Patterns of spatially refined urban built environment stocks across Chinese cities

We developed an approach combining big data mining technology, bottom-up stock modeling and Chinese temperature-zone MCI database to calculate and predict the urban built environment stocks in 2020. We considered building, road, railway and metro, and aimed to characterize their quality, composition, and spatial distribution. By applying this approach to 50 cities, we found considerable diversities MS quality, composition and distribution, discussed the impact factors and growth patterns among cities, and highlighted the disparities behind economic conditions, population size, built-up area, development rate, land use, geographical location, and urban form.