Urban redevelopment is the reconstruction or upgrade of current urban built-up areas; it revitalizes old towns and contributes to sustainable development. This paper proposes a methodological framework that integrates open-source street networks and point-of-interest data and aims to identify and evaluate urban redevelopment at the block level from the perspective of urban form and function. It is found that 1) urban blocks can be categorized into 8 groups regarding the spatial form of road junctions that have emerged within them over time, and blocks of each group share common features that can be automatically identified; 2) there are more blocks that have been morphologically redeveloped than functionally redeveloped, and the two types of redevelopments also significantly overlap with one another; and 3) the evaluation urban redevelopment identification results present a high accuracy rate that verifies the validity of the proposed framework. Based on the identification results, the impact factors of urban redevelopment are explored on both the inter- and intracity levels. The intercity analysis indicates that Chinese cities with a lower administrative level, lower urbanization rate and higher density of road junctions tend to be associated with a higher proportion of urban redevelopment. Meanwhile, the intracity analysis attempts to determine which kinds of urban blocks are more likely to undergo urban redevelopment, which are found to be the blocks with lower points of interest (POIs) density, a smaller distance to city centers, higher transit accessibility, a higher land use mixed index and larger size.
We are sharing the analysis data related to this paper as follows. Please cite the paper as a courtesy of using our shared data (Han Z, Long Y, Wang X, Hou J. 2019. Urban redevelopment at the block level: Methodology and its application to all Chinese cities. Environment and Planning B: Urban Analytics and City Science. DOI: 10.1177/2399808319843928).
The ShapeFiles of all urban blocks with estimated redevelopment information are available upon request addressed to Dr Ying LONG (firstname.lastname@example.org). Note that we only reply emails with good manner.