基于多源数据的北京市朝阳区人口时空格局评估与预测
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摘要
城市人口分布与活动呈现高度的时空动态变化,掌握人口时空变化特征并进行未来预测,对于精准人口评估、有效的政策措施制定、实时的人口预警与调控等具有重要意义。本研究利用以手机信令数据为主的多源时空数据,首先利用地理探测q统计进行探索性数据分析,其次结合贝叶斯模型进行北京市朝阳区居住人口的时空变化探究及时空预测,以期达到对朝阳区人口的动态评估与预测。首先,选用地理探测q统计进行空间异质性探测,用贝叶斯时空层次模型探究基于手机信令数据推算的北京市朝阳区居住人口的总体空间效应、总体时间效应以及局部变化趋势;其次,选用贝叶斯高斯预测过程模型,基于朝阳区各街乡的居住人口及相关人口影响因子数据进行朝阳区各街乡2017年12月的居住人口预测。时空探究表明:朝阳区居住人口在空间上存在完美空间分异,整体呈现沿环路由内向外递增的空间分布格局,整体时间趋势表现为增长,各街乡局部时间变化趋势呈现一定差异。预测的空间分布与实测空间分布整体一致,精度较高,各街乡预测精度不一。结果表明基于贝叶斯理论的时空层次模型和高斯预测过程模型可以为多源时空数据下的多尺度精准识别与人口时空模式挖掘提供有效的方法支撑。
Urban population distribution and activities are always the hot research topics. Identifying the spatialtemporal variation and predicting future trends are of great significance for estimating population accurately,making policy effectively, and warning of population booming timely. With the availability of data and the development of data processing technique, multisource data with both spatial and temporal features, such as mobile signaling data, have been used in population studies. In this paper, q-statistic was firstly applied as an exploratory analysis, then Bayesian spatial-temporal models were used to evaluate patterns of urban population and make prediction of future trends. The Chaoyang, Beijing in 2017 was selected as empirical study of this model. The spatially stratified heterogeneity was detected by q-statistic in Geodetector firstly. Then we explored the overall spatial variation, overall time trend and the departures of the local trends from the overall trend of resident population in Chaoyang by use of Bayesian spatial-temporal hierarchical model. Secondly, we applied Bayesian Gaussian predictive process to predict the resident population in December of 2017 by incorporating other relevant influential factors. The results show the perfect spatial stratified heterogeneity for resident population in Chaoyang, and the overall spatial variation demonstrates an increasing trend of population from center to the outside along the main ring road in Beijing. The overall time trend is still growing all over Chaoyang district, while the local trends, which departure from the overall trend of resident population, are different between each sub-districts in Chaoyang. Moreover, the spatial distribution of predicted resident population shows a high consistency with the observed resident population, and the prediction accuracy can be well accepted on the scale of Chaoyang district. However, prediction accuracy shows obvious difference on scale of sub-districts, with the worst prediction accuracy in the capital airport area. These findings show that Bayesian hierarchical model and Bayesian Gaussian predictive process are reliable in empirical study of population evaluation and prediction by effective application of multisource spatial-temporal data. Researches in this paper can be an excellent theoretical and practical support for mining multisource spatial-temporal data and assisting multiscale analysis with Bayesian spatial-temporal model, and provide an important basis for population controlling and early warning in urban population management.
