融合多源大数据的武汉城市空置区域评估与分析

doi:10.12082/dqxxkx.2020.190620

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (5): 997-1007.doi: 10.12082/dqxxkx.2020.190620

• “空间综合人文学与社会科学”专辑 • 上一篇下一篇

融合多源大数据的武汉城市空置区域评估与分析

杨建思¹, 柳帅¹, 王艳东^2,^*(), 廖明生²

1.武汉大学城市设计学院,武汉 430072
2.武汉大学测绘遥感信息工程国家重点实验室,武汉 430079

收稿日期:2019-10-23 修回日期:2020-02-29 出版日期:2020-05-25 发布日期:2020-07-25
通讯作者: 王艳东 E-mail:ydwang@whu.edu.cn
作者简介:杨建思（1963— ）,江西于都人,教授,博士生导师,主要从事遥感及空间信息技术在城市规划与管理中的应用。E-mail:Jsyang@whu.edu.cn
基金资助:
国家重点研发计划项目(2018YFB2100500)

The Assessment and Analysis of the Phenomenon of Vacancy within Wuhan City Using Multi-Source Datasets

YANG Jiansi¹, LIU Shuai¹, WANG Yandong^2,^*(), LIAO Mingsheng²

1. School of Urban Design, Wuhan University, Wuhan 430072, China
2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Received:2019-10-23 Revised:2020-02-29 Online:2020-05-25 Published:2020-07-25
Contact: WANG Yandong E-mail:ydwang@whu.edu.cn
Supported by:
National Key Research and Development Program of China(2018YFB2100500)

摘要/Abstract

摘要：

在城市不断发展与扩展的同时,许多建成区可能会出现空置,表现为人口、企业稀少,生产水平严重低下,对城市空置区域的评估能够反映当前城市内部各区域发展状况,能够作为城市改造更新的依据。本文提出了一个以空置指数来量化评估城市内部层面空置现象,并对其影响因素进行探究的方法。根据“空城”的外在特征：建筑水平与社会活动水平,空置指数通过较高分辨率夜光遥感影像（Luojia1-01）和土地覆盖信息得到;同时依据城市空间活力营造原则,通过社会感知数据与路网数据量化产生空置现象的内在影响因素;并通过随机森林模型对空置指数精细化以及对影响因素重要性排序。以武汉市城区为例,用该方法对空置指数可视化,可识别出空置现象严重的区域,主要为老旧城区、工厂库房、单一功能的公共设施区域等,同时道路的可达性与人流密度对空置现象影响较大,且建立的随机森林模型精度 $R 2$ 可达84.6%,对空置指数通过随机森林拟合后,其可视化分辨率由130 m优化至25 m。本研究结果可为城市中空置现象的改进提供依据。

关键词: 空置现象, 夜间灯光, 随机森林, SAR, 街区空间活力, 热力图, 兴趣点, 空间句法

Abstract:

With the rapid construction of urban areas, it is necessary to make assessment of the built-up areas. As the city develops and expands, many of the build-up areas may show the phenomenon of vacancy with a small population, few businesses, and seriously low productivity.It is a serious obstacle to urban development and leads to the decrease of street vitality. In order to promote the healthy development of cities, it is necessary to evaluate the existing phenomenon of vacancy in cities. This paper proposes a method to quantify and evaluate the phenomenon of vacancy at the city block level with avacancy index, as well as investigate its influencing factors. Firstly, a vacancy index is determined by the characteristics of a vacant town based on nighttime light data and land cover information. The nighttime light data of Luojia-1A satellite whose resolution is higher than DMSP-OLS and NPP-VIIR, reflect the intensity of nocturnal activity. In SAR interferometry, coherence is the criteria of phase stability. Stabilized objects in urban areas can be identified as buildings according to the coherence, which shows the external characteristics of vacant towns. By visualizing the vacancy index in the study area, failed construction in urban areas can be identified. Then, based on classical urban design theories on spatial vitality promotion, a series of factors of urban vitality are summarized as the internal influencing factors of the vacancy phenomenon, including the distance to the street, integration, choice, the crowd density, function of street-block, mixture of primary use, and mixture of building ages. These internal influencing factors of the vacancy phenomenon are quantified by social sensing data and road information. Lastly, the extrinsic and intrinsic features of vacancy are combined, a Random Forest model is employed to refine the vacancy index and rank the importance of the influencing factors. We use the urban area of Wuhan as a case study. Wuhan develops and expands fast in recent years. The areas with severe vacancy can be identified by visualizing the vacancy index with this proposed method. Meanwhile, the influencing factors that have the most impact on the degree of vacancy, accessibility of road network, and population density, are also identified. In this paper, the phenomenon of vacancy is analyzed from two perspectives, multi-source remote sensing data and multi-source social sensing data, both of which have time series correspondence. Thus, the results of this paper are reliable and can assist with the assessment and analysis of the phenomen on of vacancy in urban areas.

