Study on spatial distribution characteristics of service industry in western urban area based on DBSCAN clustering：A case study of Chengxi district, Xining city

doi:10.14088/j.cnki.issn0439-8114.2021.07.033

Abstract

Abstract: Based on the POI data of the service industry in Chengxi district of Xining city, this study analyzed the DBSCAN clustering results of the overall and sub industry POI data of the service industry in Chengxi district, explored the spatial layout structure and clustering characteristics of the service industry in Chengxi district, and maked a comparative analysis of the layout of the service industry in different regions. The results show that the service industry of Chengxi district of Xining city generally presents the spatial distribution characteristics of “two points and one line”, with Haihu Wanda Plaza and Limeng Commercial Pedestrian Street as the two central points, mainly distributes around the “two points” line, two points are the central agglomeration, and the difference between north and south is obvious, and the proportion of traditional service industry is too large. The distribution of boundary points and noise points is centralized and regular, which shows the potential area of service industry accumulation in Chengxi district. Although the service industry of all walks of life are affected by different factors, its agglomeration characteristics have little difference.The development level of the service industry in the Chengxi district is lower than that in the eastern and central regions, and the spatial development is unbalanced, which is significantly affected by the accessibility of transportation.

Key words: POI data, service industry, spatial layout, DBSCAN, Chengxi district of Xining city

CLC Number:

F719

ZHU Li, LI Xiao-ling, WANG Lu, YANG Cheng-zhong, XIE Fen-fen. Study on spatial distribution characteristics of service industry in western urban area based on DBSCAN clustering：A case study of Chengxi district, Xining city[J]. HUBEI AGRICULTURAL SCIENCES, 2021, 60(7): 166-174.

