
测绘学报 ›› 2023, Vol. 52 ›› Issue (3): 464-477.doi: 10.11947/j.AGCS.2023.20210514
孙立坚1, 马志勇1,2, 常迎辉1,3, 郭庆胜4, 张玉1
收稿日期:2021-09-09
修回日期:2022-01-12
发布日期:2023-04-07
通讯作者:
张玉
E-mail:zhangyu@casm.ac.cn
作者简介:孙立坚(1977-),男,博士,副研究员,研究方向为自然资源空间计算。E-mail:sunlj@casm.ac.cn
基金资助:SUN Lijian1, MA Zhiyong1,2, CHANG Yinghui1,3, GUO Qingsheng4, ZHANG Yu1
Received:2021-09-09
Revised:2022-01-12
Published:2023-04-07
Supported by:摘要: 城市是人口和资源在时空上的聚集,昼夜人群空间集聚是其中最基本、最主要、最频繁的人口时空变化模式,是理解城市复杂演变的基础微观机制。由于缺乏先进的技术手段,往往无法真实地记录和量测城市人口的个体流动与整体趋势,人们对城市昼夜人口空间集聚的了解仍然十分有限。本文基于城市人群时间地理特征,从城市大数据中挖掘并模拟城市人群的社会时空行为,在此基础上计算并分析城市昼夜人口集聚时空分布。对武汉市主城区的昼夜人口集聚分析表明:①人口密度与分布面积具有幂律关系,符合Clark模型;②傅里叶变换可有效地揭示人口分布空间变化的主方向和延伸程度;③中心城区夜间人口较白天人口分布更为集聚,研究区昼夜间人口流动主要以城区内部流动为主。归纳出城市昼夜人口集聚差异具有5种模式:交通线、三明治、光晕、椒盐和单体模式,这些模式与城市演化及空间格局有密切关系,其中交通线模式是研究区人口昼夜差异最大的一种模式。
中图分类号:
孙立坚, 马志勇, 常迎辉, 郭庆胜, 张玉. 人群时空行为与城市昼夜人口空间集聚分析——以武汉市为例[J]. 测绘学报, 2023, 52(3): 464-477.
SUN Lijian, MA Zhiyong, CHANG Yinghui, GUO Qingsheng, ZHANG Yu. Analyzing spatial and temporal dynamics of crowds and spatial agglomeration variations between the daytime and nighttime population within city—a case study in Wuhan[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(3): 464-477.
| [1] JHAMBA T, JURAN S, JONES M, et al. UNFPA strategy for the 2020 round of population and housing censuses (2015-2024)[J]. Statistical Journal of the IAOS, 2020,36(1):43-50. [2] 陆杰华. 人口负增长时代:特征、风险及其应对策略[J]. 社会发展研究, 2019, 6(1):21-32, 242. LU Jiehua. The era of negative population growth:characteristics, risks and strategies[J]. Journal of Social Development, 2019, 6(1):21-32, 242. [3] ZHAO Qing, PEPE A, DEVLIN A, et al. Impact of Sea-Level-Rise and human activities in coastal regions:an overview[J]. Journal of Geodesy and Geoinformation Science, 2021,4(1):124-143. [4] WIRTH L. Urbanism as a way of life[M]. Chicago:Chicago University Press, 1964. [5] WU Shuosheng, QIU Xiaomin, WANG Le. Population estimation methods in GIS and remote sensing:a review[J]. GIScience & Remote Sensing, 2005, 42(1):80-96. [6] 李素, 庄大方. 基于RS和GIS的人口估计方法研究综述[J]. 地理科学进展, 2006, 25(1):109-121. LI Su, ZHUANG Dafang. A review on RS- and GIS-based population estimation methods[J]. Progress in Geography, 2006, 25(1):109-121. [7] 冯甜甜. 基于高分辨率遥感数据的城市精细尺度人口估算研究[D]. 