Exploring the spatio-temporal distributions of industrial parks in Xining City from the perspective of buildings made of color steel plates
LI Yuqing1(), YANG Shuwen1,2,3(), HONG Weili1, SU Hang1, LUO Yawen1
1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730700, China 2. National - Local Joint Engineering Research Center for Application of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China 3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
Industrial parks are like the engine of urban economic development. Exploring their spatio-temporal distributions holds critical significance for ascertaining urban spatial structures and sustaining the development of industrial parks. To objectively characterize the spatio-temporal distributions of industrial parks, this study employed the data of buildings made of color steel plates as auxiliary data for investigating industrial parks in Xining City, Qinghai Province. Combined with some information and road network data of industrial parks in the main urban area of Xining City from 2005 to 2020, this study delved into the spatio-temporal distributions of industrial parks over a long period in Xining City using network kernel density analysis, standard deviational ellipse, and equal sector analysis. The results show that: ① From 2005 to 2020, industrial parks in Xining City continued to increase at a growth rate of 73%, with the fastest growth rate observed in Chengbei District; ② Highly clustered industrial parks developed from single to multiple zones. Concerning the changes in the density of buildings made of color steel plates, newly built industrial parks were mostly distributed on the urban edge, and the cluster areas exhibited north-south crossing banded distributions, aligning with the urban spatial structure. Additionally, all industrial parks showed a northwest-southeast distribution and a less significant clustering trend from 2005 to 2020; ③ The expansion of industrial parks manifested phased and zonal development, with a gradually decreased expansion intensity, suggesting the tendency of stable development. The results of this study will provide objective spatio-temporal data support and methodology for the urbanization development research or structural transformation of industrial parks in Xining.
李玉清, 杨树文, 洪卫丽, 苏航, 雒亚文. 彩钢板建筑群视角下的西宁市产业园区时空分布研究[J]. 自然资源遥感, 2024, 36(2): 239-247.
LI Yuqing, YANG Shuwen, HONG Weili, SU Hang, LUO Yawen. Exploring the spatio-temporal distributions of industrial parks in Xining City from the perspective of buildings made of color steel plates. Remote Sensing for Natural Resources, 2024, 36(2): 239-247.
Kang L, Ma L. Expansion of industrial parks in the Beijing-Tianjin-Hebei urban agglomeration: A spatial analysis[J]. Land, 2021(11):1118.
[2]
Bai Y, Li X W. Creative destruction path selection for industrial park transformation and upgrading under the concept of character town in the era of big data[J]. Security and Communication Networks, 2022,2022.
[3]
Dong Y, Guo B S, He D W, et al. Industrial transformation and urban economic efficiency evolution:An empirical study of the Yangtze River economic belt[J]. International Journal of Environmental Research and Public Health, 2022, 19(7):4154
[4]
Zeng D Z, Cheng L, Shi L, et al. China’s green transformation through eco-industrial parks[J]. World Development, 2021, 140:105249.
[5]
Wang M, Yuan X H, Yang S Q, et al. Research on spatial planning of petrochemical industrial parks from the perspective of symbiosis:Example of Yueyang green chemical industry park[J]. Sustainability, 2022, 14(8):1-17.
[6]
Hei W, Li X, Gao G, et al. Air Pollutants and CO2 emissions in industrial parks and evaluation of their green upgrade on regional air quality improvement:A case study of seven cities in Henan Province[J]. Atmosphere, 2022, 13(3):383.
[7]
Meng Q, Hu D, Zhang Y, et al. Do industrial parks generate intra-heat island effects in cities? New evidence,quantitative methods,and contributing factors from a spatiotemporal analysis of top steel plants in China[J]. Environmental Pollution, 2022, 292:118383.
[8]
Tang J X, Zhu Y L, Xiang B, et al. Multiple pollutants in groundwater near an abandoned Chinese fluorine chemical park:Concentrations,correlations,and health risk assessments[J]. Scientific Reports, 2022, 12(1):3370.
