Quantifying the Spatiotemporal Dynamics of Industrial Land Uses through Mining Free Access Social Datasets in the Mega Hangzhou Bay Region, China

Lingyan Huang, Yani Wu, Qing Zheng, Qiming Zheng, Xinyu Zheng, Muye Gan, Ke Wang, AmirReza Shahtahmassebi, Jingsong Deng, Jihua Wang and Jing Zhang
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Lingyan Huang: Institute of Agricultural Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Yani Wu: Institute of Agricultural Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Qing Zheng: Institute of Agricultural Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Qiming Zheng: Institute of Agricultural Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Xinyu Zheng: School of Information Engineering, Zhejiang Agriculture and Forestry University, Hangzhou 311300, China
Muye Gan: Institute of Agricultural Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Ke Wang: Institute of Agricultural Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
AmirReza Shahtahmassebi: Institute of Agricultural Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Jingsong Deng: Institute of Agricultural Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Jihua Wang: Beijing Research Center for Agri-Food Testing and Farmland Monitoring, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Jing Zhang: Institute of Agricultural Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China

Sustainability, 2018, vol. 10, issue 10, 1-24

Abstract: China has experienced rapid industrial growth over the last three decades, leading to diverse social and environmental issues. In the new industrialization era, it is urgent to quantify industrial land use (ILU) dynamics for sustainable industrial management, yet there have been limited attempts to systematically quantify these changes, especially in large-scale areas. Through points-of-interest (POIs), a free access geospatial big data, we developed a new framework for exploring ILU dynamics in the mega Hangzhou Bay region (MHBR). The ILU was identified by using natural language processing to mine the semantic information of industrial POIs from 2005, 2011, and 2016. Then, a two-step approach that integrated statistical analysis and hotspots detection was introduced to quantify the changes. The results revealed that traditional industries such as textile products and apparel manufacturing, unspecific equipment manufacturing, and electrical machinery and components manufacturing were dominant types across MHBR, with the enterprise number reaching 14,543, 9412, and 4374, respectively, in 2016. The growth rates of these traditional industries dropped during 2011–2016, while the growth rates of new industries such as Internet information industry and logistics industry increased remarkably, particularly in Hangzhou and Ningbo. Additionally, traditional industrial factories mainly expanded in the urban periphery and coastal zones, whereas new industries mainly grew in the urban center. Shrinkages in the hotspots of traditional industries between 2011 and 2016 were also observed. Our study provides a detailed spatial view of ILU, indicating that MHBR has undergone an industrial transition from traditional industry to new industry.

Keywords: industrial land uses; mega Hangzhou Bay region; points-of-interest; natural language processing; hotspots detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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