基于POI数据的城市蔓延测度研究——以沈阳市为例
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摘要
研究目的:基于POI地图大数据建立一个新的城市蔓延测算方法,并以沈阳市为例探讨城市蔓延的治理途径。研究方法:借鉴LOPEZ的居住(人口)密度测算法,从公共服务和设施配置的角度出发,提取沈阳市POI(Points of Interest)和建筑单元地理大数据,利用核密度分析法、CRITIC赋权法和自然断点分类法构建基于POI的城市蔓延指数。研究结果:沈阳市二三环间是城市蔓延的重灾区,重度蔓延面积最大,而三四环间重度蔓延比例最高;方向上,形成以NE-N方位为主要蔓延翼,NE-E和SW-S为次要蔓延翼的分布格局;行政区划上,蔓延程度最高的是浑南区和于洪区。研究结论:基于POI数据的城市蔓延测算方法,数据收集难度低且能够直观地反映出中国城市蔓延城中与城郊地区的差异,可为地方政府进行城市蔓延的分区治理提供参考。
The purpose of the paper is to establish a new method for measuring urban sprawl based on the POI big data, and to discuss the paths to better governance of urban sprawl via a case of Shenyang City in China. Based on Lopez's method, the new method applies density of public services instead of density of population. In order to build the POI-based urban sprawl index, the methods of nuclear density analysis, CRITIC weighting and natural break point classification were used. We also took Shenyang city as an empirical case to examine the new method. The result shows that the district between the 2 nd and 3 rd ring roads takes up the most quantities of serious sprawl area(region-Ⅴ),while the district between the 3 rd and 4 th ring roads has the highest percentage of serious sprawl area(region-Ⅴ); the orientation of NE-N is the first main spreading wing of urban sprawl, and NE-E, SW-S are the second main spreading wings; and Hunnan and Yuhong are the two districts of the highest degree of urban sprawl. In conclusion, the urban sprawl index based on the POI data can eliminate the shortage of high-precision population spatial data, and can directly reflect the differences between urban and suburban areas, which can provide references for local government to conduct a zoning governance of urban sprawl.
The purpose of the paper is to establish a new method for measuring urban sprawl based on the POI big data, and to discuss the paths to better governance of urban sprawl via a case of Shenyang City in China. Based on Lopez's method, the new method applies density of public services instead of density of population. In order to build the POI-based urban sprawl index, the methods of nuclear density analysis, CRITIC weighting and natural break point classification were used. We also took Shenyang city as an empirical case to examine the new method. The result shows that the district between the 2 nd and 3 rd ring roads takes up the most quantities of serious sprawl area(region-Ⅴ),while the district between the 3 rd and 4 th ring roads has the highest percentage of serious sprawl area(region-Ⅴ); the orientation of NE-N is the first main spreading wing of urban sprawl, and NE-E, SW-S are the second main spreading wings; and Hunnan and Yuhong are the two districts of the highest degree of urban sprawl. In conclusion, the urban sprawl index based on the POI data can eliminate the shortage of high-precision population spatial data, and can directly reflect the differences between urban and suburban areas, which can provide references for local government to conduct a zoning governance of urban sprawl.
引文
[1]程玉鸿,卢婧.城市蔓延研究述评[J].城市发展研究,2016,23(4):45-50.
[2]秦蒙,刘修岩,仝怡婷.蔓延的城市空间是否加重了雾霾污染——来自中国PM2.5数据的经验分析[J].财贸经济, 2016(11):146-160.
[3]YAN Y, LIU X, WANG F, et al. Assessing the impacts of urban sprawl on net primary productivity using fusion of Landsat and MODIS data[J]. Science of The Total Environment, 2018, 613-614:1417-1429.
[4]WISSENHAYEKU,JAEGERJAG,SCHWICKC,et al. Measuring and assessing urban sprawl:what are the remaining options for future settlement development in Switzerland for 2030?[J]. Applied Spatial Analysis and Policy, 2011, 4(4):249-279.
[5]饶传坤,韩卫敏.我国城市蔓延研究进展与思考[J].城市规划学刊, 2011(5):55-62.
[6]TRAVISI C M, CAMAGNI R, NIJKAMP P. Impacts of urban sprawl and commuting:a modelling study for Italy[J].Journal of Transport Geography, 2010, 18(3):382-392.
[7]RABBANI G, SHAFAQI S, RAHNAMA M R. Urban sprawl modeling using statistical approach in Mashhad, northeastern Iran[J]. Modeling Earth Systems and Environment, 2018,4(1):141-149.
