融合多源地理大数据的城市街区综合活力评价

摘要/Abstract

摘要： 随着城市人口、物资、信息流动的日益频繁,城市居民活动特征和生产生活方式更加复杂多变,同时,城市空间无序扩张,发展规划不足,引发了交通堵塞、人口流失、公共空间缺乏等一系列问题,最终引发了城市活力消解难题。因此,如何科学高效地进行城市活力定量分析成为了重点研究问题。本文基于OpenStreetMap、百度地图兴趣点（Point of Interest,POI）、微信宜出行、美团、高德建筑物轮廓等多源地理大数据,从人与空间双重角度,分别对人群活力、活力多样性、活动满意度和空间交互潜能进行量化研究;引入空间权重矩阵,构建了改进的空间优劣解距离法（Technique for Order Preference by Similarity toIdeal Solution,TOPSIS）综合活力评价模型,实现对南京市中心城区综合活力的评价,最后分析了工作日、周末的街区活力空间分布特征及活力极的异同,并比较了传统的熵值TOPSIS综合活力评价结果,以此探究空间关系对城市街区活力的影响,以求帮助城市规划者系统的认识当前城市活力现状,为城市规划研究提供一种可行性方案。

关键词: 地理大数据, OSM路网, POI, 南京市, 城市街区活力, 引力模型, 优劣解距离法, 空间相互作用

Abstract: How to conduct quantitative analysis of urban vitality in a scientific and efficient manner has become a key research issue nowadays. Based on the multi-source geographic big data such as OpenStreetMap, Baidu Map POI, WeChat Travel, Meituan, Gaode building outlines, etc., and from the dual perspectives of people and space, this study selects indicators from four aspects, including crowd vitality, diversity of vitality, activity satisfaction, and spatial interaction potential, to construct a comprehensive vitality evaluation model of Spatial TOPSIS. Using the model, this study evaluates the comprehensive vitality of the downtown area of Nanjing, analyzes the spatial distribution characteristics of the vitality of the neighborhood, and explores the similarities and differences of vitality poles between weekdays and weekends. The evaluation results are compared with that of Entropy TOPSIS, in an effort to explore the impact of spatial interaction on the vitality of blocks. This study aims to help urban planners to understand the current status of urban vitality systematically, and provide a feasible plan for urban planning research. The research shows that, firstly, the spatial distribution characteristics of the comprehensive vitality of the downtown blocks in Nanjing urban center are similar between weekdays and weekends. However, the comprehensive vitality of the blocks on weekdays is higher than that on weekends. From the perspective of block functions, the high-value areas of comprehensive vitality are mainly concentrated in commercial centers, tourist attractions, and transportation hubs, which are closely related to the distribution of transportation stations (e.g. subway stations). Secondly, based on the vitality analysis of weekdays and weekends, it is found that Hunanlu - Xinjiekou - Confucius temple scenic area is the largest and most stable vitality pole. Among the small vitality poles, only Longjiang Metro Station has begun to take shape. Other small vitality poles, including Jiqingmen Street, Olympic Sports Center, Baijiahu Commercial District, and Wanda Commercial District, are unstable. Their vitality is still growing. Thus, they may become bigger vitality poles in the future. Thirdly, both the high vitality blocks in the center of the study area and the low vitality blocks receive less spatial effects. The areas with the greatest vitality change are generally distributed in a ring shape around the periphery of the central city. Combining the block functions, it is found that the comprehensive vitality of block units of other land and industrial land is less affected by the spatial interaction. In comparison, residential, commercial, scientific, educational, and cultural land are greatly affected by spatial interaction.

Key words: geographic big data, OSM road network, POI, Nanjing, urban block vitality, gravity model, TOPSIS, spatial interaction

唐璐, 许捍卫, 丁彦文. 融合多源地理大数据的城市街区综合活力评价[J]. 地球信息科学学报, , (): 3-4.

TANG Lu, XU Hanwei, DING Yanwen. Comprehensive Vitality Evaluation of Urban Blocks based on Multi-source Geographic Big Data[J]. Journal of Geo-information Science, , (): 3-4.

