空间微观模拟方法及在城市研究中的应用
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摘要
基于活动主体的城市系统微观模拟可能在未来城市研究中发挥重要作用,但其通常受到微观个体数据稀缺的限制。空间微观模拟方法(spatial microsimulation)主要基于家庭、个人等微观分析单元,通过整合不同层面的数据源,如宏观汇总层面的人口普查统计表以及微观层面的家庭活动日志调查等,合成大样本微观个体数据集,可以在精细化空间尺度上对微观个体行为进行模拟研究。该方法在城市系统微观模拟、空间分析以及政策评估等方面具有一定优势,在西方国家城市研究中的应用逐渐增多,但在国内较为缺乏。本文尝试对空间微观模拟方法的起源、三种核心算法,包括条件概率(conditional probability)、确定性加权(deterministic reweighting)以及模拟退火(simulated annealing)进行介绍,并从国际层面综述该方法在城市研究,如收入与贫困、交通出行、健康等领域中的应用,为我国相关研究的开展提供借鉴。
Individual agent-based microsimulation might be an important research direction for future urban modeling. However, possibly due to confidentiality issues, the spatially detailed microdata sets with a wide range of individual or household characteristics are usually not publicly accessible in many countries. There is a strong demand for the development of small area estimates of socio-demographics and the potential effects of policy changes, which could help the government acquire detailed information on population's attributes at a fine geographic scale, better allocate the limited resources to the most needed places, and evaluate the potential impacts of policy decisions. Using individuals or households as the basic analytical unit, spatial microsimulation can synthesize much individual-level spatial microdata for large populations through combining different data sources, such as household activity diary survey and aggregate population census tabulates. Spatial microsimulation can simulate the virtual populations in a spatial setting, and it involves three major procedures, including the construction of small area microdata, static what-if simulations for one time point and dynamic microsimulation over a period. This approach can simulate the synthetic population's behavior at fine geographic resolution, and perform different what-if simulations to explore the impacts of policy scenarios. In general, spatial microsimulation has multiple advantages for urban research, spatial analysis and policy evaluation, and thus has been increasingly applied in the fields of geography, transport, and social sciences, particularly in developed countries.However, in China, microsimulation studies has been very scarce to date, possibly due to the fact that the microsimulation development is challenging requiring a high level of programming skills, there is little publicly available software suited to microsimulation models,and there is a lack of data at an appropriate scale. This paper aims to first provide a comprehensive review of spatial microsimulation techniques, including conditional probability,deterministic reweighting, and simulated annealing, which have been widely used for creating synthetic populations in microsimulation studies. Further, this paper also reviews the recent applications of spatial microsimulation approach in urban research worldwide, focusing on income distribution and deprivation evaluation, travel behavior and transport carbon emission,and health behavior and outcomes. The paper ends with the discussion and conclusion.
Individual agent-based microsimulation might be an important research direction for future urban modeling. However, possibly due to confidentiality issues, the spatially detailed microdata sets with a wide range of individual or household characteristics are usually not publicly accessible in many countries. There is a strong demand for the development of small area estimates of socio-demographics and the potential effects of policy changes, which could help the government acquire detailed information on population's attributes at a fine geographic scale, better allocate the limited resources to the most needed places, and evaluate the potential impacts of policy decisions. Using individuals or households as the basic analytical unit, spatial microsimulation can synthesize much individual-level spatial microdata for large populations through combining different data sources, such as household activity diary survey and aggregate population census tabulates. Spatial microsimulation can simulate the virtual populations in a spatial setting, and it involves three major procedures, including the construction of small area microdata, static what-if simulations for one time point and dynamic microsimulation over a period. This approach can simulate the synthetic population's behavior at fine geographic resolution, and perform different what-if simulations to explore the impacts of policy scenarios. In general, spatial microsimulation has multiple advantages for urban research, spatial analysis and policy evaluation, and thus has been increasingly applied in the fields of geography, transport, and social sciences, particularly in developed countries.However, in China, microsimulation studies has been very scarce to date, possibly due to the fact that the microsimulation development is challenging requiring a high level of programming skills, there is little publicly available software suited to microsimulation models,and there is a lack of data at an appropriate scale. This paper aims to first provide a comprehensive review of spatial microsimulation techniques, including conditional probability,deterministic reweighting, and simulated annealing, which have been widely used for creating synthetic populations in microsimulation studies. Further, this paper also reviews the recent applications of spatial microsimulation approach in urban research worldwide, focusing on income distribution and deprivation evaluation, travel behavior and transport carbon emission,and health behavior and outcomes. The paper ends with the discussion and conclusion.