[8]LOPEZ R, HYNES H P. Sprawl in the 1990s:measurement,distribution and trends[J]. Urban Affairs Review, 2003,38(3):325-355.
[9]蒋芳,刘盛和,袁弘.北京城市蔓延的测度与分析[J].地理学报,2007,62(6):649-658.
[10]JAT M K, GARG P K, KHARE D. Monitoring and modelling of urban sprawl using remote sensing and GIS techniques[J]. International Journal of Applied Earth Observation&Geoinformation, 2008, 10(1):26-43.
[11]孙平军,封小平,孙弘,等. 2000—2009年长春、吉林城市蔓延特征、效应与驱动力比较研究[J].地理科学进展,2013,32(3):381-388.
[12]WILLIAM K REILLY. The Use of Land[M]. New York:Crowell, 1973:1-30.
[13]JOHN A DUTTON. New American Urbanism:Re-forming the Suburban Metropolis[M]. Milano:Skira Architecture Library, 2001:1-55.
[14][美]R.罗伯特·布鲁格曼.城市蔓延简史[M].吕晓惠,许明修,孙晶,译.北京:中国电力出版社, 2007:163-166.
[15]邓涛涛,王丹丹.中国高速铁路建设加剧了“城市蔓延”吗?——来自地级城市的经验证据[J].财经研究,2018,44(10):125-137.
[16]单葆国,邵玺,余姗,等.中国城市蔓延阶段特征及驱动因素分析[J].地球信息科学学报,2018,20(3):302-310.
[17]王家庭,臧家新,卢星辰,等.城市私人交通和公共交通对城市蔓延的不同影响——基于我国65个大中城市面板数据的实证检验[J].经济地理,2018,38(2):74-81.
[18]张琳琳,岳文泽,范蓓蕾.中国大城市蔓延的测度研究——以杭州市为例[J]. 2014,34(4):394-400.
[19]刘和涛,田玲玲,田野,等.武汉市城市蔓延的空间特征与管治[J].经济地理,2015,35(4):47-53.
[20]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.
[21]谷岩岩,焦利民,董婷,等.基于多源数据的城市功能区识别及相互作用分析[J].武汉大学学报:信息科学版,2018,43(7):1113-1121.
[22]郭洁,吕永强,沈体雁.基于点模式分析的城市空间结构研究——以北京都市区为例[J].经济地理,2015,35(8):68-74,97.
[23]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.
[24]陈吉煜,刘勇.我国城市蔓延的成因与机理研究述评[J].现代城市研究,2018,33(7):31-36,85.
[25]孙萍,唐莹,ROBERT J MASON,等.国外城市蔓延控制及对我国的启示[J].经济地理,2011,31(5):748-753.
[26]赵燕菁.城市化2.0与规划转型——一个两阶段模型的解释[J].城市规划,2017,41(3):84-93,116.
[27]刘瑞超,陈东景,路兰.土地财政对城市蔓延的影响[J].城市问题,2018(5):85-91.
[28]张卫东,石大千.基础设施建设对人口城市化水平的影响[J].城市问题,2015(11):31-37.
[29]CHU H J, LIAU C J, LIN C H, et al. Integration of fuzzy cluster analysis and kernel density estimation for tracking typhoon trajectories in the Taiwan region[J]. Expert Systems with Applications, 2012, 39(10):9451-9457.
[30]XIE Z, YAN J. Kernel density estimation of traffic accidents in a network space[J]. Computers, environment and urban systems, 2008, 32(5):396-406.
[31]DIAKOULAKI D, MAVROTAS G, PAPAYANNAKIS L.Determining objective weights in multiple criteria problems:the critic method[J]. Computers&Operations Research,1995, 22(7):763-770.
[32]陈卓,吴伟光,吴维聪,等.浙江省现代农业园区建设绩效评价及其影响因素分析——以蔬菜瓜果产业为例[J].中国农业资源与区划,2016,37(3):169-175.
[33]张惠芳,王兆文,朱斌.基于CRITIC法的医疗工作质量综合评价[J].中国卫生统计,2013,30(3):421-423.
[34]贺玉德,马祖军.基于CRITIC-DEA的区域物流与区域经济协同发展模型及评价——以四川省为例[J].软科学,2015,29(3):102-106.
[35]孙宗耀,翟秀娟,孙希华,等.基于POI数据的生活设施空间布局及配套情况研究——以济南市内五区为例[J].地理信息世界,2017,24(1):65-70.
