Urban landscape as a spatial representation of land rent: A quantitative analysis. (March 2019)
- Record Type:
- Journal Article
- Title:
- Urban landscape as a spatial representation of land rent: A quantitative analysis. (March 2019)
- Main Title:
- Urban landscape as a spatial representation of land rent: A quantitative analysis
- Authors:
- Wu, Jiayu
Wang, Shantong
Zhang, Yang
Zhang, Anqi
Xia, Chang - Abstract:
- Abstract: Due to the emergence of geographical 'big data, ' the field of urban studies is enjoying many new research opportunities. By using several sources of geographical 'big data', an analysis framework was structured to measure the urban landscape based on three aspects: city plan, pattern of building form, and urban land use. An association rule analysis was used to explore the relationship between land rent and the urban landscape, and the results indicate that the urban landscape differs across urban areas. The blocks classified as being located in main centers were associated with more convenient public transportation, denser road networks, more vertical street space, more diverse block patterns, more flexible architecture arrangements, mixed uses, high density, and more services. By contrast, non-center areas usually comprised blocks that were larger, tabular, single-purpose, and more regular. Non-center areas often cannot provide high-quality public goods, and they contain scattered large industrial enterprises. The urban landscapes of sub-center blocks fell in between these two urban areas. To our knowledge, this is the first paper that attempts to explain the relationship between land rent and urban landscapes based on 'big data, ' and our study may provide meaningful insights into urban design for government officials and academics. Highlights: Urban landscape is verified as a spatial representation of land rent by a quantitative approach. We structure anAbstract: Due to the emergence of geographical 'big data, ' the field of urban studies is enjoying many new research opportunities. By using several sources of geographical 'big data', an analysis framework was structured to measure the urban landscape based on three aspects: city plan, pattern of building form, and urban land use. An association rule analysis was used to explore the relationship between land rent and the urban landscape, and the results indicate that the urban landscape differs across urban areas. The blocks classified as being located in main centers were associated with more convenient public transportation, denser road networks, more vertical street space, more diverse block patterns, more flexible architecture arrangements, mixed uses, high density, and more services. By contrast, non-center areas usually comprised blocks that were larger, tabular, single-purpose, and more regular. Non-center areas often cannot provide high-quality public goods, and they contain scattered large industrial enterprises. The urban landscapes of sub-center blocks fell in between these two urban areas. To our knowledge, this is the first paper that attempts to explain the relationship between land rent and urban landscapes based on 'big data, ' and our study may provide meaningful insights into urban design for government officials and academics. Highlights: Urban landscape is verified as a spatial representation of land rent by a quantitative approach. We structure an analysis framework to measure the urban landscape based on three aspects. Association rule analysis is used to explore the relationship between land rent and the urban landscape. The results indicate that the urban landscape differs across urban areas. … (more)
- Is Part Of:
- Computers, environment and urban systems. Volume 74(2019)
- Journal:
- Computers, environment and urban systems
- Issue:
- Volume 74(2019)
- Issue Display:
- Volume 74, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 74
- Issue:
- 2019
- Issue Sort Value:
- 2019-0074-2019-0000
- Page Start:
- 62
- Page End:
- 73
- Publication Date:
- 2019-03
- Subjects:
- Land rent -- Urban landscape -- Association rules analysis -- Big data -- China
City planning -- Data processing -- Periodicals
Regional planning -- Data processing -- Periodicals
303.4834 - Journal URLs:
- http://www.sciencedirect.com/science/journal/01989715 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.compenvurbsys.2018.12.004 ↗
- Languages:
- English
- ISSNs:
- 0198-9715
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.914000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 9439.xml