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城市家庭居住地选址的空间异质性分析——以美国佛罗里达州橙县为例

DOI: 10.11820/dlkxjz.2012.08.005, PP. 1024-1031

Keywords: 多项Logit模型,家庭居住地选址,空间同质性,空间效应,空间异质性,美国佛罗里达州

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Abstract:

家庭选择居住地的行为天然具有空间性,因而空间异质性效应是家庭居住地选址建模不可忽视的因素。传统的居住地选址模型基于空间一致性假设,即假设影响因素对家庭的居住地选择行为的影响在空间上一致,因而忽略了空间异质性效应。基于多项Logit模型构建了居住地选址模型,并在两个空间尺度和5个子区域中分别应用该模型,来反映影响因素的影响作用在空间上的变化。以美国佛罗里达州橙县家庭选址为例进行实证研究,结果表明家庭居住地选址行为的影响因素在不同的空间位置和空间尺度上具有不同的作用,因而存在显著的空间异质性。尽管以美国地区为例,但所得结论对国内案例区研究同样具有借鉴意义。

References

[1]  Anselin L. Spatial Econometrics: Methods and Models.Dordrecht, The Netherlands: Kluwer Academic Publishers:1988.
[2]  Tobler W. Cellular geography//Gale S, Olsson G. Philosophyin Geography. Dordrecht: Reidel Publishing Company,1979: 379-386.
[3]  Miller H J. Potential contributions of spatial analysis togeographic information systems for transportation(GIS-T). Geographical Analysis, 1999, 31(4):373-399.
[4]  Gilbert A, Chakraborty J. Using geographically weightedregression for environmental justice analysis: Cumulativecancer risks from air toxics in Florida. Social Science Research,2011, 40(1): 273-286.
[5]  Kazuaki M, Varameth V, Naoki S, et al. Discrete choicemodel with structuralized spatial effects for location analysis.Transportation Research Record, 2004, 1898:183-190.
[6]  Fotheringham A S, Charlton M E, Brunsdon C. Geographicallyweighted regression: A natural evolution of the expansionmethod for spatial data analysis. Environmentand Planning A, 1998, 30(11): 1905-1927.
[7]  Fotheringham A S, Brunsdon C, Charlton M. GeographicallyWeighted Regression: The Analysis of SpatiallyVarying Relationships. Hoboken, NJ: Wiley, Chichester,West Sussex, 2002.
[8]  Tu J, Xia Z. Examining spatially varying relationships betweenland use and water quality using geographicallyweighted regression I: Model design and evaluation. Scienceof the Total Environment, 2008, 407(1): 358-378.
[9]  McMillen D P, McDonald J F. Land use before zoning.The case of 1920's Chicago. Regional Science and UrbanEconomics,1999, 29(4): 473-489.
[10]  Luo J, Kanala N K. Modeling urban growth with geographicallyweighted multinomial logistic Regression//Liu L, Li X, Liu K, et al. Proceedings of Geoinformatics2008 and Joint Conference on GIS and Built Environment:The Built Environment and Its Dynamics. Guangzhou,China: SPIE, 2008, 71440:71440M-71440M-11.
[11]  Tanaka K, Yoshida K, Kawase Y. Applying geographicallyweighted regression to conjoint analysis: Empiricalfindings from urban park amenities//American AgriculturalEconomics Association(New Name 2008: Agriculturaland Applied Economics Association), 2008.
[12]  Zhou B, Kockelman K M. Microsimulation of residentialland development and household location choices: Bid-ding for land in Austin, Texas. Transportation ResearchRecord, 2008, 2077(1): 106-112.
[13]  Orange County. Orange County 2010-2011 Annual Report.2011-08-16[2012-01-. http://www.orangecountyfl.net/Portals/0/Resources/Internet/Homepage/docs/OrangeCountyAnnualReport2010-11_Final.pdf
[14]  Getis A, Ord J K. The analysis of spatial association byuse of distance statistics. Geographical Analysis, 1992, 24(3): 189-206.
[15]  Fischer M M, Hewings G J D, Nijkamp P, et al. ResidentialLocation Choice: Models and Applications. New York: Springer, 2010.
[16]  Tang K. Green CITYnomics: The Urban War against ClimateChange. Sheffield: Greenleaf Publishing, 2009.
[17]  Choocharukul K, Van H T, Fujii S. Psychological effectsof travel behavior on preference of residential locationchoice. Transportation Research A, 2008, 42(1): 116-124.
[18]  Anselin L. Spatial Econometrics: Methods and Models.Dordrecht, The Netherlands: Kluwer Academic Publishers:1988.
[19]  Tobler W. Cellular geography//Gale S, Olsson G. Philosophyin Geography. Dordrecht: Reidel Publishing Company,1979: 379-386.
