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Data Details

Boundary Dataset of Built-up Areas in Chengdu-Chongqing Urban Economic Circle Based on POI & ISA Composite Index (2010, 2020)


ZHU Yonglu1,2ZHANG Yang*1,2,3YANG Renzhi1ASHUO Ayi1NAIGUME Erwai1
1 College of Tourism and Urban-Rural Planning，Chengdu University of Technology，Chengdu 610059，China2 Key Laboratory of Digital Drafting and Land Information Application，Ministry of Natural Resources，Wuhan 430079，China3 College of Architecture，Southeast University，Nanjing 210096，China

DOI:10.3974/geodb.2023.09.02.V1

Published:Sep. 2023

Visitors：4280       Data Files Downloaded：80      
Data Downloaded：47.75 MB      Citations：

Key Words：

built-up area,POI & ISA Composite Index,Chengdu-Chongqing Economic Circle,point of interest,impervious surface

Abstract：

Authors integrated Landsat TM/OLI images in 2010, 2020, Point of Interest in Baidu e-Map to create a comprehensive index (POI & ISA) and to obtain the boundary dataset of built-up areas in Chengdu-Chongqing Urban Economic Circle based on POI & ISA Composite Index (2010, 2020). The dataset includes the spatial data and statistical area data of built-up areas of 16 cities in 2010 and 2020. The dataset is archived in .shp and .xlsx data formats, and consists of 257 data files with data size of 974 KB (Compressed to one file with 611 KB).Browse

Foundation Item：

Ministry of Natural Resources of P. R. China (ZRZYBWD202201)

Data Citation：

ZHU Yonglu, ZHANG Yang*, YANG Renzhi, ASHUO Ayi, NAIGUME Erwai. Boundary Dataset of Built-up Areas in Chengdu-Chongqing Urban Economic Circle Based on POI & ISA Composite Index (2010, 2020)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2023. https://doi.org/10.3974/geodb.2023.09.02.V1.

ZHU Yonglu, ZHANG Yang, YANG Renzhi, et al. Boundary dataset of built-up areas in Chengdu-Chongqing economic circle based on POI&ISA composite index (2010, 2020) [J]. Journal of Global Change Data & Discovery, 2023, 7(4): 382-390.

References：


     [1] The Ministry of Construction of the People's Republic of China. Basic terminology standard for urban planning: GB/T50280-98 [S]. Beijing: China State Construction Industry Press, 1998.
     [2] Hu, T., Huang, X., Li, D., et al. Comprehensive assessment of extraction capacity in urban built-up areas in China [J]. Journal of Surveying and Mapping, 2023, 52(3): 432-442.
     [3] Ao, Y., Wu, B. L., Bai, Z. D., et al. Spatiotemporal dynamic characteristics of urban built-up areas of the Guangdong-Hong Kong-Macao Greater Bay Area based on NPP-VIIRS night light data [J]. Journal of Earth Science and Environment, 2022, 44(3): 513-523.
     [4] Tan, X. Y., Chen, Y. G., Urban boundary identification based on the neighborhood extension quantification method [J]. Progress in Geography, 2015, 34(10): 1259-1265.
     [5] Bhatti, S. S., Tripathi, N. K. Built-up area extraction using Landsat 8 OLI imagery [J]. GIScience & Remote Sensing, 2014, 51(4): 445-467.
     [6] Xu, Z. N., Gao, X. L., Border identification method in urban built-up areas based on point of interest of electronic map [J]. Journal of Geography, 2016, 71(6): 928-939.
     [7] Lin, L. L., Xu, H. Q., Huang S.L., Dynamic monitoring of urban expansion in eastern coastal areas of China based on DMSP / OLS night light images [J]. Journal of Earth Information Science, 2019, 21(7): 1074-1085.
     [8] Li, F., Yan, Q. W., Zou, Y. J., et al. Study on the extraction accuracy of urban built-up area using night light POI—Take Luojia 101 star and NPP / VIIRS night light images as an example [J]. Journal of Wuhan University (Information Science Edition), 2021, 46(6): 825-835.
     [9] The Land and Space Planning Bureau of the Ministry of Natural Resources, Tongji University, Peking University, etc. Urban area scope determination code [S]. Ministry of Natural Resources, PRC, 2021.
     [10] Huang, M., Zhang, M., Zhang, B., et al. Analysis of the concept of urban area scope and exploration of its determination methods——takes 115 cities as the practice objects [J]. Urban Planning, 2022, 46(5): 17-26.
     [11] Zhang, Y., Zheng, F. J., Liu, Y. F., et al. Extraction of urban built-up areas based on POI and impervious surface index [J]. Geographical Sciences, 2022, 42(3): 506-514.
     [12] Jiang, H. P., Sun, Z. C., Guo, H. D., et al. A standardized dataset of built-up areas of China’s cities with populations over 300,000 for the period 1990-2015 [DS/OL]. V1. Science Data Bank, 2021.
     [13] Sun, J., Sun, Z. C., Guo, H. D., et al. Dataset of Chinese urban 2020 [J]. Scientific Data of China (Chinese and English online version), 2022,7(1): 190-204.
     [14] Cao, F. J., Qiu, Y., Zou, Y., Rapid extraction method of built-up area based on H/T fracture method and POI data [J]. Geography and Geographic Information Science, 2020, 36(6): 48-54.
     [15] Ridd, M. K. Exploring a V-I-S (Vegetation-Impervious surface-Soil) model for urban ecosystem analysis through remote sensing comparative anatomy for cities [J]. International Journal of Remote Sensing, 1995, 16(12): 2165-2185.
     [16] Zhang, Y., Liu, Y. F., Liu, Y., Analysis of the space-temporal pattern of impervious surface in Wuhan [J]. Geographic Sciences, 2017, 37(12): 1917-1924.
     [17] Xu, H. Q., Study of water information extraction using improved normalized differential water index (MNDWI) [J]. Journal of Remote Sensing, 2005(5): 589-595.
     [18] Wilson, E. H., Hurd, J. D., Civco, D. L., et al. Development of a geospatial model to quantify, describe and map urban growth [J]. Remote Sensing of Environment, 2003, 86(3):275-285.
     

Data Product:

ID Data Name Data Size Operation
0Datapaper_ChengYuUrbanArea_2010_2020.pdf3363.00kbDownLoad
1 ChengYuUrbanArea_2010_2020.rar 611.15KB
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