[36]汤放华,莫文波,张曦,等.基于POI数据的公共服务设施空间分布研究[J].城市建筑,2017(27):35-39.
[37]张景奇. 1975-2011年沈阳市城市空间演变与发展政策的相关性分析[J].干旱区资源与环境,2016,30(6):43-48.
[38]朱查松,张京祥,罗震东.城市非建设用地规划主要内容探讨[J].现代城市研究,2010,25(3):32-35,80.
[39]柳英华,白光润.城市娱乐休闲设施的空间结构特征——以上海市为例[J].人文地理,2006(5):6-9.
[40]HEIDENREICH N B, SCHINDLER A, SPERLICH S.Bandwidth selection for kernel density estimation:a review of fully automatic selectors[J]. Advances in Statistical Analysis, 2013, 97(4):403-433.
[41]KING T L, THORNTON L E, BENTLEY R J, et al. The use of kernel density estimation to examine associations between neighborhood destination intensity and walking and physical activity[J]. Plos One, 2015, 10(9):e0137402.
[42]HOU J, LIU J, LI S, et al. The spatial distribution pattern analysis of city infrastructure cases of urban management[J].Journal of Geoscience and Environment Protection, 2018(6):21-42.
[43]段亚明,刘勇,刘秀华,等.基于POI大数据的重庆主城区多中心识别[J].自然资源学报,2018,33(5):788-800.
[44]HEIDENREICH N B, SCHINDLER A, SPERLICH S.Bandwidth selection for kernel density estimation:a review of fully automatic selectors[J]. Advances in Statistical Analysis, 2013, 97(4):403-433.
[2]秦蒙,刘修岩,仝怡婷.蔓延的城市空间是否加重了雾霾污染——来自中国PM2.5数据的经验分析[J].财贸经济, 2016(11):146-160.
[3]YAN Y, LIU X, WANG F, et al. Assessing the impacts of urban sprawl on net primary productivity using fusion of Landsat and MODIS data[J]. Science of The Total Environment, 2018, 613-614:1417-1429.
[4]WISSENHAYEKU,JAEGERJAG,SCHWICKC,et al. Measuring and assessing urban sprawl:what are the remaining options for future settlement development in Switzerland for 2030?[J]. Applied Spatial Analysis and Policy, 2011, 4(4):249-279.
[5]饶传坤,韩卫敏.我国城市蔓延研究进展与思考[J].城市规划学刊, 2011(5):55-62.
[6]TRAVISI C M, CAMAGNI R, NIJKAMP P. Impacts of urban sprawl and commuting:a modelling study for Italy[J].Journal of Transport Geography, 2010, 18(3):382-392.
[7]RABBANI G, SHAFAQI S, RAHNAMA M R. Urban sprawl modeling using statistical approach in Mashhad, northeastern Iran[J]. Modeling Earth Systems and Environment, 2018,4(1):141-149.
[8]LOPEZ R, HYNES H P. Sprawl in the 1990s:measurement,distribution and trends[J]. Urban Affairs Review, 2003,38(3):325-355.
[9]蒋芳,刘盛和,袁弘.北京城市蔓延的测度与分析[J].地理学报,2007,62(6):649-658.
[10]JAT M K, GARG P K, KHARE D. Monitoring and modelling of urban sprawl using remote sensing and GIS techniques[J]. International Journal of Applied Earth Observation&Geoinformation, 2008, 10(1):26-43.
[11]孙平军,封小平,孙弘,等. 2000—2009年长春、吉林城市蔓延特征、效应与驱动力比较研究[J].地理科学进展,2013,32(3):381-388.
[12]WILLIAM K REILLY. The Use of Land[M]. New York:Crowell, 1973:1-30.
[13]JOHN A DUTTON. New American Urbanism:Re-forming the Suburban Metropolis[M]. Milano:Skira Architecture Library, 2001:1-55.
[14][美]R.罗伯特·布鲁格曼.城市蔓延简史[M].吕晓惠,许明修,孙晶,译.北京:中国电力出版社, 2007:163-166.
[15]邓涛涛,王丹丹.中国高速铁路建设加剧了“城市蔓延”吗?——来自地级城市的经验证据[J].财经研究,2018,44(10):125-137.
[16]单葆国,邵玺,余姗,等.中国城市蔓延阶段特征及驱动因素分析[J].地球信息科学学报,2018,20(3):302-310.
