沈阳市城市功能区分布与人口活动研究

doi:10.12082/dqxxkx.2018.180075

摘要/Abstract

摘要：

从精细尺度上研究城市功能区分布与人口活动规律对于政府和相关部门合理调节城市内部资源配置、安排城市设施布局具有重要意义。以沈阳市中心城区为例,根据核密度估计原理,基于兴趣点（POI）数据探索沈阳市中心功能区分布情况,通过解读多时相百度热力图数据,探索工作日和周末城区人口时空分布规律,即从城市人口活动和城市功能设施分布2个角度解读沈阳市中心城区空间结构与组织形式。借助SPSS 20.0分析人口热度和设施分布的相关性关系,建立多元线性回归模型。主要结论如下：① 沈阳市城市活力区呈现多中心分布模式,大多位于商业中心、金融中心或者城市功能复合中心。② 工作日人口高热区空间分布较周末更为分散,面积较大,早晚波动较大。周末人口高热区主要集中在商业中心和城市复合功能区且白天存在较大波动。③ 功能区分布与人口热度显著相关,其中生活服务设施、金融设施、交通设施、住宿设施和景点设施是工作日人口热度的主要影响因子;教育设施、餐饮设施、交通设施、金融设施和公共服务设施是周末人口热度的主要影响因子。

关键词: 沈阳市, 热力图, 空间结构, 人口活动, 多元线性回归

Abstract:

Studying the distribution of urban functional areas and the laws of population activities on a fine scale has important implications for the government and relevant departments to rationally adjust the allocation of urban internal resources and arrange the layout of urban facilities. Taking the center area of Shenyang City in Liaoning Province as research area, based on the principle of nuclear density estimation, the distribution of functional areas in downtown Shenyang is explored based on point-of-interest (POI) data, and the temporal and spatial distribution patterns of urban population on working days and weekends are explored by interpreting the multi-temporal Baidu thermogram data. This paper analyzed the spatial structure of Shenyang city center from two perspectives: the distribution of the urban physical facilities and the law of the population activities. Finally, we used SPSS to analyze the correlation between population heat and urban physical facilities and established multiple linear regression models. The results showed that: (1) Shenyang City vitality area showed a multi-center distribution model, mostly in the commercial center, financial center or urban function complex center. (2) The spatial distribution of the hot spots in the working day was more scattered than that on the weekend. The area was larger and fluctuated greatly. However, population hot spots were mainly concentrated in the commercial center and urban complex functional area and there was a large fluctuation during the daytime on weekend. (3) The distribution of functional areas was significantly correlated with the population heat. Life service facilities, financial facilities, transports, accommodation facilities and attractions are the main factors influencing the population heat on the working day. While educational institutions, transports, public service facilities, catering facilities and financial facilities are the main influential factors on weekend.

Key words: Shenyang City, Baidu thermal chart, special structure, population activities, multiple linear regression

刘彤, 周伟, 曹银贵. 沈阳市城市功能区分布与人口活动研究[J]. 地球信息科学学报, 2018, 20(7): 988-995.DOI:10.12082/dqxxkx.2018.180075

LIU Tong,ZHOU Wei,CAO Yingui. Distribution of Functional Areas and Population Activities in Shenyang City[J]. Journal of Geo-information Science, 2018, 20(7): 988-995.DOI:10.12082/dqxxkx.2018.180075