[20]  Miller H J. Potential contributions of spatial analysis togeographic information systems for transportation(GIS-T). Geographical Analysis, 1999, 31(4):373-399.
[21]  Gilbert A, Chakraborty J. Using geographically weightedregression for environmental justice analysis: Cumulativecancer risks from air toxics in Florida. Social Science Research,2011, 40(1): 273-286.
[22]  Kazuaki M, Varameth V, Naoki S, et al. Discrete choicemodel with structuralized spatial effects for location analysis.Transportation Research Record, 2004, 1898:183-190.
[23]  Fotheringham A S, Charlton M E, Brunsdon C. Geographicallyweighted regression: A natural evolution of the expansionmethod for spatial data analysis. Environmentand Planning A, 1998, 30(11): 1905-1927.
[24]  Fotheringham A S, Brunsdon C, Charlton M. GeographicallyWeighted Regression: The Analysis of SpatiallyVarying Relationships. Hoboken, NJ: Wiley, Chichester,West Sussex, 2002.
[25]  Tu J, Xia Z. Examining spatially varying relationships betweenland use and water quality using geographicallyweighted regression I: Model design and evaluation. Scienceof the Total Environment, 2008, 407(1): 358-378.
[26]  McMillen D P, McDonald J F. Land use before zoning.The case of 1920's Chicago. Regional Science and UrbanEconomics,1999, 29(4): 473-489.
[27]  Luo J, Kanala N K. Modeling urban growth with geographicallyweighted multinomial logistic Regression//Liu L, Li X, Liu K, et al. Proceedings of Geoinformatics2008 and Joint Conference on GIS and Built Environment:The Built Environment and Its Dynamics. Guangzhou,China: SPIE, 2008, 71440:71440M-71440M-11.
[28]  Tanaka K, Yoshida K, Kawase Y. Applying geographicallyweighted regression to conjoint analysis: Empiricalfindings from urban park amenities//American AgriculturalEconomics Association(New Name 2008: Agriculturaland Applied Economics Association), 2008.
[29]  Zhou B, Kockelman K M. Microsimulation of residentialland development and household location choices: Bid-ding for land in Austin, Texas. Transportation ResearchRecord, 2008, 2077(1): 106-112.
[30]  Getis A, Ord J K. The analysis of spatial association byuse of distance statistics. Geographical Analysis, 1992, 24(3): 189-206.
[31]  Orange County. Orange County 2010-2011 Annual Report.2011-08-16[2012-01-01].
[32]  Xin Y, Karthik K, Ram P, et al. Methodology to MatchDistributions of Both Household and Person Attributes inGeneration of Synthetic Populations//Proceedings of the88th Annual Meeting of the Transportation ResearchBoard (DVD),Washington, D.C., 2009: 11-15.
[33]  Fischer M M, Hewings G J D, Nijkamp P, et al. ResidentialLocation Choice: Models and Applications. New York: Springer, 2010.
[34]  Tang K. Green CITYnomics: The Urban War against ClimateChange. Sheffield: Greenleaf Publishing, 2009.
[35]  Bhat C R, Guo J. A mixed spatially correlated logit model:Formulation and application to residential choice modeling.Transportation Research Part B: Methodological,2004, 38(2):147-168.
[36]  Guo J, Bhat C. Modifiable areal units: Problem or perceptionin modeling of residential location choice? TransportationResearch Board, 2004, 1898: 138-147.
[37]  郑思齐, 符育明, 刘洪玉. 利用排序多元Logit 模型研究城市居民的居住区位选择. 地理科学进展, 2004, 23(5):86-93.
[38]  郑思齐, 符育明, 刘洪玉. 城市居民对居住区位的偏好:支付意愿梯度模型的估计. 地理科学进展, 2005, 24(1):97-104.
[39]  党云晓, 张文忠, 武文杰. 北京市居民住房消费行为的空间差异. 地理科学进展, 2011, 30(10): 1023-1029.
[40]  Uyar B, Kenneth H B. Impact of local public services andtaxes on dwelling choice within a single taxing jurisdiction:A discrete choice model. Journal of Real Estate Research,2005, 27(4): 427-444.
[41]  Guo J, Bhat C R. Operationalizing the concept of neighborhood:Application to residential location choice analysis.Journal of Transport Geography, 2007, 15(1): 31-45.
[42]  Choocharukul K, Van H T, Fujii S. Psychological effectsof travel behavior on preference of residential locationchoice. Transportation Research A, 2008, 42(1): 116-124.