[17]王家庭,臧家新,卢星辰,等.城市私人交通和公共交通对城市蔓延的不同影响——基于我国65个大中城市面板数据的实证检验[J].经济地理,2018,38(2):74-81.
[18]张琳琳,岳文泽,范蓓蕾.中国大城市蔓延的测度研究——以杭州市为例[J]. 2014,34(4):394-400.
[19]刘和涛,田玲玲,田野,等.武汉市城市蔓延的空间特征与管治[J].经济地理,2015,35(4):47-53.
[20]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.
[21]谷岩岩,焦利民,董婷,等.基于多源数据的城市功能区识别及相互作用分析[J].武汉大学学报:信息科学版,2018,43(7):1113-1121.
[22]郭洁,吕永强,沈体雁.基于点模式分析的城市空间结构研究——以北京都市区为例[J].经济地理,2015,35(8):68-74,97.
[23]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.
[24]陈吉煜,刘勇.我国城市蔓延的成因与机理研究述评[J].现代城市研究,2018,33(7):31-36,85.
[25]孙萍,唐莹,ROBERT J MASON,等.国外城市蔓延控制及对我国的启示[J].经济地理,2011,31(5):748-753.
[26]赵燕菁.城市化2.0与规划转型——一个两阶段模型的解释[J].城市规划,2017,41(3):84-93,116.
[27]刘瑞超,陈东景,路兰.土地财政对城市蔓延的影响[J].城市问题,2018(5):85-91.
[28]张卫东,石大千.基础设施建设对人口城市化水平的影响[J].城市问题,2015(11):31-37.
[29]CHU H J, LIAU C J, LIN C H, et al. Integration of fuzzy cluster analysis and kernel density estimation for tracking typhoon trajectories in the Taiwan region[J]. Expert Systems with Applications, 2012, 39(10):9451-9457.
[30]XIE Z, YAN J. Kernel density estimation of traffic accidents in a network space[J]. Computers, environment and urban systems, 2008, 32(5):396-406.
[31]DIAKOULAKI D, MAVROTAS G, PAPAYANNAKIS L.Determining objective weights in multiple criteria problems:the critic method[J]. Computers&Operations Research,1995, 22(7):763-770.
[32]陈卓,吴伟光,吴维聪,等.浙江省现代农业园区建设绩效评价及其影响因素分析——以蔬菜瓜果产业为例[J].中国农业资源与区划,2016,37(3):169-175.
[33]张惠芳,王兆文,朱斌.基于CRITIC法的医疗工作质量综合评价[J].中国卫生统计,2013,30(3):421-423.
[34]贺玉德,马祖军.基于CRITIC-DEA的区域物流与区域经济协同发展模型及评价——以四川省为例[J].软科学,2015,29(3):102-106.
[35]孙宗耀,翟秀娟,孙希华,等.基于POI数据的生活设施空间布局及配套情况研究——以济南市内五区为例[J].地理信息世界,2017,24(1):65-70.
[36]汤放华,莫文波,张曦,等.基于POI数据的公共服务设施空间分布研究[J].城市建筑,2017(27):35-39.
[37]张景奇. 1975-2011年沈阳市城市空间演变与发展政策的相关性分析[J].干旱区资源与环境,2016,30(6):43-48.
[38]朱查松,张京祥,罗震东.城市非建设用地规划主要内容探讨[J].现代城市研究,2010,25(3):32-35,80.
[39]柳英华,白光润.城市娱乐休闲设施的空间结构特征——以上海市为例[J].人文地理,2006(5):6-9.
[40]HEIDENREICH N B, SCHINDLER A, SPERLICH S.Bandwidth selection for kernel density estimation:a review of fully automatic selectors[J]. Advances in Statistical Analysis, 2013, 97(4):403-433.
[41]KING T L, THORNTON L E, BENTLEY R J, et al. The use of kernel density estimation to examine associations between neighborhood destination intensity and walking and physical activity[J]. Plos One, 2015, 10(9):e0137402.
[42]HOU J, LIU J, LI S, et al. The spatial distribution pattern analysis of city infrastructure cases of urban management[J].Journal of Geoscience and Environment Protection, 2018(6):21-42.
[43]段亚明,刘勇,刘秀华,等.基于POI大数据的重庆主城区多中心识别[J].自然资源学报,2018,33(5):788-800.
[44]HEIDENREICH N B, SCHINDLER A, SPERLICH S.Bandwidth selection for kernel density estimation:a review of fully automatic selectors[J]. Advances in Statistical Analysis, 2013, 97(4):403-433.