武汉:武汉大学, 2010. FENG Tiantian. Urban small area population estimation based on high-resolution remote sensing data[D]. Wuhan:Wuhan University, 2010. [8] FISHER P F, LANGFORD M. Modelling the errors in areal interpolation between zonal systems by Monte Carlo simulation[J]. Environment and Planning A:Economy and Space, 1995, 27(2):211-224. [9] LANGFORD M. Obtaining population estimates in non-census reporting zones:an evaluation of the 3-class dasymetric method[J]. Computers, Environment and Urban Systems, 2006,30 (2):161-180. [10] NORBECK S. The law of allometric growth[M]. Michigan:University of Michigan,1965. [11] HARDIN P J, JACKSON M W, JENSEN R R. Modelling housing unit density from land cover metrics:a Midwestern US example[J]. Geocarto International, 2008, 23(5):393-411. [12] LO C P. Population estimation using geographically weighted regression[J]. GIScience & Remote Sensing, 2008, 45(2):131-148. [13] ZHANG Junlin, XU Wei, QIN Lianjie, et al. Spatial distribution estimates of the urban population using DSM and DEM data in China[J]. ISPRS International Journal of Geo-Information, 2018, 7(11):435. [14] LI Guiying, WENG Qihao. Fine-scale population estimation:how Landsat ETM+ imagery can improve population distribution mapping[J]. Canadian Journal of Remote Sensing, 2010, 36(3):155-165. [15] BAKILLAH M, LIANG S, MOBASHERI A, et al. Fine-resolution population mapping using OpenStreetMap points-of-interest[J]. International Journal of Geographical Information Science, 2014, 28(9):1940-1963. [16] 卓莉, 黄信锐, 陶海燕, 等. 基于多智能体模型与建筑物信息的高空间分辨率人口分布模拟[J]. 地理研究, 2014, 33(3):520-531. ZHUO Li, HUANG Xinrui, TAO Haiyan, et al. High spatial resolution population distribution simulation based on building information and multi-agent[J]. Geographical Research, 2014, 33(3):520-531. [17] LI Xiaoma, ZHOU Weiqi. Dasymetric mapping of urban population in China based on radiance corrected DMSP-OLS nighttime light and land cover data[J]. Science of the Total Environment, 2018, 643:1248-1256. [18] SUN Weichao, ZHANG Xia, WANG Nan, et al. Estimating population density using DMSP-OLS night-time imagery and land cover data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(6):2674-2684. [19] 戚伟, 李颖, 刘盛和, 等. 城市昼夜人口空间分布的估算及其特征:以北京市海淀区为例[J]. 地理学报, 2013, 68(10):1344-1356. QI Wei, LI Ying, LIU Shenghe, et al. Estimation of urban population at daytime and nighttime and analyses of their spatial pattern:a case study of Haidian District, Beijing[J]. Acta Geographica Sinica, 2013, 68(10):1344-1356. [20] 江晴晴. 城市昼夜人口空间分布模拟方法:以南京市建邺区为例[D]. 南京:东南大学, 2015. JIANG Qingqing. Modeling spatial distribution of urban population at daytime and nighttime:a case study of Jianye district, Nanjing[D]. Nanjing:Southeast University, 2015. [21] 罗阳欢, 祝善友, 张桂欣, 等. 南京市秦淮区昼夜人口空间分布估算与分析[J]. 