引文
[1]Orcutt G H.A new type of socio-economic system.The Review of Economics and Statistics,1957,39:116-123.
[2]Merz J.Microsimulation:A survey of principles,developments and applications.International Journal of Forecasting,1991,7:77-104.
[3]Clarke G P.Microsimulation for Urban and Regional Oolicy Analysis.London:Pion,1996.
[4]龙瀛,沈振江,毛其智.城市系统微观模拟中的个体数据获取新方法.地理学报,2011,66(3):416-426.[Long Ying,Shen Zhenjiang,Mao Qizhi.Retrieving individual attributes from aggregate dataset for urban micro-simulation:A preliminary exploration.Acta Geographica Sinica,2011,66(3):416-426.]
[5]Orcutt G,Caldwell S,Wertheimer R.Policy exploration through microanalytic simulation.The Urban Institute,Washington D.C.,1976.
[6]Orcutt G,Merz J,Quinke H,eds.Microanalytic Simulation Models to Support Social und Financral Policy.Amsterdam:North-Holland,1986.
[7]Ballas D,Clarke G,Dorling D,et al.Using geographical information systems and spatial microsimulation for the analysis of health inequalities.Health Informatics Journal,2006,12:65-79.
[8]Edwards K,Clarke G.The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds,UK:SimObesity.Social Science&Medicine,2009,69:1127-1134.
[9]Tanton R.A review of spatial microsimulation methods.International Journal of Microsimulation,2014,7(1):4-25.
[10]Birkin M,Clarke M.SYNTHESIS:A synthetic spatial information system for urban and regional analysis:Methods and examples.Environment and Planning A,1988,20:1645-1671.
[11]Birkin M,Clarke M.The generation of individual and household incomes at the small area level using synthesis.Regional Studies,1989,23:535-548.
[12]Ballas D,Clarke G.GIS and microsimulation for local labour market analysis.Computers,Environment and Urban Systems,2000,24:305-330.
[13]Brown L,Harding A.Social modelling and public policy:Application of microsimulation modelling in Australia.Journal of Artificial Societies and Social Simulation,2002,5(4):6.
[14]Vovsha P,Petersen E,Donnelly R.Microsimulation in travel demand modeling:Lessons learned from the New York best practice model.Transportation Research Record,2002,1805:68-77.
[15]黎夏,叶嘉安.基于神经网络的元胞自动机及模拟复杂土地利用系统.地理研究,2005,24(1):19-27.[Li Xia,Yeh Gar-On.Cellular automata for simulating complex land use systems using nueral networks.Geographical Research,2005,24(1):19-27.]
[16]陈彦光,周一星.细胞自动机与城市系统的空间复杂性模拟:历史、现状与前景.经济地理,2000,20(3):35-39.[Chen Yanguang,Zhou Yixing.Cellular automata and simulation of spatial complexity of urban systems:History,present situation and future.Economic Geography,2000,20(3):35-39.]
[17]龙瀛,毛其智,杨东峰,等.城市形态、交通能耗和环境影响集成的多智能体模型.地理学报,2011,66(8):1033-1044.[Long Ying,Mao Qizhi,Yang Dongfeng,et al.A multi-agent model for urban form,transportation energy consumption and environmental impact integrated simulation.Acta Geographica Sinica,2011,66(8):1033-1044.]
[18]Ballas D,Clarke G,Dorling D,et al.SimBritain:A spatial microsimulation approach to population dynamics.Population,Space and Place,2005,11:13-34.
[19]Miller E J,Douglas J,Abraham J E,et al.Microsimulating urban systems.Computers,Environment and Urban Systems,2004,28:9-44.
[20]Mannion O,Lay-Yee R,Wrapson W,et al.JAMSIM:A microsimulation modelling policy tool.Journal of Artificial Societies and Social Simulation,2012,15(1):8.
[21]Birkin M,Clarke M.Spatial microsimulation models:A review and a glimpse into the future.Population Dynamics and Projection Methods.Springer,2011.
[22]龙瀛,茅明睿,毛其智,等.大数据时代的精细化城市模拟:方法、数据和案例.人文地理,2014,29(3):7-13.[Long Ying,Mao Mingrui,Mao Qizhi,et al.Fine-scale urban modeling and its opportunities in the“big data”era:Methods,data and empirical studies.Human Geography,2014,29(3):7-13.]
[23]Hermes K,Poulsen M.A review of current methods to generate synthetic spatial microdata using reweighting and future directions.Computers,Environment and Urban Systems,2012,36:281-290.