Urban population distribution and activities are always the hot research topics. Identifying the spatialtemporal variation and predicting future trends are of great significance for estimating population accurately,making policy effectively, and warning of population booming timely. With the availability of data and the development of data processing technique, multisource data with both spatial and temporal features, such as mobile signaling data, have been used in population studies. In this paper, q-statistic was firstly applied as an exploratory analysis, then Bayesian spatial-temporal models were used to evaluate patterns of urban population and make prediction of future trends. The Chaoyang, Beijing in 2017 was selected as empirical study of this model. The spatially stratified heterogeneity was detected by q-statistic in Geodetector firstly. Then we explored the overall spatial variation, overall time trend and the departures of the local trends from the overall trend of resident population in Chaoyang by use of Bayesian spatial-temporal hierarchical model. Secondly, we applied Bayesian Gaussian predictive process to predict the resident population in December of 2017 by incorporating other relevant influential factors. The results show the perfect spatial stratified heterogeneity for resident population in Chaoyang, and the overall spatial variation demonstrates an increasing trend of population from center to the outside along the main ring road in Beijing. The overall time trend is still growing all over Chaoyang district, while the local trends, which departure from the overall trend of resident population, are different between each sub-districts in Chaoyang. Moreover, the spatial distribution of predicted resident population shows a high consistency with the observed resident population, and the prediction accuracy can be well accepted on the scale of Chaoyang district. However, prediction accuracy shows obvious difference on scale of sub-districts, with the worst prediction accuracy in the capital airport area. These findings show that Bayesian hierarchical model and Bayesian Gaussian predictive process are reliable in empirical study of population evaluation and prediction by effective application of multisource spatial-temporal data. Researches in this paper can be an excellent theoretical and practical support for mining multisource spatial-temporal data and assisting multiscale analysis with Bayesian spatial-temporal model, and provide an important basis for population controlling and early warning in urban population management.
引文
[1]刘瑜,肖昱,高松,等.基于位置感知设备的人类移动研究综述[J].地理与地理信息科学,2011,27(4):8-13.[Liu Y,Xiao Y, Gao S, et al. A review of human mobility research based on location aware devices[J]. Geography and Geo-information Science, 2011,27(4):8-13.]
[2]郭璨,甄峰,朱寿佳.智能手机定位数据应用于城市研究的进展与展望[J].人文地理,2014,29(6):18-23.[Guo C,Zhen F, Zhu S J. Progress and prospect of the application of amart phone lbs data in urban researches[J]. Human Geography, 2014,29(6):18-23.]
[3] Vieira M R, Inez V F I, Oliver N, et al. Characterizing dense urban areas from mobile phone-call data:discovery and social dynamics[C]. Proceedings of 2010 IEEE Second International Conference, 2010:241-248.
[4]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.]
[5] Deville P, Linard C, Martin S, et al. Dynamic population mapping using mobile phone data[J]. Proceedings of the National AcademyofSciences,2014,111(45):15888-15893.
[6]曹劲舟,涂伟,李清泉,等.基于大规模手机定位数据的群体活动时空特征分析[J].地球信息科学学报,2017,19(4):467-474.[Cao J Z,Tu W, LI Q Q, et al. Spatio-temporal analysis of aggregated human activities based on massive mobile phone tracking data[J]. Journal of Geo-information Science, 2017,19(4):467-474.]
[7]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 populationdistribution using cell phone signaling data[J]. Geographical Research, 2017,36(5):972-984.]
[8]孙斌栋,魏旭红.上海都市区就业——人口空间结构演化特征[J].地理学报,2014,69(6):747-758.[Sun B D, Wei X H. Spatial distribution and structure evolution of employment and population in Shanghai metropolitan area[J].Acta Geographica Sinica, 2014,69(6):747-758.]
[9] Lee R D, Tuljapurkar S. Stochastic population forecasts for the United States:Beyond high, medium, and low[J].Publications of the American Statistical Association,1994,89(428):1175-1189.
[10] Karlsson A. Statistical demography and forecasting[J].Psychometrika, 2007,72(2):273-274.
[11] Raftery A E, Li N,?ev?íkováH, et al. Inaugural article by a recently elected academy member:Bayesian probabilistic population projections for all countries[J]. Proceedings of the National Academy of Sciences of the United States of America, 2012,109(35):13915-13921.
[12]顾川.山东省人口预测与分析[D].济南:山东师范大学,2017.[Gu C. Prediction and analysis of population in Shandong[D]. Jinan:Shandong Normal University, 2017.]
[13]仰园.全面二孩政策下中国人口总量的预测[D].合肥:中国科学技术大学,2017.[Yang Y. Prediction of China's population based on the universal two-child policy[D]. Hefei:University of Science and Technology of China,2017.]
[14]安亚革.基于人口生育政策变动的中国人口预测分析[D].沈阳:辽宁大学,2017.[An Y G. Analysis of chinese population prediction based on the population birth policy change[D]. Shenyang:Liaoning University, 2017.]