长江流域资源与环境, 2018, 27(5):1020-1030. LUO Yanghuan, ZHU Shanyou, ZHANG Guixin, et al. Estimation and analysis of spatial distribution of urban population during the daytime and nighttime in Qinhuai district of Nanjing[J]. Resources and Environment in the Yangtze Basin, 2018, 27(5):1020-1030. [22] 杨松. 基于建筑空间的城市昼夜人口时空分布研究[D]. 成都:西南石油大学, 2019. YANG Song. Study on spatial and temporal distribution of urban day and night population based on architectural space[D]. Chengdu:Southwest Petroleum University, 2019. [23] 陈慕琳. 基于互联网挖掘信息及建筑数据估算的精细尺度昼夜人口空间格局分析[D]. 武汉:华中师范大学, 2019. CHEN Mulin. Spatial distribution analysis of fine scale daytime and nighttime population based on the Internet mining information and building data estimation[D]. Wuhan:Central China Normal University, 2019. [24] NEMESKAL J, OUŘEDNÍČEK M, POSPÍILOVÁ L. Temporality of urban space:daily rhythms of a typical week day in the Prague metropolitan area[J]. Journal of Maps, 2020, 16(1):30-39. [25] 武汉市统计局. 武汉统计年鉴(2018)[M].北京:中国统计出版社, 2019. Wuhan Municipal Bureau of Statistics. Wuhan Statistical Yearbook(2018)[M]. Beijing:China Statistics Press. 2019. [26] BOEING G. Estimating local daytime population density from census and payroll data[J]. Regional Studies, Regional Science, 2018, 5(1):179-182. [27] 罗名海,刘艳芳,詹庆明,等. 武汉市地理国情监测与城市协调发展研究:2019[M].北京:测绘出版社, 2019. LUO Minghai, LIU Yanfang, ZHAN Qingming, et al. Wuhan city geographical national conditions monitoring and urban coordinated development research:2019[M].Beijing:Surveying and Mapping Press, 2019. [28] 极光大数据. 2018年中国城市通勤研究报告[R]. 深圳:极光大数据,2019. Aurora Big Data. 2018 China Urban Commuter Research Report[R]. Shenzhen:Aurora Big Data, 2019. [29] COX K R, GOLLEDGE R G. Behavioral problems in geography:a symposium[D]. Evanston:Northwestern University Press, 1969. [30] ORBAN K, EDBERG A K, ERLANDSSON L K. Using a time-geographical diary method in order to facilitate reflections on changes in patterns of daily occupations[J]. Scandinavian Journal of Occupational Therapy, 2012, 19(3):249-259. [31] 李燕, 张艳艳. 城乡青少年智能手机使用现状调查与分析[J]. 济宁学院学报, 2020, 41(5):60-65. LI Yan, ZHANG Yanyan. Survey and analysis of the current situation of the use of smart-phone by urban and rural adolescents[J]. Journal of Jining University, 2020, 41(5):60-65. [32] 柴祥德. 中小学生使用智能手机现象及学校管理思考[J]. 数字教育, 2016, 2(3):69-72. CHAI Xiangde. Thoughts on the phenomenon of primary and secondary school students' using smart phones and school management[J]. Digital Education, 2016, 2(3):69-72. [33] BELLONE F, CUNNINGHAM R. All roads lead to center Laxton[J]. Journal of Economic Integration, 1993,13(3):47-52. [34] 白雪. 1984-2013年中国经济重心、人口重心轨迹演变及机制探讨[J]. 