Key words: vacancy, nighttime light, random forest, SAR, vitality of block, heat map, point of interests, space syntax

杨建思, 柳帅, 王艳东, 廖明生. 融合多源大数据的武汉城市空置区域评估与分析[J]. 地球信息科学学报, 2020, 22(5): 997-1007.DOI:10.12082/dqxxkx.2020.190620

YANG Jiansi, LIU Shuai, WANG Yandong, LIAO Mingsheng. The Assessment and Analysis of the Phenomenon of Vacancy within Wuhan City Using Multi-Source Datasets[J]. Journal of Geo-information Science, 2020, 22(5): 997-1007.DOI:10.12082/dqxxkx.2020.190620

图/表 10

图1

图2

表1

基于空间活力营造原则量化的空置现象影响因素"

空间活力营造原则	量化的空置现象影响因素
街道可达性	到街道的距离 $D$
	集成度 $I$ ,表示系统中某一道路与其它道路集聚或离散的程度。
	穿行度 $C$ ,表示被其他最短路径穿行的可能性。
活动强度与形态	人流密度count
	所在街区属于何种类型（居民区,商业区等）
街区混合度	街区功能混合度 $H$
	街区建筑年代混合度（高,低）

表1

图3

图4

图5

图6

图7

图8

图9

参考文献 34

[1]	聂翔宇, 刘新静. 城市化进程中“鬼城”的类型分析及其治理研究[J]. 南通大学学报(社会科学版), 2013,29(4):111-117.
	[ Nie X Y, Liu X J. Analysis of the types of "Ghost City" in the process of urbanization and its governance[J]. Journal of Nantong University(Social Science Edition), 2013,29(4):111-117. ]
[2]	Shepard W. Ghost cities of China: The story of cities without people in the world's most populated country[M]. London: Zed Books Ltd., 2015.
[3]	Dubeaux S, Sabot E C. Maximizing the potential of vacant spaces within shrinking cities, a German approach[J]. Cities, 2018,75:6-11.
[4]	段禄峰, 魏明, 车志晖. 新型城镇化背景下规划型“空城”现象解析[J]. 生态经济, 2017,33(10):116-121.
	[ Duan L F, Wei M, Che Z H. Analysis of the planning-type "empty city" phenomenon under the background of new urbanization[J]. Ecology and Economy, 2017,33(10):116-121. ]
[5]	Jin X, Long Y, Sun W, et al. Evaluating cities' vitality and identifying ghost cities in China with emerging geographical data[J]. Cities, 2017,63:98-109. doi: 10.1016/j.cities.2017.01.002
[6]	Ge W, Yang H, Zhu X, et al. Ghost city extraction and rate estimation in China based on NPP-VIIRS night-time light data[J]. ISPRS International Journal of Geo-Information, 2018,7(6):219-235. doi: 10.3390/ijgi7060219
[7]	董磊磊, 潘竟虎, 冯娅娅, 等. 基于夜间灯光的中国房屋空置的空间分异格局[J]. 经济地理, 2017,37(9):62-69,176.
	[ Dong L L, Pan J H, Feng Y Y, et al. Spatial differentiation pattern of Chinese house vacant based on nighttime light[J]. Economic Geography, 2017,37(9):62-69,176. ]
[8]	Zheng Q, Deng J, Jiang R, et al. Monitoring and assessing “ghost cities” in Northeast China from the view of nighttime light remote sensing data[J]. Habitat International, 2017,70:34-42. doi: 10.1016/j.habitatint.2017.10.005
[9]	Zheng Q, Zeng Y, Deng J, et al. “Ghost cities” identification using multi-source remote sensing datasets: A case study in Yangtze River Delta[J]. Applied geography, 2017,80:112-121. doi: 10.1016/j.apgeog.2017.02.004
[10]	Lu H, Zhang C, Liu G, et al. Mapping China's ghost cities through the combination of nighttime satellite data and daytime satellite data[J]. Remote Sensing, 2018,10(7):1037-1055.
[11]	沈晓艳, 黄贤金. 基于土地供应侧的中国商品住宅空置效应分析——以35个大中城市为例[J]. 现代城市研究, 2017(10):12-17.
	[ Shen X L, Huang X J. Analysis on the vacancy effect of Chinese commercial residential buildings based on land supply side: A case study of 35 large and medium-sized cities[J]. Modern Urban Research, 2018,10(7):12-17.]
[12]	Chi G, Liu Y, Wu Z, et al. Ghost cities analysis based on positioning data in China[J]. arXiv preprint arXiv:1510.08505, 2015,11:1-14.
[13]	唐如建, 付光辉. 南京市新建商品住宅空置率空间差异分析[J]. 城市问题, 2017(2):77-82.
	[ Tang R J, Fu G H. Analysis on spatial difference of vacancy rate of new commercial residential buildings in Nanjing[J]. City Problems, 2017(2):77-82. ]
[14]	刘云舒, 赵鹏军, 梁进社. 基于位置服务数据的城市活力研究——以北京市六环内区域为例[J]. 地域研究与开发, 2018,37(6):64-69,87.
	[ Liu Y S, Zhao P J, Liang J S. A study on urban vitality based on location service data: a case study of the area within the sixth ring road of Beijing[J]. Regional Research and Development, 2018,37(6):64-69,87. ]
[15]	Yue Y, Zhuang Y, Yeh A G O, et al. Measurements of POI-based mixed use and their relationships with neighborhood vibrancy[J]. International Journal of Geographical Information Science, 2017,31(4):658-675.
[16]	Cai J, Huang B, Song Y. Using multi-source geospatial big data to identify the structure of polycentric cities[J]. Remote Sensing of Environment, 2017,202:210-221.
