Land-use decision support in brownfield redevelopment for urban renewal based on crowdsourced data and a presence-and-background learning (PBL) method. (November 2019)

• Record Type: Journal Article
• Authors: Liu, Yilun; Zhu, A-Xing; Wang, Jingli; Li, Wenkai; Hu, Guohua; Hu, Yueming
• Is Part Of: Land use policy. Volume 88(2019)
• Publication Date: 2019-11
• Subjects: Urban renewal -- Suitability assessment -- Crowdsourced data -- PBL -- Shenzhen; Land use -- Periodicals; Land use -- Government policy -- Periodicals; Sol, Utilisation du -- Périodiques; Sol, Utilisation du -- Politique gouvernementale -- Périodiques; Electronic journals; 333.7305
• DOI: 10.1016/j.landusepol.2019.104188
• Languages: English
• ISSNs: 0264-8377

Abstract

Highlights: The open-access crowdsourced datasets are introduced to achieve the urban dynamic information for urban renewal decision supporting. A presence-and-background data machine learning (PBL) method is applied to mass assessment of brownfield redevelopment suitability. The assessment based on crowdsourced data and traditional data are accurate and reliable for the residential and commercial land parcels. Abstract: Brownfield assessment is a crucial precondition for sustainable urban renewal. In particular, brownfield assessment usually involve a large amount of data and information relevant to urban or land dynamic. However, obtaining these data and information in fine scale has been especially challenging via traditional datasets (like surveyed or statistics datasets). This paper proposes a method for integrating crowdsourced datasets and traditional datasets to measure dynamic information of land parcels and applies a presence and background data machine learning (PBL) model to assess the redevelopment suitability in mass. The work focuses on the study area of Shenzhen, a high-density urban context with an adequate amount of urban renewal cases and sufficient crowdsourced data. Results indicated that assessments based on the combination of crowdsourced datasets and traditional datasets were accurate and reliable for the residential and commercial land parcels. Furthermore, mass assessments at a fine spatial-temporal scale are viable if introduce crowdsourced datasets due to the cost of obtaining the crowdsourced data is much lower, and the spatial-temporal scale is much finer than investigated or surveyed data.