[15] Yue T X, Wang Y A, Chen S P, et al. Numerical simulation of population distribution in China[J]. Population&Environment, 2003,25(2):141-163.
[16]廖一兰,王劲峰,孟斌,等.人口统计数据空间化的一种方法[J].地理学报,2007,62(10):1110-1119.[Liao Y L,Wang J F, Meng B, et al. A method of spatialization of statistical population[J]. Acta Geographica Sinica, 2007,62(10):1110-1119.]
[17] Zhang J, Zheng Y, Qi D, et al. DNN-based prediction model for spatio-temporal data[C]. ACM Sigspatial International Conference on Advances in Geographic Information Systems. ACM, 2016:92.
[18]胡曾曾,赵志龙,张贵祥.非首都功能疏解背景下北京市人口空间分布形态模拟[J].地球信息科学学报,2018,20(2):205-216.[Hu Z Z, Zhao Z L, Zhang G X. Simulation and projection of the spatial pattern of the population in Beijing under the background of non-capital function extraction[J]. Journal of Geo-information Science, 2018,20(2):205-216.]
[19]陈会宴,廖一兰,张宁旭,等.山西省原平市神经管畸形时空分析[J].地球信息科学学报,2017,19(4):502-510.[Chen H Y,Liao Y L, Zhang N X, et al. Spatial and temporal analysis of neural tube defects in Yuanping County, Shanxi Province[J]. Journal of Geo-information Science, 2017,19(4):502-510.]
[20]李世元,王学梅.贝叶斯时空模型在空间流行病学中的研究进展[J].世界最新医学信息文摘,2017,17(34):55-57.[Li S Y, Wang X M. Research Progress of Bayesian spatial temporal model in spatial epidemiology[J]. World Latest Medicine Information, 2017,17(34):55-57.]
[21] Knorr-Held L. Bayesian modelling of inseparable spacetime variation in disease risk[J]. Statistics in Medicine,2000,19(17-18):2555–2567.
[22] Li G, Haining R, Richardson S, et al. Space-time variability in burglary risk:A Bayesian spatio-temporal modelling approach[J]. Spatial Statistics, 2014,9:180-191.
[23] Law J, Quick M, Chan P. Bayesian spatio-temporal modeling for analyzing local patterns of crime over time at the small-area level[J]. Journal of Quantitative Criminology, 2014,30(1):57-78.
[24]王晓荷,田茂再.基于分层贝叶斯时空模型的雾霾天气过程计数分析[J].数理统计与管理,2017,36(6):970-982.[Wang X H, Tian M Z. Analysis of haze counts using hierarchical Bayesian spatiotemporal models[J]. Journal of Applied Statistics&Management, 2017,36(6):970-982.]
[25]梅波,田茂再.贝叶斯时空分位回归模型及其对北京PM2.5浓度的研究[J].统计研究,2016,33(12):91-100.[Mei Bo,Tian M Z. Bayesian spatio-temporal quantile regression model and its application in PM2.5concentration in Beijing[J]. Statistical Research, 2016,33(12):91-100.]
[26] Bakar, Shuvo K. Bayesian analysis of daily maximum ozone levels[D]. Southampton:University of Southampton, 2012.
[27] Sarto S D, Ranalli M G, Bakar K S, et al. Bayesian spatiotemporal modeling of urban air pollution dynamics[C].Joint Meeting Metma Vii-Graspa, 2014.
[28]伍维模,王家强,曹琦,等.塔里木盆地北缘盐土有机碳含量的贝叶斯地统计预测[J].应用生态学报,2017,28(2):439-448.[Wu W M, Wang J Q, Cao Q, et al. Bayesian geostatistical prediction of soil organic carbon contents of solonchak soils in northern Tarim Basin, Xinjiang, China[J]. Chinese Journal of Applied Ecology, 2017,28(2):439-448.]
[29]李俊明.基于Bayesian层次时空模型的我国老龄化分析与预测[J].统计研究,2016,33(8):89-94.[Li J M. Spacetime variation of Chinese aging based on Bayesian hierarchy spatio-temporal model[J]. Statistical Research, 2016,33(8):89-94.]
