Abstract
Urban blue and green space is a key element supporting the normal operation of urban landscape ecosystems and guaranteeing and improving people's lives. In this paper, 97.1k photos of Beijing were captured by using web crawler technology, and the blue sky and green vegetation objects in the photos were extracted by using the Image Cascade Network (ICNet) neural network model. We analyzed the distribution characteristics of the blue–green space area proportion index and its relationships with the background economic and social factors. The results showed the following. (1) The spatial distribution of Beijing's blue–green space area proportion index showed a pattern of being higher in the west and lower in the middle and east. (2) There was a positive correlation between the satellite remote sensing normalized difference vegetation index (NDVI) and the proportion index of green space area, but the fitting degree of geospatial weighted regression decreased with an increasing analysis scale. (3) There were differences in the relationship between the housing prices in different regions and the proportion index of blue–green space, but the spatial fitting degree of the two increased with the increase of study scale. (4) There was a negative correlation between the proportion index of blue–green space and population density, and the low-population areas per unit blue–green space were mainly distributed in the south of the city and the urban fringe areas beyond the Third Ring Road. The urban blue–green space analysis that was constructed by this study provides new aspect for urban landscape ecology study, and the results proposed here also provide support for government decision-makers to optimize urban ecological layouts.
Highlights
In 2012, the United Nations Conference on Sustainable Development (Rio + 20) advocated for the construction of “green cities” around the world
(2) There was a positive correlation between the satellite remote sensing normalized difference vegetation index (NDVI) and the proportion index of green space area, but the fitting degree of geospatial weighted regression decreased with an increasing analysis scale
The results showed that there was a positive correlation between the satellite remote sensing NDVI and green space area proportion index (RG), but the fitting degree of Geographically weighted regression (GWR) decreased as the analysis scale increased
Summary
In 2012, the United Nations Conference on Sustainable Development (Rio + 20) advocated for the construction of “green cities” around the world. A lot of studies exist that have investigated the distribution pattern of urban blue–green space, analyzed the spatial rules of differences in blue–green space, and summarized the relationship between blue–green space, natural geographical factors, and economic and social factors This topic is of great significance to city planners, builders, and residents. It is convenient to explore all imagery in different locations, and the cost to obtain and download the imagery is almost zero It is an important and fundamental way forward urban studies to make full use of satellite images and street view photos. In view of the above problems, the authors planned to apply internet big data and image recognition technology supported by artificial intelligence to extract urban blue–green space information. What are the quantitative relationships between blue–green space and environmental and social–economic factors?
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