[24]Harland K,Heppenstall A,Smith D,et al.Creating realistic synthetic populations at varying spatial scales:A comparative critique of population synthesis techniques.Journal of Artificial Societies and Social Simulation,2012,15:1-24.
[25]Voas D,Williamson P.An evaluation of the combinatorial optimisation approach to the creation of synthetic microdata.International Journal of Population Geography,2000,6:349-366.
[26]Anderson B.Estimating Small Area Income Deprivation:An Iterative Proportional Fitting Approach.Centre for Research in Economic Sociology and Innovation(CRESI)Working Paper 2011-02,University of Essex:Colchester,2011.
[27]Smith D M,Clarke G P,Harland K.Improving the synthetic data generation process in spatial microsimulation models.Environment and Planning A,2009,41:1251-1268.
[28]Williamson P,Birkin M,Rees P.The estimation of population microdata by using data from small area statistics and samples of anonymised records.Environment and Planning A,1998,30:785-816.
[29]Ballas D,Clarke G P.The local implications of major job transformations in the city:A spatial microsimulation approach.Geographical Analysis,2001,33:291-311.
[30]Knudsen D C,Fotheringham A S.Matrix comparison,goodness-of-fit,and spatial interaction modeling.International Regional Science Review,1986,10:127-147.
[31]Tanton R.Spatial microsimulation as a method for estimating different poverty rates in Australia.Population,Space and Place,2011,17:222-235.
[32]Clark S,Birkin M,Heppenstall A.Sub regional estimates of morbidities in the English elderly population.Health&Place,2014,27:176-185.
[33]Tomintz M,Clarke G,Rigby J.The geography of smoking in Leeds:Estimating individual smoking rates and the implications for the location of stop smoking services.Area,2008,40(3):341-353.
[34]Lymer S,Brown L,Yap M,et al.2001 Regional disability estimates for New South Wales,Australia,using spatial microsimulation.Applied Spatial Analasis and Policy,2008,1(2):99-116
[35]Goulias K G.Forecasting the impact of sociodemographic changes on travel demand:Experiments with a dynamic microsimulation model system.University of California Transportation Center,1992.
[36]Bhat C,Guo J,Srinivasan S,et al.Comprehensive econometric microsimulator for daily activity-travel patterns.Transportation Research Record,2004,1894:57-66.
[37]Vovsha P,Petersen E,Donnelly R.Microsimulation in travel demand modeling:Lessons learned from the New York best practice model.Transportation Research Record,2002,1805:68-77.
[38]Veldhuisen J,Kapoen L,Timmermans H.RAMBLAS:A regional planning model based on the microsimulation of daily activity travel patterns.Environment and Planning A,2000,32:427-444.
[39]Hunt J D,Kriger D S,Miller E.Current operational urban land-use-transport modelling frameworks:A review.Transport Reviews,2005,25:329-376.
[40]Waddell P.Modeling urban development for land use,transportation,and environmental planning.Journal of the American Planning Association,2002,68(3):297-314.
[41]Lovelace R,Philips I.The‘oil vulnerability’of commuter patterns:A case study from Yorkshire and the Humber,UK.Goeforum,2014,51:169-182.
[42]Yagi S,Mohammadian A K.An activity-based microsimulation model of travel demand in the Jakarta Metropolitan Area.Journal of Choice Modelling,2010,3:32-57.
[43]Ma J,Heppenstall A,Harland K,et al.Synthesising carbon emission for mega-cities:A static spatial microsimulation of transport CO2from urban travel in Beijing.Computers,Environment and Urban Systems,2014,45:78-88.
[44]Ma J,Mitchell G,Heppenstall A.Exploring transport carbon futures using population microsimulation and travel diaries:Beijing to 2030.Transportation Research Part D,2015,37:108-122.
[45]Edwards K L,Clarke G,Thomas J,et al.Internal and external validation of spatial microsimulation models:Small area estimates of adult obesity.Applied Spatial Analasis and Policy,2011,4(4):281-300.
[46]柴彦威,赵莹,刘云刚.城市地理学研究方法的进展与展望.中国科学院院刊,2011,26(4):430-435.[Chai Yanwei,Zhao Ying,Liu Yungang.Research Progress and prospect of urban geography methodologies and methods.Bulletin of Chinese Academy of Sciences,2011,26(4):430-435.]
[47]陈彦光.地理学理论研究和科学分析的一般方法探讨.地理科学,2009,29(3):316-322.[Chen Yanguang.Exploring general research method of theoretical geography with three steps.Scientia Geographica Sinica,2009,29(3):316-322.]