[9]
Chen Y F, Xu Y, Wang F Y. Air pollution effects of industrial transformation in the Yangtze River Delta from the perspective of spatial spillover[J]. Journal of Geographical Sciences, 2022, 32(1):156-176.
doi: 10.1007/s11442-021-1929-6
[10]
Xiao X D, Zhang L L, Xiong Y M, et al. Influence of spatial characteristics of green spaces on microclimate in Suzhou Industrial Park of China[J]. Scientific Reports, 2022, 12(1):9121.
doi: 10.1038/s41598-022-13108-1
pmid: 35650391
[11]
Xu Y Y, Liu M, Hu Y M, et al. Analysis of Three-Dimensional space expansion characteristics in old industrial area renewal using GIS and Barista:A case study of Tiexi district,Shenyang,China[J]. Sustainability, 2019, 11(7):1-22.
[12]
Park J I, Kim J O. Does industrial land sprawl matter in land productivity? A case study of industrial parks of South Korea[J]. Journal of Cleaner Production, 2022, 334:130209.
[13]
Zhuang L, Ye C. Changing imbalance:Spatial production of national high-tech industrial development zones in China (1988-2018)[J]. Land Use Policy, 2020, 94:104512.
[14]
Wang S S, Lu C Y, Gao Y, et al. Life cycle assessment of reduction of environmental impacts via industrial symbiosis in an energy-intensive industrial park in China[J]. Journal of Cleaner Production, 2019, 241:118358.
[15]
Vallentin C, Harfenmeister K, Itzerott S, et al. Suitability of satellite remote sensing data for yield estimation in northeast Germany[J]. Precision Agric ulture, 23(1),52-82.
Ma J J, Yang S W, Jia X, et al. Temporal and spatial change of color steel sheds in Anning district of Lanzhou City[J]. Science of Surveying and Mapping, 2018, 43(12):34-37,71.
[17]
宋郃. 银川市彩钢板建筑与产业园区时空格局演变关系研究[D]. 兰州: 兰州交通大学, 2021.
Song H. Research on spatial and temporal pattern evolution relationship of color steel building and industrial parks in Yinchuan[D]. Lanzhou: Lanzhou Jiatong University, 2021.
Gao L Y, Li Y K, Yang S W, et al. Analysis on fire rescue optimization of color steel building complexes in northwest China:A case study of Lanzhou[J]. Geomatics & Spatial Geographic Information, 2021, 44(11):31-35,39.
Zhang N X, Yang S W, Fu Y K, et al. Spatial-temporal aggregation characteristics of color steel plate buildings in Lanzhou and their influence on urban heat island effect[J]. Geography and Geo-Information Science, 2022, 38(3):43-49.
Yu W H, Ai T H, Liu P C, et al. Network kernel density estimation for the analysis of facility POI hotspots[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(12):1378-1383,1400.
doi: 10.11947/j.AGCS.2015.20140538
Ma C, Wang H W, Tan B, et al. Characteristics and spatial reconstruction of an urban-rural settlement scale system in a typical oasis in Xinjing:A case study of the Ugan-Kuqa River Delta oasis[J]. Acta Geographica Sinica, 2022, 77(4):852-868.
Yu W H, Ai T H. The visualization and analysis of POI features under network space supported by kernel density estimation[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(1):82-90.
doi: 10.11947/j.AGCS.2015.20130538
[23]
Delso J, Martín B, Ortega E. A new procedure using network analysis and kernel density estimations to evaluate the effect of urban configurations on pedestrian mobility[J]. The Case Study of Vitoria-gasteiz.J.T Rasp.
[24]
Shen B X, Xu X, Li J, et al. Unfolding spatial-temporal patterns of taxi trip based on an improved network kernel density estimation[J]. ISPRS International Journal of Geo-Information, 2020, 9(11):683
Tian P, Li J L, Cao L D, et al. Evaluation and development trend prediction of China’s fishery economy[J]. China’s Agricultural Resources and Regional Planning:1-15.
Li L T, Zhang S S, Zhao X Z, et al. The influence of spatial growth of cultural Industry on the evolution of urban spatial morphology:Taking the core region of Xi’an as a case study[J]. Geographical Research, 2021, 40(2):431-445.
Wang H J, Xia C, Zhang A Q, et al. Space syntax expand intensity index and its applications to quantitative analysis of urban expansion[J]. Acta Geographica Sinica, 2016, 71(8):1302-1314.
[29]
Wang H, Zhang B, Liu Y, et al. Urban expansion patterns and their driving forces based on the center of gravity-GTWR model:A case study of the Beijing-Tianjin-Hebei urban agglomeration[J]. Journal of Geographical Sciences, 2020, 30(2):297-318.