu street view images of 6670 sample points in the four central districts of the city were selected as test data to explore the possibility of urban poverty measurement in an urban built environment. Firstly, after training the street view picture-classified model based on FCN-8s deep neural network, the street view features were semantically divided into categories such as buildings, sky, trees, and so on. The street view indicators of communities were calculated by buffer analysis. Four street view main factors including building enclosure, vegetation enclosure, sky openness, and road openness, were extracted by principal component analysis; the correlation between them and multiple poverty index (IMD) was verified. Finally, the multi-linear regression model to estimate the poverty level, which is based on street view indicators, was constructed from 61 communities through a simple random sampling method. The prediction model was used for the remaining 60 communities in order to verify the accuracy of the street view measurement. It was found that IMD is positively correlated with building enclosure, and negatively correlated with vegetation enclosure, sky openness, and road openness. In addition, the measurement results from street view were basically consistent with the spatial patterns of traditional urban poverty measurement, which were usually higher than the poverty level in traditional poverty measurement. This is because the two methods produce different results that are affected by the measurement content, community type, street attributes, and other factors. For example, street view can depict the real living environment of the poor urban people, which is more suitable for poor communities that occupy terribly built environments, such as crowded and dilapidated old neighborhoods, urban villages with serious construction violations, rural homes, and under-construction industrial parks and so on. On the other hand, the traditional IMD measurement method is more suitable for those areas where the external and surrounding built environments are not very bad, such as redeveloped constructions that replaced decaying structures, affordable housing with pleasing environments, and so on. To a certain extent, the two methods can complement each other, and street view can provide timely monitoring of changes in urban built-up areas. It can also help poor communities put forward concrete proposals to effectively improve their living environment, in terms of physical amenities, such as roads, buildings, and green spaces. In this paper, a series of street view indicators related to urban poverty were extracted by analyzing the street view data of communities in the four central districts of Guangzhou. Based on these indicators, a model of urban poverty was successfully constructed. Findings about the characteristics of street view, factors influencing street view measurement, and the scope of application when compared with the traditional urban poverty method are presented. The use of an urban poverty measurement technology with high precision, wide coverage, fast updating, and information-rich streetscapes can benefit and refine urban poverty research and enrich the dimension of urban poverty measurement indicators. Such measurement also has practical significance for the redevelopment of decrepit structures and areas inhabited by poor communities.

Key words: urban poverty, street view, built urban environment, Guangzhou