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Dataset of Spatio-temporal Changes of Enterprises in Tibet Autonomous Region (2010-2020)


CHEN Jiarui1ZHANG Wenzhong*2MA Renfeng1LIU Lidong1SHENG Yuting1WANG Weiqing1LI Jiaming2XU Liuji3
1 Department of Geography and Spatial Information Techniques,Ningbo Universities Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research,Ningbo University,Ningbo 315211,China2 Key Laboratory of Regional Sustainable Development Modeling,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China3 Beijing Institute of Surveying and Mapping,Beijing 100038,China

DOI:10.3974/geodb.2022.07.09.V1

Published:July 2022

Visitors:915       Data Files Downloaded:46      
Data Downloaded:1380.56 MB      Citations:

Key Words:

Tibet Autonomous Region, enterprise, three industries, spatial and temporal distribution,2010-2020

Abstract:

Spatial and temporal changes of regional enterprise distribution pattern reflect the structure and level of local industrial and economic development. By the data mining methodology and using the enterprise information at the National Enterprise Credit Information Public Display System (www.gsxt.gov.cn), Tian-yan-cha (www.tianyancha.com) and Qi-cha-cha (www.qcc.com), the dataset of spatio-temporal changes of enterprises in Tibet Autonomous Region (2010-2020) was developed. This dataset is consisted of the information on enterprises in the primary industry, the secondary industry and the tertiary industry of Tibet during 2010-2020. The results of this dataset show that the number of enterprises in the three industries in Tibet multiplied and their business scope expanded from 2010 to 2020, and the temporal and spatial distribution of enterprises in the three industries tended to evolve over time with Lhasa and Shigatse as the first-level core, Nyingtri and Lhoka as the secondary centers, and a belt-like distribution along the Yarlung Tsangpo River valley. The dataset is archived in .shp formats, and consists of 73 data files with data size of 2.16 GB (Compressed to one file with 30.0 MB).

Foundation Item:

Ministry of Science and Technology of P. R. China (2019QZKK0406);

Data Citation:

CHEN Jiarui, ZHANG Wenzhong*, MA Renfeng, LIU Lidong, SHENG Yuting, WANG Weiqing, LI Jiaming, XU Liuji. Dataset of Spatio-temporal Changes of Enterprises in Tibet Autonomous Region (2010-2020)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2022. https://doi.org/10.3974/geodb.2022.07.09.V1.

References:

[1] Ma, R. F., Li, Q. Q., Dou, S. M., et al. Hot Topics, methods and data challenges in regional and urban industry research [J]. Think Tank : Theory & Practice, 2022, 7(2): 154-164.
     [2] Lavoratori, K., Mariotti, S., Piscitello, L. The role of geographical and temporary proximity in MNEs' location and intra-firm co-location choices [J]. Regional Studies, 2020, 54(10): 1442-1456.
     [3] Li, J. M., Zhang, W. Z., Li, Y., et al. Analysis of industrial spatial agglomeration characteristics based on micro-enterprise data [J]. Geographical Research, 2016, 35(1): 95-107.
     [4] Wu, D. D., Ma, R. F., Zhang, Y., et al. Cluster characteristics and spatial and temporal pattern evolution of Hangzhou cultural and creative industries [J]. Economic Geography, 2018, 38(10): 127-135.
     [5] Ma, R. F., Wang, T. F., Zhang, W. Z., et al. Location model of cultural and creative industries and empirical evidence in Zhejiang [J]. Geographical Research, 2018, 37(2): 379-390.
     [6] Li, J. M., Sun, T. S., Zhang, W. Z. Study on Spatial Agglomeration Characteristics and Patterns of China's Productive Service Industry [J]. Scientia Geographica Sinica, 2014, 34(4): 385-393.
     [7] Dou, C. C., Chen, L. , Xie, Y. Y. et al. Spatial Agglomeration and Influencing Factors of Urban Children's Education and Tutoring Institutions in Beijing [J]. Geographical Research, 2022, 41(4): 1170-1182.
     [8] Zhan, D. S., Zhan, Q. Y., Zhang, W. Z., et al. Spatial clustering characteristics and location selection of real estate enterprises in Hangzhou [J]. Progress in Geography, 2021, 40(5): 736-745.
     [9] National Enterprise Credit Information Publicity System [EB/OL]. https://www.gsxt.gov.cn.
     [10] https://www.tianyancha.com.
     [11] https://www.qcc.com.
     

Data Product:

ID Data Name Data Size Operation
1 ChangeEnt_Tibet_2010-2020.rar 30732.46KB
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