[43]  Sermons M W, Koppelman F S. Representing the differencesbetween female and male commute behavior in residentiallocation choice models. Journal of Transport Geography,2001, 9(2): 101-110.
[44]  Bhat C R. Covariance Heterogeneity in nested logit models:Econometric structure and application to intercitytravel. Transportation Research B, 1997, 31(1): 11-21.
[45]  Bhat C R. Quasi-random maximum simulated likelihoodestimation of the Mixed Multinomial Logit Model. TransportationResearch B, 2001, 35(7): 677-693.
[46]  Timmermans H, Borgers A, Dijk J V. Residential choicebehaviour of Dual Earner households: A decompositionaljoint choice model. Environment and Planning A, 1992,24(4): 517-533.
[47]  Ben-Akiva M, Bowman J. Integration of an activity-based model system and a residential location model.Urban Studies, 1998, 35(7):1131-1153.
[48]  Wadell P A behavioral simulation model for metropolitanpolicy analysis and planning: Residential location andhousing markets components of UrbanSim. Environmentand Planning B, 2000, 27(2): 247-263.
[49]  Sener I N, Pendyala R M, Bhat C R. Accommodating spatialcorrelation across choice alternatives in discretechoice models: An application to modeling residential locationchoice behavior. Journal of Transport Geography,2011, 19(2): 294-303.
[50]  Xin Y, Karthik K, Ram P, et al. Methodology to MatchDistributions of Both Household and Person Attributes inGeneration of Synthetic Populations//Proceedings of the88th Annual Meeting of the Transportation ResearchBoard (DVD),Washington, D.C., 2009: 11-15.
[51]  Bhat C R, Guo J. A mixed spatially correlated logit model:Formulation and application to residential choice modeling.Transportation Research Part B: Methodological,2004, 38(2):147-168.
[52]  Guo J, Bhat C. Modifiable areal units: Problem or perceptionin modeling of residential location choice? TransportationResearch Board, 2004, 1898: 138-147.
[53]  郑思齐, 符育明, 刘洪玉. 利用排序多元Logit 模型研究城市居民的居住区位选择. 地理科学进展, 2004, 23(5):86-93.
[54]  郑思齐, 符育明, 刘洪玉. 城市居民对居住区位的偏好:支付意愿梯度模型的估计. 地理科学进展, 2005, 24(1):97-104.
[55]  党云晓, 张文忠, 武文杰. 北京市居民住房消费行为的空间差异. 地理科学进展, 2011, 30(10): 1023-1029.
[56]  Uyar B, Kenneth H B. Impact of local public services andtaxes on dwelling choice within a single taxing jurisdiction:A discrete choice model. Journal of Real Estate Research,2005, 27(4): 427-444.
[57]  Guo J, Bhat C R. Operationalizing the concept of neighborhood:Application to residential location choice analysis.Journal of Transport Geography, 2007, 15(1): 31-45.
[58]  Sermons M W, Koppelman F S. Representing the differencesbetween female and male commute behavior in residentiallocation choice models. Journal of Transport Geography,2001, 9(2): 101-110.
[59]  Bhat C R. Covariance Heterogeneity in nested logit models:Econometric structure and application to intercitytravel. Transportation Research B, 1997, 31(1): 11-21.
[60]  Bhat C R. Quasi-random maximum simulated likelihoodestimation of the Mixed Multinomial Logit Model. TransportationResearch B, 2001, 35(7): 677-693.
[61]  Timmermans H, Borgers A, Dijk J V. Residential choicebehaviour of Dual Earner households: A decompositionaljoint choice model. Environment and Planning A, 1992,24(4): 517-533.
[62]  Ben-Akiva M, Bowman J. Integration of an activity-based model system and a residential location model.Urban Studies, 1998, 35(7):1131-1153.
[63]  Wadell P A behavioral simulation model for metropolitanpolicy analysis and planning: Residential location andhousing markets components of UrbanSim. Environmentand Planning B, 2000, 27(2): 247-263.
[64]  Sener I N, Pendyala R M, Bhat C R. Accommodating spatialcorrelation across choice alternatives in discretechoice models: An application to modeling residential locationchoice behavior. Journal of Transport Geography,2011, 19(2): 294-303.

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