[30]李原.贝叶斯层次时空模型在省际人口流入分析中的应用[J].统计与决策,2018(6):73-77.[Li Y. The application of Bayesian hierarchy spatio-temporal model in analyzing population inflows in provinces[J]. Statistics&Decision, 2018(6):73-77.]
[31]王劲峰,徐成东.地理探测器:原理与展望[J].地理学报,2017,72(1):116-134.[Wang J F, Xu C D. Geodetector:Principle and prospective[J]. Acta Geographica Sinica,2017,72(1):116-134.]
[32] Haining R. Spatial data analysis:Theory and practice[M].UK:Cambridge University Press, 2003:307-377.
[33] Bakar K S, Sahu S K. spTimer:spatio-temporal Bayesian modeling using R[J]. Journal of Statistical Software,2015,63(15):1-32.
[34] Smith A F M, Roberts G O. Bayesian computation via the Gibbs sampler and related Markou chain Monto Carlo methods[J]. Journal of the Royal Statistical Society Series B, 1993,55(1):3-23.
[35] Dryden I L, Kent J T. 13 Bayesian forecasting using spatiotemporal models with applications to ozone concentration levels in the Eastern United States[M]. Geometry:Driven Statistics John Wiley&Sons, 2015.
[36] Sahu S K, Gelfand A E, Holland D M. High resolution spacetime ozone modeling for assessing trends[J]. Publications of the American Statistical Association, 2007,102(480):1221-1234.
[37] Sahu S K, Bakar K S. Hierarchical Bayesian auto-regressive models for large space time data with applications to ozone concentration modelling by Sujit Kumar Sahu and Khandoker Shuvo Bakar:Rejoinder[J]. Applied Stochastic Models in Business&Industry, 2012,28(5):395-415.
[38]北京市交通委:春节期间在京人口减少约848万[ED/OL].[2017/2/2]. http://www.xinhuanet.com/2017-02/02/c_1120400764.htm[Beijing Traffic Committee:population in Beijing declined about 8.48 million during the Spring Festival[ED/OL].[2017/2/2]. http://www.xinhuanet.com/2017-02/02/c_1120400764.htm.]
[39]中共北京市委北京市人民政府印发《关于率先行动改革优化营商环境实施方案》的通知[ED/OL].[2017/9/16].http://zhengce.beijing.gov.cn/library/192/33/50/438650/1283720/index.html.[Beijing Committee of the Communist Party of China The people's Government of Beijing City print and distribute the notice of《Reforming and optimizing the implementation plan for business environment are the first to act.》[ED/OL].[2017/9/16]. http://zhengce.beijing.gov.cn/library/192/33/50/438650/1283720/index.html.]
[2]郭璨,甄峰,朱寿佳.智能手机定位数据应用于城市研究的进展与展望[J].人文地理,2014,29(6):18-23.[Guo C,Zhen F, Zhu S J. Progress and prospect of the application of amart phone lbs data in urban researches[J]. Human Geography, 2014,29(6):18-23.]
[3] Vieira M R, Inez V F I, Oliver N, et al. Characterizing dense urban areas from mobile phone-call data:discovery and social dynamics[C]. Proceedings of 2010 IEEE Second International Conference, 2010:241-248.
[4]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.]
[5] Deville P, Linard C, Martin S, et al. Dynamic population mapping using mobile phone data[J]. Proceedings of the National AcademyofSciences,2014,111(45):15888-15893.
[6]曹劲舟,涂伟,李清泉,等.基于大规模手机定位数据的群体活动时空特征分析[J].地球信息科学学报,2017,19(4):467-474.[Cao J Z,Tu W, LI Q Q, et al. Spatio-temporal analysis of aggregated human activities based on massive mobile phone tracking data[J]. Journal of Geo-information Science, 2017,19(4):467-474.]
[7]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 populationdistribution using cell phone signaling data[J]. Geographical Research, 2017,36(5):972-984.]