热带地理, 2015, 35(5):762-769. BAI Xue. Evolution and mechanism of economic and population gravity center in China during 1984-2013[J]. Tropical Geography, 2015, 35(5):762-769. [35] CLARK C. Urban population densities[J]. Journal of the Royal Statistical Society Series A (General), 1951, 114(4):490. [36] TANNER J C. Factors affecting the amount of travel[M]. London:H. M. Stationery Office, 1961. [37] SHERRATT G G. A model for general urban growth[M]. Oxford:Pergamon Press,1960. [38] NEWLING, B. The spatial variation of urban population densities[J]. Geographical Review, 1969,59:242-252. [39] 吕安民, 李成名, 林宗坚, 等. 人口密度的空间连续分布模型[J]. 测绘学报, 2003, 32(4):344-348. LÜ Anmin, LI Chengming, LIN Zongjian, et al. Spatial continuous surface model of population density[J]. Acta Geodaetica et Cartographic Sinica, 2003, 32(4):344-348. [40] 王冰. 基于遥感和GIS的高分辨率城市人口密度模拟:以重庆市北碚城区为例[D]. 重庆:西南大学, 2010. WANG Bing. Simulation of high-resolution urban population density based on remote sensing and GIS:a case study in Beibei District, Chongqing[D]. Chongqing:Southwest University, 2010. [41] 张子民, 周英, 李琦, 等. 城市局域动态人口估算方法与模拟应用[J]. 地球信息科学学报, 2010, 12(4):503-509. ZHANG Zimin, ZHOU Ying, LI Qi, et al. An estimation method of dynamic population within an urban local area[J]. Journal of Geo-Information Science, 2010, 12(4):503-509. |
| [1] | 李鹏, 张家涵, 汪志翰, 王厚杰, 李振洪. 潮间带地形重建方法综述:现状、挑战与趋势[J]. 测绘学报, 2026, 55(4): 571-587. |
| [2] | 付波霖, 黄柯越, 杨艳丽, 孙伟伟, 王朝茵. 基于实测全谱段高光谱数据的多场景红树林土壤光谱响应特性解析及土壤有机碳含量反演[J]. 测绘学报, 2026, 55(4): 604-617. |
| [3] | 吴岚昕, 彭江涛, 孙伟伟, 杨冰. 面向海岸带湿地高光谱遥感的欧拉映射与互补特征建模变化检测方法[J]. 测绘学报, 2026, 55(4): 618-631. |
| [4] | 高二涛, 刘静, 李淑瑾, 周国清, 付波霖, 李淑娴. 光学遥感与SAR协同的茅尾海潮滩多维时空演变监测与分析[J]. 测绘学报, 2026, 55(4): 632-646. |
| [5] | 徐豪, 徐南, 辛会超, 马跃, 涂伟, 李清泉. 基于ICESat-2卫星高度计数据的潮间带地形光子提取方法[J]. 测绘学报, 2026, 55(4): 658-672. |
| [6] | 布金伟, 刘淑慧, 徐顺双, 向彤粟, 汪秋兰, 籍超颖, 左小清. 星载GNSS-R全球海浪波周期估计的经验模型构建[J]. 测绘学报, 2026, 55(4): 684-697. |
| [7] | 杨泽鑫. 基于点云数据的建筑物多细节层次模型重建关键技术研究[J]. 测绘学报, 2026, 55(4): 755-755. |
| [8] | 姚永祥. 基于广义相似性特征的多模态遥感影像稳健匹配[J]. 测绘学报, 2026, 55(4): 757-757. |
| [9] | 续东. 基于多源三维底座数据的全局定位关键技术研究[J]. 测绘学报, 2026, 55(4): 760-760. |
| [10] | 叶晨鸣, 康志忠, 才谨豪, 左秉正, 邵帅, 李彦. 形态特征引导的月表地理实体实景三维建模方法[J]. 测绘学报, 2026, 55(3): 525-535. |
| [11] | 王美莲. 基于手持激光雷达点云的树种分类及枝叶分离研究[J]. 测绘学报, 2026, 55(3): 569-569. |
| [12] | 彭代锋, 刘雪莲, 鲁梦飞, 管海燕. 基于多尺度跨模态特征融合的异源遥感影像洪水变化检测[J]. 测绘学报, 2026, 55(2): 328-343. |
| [13] | 胡浩鹏, 吴杭彬, 战仕浩, 温在豪, 刘春. 视觉点云质量优化支持的道路杆状物变化检测[J]. 测绘学报, 2026, 55(2): 344-358. |
| [14] | 余东行. 高分辨率遥感影像场景与目标识别技术研究[J]. 测绘学报, 2026, 55(2): 377-377. |
| [15] | 韩斌, 黄欣, 李丰毅, 卢晓珍. 一种双编码器自适应特征融合的SAR图像水体分割网络[J]. 测绘学报, 2026, 55(1): 101-113. |
| 阅读次数 | ||||||
|
全文 |
|
|||||
|
摘要 |
|
|||||