参考文献

[1] 王玉琢. 基于手机信令数据的上海中心城区城市空间活力特征评价及内在机制研究[D].南京:东南大学,2017.
[Wang Y Z.Research on urban spatial vitality characteristic evaluation and internal mechanism of Shanghai central city based on mobile phone signaling data[D]. Nanjing: Southeast University, 2017.]
[2] 范冬婉. 时空大数据支持下的城市活力测量方法及增长策略研究[D].武汉:武汉大学,2019.
[Fan D W.Quantifying urban vitality and its growth strategy using spatiotemporal big data[D].Wuhan:Wuhan University, 2019.]
[3] 翁世杰. 武汉市城市活力分析——以三环内主城区为例 [D].武汉:武汉大学,2019.
[Weng S J.Analysis of urban vitality for Wuhan: A case study in main city zone[D]. Wuhan:Wuhan University, 2019.]
[4] Jacobs J.The death and life of great American cities[M]. New York: random house, 1961.
[5] Lynch K.Good city form[M]. Boston: The MIT Press, 1984:30-45.
[6] Bentley I.Responsive environments[M]. New York: Architectural Press,1985:27-31.
[7] Gehl J.Life between buildings: Using public space[M]. Washington DC: Island press, 2011.
[8] 蒋涤非. 城市形态活力论[M].南京:东南大学出版社, 2007.
[Jiang D F.The theory of city form vitality Theory of city form vitality[M]. Nanjing: Southeast University Press, 2007.]
[9] 龚颖. 城市公共空间活力研究[D].武汉:中国地质大学, 2009.
[Gong Y.Study of the vitality of urban public space [D].Wuhan: China University of Geosciences, 2009.]
[10] 李万林. 城市公共空间的活力与特征浅述[J].重庆建筑, 2011,10(12):10-12.
[Li W L.Brief introduction of vitality and features in urban public space[J]. Chongqing Architecture, 2011,10(12):10-12.]
[11] 汤华杰. 加快城市活力空间建设的思考[J].中国国情国力,2017(5):48-49.
[Tang H J.Thoughts on speeding up the construction of urban vitality space[J]. China National Conditions and Strength, 2017(5):48-49.]
[12] Gehl Architects.Towards a fine city for people: Public spaces and public life-London[R]. London: Transport for London, 2004.
[13] Biddulph M.Radical streets: The impact of innovative street designs on liveability and activity in residential areas[J]. Urban Design International, 2012,17(3):178-205.
[14] 杨喜平,方志祥.移动定位大数据视角下的人群移动模式及城市空间结构研究进展[J].地理科学进展,2018,37(7): 880-889.
[Yang X P, Fang Z X.Recent progress in studying human mobility and urban spatial structure based on mobile location big data[J]. Progress in Geography, 2018, 37(7):880-889.]
[15] Xi L, Li G, Yongxi G, et al.Revealing travel patterns and city structure with taxi trip data[J]. Journal of Transport Geography, 2015,43:78-90.
[16] John D, Peter H, Ronan F, et al.Population Mobility Dynamics Estimated from Mobile Telephony Data[J]. Journal of Urban Technology,2014, 21(2):109-132.
[17] Meng Z, Yang Y, Qingquan L, et al.Portraying temporal dynamics of urban spatial divisions with mobile phone positioning data: A complex network approach[J]. ISPRS International Journal of Geo-information, 2016,5(12):240.
[18] 陈佳,胡波,左小清,等.利用手机定位数据的用户特征挖掘[J].武汉大学学报·信息科学版,2014,39(6):734-738, 744.
[Chen J, Hu B, Zuo X Q, et al.Personal profile mining based on mobile phone location data[J]. Geomatics and Information Science of Wuhan University, 2014,39(6):734-738,744.]
[19] 钟炜菁,王德,谢栋灿,等.上海市人口分布与空间活动的动态特征研究——基于手机信令数据的探索[J].地理研究,2017,36(5):972-984.
[Zhong W J, Wang D, Xie D C, et al.Dynamic characteristics of Shanghai's population distribution using cell phone signaling data[J]. Geographical Research, 2017,36(5):972-984.]
[20] 罗桑扎西,甄峰.基于手机数据的城市公共空间活力评价方法研究——以南京市公园为例[J].地理研究,2019,38(7):1594-1608.
[Luo S, Zhen F.How to evaluate public space vitality based on mobile phone data: An empirical analysis of Nanjing's parks[J]. Geographical Research, 2019,38(7):1594-1608.]
[21] 王波,甄峰,张浩.基于签到数据的城市活动时空间动态变化及区划研究[J].地理科学,2015,35(2):151-160.