[2]Merz J.Microsimulation:A survey of principles,developments and applications.International Journal of Forecasting,1991,7:77-104.
[3]Clarke G P.Microsimulation for Urban and Regional Oolicy Analysis.London:Pion,1996.
[4]龙瀛,沈振江,毛其智.城市系统微观模拟中的个体数据获取新方法.地理学报,2011,66(3):416-426.[Long Ying,Shen Zhenjiang,Mao Qizhi.Retrieving individual attributes from aggregate dataset for urban micro-simulation:A preliminary exploration.Acta Geographica Sinica,2011,66(3):416-426.]
[5]Orcutt G,Caldwell S,Wertheimer R.Policy exploration through microanalytic simulation.The Urban Institute,Washington D.C.,1976.
[6]Orcutt G,Merz J,Quinke H,eds.Microanalytic Simulation Models to Support Social und Financral Policy.Amsterdam:North-Holland,1986.
[7]Ballas D,Clarke G,Dorling D,et al.Using geographical information systems and spatial microsimulation for the analysis of health inequalities.Health Informatics Journal,2006,12:65-79.
[8]Edwards K,Clarke G.The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds,UK:SimObesity.Social Science&Medicine,2009,69:1127-1134.
[9]Tanton R.A review of spatial microsimulation methods.International Journal of Microsimulation,2014,7(1):4-25.
[10]Birkin M,Clarke M.SYNTHESIS:A synthetic spatial information system for urban and regional analysis:Methods and examples.Environment and Planning A,1988,20:1645-1671.
[11]Birkin M,Clarke M.The generation of individual and household incomes at the small area level using synthesis.Regional Studies,1989,23:535-548.
[12]Ballas D,Clarke G.GIS and microsimulation for local labour market analysis.Computers,Environment and Urban Systems,2000,24:305-330.
[13]Brown L,Harding A.Social modelling and public policy:Application of microsimulation modelling in Australia.Journal of Artificial Societies and Social Simulation,2002,5(4):6.
[14]Vovsha P,Petersen E,Donnelly R.Microsimulation in travel demand modeling:Lessons learned from the New York best practice model.Transportation Research Record,2002,1805:68-77.
[15]黎夏,叶嘉安.基于神经网络的元胞自动机及模拟复杂土地利用系统.地理研究,2005,24(1):19-27.[Li Xia,Yeh Gar-On.Cellular automata for simulating complex land use systems using nueral networks.Geographical Research,2005,24(1):19-27.]
[16]陈彦光,周一星.细胞自动机与城市系统的空间复杂性模拟:历史、现状与前景.经济地理,2000,20(3):35-39.[Chen Yanguang,Zhou Yixing.Cellular automata and simulation of spatial complexity of urban systems:History,present situation and future.Economic Geography,2000,20(3):35-39.]
[17]龙瀛,毛其智,杨东峰,等.城市形态、交通能耗和环境影响集成的多智能体模型.地理学报,2011,66(8):1033-1044.[Long Ying,Mao Qizhi,Yang Dongfeng,et al.A multi-agent model for urban form,transportation energy consumption and environmental impact integrated simulation.Acta Geographica Sinica,2011,66(8):1033-1044.]
[18]Ballas D,Clarke G,Dorling D,et al.SimBritain:A spatial microsimulation approach to population dynamics.Population,Space and Place,2005,11:13-34.
[19]Miller E J,Douglas J,Abraham J E,et al.Microsimulating urban systems.Computers,Environment and Urban Systems,2004,28:9-44.
[20]Mannion O,Lay-Yee R,Wrapson W,et al.JAMSIM:A microsimulation modelling policy tool.Journal of Artificial Societies and Social Simulation,2012,15(1):8.
[21]Birkin M,Clarke M.Spatial microsimulation models:A review and a glimpse into the future.Population Dynamics and Projection Methods.Springer,2011.
[22]龙瀛,茅明睿,毛其智,等.大数据时代的精细化城市模拟:方法、数据和案例.人文地理,2014,29(3):7-13.[Long Ying,Mao Mingrui,Mao Qizhi,et al.Fine-scale urban modeling and its opportunities in the“big data”era:Methods,data and empirical studies.Human Geography,2014,29(3):7-13.]
[23]Hermes K,Poulsen M.A review of current methods to generate synthetic spatial microdata using reweighting and future directions.Computers,Environment and Urban Systems,2012,36:281-290.