[8]孙斌栋,魏旭红.上海都市区就业——人口空间结构演化特征[J].地理学报,2014,69(6):747-758.[Sun B D, Wei X H. Spatial distribution and structure evolution of employment and population in Shanghai metropolitan area[J].Acta Geographica Sinica, 2014,69(6):747-758.]
[9] Lee R D, Tuljapurkar S. Stochastic population forecasts for the United States:Beyond high, medium, and low[J].Publications of the American Statistical Association,1994,89(428):1175-1189.
[10] Karlsson A. Statistical demography and forecasting[J].Psychometrika, 2007,72(2):273-274.
[11] Raftery A E, Li N,?ev?íkováH, et al. Inaugural article by a recently elected academy member:Bayesian probabilistic population projections for all countries[J]. Proceedings of the National Academy of Sciences of the United States of America, 2012,109(35):13915-13921.
[12]顾川.山东省人口预测与分析[D].济南:山东师范大学,2017.[Gu C. Prediction and analysis of population in Shandong[D]. Jinan:Shandong Normal University, 2017.]
[13]仰园.全面二孩政策下中国人口总量的预测[D].合肥:中国科学技术大学,2017.[Yang Y. Prediction of China's population based on the universal two-child policy[D]. Hefei:University of Science and Technology of China,2017.]
[14]安亚革.基于人口生育政策变动的中国人口预测分析[D].沈阳:辽宁大学,2017.[An Y G. Analysis of chinese population prediction based on the population birth policy change[D]. Shenyang:Liaoning University, 2017.]
[15] Yue T X, Wang Y A, Chen S P, et al. Numerical simulation of population distribution in China[J]. Population&Environment, 2003,25(2):141-163.
[16]廖一兰,王劲峰,孟斌,等.人口统计数据空间化的一种方法[J].地理学报,2007,62(10):1110-1119.[Liao Y L,Wang J F, Meng B, et al. A method of spatialization of statistical population[J]. Acta Geographica Sinica, 2007,62(10):1110-1119.]
[17] Zhang J, Zheng Y, Qi D, et al. DNN-based prediction model for spatio-temporal data[C]. ACM Sigspatial International Conference on Advances in Geographic Information Systems. ACM, 2016:92.
[18]胡曾曾,赵志龙,张贵祥.非首都功能疏解背景下北京市人口空间分布形态模拟[J].地球信息科学学报,2018,20(2):205-216.[Hu Z Z, Zhao Z L, Zhang G X. Simulation and projection of the spatial pattern of the population in Beijing under the background of non-capital function extraction[J]. Journal of Geo-information Science, 2018,20(2):205-216.]
[19]陈会宴,廖一兰,张宁旭,等.山西省原平市神经管畸形时空分析[J].地球信息科学学报,2017,19(4):502-510.[Chen H Y,Liao Y L, Zhang N X, et al. Spatial and temporal analysis of neural tube defects in Yuanping County, Shanxi Province[J]. Journal of Geo-information Science, 2017,19(4):502-510.]
[20]李世元,王学梅.贝叶斯时空模型在空间流行病学中的研究进展[J].世界最新医学信息文摘,2017,17(34):55-57.[Li S Y, Wang X M. Research Progress of Bayesian spatial temporal model in spatial epidemiology[J]. World Latest Medicine Information, 2017,17(34):55-57.]
[21] Knorr-Held L. Bayesian modelling of inseparable spacetime variation in disease risk[J]. Statistics in Medicine,2000,19(17-18):2555–2567.
[22] Li G, Haining R, Richardson S, et al. Space-time variability in burglary risk:A Bayesian spatio-temporal modelling approach[J]. Spatial Statistics, 2014,9:180-191.
[23] Law J, Quick M, Chan P. Bayesian spatio-temporal modeling for analyzing local patterns of crime over time at the small-area level[J]. Journal of Quantitative Criminology, 2014,30(1):57-78.
[24]王晓荷,田茂再.基于分层贝叶斯时空模型的雾霾天气过程计数分析[J].数理统计与管理,2017,36(6):970-982.[Wang X H, Tian M Z. Analysis of haze counts using hierarchical Bayesian spatiotemporal models[J]. Journal of Applied Statistics&Management, 2017,36(6):970-982.]