[17]	Jendryke M, Balz T, McClure S C, et al. Putting people in the picture: Combining big location-based social media data and remote sensing imagery for enhanced contextual urban information in Shanghai[J]. Computers, Environment and Urban Systems, 2017,62:99-112.
[18]	王明明, 王卷乐. 基于夜间灯光与土地利用数据的山东省乡镇级人口数据空间化[J]. 地球信息科学学报, 2019,21(5):699-709.
	[ Wang M M, Wang J L. Spatialization of township-level population based on nighttime light and land use data in Shandong Province[J]. Journal of Geo-information Science, 2019,21(5):699-709. ]
[19]	谭敏, 刘凯, 柳林, 等. 基于随机森林模型的珠江三角洲30m格网人口空间化[J]. 地理科学进展, 2017,36(10):1304-1312.
	[ Tan M, Liu K, Liu L, et al. Spatialization of 30 m grid population in the Pearl River Delta based on random forest model[J]. Progress in Geography, 2017,36(10):1304-1312. ]
[20]	李熙, 薛翔宇. 基于波士顿矩阵的夜光遥感电力消费估算方法[J]. 武汉大学学报·信息科学版, 2018,43(12):1994-2002.
	[ Li Xi, Xue X Y. Estimation method of nighttime light images' electric power consumption based on the Boston Matrix[J]. Journal of Wuhan University(Information Science Edition), 2018,43(12):1994-2002. ]
[21]	Zhen J, Pei T, Xie S. Kriging methods with auxiliary nighttime lights data to detect potentially toxic metals concentrations in soil[J]. Science of The Total Environment, 2019,659:363-371. doi: 10.1016/j.scitotenv.2018.12.330
[22]	Duan H, Cao Z, Shen M, et al. Detection of illicit sand mining and the associated environmental effects in China's fourth largest freshwater lake using daytime and nighttime satellite images[J]. Science of the total environment, 2019,647:606-618. doi: 10.1016/j.scitotenv.2018.07.359
[23]	李德仁, 李熙. 夜光遥感技术在人道主义灾难评估中的应用[J]. 自然杂志, 2018,40(3):169-176.
	[ Li Deren, Li Xi. Use of night-time light remote sensing in humanitarian disaster evaluation[J]. Natural Journal, 2018,40(3):169-176. ]
[24]	Jendryke M, McClure S C, Balz T, et al. Monitoring the built-up environment of Shanghai on the street-block level using SAR and volunteered geographic information[J]. International Journal of Digital Earth, 2017,10(7):675-686.
[25]	Washaya P, Balz T, Mohamadi B. Coherence change-detection with sentinel-1 for natural and anthropogenic disaster monitoring in urban areas[J]. Remote Sensing, 2018,10(7):1026-1047. doi: 10.3390/rs10071026
[26]	Jiang W, He G, Long T, et al. Potentiality of using Luojia 1-01 nighttime light imagery to investigate artificial light pollution[J]. Sensors, 2018,18(9):2900-2914. doi: 10.3390/s18092900
[27]	廖明生, 王腾. 时间序列InSAR技术与应用[M]. 北京: 科学出版社, 2014.
	[ Liao M S, Wang T. Time series InSAR technology and application[M]. Beijing: Science Press, 2014. ]
[28]	Lu D, Tian H, Zhou G, et al. Regional mapping of human settlements in southeastern China with multisensor remotely sensed data[J]. Remote Sensing of Environment, 2008,112(9):3668-3679.
[29]	叶宇, 庄宇, 张灵珠, 等. 城市设计中活力营造的形态学探究——基于城市空间形态特征量化分析与居民活动检验[J]. 国际城市规划, 2016(1):26-33.
	[ Ye Y, Zhuang Y, Zhang L Z, et al. Morphological exploration of vitality creation in urban design: Based on quantitative analysis of urban spatial form characteristics and inspection of resident activities[J]. International Urban Planning, 2016(1):26-33. ]
[30]	Hillier B. Space is the machine: A configurational theory of architecture[M]. Cambridge: Cambridge University Press, 2007.
[31]	Liu X, Niu N, Liu X, et al. Characterizing mixed-use buildings based on multi-source big data[J]. International Journal of Geographical Information Science, 2018,32(4):738-756.
[32]	Jacobs J. The death and life of great american cities[M]. London: Random House LLC, 1961.
[33]	姚尧, 任书良, 王君毅, 等. 卷积神经网络和随机森林的城市房价微观尺度制图方法[J]. 地球信息科学学报, 2019,21(2):168-177. doi: 10.12082/dqxxkx.2019.180508
	[ Yao Y, Ren S L, Wang J Y, et al. Mapping the fine-scale housing price distribution by integrating a convolutional neural network and random forest[J]. Journal of Geo-information Science, 2019,21(2):168-177. ] doi: 10.12082/dqxxkx.2019.180508
[34]	Li K, Chen Y, Li Y. The random forest-based method of fine-resolution population spatialization by using the international space station nighttime photography and social sensing data[J]. Remote Sensing, 2018,10(10):1650-1668. doi: 10.3390/rs10101650