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参考文献 24

[1]	渠涛,张理茜,武占云.不同历史时期特殊事件影响下的城市空间结构演变研究——以天津市为例[J].地理科学,2014,34(6):656-663.
	[ Qu T, Zhang L X, Wu Z Y.City spatial structure evolution affected by special events in different historical periods: A case study of Tianjin[J]. Scientia Geogrephica Sinica, 2014,34(6):656-663. ]
[2]	高宜程,申玉铭,邱灵.山西省晋城市空间结构演化与重构[J].地理研究,2013,32(7):1231-1242.
	[ Gao Y C, Shen Y M, Qiu L.The evolvement and restructuring of the urban spatial structure of Jincheng City, Shanxi Province[J]. Geographical Research, 2013,32(7):1231-1242. ]
[3]	陈菁,罗家添,吴端旺.基于图谱特征的中国典型城市空间结构演变分析[J].地理科学,2011,31(11):1313-1321.
	[ Chen J, Luo J T, Wu D W.The evolution of spatial structure of typical cities in China on the basis of graph spectral features[J]. Scientia Geogrephica Sinica, 2011,31(11):1313-1321. ]
[4]	张善余. 人口地理学概论(第三版)[M].上海:华东师范大学出版社,2013.
	[ Zhang S Y.Population geography (The Third Edition)[M]. Shanghai: East China Normal University Press, 2013. ]
[5]	周春山,叶昌东.中国城市空间结构研究评述[J].地理科学进展,2013,32(7):1030-1038. doi: 10.11820/dlkxjz.2013.07.006
	[ Zhou C S, Ye C D.Progress on studies of urban spatial structure in China[J]. Progress in Geography, 2013,32(7):1030-1038. ] doi: 10.11820/dlkxjz.2013.07.006
[6]	李传斌. 西安市城市空间结构演替研究[D].西安:西北大学,2002.
	[ Li C B.Research on evolution of Xi'an's urban spatial structure[D]. Xi'an: Northwest University, 2002. ]
[7]	余颖,扈万泰.紧凑城市——重庆都市区空间结构模式研究[J].城市发展研究,2004,11(4):59-63. doi: 10.3969/j.issn.1006-3862.2004.04.014
	[ Yu Y, Hu W T.Compact city: A study on the space structure pattern of Chongqing urban area[J]. Urban Studies, 2004,11(4):59-63. ] doi: 10.3969/j.issn.1006-3862.2004.04.014
[8]	张水清,杜德斌.上海中心城区职能转移与城市空间结构优化[J].城市发展研究,2001,8(6):44-49.
	[ Zhang S Q, Du D B.Function change of Shanghai's central urban area and urban spatial restructure[J]. Urban Studies, 2001,8(6):44-49. ]
[9]	王芳,高晓路,颜秉秋.基于住宅价格的北京城市空间结构研究[J].地理科学进展,2014,33(10):1322-1331. doi: 10.11820/dlkxjz.2014.10.004
	[ Wang F, Gao X L, Yan B Q.Research on urban spatial structure in Beijing based on housing prices[J]. Progress in Geography, 2014,33(10):1322-1331. ] doi: 10.11820/dlkxjz.2014.10.004
[10]	梁辰,王诺,佟士祺,等.大连临港产业集聚与城市空间结构演变研究[J].经济地理,2012,32(8):84-90.
	[ Liang C, Wang N, Tong S Q, Liu Z B, et al.Urban spatial structure evolution under the influence of port industrial aggregation:a case study of Dalian[J]. Economic Geography, 2012,32(8):84-90. ]
[11]	钮心毅,丁亮,宋小冬.基于手机数据识别上海中心城的城市空间结构[J].城市规划学刊,2014(6):61-67.
	[ Niu X Y, Ding L, Song X D. Understanding urban spatial structure of Shanghai central city based on mobile phone data[J]. Urban Planning Forum, 2014(6):61-67. ]
[12]	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. ]
[13]	李明晓,陈洁,张恒才,等.上海市精细时空尺度人口分布估计与特征分析[J].地球信息科学学报,2017,19(6):800-807.
	[ Li M X, Chen J, Zhang H C, et al.Fine-grained population estimation and distribution characteristics in Shanghai[J]. Journal of Geo-information Science, 2017,19(6):800-807. ]