[24]Harland K,Heppenstall A,Smith D,et al.Creating realistic synthetic populations at varying spatial scales:A comparative critique of population synthesis techniques.Journal of Artificial Societies and Social Simulation,2012,15:1-24.
[25]Voas D,Williamson P.An evaluation of the combinatorial optimisation approach to the creation of synthetic microdata.International Journal of Population Geography,2000,6:349-366.
[26]Anderson B.Estimating Small Area Income Deprivation:An Iterative Proportional Fitting Approach.Centre for Research in Economic Sociology and Innovation(CRESI)Working Paper 2011-02,University of Essex:Colchester,2011.
[27]Smith D M,Clarke G P,Harland K.Improving the synthetic data generation process in spatial microsimulation models.Environment and Planning A,2009,41:1251-1268.
[28]Williamson P,Birkin M,Rees P.The estimation of population microdata by using data from small area statistics and samples of anonymised records.Environment and Planning A,1998,30:785-816.
[29]Ballas D,Clarke G P.The local implications of major job transformations in the city:A spatial microsimulation approach.Geographical Analysis,2001,33:291-311.
[30]Knudsen D C,Fotheringham A S.Matrix comparison,goodness-of-fit,and spatial interaction modeling.International Regional Science Review,1986,10:127-147.
[31]Tanton R.Spatial microsimulation as a method for estimating different poverty rates in Australia.Population,Space and Place,2011,17:222-235.
[32]Clark S,Birkin M,Heppenstall A.Sub regional estimates of morbidities in the English elderly population.Health&Place,2014,27:176-185.
[33]Tomintz M,Clarke G,Rigby J.The geography of smoking in Leeds:Estimating individual smoking rates and the implications for the location of stop smoking services.Area,2008,40(3):341-353.
[34]Lymer S,Brown L,Yap M,et al.2001 Regional disability estimates for New South Wales,Australia,using spatial microsimulation.Applied Spatial Analasis and Policy,2008,1(2):99-116
[35]Goulias K G.Forecasting the impact of sociodemographic changes on travel demand:Experiments with a dynamic microsimulation model system.University of California Transportation Center,1992.
[36]Bhat C,Guo J,Srinivasan S,et al.Comprehensive econometric microsimulator for daily activity-travel patterns.Transportation Research Record,2004,1894:57-66.
[37]Vovsha P,Petersen E,Donnelly R.Microsimulation in travel demand modeling:Lessons learned from the New York best practice model.Transportation Research Record,2002,1805:68-77.
[38]Veldhuisen J,Kapoen L,Timmermans H.RAMBLAS:A regional planning model based on the microsimulation of daily activity travel patterns.Environment and Planning A,2000,32:427-444.
[39]Hunt J D,Kriger D S,Miller E.Current operational urban land-use-transport modelling frameworks:A review.Transport Reviews,2005,25:329-376.
[40]Waddell P.Modeling urban development for land use,transportation,and environmental planning.Journal of the American Planning Association,2002,68(3):297-314.
[41]Lovelace R,Philips I.The‘oil vulnerability’of commuter patterns:A case study from Yorkshire and the Humber,UK.Goeforum,2014,51:169-182.
[42]Yagi S,Mohammadian A K.An activity-based microsimulation model of travel demand in the Jakarta Metropolitan Area.Journal of Choice Modelling,2010,3:32-57.
[43]Ma J,Heppenstall A,Harland K,et al.Synthesising carbon emission for mega-cities:A static spatial microsimulation of transport CO2from urban travel in Beijing.Computers,Environment and Urban Systems,2014,45:78-88.
[44]Ma J,Mitchell G,Heppenstall A.Exploring transport carbon futures using population microsimulation and travel diaries:Beijing to 2030.Transportation Research Part D,2015,37:108-122.
[45]Edwards K L,Clarke G,Thomas J,et al.Internal and external validation of spatial microsimulation models:Small area estimates of adult obesity.Applied Spatial Analasis and Policy,2011,4(4):281-300.
[46]柴彦威,赵莹,刘云刚.城市地理学研究方法的进展与展望.中国科学院院刊,2011,26(4):430-435.[Chai Yanwei,Zhao Ying,Liu Yungang.Research Progress and prospect of urban geography methodologies and methods.Bulletin of Chinese Academy of Sciences,2011,26(4):430-435.]
[47]陈彦光.地理学理论研究和科学分析的一般方法探讨.地理科学,2009,29(3):316-322.[Chen Yanguang.Exploring general research method of theoretical geography with three steps.Scientia Geographica Sinica,2009,29(3):316-322.]
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