融合多源大数据的武汉城市空置区域评估与分析

The Assessment and Analysis of the Phenomenon of Vacancy within Wuhan City Using Multi-Source Datasets

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 34

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	应申, 窦小影, 徐雅洁, 苏俊如, 李霖. 新型冠状病毒肺炎疫情可视化进展与分析[J]. 地球信息科学学报, 2021, 23(2): 211-221.
[2]	赵鹏军, 曹毓书. 基于多源地理大数据与机器学习的地铁乘客出行目的识别方法[J]. 地球信息科学学报, 2020, 22(9): 1753-1765.
[3]	孙阳, 刘新, 苏亚聪, 徐爽, 纪兵, 张志杰. 基于夜间灯光数据估算安徽省县级尺度城镇化水平[J]. 地球信息科学学报, 2020, 22(9): 1837-1847.
[4]	蒲东川, 王桂周, 张兆明, 牛雪峰, 何国金, 龙腾飞, 尹然宇, 江威, 孙嘉悦. 基于独立成分分析和随机森林算法的城镇用地提取研究[J]. 地球信息科学学报, 2020, 22(8): 1597-1606.
[5]	朱守杰, 杜世宏, 李军, 商硕硕, 杜守基. 融合多源空间数据的城镇人口分布估算[J]. 地球信息科学学报, 2020, 22(8): 1607-1616.
[6]	李婉, 牛陆, 陈虹, 吴骅. 基于随机森林算法的地表温度鲁棒降尺度方法[J]. 地球信息科学学报, 2020, 22(8): 1666-1678.
[7]	张佰发, 苗长虹, 宋雅宁, 王娟娟. 一种DMSP/OLS稳定夜间灯光影像中国区域的校正方法[J]. 地球信息科学学报, 2020, 22(8): 1679-1691.
[8]	毛亚萍, 房世峰. 基于机器学习的参考作物蒸散量估算研究[J]. 地球信息科学学报, 2020, 22(8): 1692-1701.
[9]	闫庆武, 厉飞, 李玲. 基于2种夜间灯光影像亮度修正指数的城市建成区提取研究[J]. 地球信息科学学报, 2020, 22(8): 1714-1724.
[10]	赵忠旭, 张燕杰, 潘影, 武俊喜, 李振男. 夜间灯光数据支持下西藏人类活动强度变化对生态系统调节服务的影响[J]. 地球信息科学学报, 2020, 22(7): 1544-1554.
[11]	崔成, 任红艳, 赵璐, 庄大方. 基于街景影像多特征融合的广州市越秀区街道空间品质评估[J]. 地球信息科学学报, 2020, 22(6): 1330-1338.
[12]	曹元晖, 刘纪平, 王勇, 王良杰, 吴文周, 苏奋振. 基于POI数据的城市建筑功能分类方法研究[J]. 地球信息科学学报, 2020, 22(6): 1339-1348.
[13]	贾涛, 杨仕浩, 李欣, 鄢鹏高, 喻雪松, 罗希, 陈凯. 武汉居民建筑物碳排放反演计算和时空分析[J]. 地球信息科学学报, 2020, 22(5): 1063-1072.
[14]	董鹤松, 李仁杰, 李建明, 李帅. 基于DMSP-OLS与NPP-VIIRS整合数据的中国三大城市群城市空间扩展时空格局[J]. 地球信息科学学报, 2020, 22(5): 1161-1174.
[15]	柳林, 梁斯毅, 宋广文. 基于潜在受害者动态时空分布的街面接触型犯罪研究[J]. 地球信息科学学报, 2020, 22(4): 887-897.