[Wang B, Zhen F, Zhang H.The dynamic changes of urban space- time activity and activity zoning based on check- in data in sina web[J]. Scientia Geographica Sinica, 2015,35(2):151-160.]
[22] 张莹. 城市街区活力测度及影响机制研究——以武汉市主城区为例[D]. 武汉: 武汉大学,2019.
[Zhang Y.Research on the measurement and influence mechanism of urban block vitality: Taking the main city of Wuhan as an example[D].Wuhan:Wuhan University, 2019.]
[23] Cranshaw J, Schwartz R, Hong J I, et al.The Livehoods project: Utilizing social media to understand the dynamics of a city[J]. ICWSM 2012 - Proceedings of the 6th International AAAI Conference on Weblogs and Social Media, 2012:58-65.
[24] Zeng C, Song Y, He Q, et al.Spatially explicit assessment on urban vitality: Case studies in Chicago and Wuhan[J]. Sustainable Cities and Society, 2018,40:296-306.
[25] Huang B, Zhou Y, Li Z, et al.Evaluating and characterizing urban vibrancy using spatial big data: Shanghai as a case study[J]. Environment and Planning B: Urban Analytics and City Science, SAGE Publications Sage UK: London, England, 2019: 2399808319828730.
[26] Wu J, Ta N, Song Y, et al.Urban form breeds neighborhood vibrancy: A case study using a GPS-based activity survey in suburban Beijing[J]. Cities, 2018,74:100-108.
[27] Mehta V.Evaluating Public Space[J]. Journal of Urban Design, 2014,19(1):53-88.
[28] 宁晓平. 土地利用结构与城市活力的影响分析[D].深圳: 深圳大学,2016.
[Ning X P.Analysis on the influence of land use structure and urban vitality[D]. Shenzhen: Shenzhen University, 2016.]
[29] 赵卫锋,李清泉,李必军.利用城市POI数据提取分层地标[J].遥感学报,2011,15(5):973-988.
[Zhao W F, Li Q Q, Li B J.Extracting hierarchical landmarks from urban POI data[J]. Journal of Remote Sensing, 2011,15(5):973-988.]
[30] 谢超. 城市网络格局下的新城活力表征研究——以广州珠江新城为例[D].哈尔滨:哈尔滨工业大学,2020.
[Xie C.A research on the urban vitality characterization of new town from a network perspective: A case study from the pearl river new town in Guangzhou[D]. Harbin: Harbin Institute of Technology, 2020.]
[31] 倪艳亭,崔雁冰.基于改进TOPSIS 法的济南都市圈城市创新能力空间分异研究[J].地域研究与开发,2019,38(6): 63-67.
[Ni Y T, Cui Y B.Spatial differentiation of urban innovation capability in Jinan metropolitan circle based on improved TOPSIS method[J]. Areal Research and Development, 2019,38(6):63-67.]
[32] Ord J K, Getis A.Local spatial autocorrelation statistics: Distributional issues and an application[J]. Geographical Analysis, 2010,27(4):286-306.
[33] OpenStreetMap 基金会.OpenStreetMap[EB/OL].https:// www.openstreetmap.org/,2021.
[OSMF. OpenStreetMap [EB/OL]. https://www.openstreetmap.org/, 2021.]
[34] 中华人民共和国住房和城乡建设部.中华人民共和国城镇建设工程标准:城市道路工程设计规范CJJ 37-2012 [S].北京:中国建筑工业出版社,2012.
[Ministry of Housing and Urban-Rural Development of the People's Republic of China. Code for design of urban road engineering. CJJ 37-2012[S]. Beijing: China Architecture & Building Press, 2012.]
[35] Yuan Zhang, et al.The Combined Use of Remote Sensing and Social Sensing Data in Fine- Grained Urban Land Use Mapping: A Case Study in Beijing, China[J]. Remote Sensing, 2017,9(9):865.
[36] Liu Xingjian, Long Ying.Automated identification and characterization of parcels with OpenStreetMap and points of interest[J]. Environment and Planning B: Planning and Design, 2016,43(2):341-360.
[37] 中华人民共和国住房和城乡建设部.中华人民共和国国家标准:城市用地分类与规划建设用地标准GB 50137- 2011[S].北京:中国计划出版社,2012.
[Ministry of Housing and Urban-Rural Development of the People's Republic of China. National Standard (Mandatory) of the People's Republic of China: Code for classification of urban land use and planning standards of development land. GB 50137-2011[S]. Beijing: China Planning Press, 2012.]