[25]梅波,田茂再.贝叶斯时空分位回归模型及其对北京PM2.5浓度的研究[J].统计研究,2016,33(12):91-100.[Mei Bo,Tian M Z. Bayesian spatio-temporal quantile regression model and its application in PM2.5concentration in Beijing[J]. Statistical Research, 2016,33(12):91-100.]
[26] Bakar, Shuvo K. Bayesian analysis of daily maximum ozone levels[D]. Southampton:University of Southampton, 2012.
[27] Sarto S D, Ranalli M G, Bakar K S, et al. Bayesian spatiotemporal modeling of urban air pollution dynamics[C].Joint Meeting Metma Vii-Graspa, 2014.
[28]伍维模,王家强,曹琦,等.塔里木盆地北缘盐土有机碳含量的贝叶斯地统计预测[J].应用生态学报,2017,28(2):439-448.[Wu W M, Wang J Q, Cao Q, et al. Bayesian geostatistical prediction of soil organic carbon contents of solonchak soils in northern Tarim Basin, Xinjiang, China[J]. Chinese Journal of Applied Ecology, 2017,28(2):439-448.]
[29]李俊明.基于Bayesian层次时空模型的我国老龄化分析与预测[J].统计研究,2016,33(8):89-94.[Li J M. Spacetime variation of Chinese aging based on Bayesian hierarchy spatio-temporal model[J]. Statistical Research, 2016,33(8):89-94.]
[30]李原.贝叶斯层次时空模型在省际人口流入分析中的应用[J].统计与决策,2018(6):73-77.[Li Y. The application of Bayesian hierarchy spatio-temporal model in analyzing population inflows in provinces[J]. Statistics&Decision, 2018(6):73-77.]
[31]王劲峰,徐成东.地理探测器:原理与展望[J].地理学报,2017,72(1):116-134.[Wang J F, Xu C D. Geodetector:Principle and prospective[J]. Acta Geographica Sinica,2017,72(1):116-134.]
[32] Haining R. Spatial data analysis:Theory and practice[M].UK:Cambridge University Press, 2003:307-377.
[33] Bakar K S, Sahu S K. spTimer:spatio-temporal Bayesian modeling using R[J]. Journal of Statistical Software,2015,63(15):1-32.
[34] Smith A F M, Roberts G O. Bayesian computation via the Gibbs sampler and related Markou chain Monto Carlo methods[J]. Journal of the Royal Statistical Society Series B, 1993,55(1):3-23.
[35] Dryden I L, Kent J T. 13 Bayesian forecasting using spatiotemporal models with applications to ozone concentration levels in the Eastern United States[M]. Geometry:Driven Statistics John Wiley&Sons, 2015.
[36] Sahu S K, Gelfand A E, Holland D M. High resolution spacetime ozone modeling for assessing trends[J]. Publications of the American Statistical Association, 2007,102(480):1221-1234.
[37] Sahu S K, Bakar K S. Hierarchical Bayesian auto-regressive models for large space time data with applications to ozone concentration modelling by Sujit Kumar Sahu and Khandoker Shuvo Bakar:Rejoinder[J]. Applied Stochastic Models in Business&Industry, 2012,28(5):395-415.
[38]北京市交通委:春节期间在京人口减少约848万[ED/OL].[2017/2/2]. http://www.xinhuanet.com/2017-02/02/c_1120400764.htm[Beijing Traffic Committee:population in Beijing declined about 8.48 million during the Spring Festival[ED/OL].[2017/2/2]. http://www.xinhuanet.com/2017-02/02/c_1120400764.htm.]
[39]中共北京市委北京市人民政府印发《关于率先行动改革优化营商环境实施方案》的通知[ED/OL].[2017/9/16].http://zhengce.beijing.gov.cn/library/192/33/50/438650/1283720/index.html.[Beijing Committee of the Communist Party of China The people's Government of Beijing City print and distribute the notice of《Reforming and optimizing the implementation plan for business environment are the first to act.》[ED/OL].[2017/9/16]. http://zhengce.beijing.gov.cn/library/192/33/50/438650/1283720/index.html.]