• Journal of Geographical Sciences
  • Vol. 30, Issue 4, 669 (2020)
Xiaoqing SONG1、2、*, Mengmeng WEN1, Yajing SHEN1, Qi FENG1, Jingwei XIANG3, Weina ZHANG2, Guosong ZHAO1, and Zhifeng WU4
Author Affiliations
  • 1Research Center for Spatial Planning and Human-Environment System Simulation, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
  • 2Hunan Key Laboratory of Land Resources Evaluation and Utilization, Hunan Planning Institute of Land and Resources, Changsha 410007, China
  • 3School of Public Administration, China University of Geosciences, Wuhan 430074, China
  • 4School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
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    DOI: 10.1007/s11442-020-1749-0 Cite this Article
    Xiaoqing SONG, Mengmeng WEN, Yajing SHEN, Qi FENG, Jingwei XIANG, Weina ZHANG, Guosong ZHAO, Zhifeng WU. Urban vacant land in growing urbanization: An international review[J]. Journal of Geographical Sciences, 2020, 30(4): 669 Copy Citation Text show less
    Images of urban vacant land in the case study area in Guangzhou City, China, 2016Note: The data were produced by the authors using a high-resolution remote sensing image, combining a street view and field survey (1. Wild grassland; 2. Abandoned building land; 3. Wild grass mixed with shrub and tree land; 4. Abandoned building mixed with bare land; 5. Bare land; 6. Abandoned building mixed with wild grassland).
    Fig. 1. Images of urban vacant land in the case study area in Guangzhou City, China, 2016Note: The data were produced by the authors using a high-resolution remote sensing image, combining a street view and field survey (1. Wild grassland; 2. Abandoned building land; 3. Wild grass mixed with shrub and tree land; 4. Abandoned building mixed with bare land; 5. Bare land; 6. Abandoned building mixed with wild grassland).
    Comparison of the morphologies of urban vacant land in the case study area in Guangzhou and New York City, 2016Note: The vacant land data pertaining to New York City were obtained from the NYC Department of City Planning (https://www1.nyc.gov/site/planning/data-maps/open-data.page); the vacant land data pertaining to Guangzhou City in southern China were produced by the authors using a high-resolution remote sensing image, combining a street view and field survey.
    Fig. 2. Comparison of the morphologies of urban vacant land in the case study area in Guangzhou and New York City, 2016Note: The vacant land data pertaining to New York City were obtained from the NYC Department of City Planning (https://www1.nyc.gov/site/planning/data-maps/open-data.page); the vacant land data pertaining to Guangzhou City in southern China were produced by the authors using a high-resolution remote sensing image, combining a street view and field survey.
    Distribution pattern of urban vacant land in typical transects in Guangzhou, 2016Note: The data were produced by the authors using a high- resolution remote sensing image, combining a street view and field survey
    Fig. 3. Distribution pattern of urban vacant land in typical transects in Guangzhou, 2016Note: The data were produced by the authors using a high- resolution remote sensing image, combining a street view and field survey
    Distribution pattern of urban vacant land in New York City, 2016.Note: The data were obtained from the NYC Department of City Planning (https://www1.nyc.gov/site/planning/data-maps/open-data.page).
    Fig. 4. Distribution pattern of urban vacant land in New York City, 2016.Note: The data were obtained from the NYC Department of City Planning (https://www1.nyc.gov/site/planning/data-maps/open-data.page).
    Distribution pattern of urban vacant land in Chicago, 2017Note: The space coordinates of vacant land parcels were from the City of Chicago (https://www.cityofchicago.org/city/en.html).
    Fig. 5. Distribution pattern of urban vacant land in Chicago, 2017Note: The space coordinates of vacant land parcels were from the City of Chicago (https://www.cityofchicago.org/city/en.html).
    Distribution pattern of share of urban vacant land area in Philadelphia, 2010Note: The data were obtained from ATACANGROUP (https://www.centercityrealestate.com/philadelphia-real-estate-blog/philadelphias-vacant-landissuses/).
    Fig. 6. Distribution pattern of share of urban vacant land area in Philadelphia, 2010Note: The data were obtained from ATACANGROUP (https://www.centercityrealestate.com/philadelphia-real-estate-blog/philadelphias-vacant-landissuses/).
    Distribution pattern of urban vacant land in Saskatoon, Canada, 2016Note: “Density” denotes the density of urban vacant land parcel; “economic density” denotes the density of commercial and industrial businesses at the neighborhood level. Vacant land data and data of commercial and industrial businesses were taken from the Vacant Lot Inventory and the Annual Report of Businesses Information for the City of Saskatoon, which were released by the city hall (https://www.saskatoon.ca/).
    Fig. 7. Distribution pattern of urban vacant land in Saskatoon, Canada, 2016Note: “Density” denotes the density of urban vacant land parcel; “economic density” denotes the density of commercial and industrial businesses at the neighborhood level. Vacant land data and data of commercial and industrial businesses were taken from the Vacant Lot Inventory and the Annual Report of Businesses Information for the City of Saskatoon, which were released by the city hall (https://www.saskatoon.ca/).
    Distribution pattern of urban vacant housing land in 65 cities, U.S., 2015
    Fig. 8. Distribution pattern of urban vacant housing land in 65 cities, U.S., 2015
    Number of parcelsArea of UVL (ha)Occurrence probability (%)
    Share of the number of UVL parcels to that of land parcelsShare of the area of UVL to the total land area
    Wild grassland831845.884.033.66
    Wild grass mixed with shrub and tree land319487.681.552.11
    Bare land341279.351.651.21
    Abandoned building mixed with wild grassland207201.061.000.87
    Abandoned building mixed with bare land122131.130.590.57
    Abandoned building land1210.930.060.05
    Sum18321956.028.888.46
    Table 1.