References 34

[1]	DANIELS P W.Services and space: Key aspects of urban and regional development[J]. Journal of retailing and consumer services, 1995, 4(3):212-213.
[2]	COE N M, TOWNSEND A R.Debunking the myth of localized agglomerations: The development of a regionalized service economy in South-East England[J]. Transactions of the institute of British geographers, 1998,23(3):385-404.
[3]	BURGER M J,MEIJERS E J.Spatial structure and productivity in US metropolitan areas[J]. Environment and planning A,2010,42(6):1383-1402.
[4]	李惠娟. 国外服务业集聚研究综述[J]. 合作经济与科技, 2013(13): 18-20.
[5]	UYANGA J T.Implications of the service sector growth for population change in small towns in South Australia[J]. Indonesian journal of geography,1981,11(42):1-7.
[6]	陈曦, 吕斌. 中小城市服务业集聚区发展模式研究[J]. 经济地理, 2014, 34(4): 105-111.
[7]	方远平, 阎小培. 1990年代以来我国沿海中心城市服务业特征与趋势比较研究——以北京、上海与广州为例[J]. 经济地理, 2004(5): 614-619,637.
[8]	徐志华,杨强,申玉铭. 区域中心城市服务业发展综合评价及其影响因素[J].地域研究与开发, 2016,35(3):40-45.
[9]	汪欢欢. 服务业视角下的内陆城市发展道路:影响及分类模式[J]. 中国西部, 2019(3): 34-44.
[10]	仲莲, 颜丽, 毛红魁. 青海省服务业转型升级探索研究[J]. 中国工程咨询, 2017(10): 51-54.
[11]	周成虎. 大数据时代的空间数据价值——《空间数据挖掘理论与应用》评介[J]. 地理学报, 2016, 71(7): 1281.
[12]	张铁映, 李宏伟, 许栋浩, 等. 采用密度聚类算法的兴趣点数据可视化方法[J]. 测绘科学, 2016, 41(5): 157-162.
[13]	李阳, 陈晓红. 哈尔滨市商业中心时空演变与空间集聚特征研究[J]. 地理研究, 2017, 36(7): 1377-1385.
[14]	陈红霞. 北京市生产性服务业空间分布与集聚特征的演变[J]. 经济地理, 2018, 38(5): 108-116.
[15]	张志斌, 公维民, 张怀林, 等. 兰州市生产性服务业的空间集聚及其影响因素[J]. 经济地理, 2019, 39(9): 112-121.
[16]	孙克, 聂坚, 游细斌, 等. 长江中游城市群生产性服务业的分工特征及空间效应分析[J]. 经济地理, 2018, 38(2): 123-132.
[17]	张海平, 周星星, 汤国安, 等. 基于GIS场模型的城市餐饮服务热点探测及空间格局分析[J]. 地理研究, 2020, 39(2): 354-369.
[18]	何伟纯, 李二玲, 崔之珍, 等. 开封市主城区零售商业空间布局及其影响因素[J]. 经济地理, 2018, 38(9): 158-167.
[19]	潘裕娟, 曹小曙. 城市物流业的基本-非基本经济活动分析——以广州市为例[J]. 经济地理, 2011, 31(9): 1483-1488.
[20]	冉钊, 周国华, 吴佳敏, 等. 基于POI数据的长沙市生活性服务业空间格局研究[J]. 世界地理研究, 2019, 28(3): 163-172.
[21]	薛冰, 肖骁, 李京忠, 等. 基于POI大数据的城市零售业空间热点分析——以辽宁省沈阳市为例[J]. 经济地理, 2018, 38(5): 36-43.
[22]	刘雅轩, 陈彤. 基于POI数据的乌鲁木齐市城市公园绿地对周边住宅价格的影响研究[J]. 干旱区资源与环境, 2020, 34(11): 36-43.
[23]	李钢, 杨兰, 贺建雄, 等. 基于POI数据的西安市快递自提点空间格局及空间关系研究——以菜鸟驿站为例[J]. 地理科学, 2018, 38(12): 2024-2030.
[24]	李维维, 陈田, 马晓龙. 西安城市旅游休闲业态空间热点特征及形成机制[J]. 地理科学, 2020, 40(3): 437-446.
[25]	涂建军, 唐思琪, 张骞, 等. 山地城市格局对餐饮业区位选择影响的空间异质性[J]. 地理学报,2019,74(6): 1163-1177.
[26]	吴先赋, 李永树, 王金明, 等. 基于POI数据的成都市区生活设施空间格局分析[J]. 测绘地理信息,2019,44(3):122-126.
[27]	魏中宇, 苏惠敏, 黄荣静. 基于POI数据西安市商业集聚特征分析[J]. 西南大学学报(自然科学版), 2020, 42(4): 97-104.
[28]	范梦余, 陈怡宁, 张辉. 呼伦贝尔游客POI空间格局与移动轨迹研究——基于地理标记照片的实证分析[J].干旱区资源与环境, 2019, 33(6): 203-208.
[29]	查小磊, 张明山, 高晴, 等. 基于POI数据的长沙市生活服务业空间配套研究[J]. 科技和产业, 2018, 18(9): 36-39.
[30]	刘畅, 李治军, 姜守旭. 基于DBSCAN算法的城市交通拥堵区域发现[J]. 智能计算机与应用, 2015, 5(3): 69-71.
[31]	李江苏, 梁燕, 王晓蕊. 基于POI数据的郑东新区服务业空间聚类研究[J]. 地理研究, 2018, 37(1): 145-157.
[32]	李新延, 李德仁. DBSCAN空间聚类算法及其在城市规划中的应用[J]. 测绘科学, 2005(3): 51-53,5.
[33]	樊东卫, 何勃亮, 李长华, 等. 球面距离计算方法及精度比较[J]. 天文研究与技术, 2019, 16(1): 69-76.
[34]	ESTER M, KRIEGEL H P, SANDER J, et al.A density-based algorithm for discovering clusters in large spatial databases with noise[A]. KDD'96: Proceedings of the second international conference on knowledge discovery and data mining[C].Portland:AAAI Press,1996.226-231.