[14]	池娇,焦利民,董婷,等.基于POI数据的城市功能区定量识别及其可视化[J].测绘地理信息,2016,41(2):68-73. doi: 10.14188/j.2095-6045.2016.02.017
	[ Chi J, Jiao L M, Dong T, et al.Quantitative identification and visualization of urban functional area based on POI Data[J]. Journal of Geomatics, 2016,41(2):68-73. ] doi: 10.14188/j.2095-6045.2016.02.017
[15]	陈蔚珊,柳林,梁育填.基于POI数据的广州零售商业中心热点识别与业态集聚特征分析[J].地理研究,2016,35(4):703-716.
	[ Chen W S, Liu L, Liang Y T.Retail center recognition and spatial aggregating feature analysis of retail formats in Guangzhou based on POI data[J]. Geographical Research, 2016,35(4):703-716. ]
[16]	孙宗耀,翟秀娟,孙希华,等.基于POI数据的生活设施空间分布及配套情况研究——以济南市内五区为例[J].地理信息世界,2017,24(1):65-70.
	[ Sun Z Y, Zhai X J, Sun X H, et al.Study on spatial distribution and matching situation of living facilities based on POI—— Taking five districts of Ji'nan as a case[J]. Geomatics World, 2017,24(1):65-70. ]
[17]	康雨豪,王玥瑶,夏竹君,等.基于POI数据的武汉城市功能区划分与识别[J].测绘地理信息,2017,43(1):1-5.
	[ Kang Y H, Wang Y Y, Xia Z J, et al.Identification and classification of Wuhan urban districts based on POI[J]. Journal of Geomatics, 2017,43(1):1-5. ]
[18]	周晨,冯宇东,肖匡心,等.基于多元线性回归模型的东北地区需水量分析[J].数学的实践与认识,2014,44(1):118-123.
	[ Zhou C, Feng Y D, Xiao K X, et al.Reseaech on water requirement in northeast area based on multiple linear regression model[J]. Mathematics in Practice and Theory, 2014,44(1):118-123. ]
[19]	田兵. 多元线性回归分析及其实际应用[J].阴山学刊(自然科学),2011,25(1):16-19. doi: 10.3969/j.issn.1004-1869-B.2011.01.005
	[ Tian B.Multiple linear regression analysis and its application[J]. Yinshang Academic Journal (natural science), 2011,25(1):16-19. ] doi: 10.3969/j.issn.1004-1869-B.2011.01.005
[20]	吴康敏,张虹鸥,王洋,等.广州市多类型商业中心识别与空间模式[J].地理科学进展,2016,35(8):963-974. doi: 10.18306/dlkxjz.2016.08.005
	[ Wu K M, Zhang H O, Wang Y, et al.Identify of the multiple types of commercial center in Guangzhou and its spatial pattern[J]. Progress in Geography, 2016,35(8):963-974. ] doi: 10.18306/dlkxjz.2016.08.005
[21]	Thurstain-Goodwin M, Unwin D.Defining and delineating the central areas of towns for statistical monitoring using continuous surface representations[J]. Transactions in GIS, 2000,4(4):305-317. doi: 10.1111/1467-9671.00058
[22]	禹文豪,艾廷华.核密度估计法支持下的网络空间 POI 点可视化与分析[J].测绘学报,2015,44(1):82-90. doi: 10.11947/j.AGCS.2015.20130538
	[ Yu W H, Ai T H.The visualization and analysis of POI features under network space supported by Kernel Density Estimation. Acta Geodaetica et Cartographica Sinica, 2015,44(1):82-90. ] doi: 10.11947/j.AGCS.2015.20130538
[23]	Liu X L, Wei X J, Jiao L M, et al.Relationships between street centrality and land use intensity in Wuhan, China[J]. Journal of Urban Planning and Development, 2015,DOI: 10.2007/s11769-015. doi: 10.1061/(ASCE)UP.1943-5444.0000274
[24]	Okabe A, Satoh T, Sugihara K.A kernel density estimation method for networks, its computational method and a GIS- based tool[J]. International Journal of Geographical Information Science, 2009,23(1):7-32. doi: 10.1080/13658810802475491