    Types of urban vacant land in the Guangzhou City case study, 2016

    Number of UVL parcelsArea of UVL (ha)Occurrence probability (%)
    Share of the number of UVL parcels to that of land parcelsShare of the area of UVL to the total land area
    Private ownership211081724.902.462.94
    City ownership4965949.440.581.62
    Public authority, state or federal ownership14038.900.020.07
    Mixed city and private ownership2652.060.000.09
    Other ownership which excludesland with the above ownerships915212.760.110.36
    Sum271542978.063.175.08
    Table 2.

    Types of urban vacant land in New York City, 2016 (Parcel, ha, %)

    DistrictsNew York CityManhattanBronxBrooklynQueensStaten Island
    Occurrence probabilityShare of the number of the vacant parcels to that of the whole city (%)3.172.724.482.452.535.65
    Share of the area of the vacant parcels to that of the whole city (%)5.082.062.972.482.5314.40
    Mean area of UVL (ha)0.110.0750.0600.0480.0730.247
    Average shape index value of UVL1.461.501.461.461.491.38
    Table 3.

    Morphologic characteristics of urban vacant land parcels in New York City, 2016

    DistrictNumber of UVL parcelsArea of UVL (ha)Mean area of UVL parcel (ha)Occurrence probability (%)Average shape index value
    Share of the number of the vacant parcels to that of the whole cityShare of the area of the vacant parcels to that of the whole city
    Transect 18354.170.653.752.491.45
    Transect 2144176.111.223.884.901.46
    Transect 3484597.401.2311.039.941.48
    Transect 4456536.471.1812.0910.841.37
    Transect 5413361.310.8710.199.491.31
    Transect 6252230.550.9110.068.891.27
    Total18321956.021.078.888.461.39
    Table 4.

    Morphologic characteristics of urban vacant land parcels in the typical transect area of Guangzhou City, China, 2016

    Xiaoqing SONG, Mengmeng WEN, Yajing SHEN, Qi FENG, Jingwei XIANG, Weina ZHANG, Guosong ZHAO, Zhifeng WU. Urban vacant land in growing urbanization: An international review[J]. Journal of Geographical Sciences, 2020, 30(4): 669
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