基于尺度收缩方法的中国县级尺度房屋结构数据推算
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摘要
以国家统计局公开发布的2005年全国1%人口抽样调查数据为基数,对全国县级尺度农村房屋结构的比例构成进行了推定。针对全国性普查数据存在的问题,开发了基于不同空间精度等级的数据收缩模型,利用中国建筑气候区划、建筑热工区划、人口和社会经济指标等因子,推定了2300多个县级空间单元的农村地区不同建筑结构的房屋比例和数量。同时,对模型结构和推定精度进行了校验,结果表明,由地级和省级单元的五种房屋结构比例来推定县级单元数据,模型精度(R2)分别达到70%和40%。这项工作为从区域尺度把握农村房屋结构状况,评价各地对地震灾害的抵御能力和制定相关规划提供了基础数据支撑。
County-level housing structure data,especially those in rural areas,are critical for evaluating regional seismic vulnerability and making anti-disaster planning.The data open to the public,however,are incomplete and inconsistent in scale.This paper addressed this issue and estimated the county-level rural housing structure data across the country,including rural and urban housing number and housing ratios in five structures,including wood,brick,mixed,and reinforced concrete.The national 1/100 census data conducted in 2005 were taken as the basic data.Based on an evaluation of the original data,four spatial accuracy levels were specified according to their statistical scales.Separation method was used to identify rural and urban housing data individually from total housing data.Besides,spatial down-scaling models were established to transfer housing data from province-level or prefecture-level to county-level.The influences of geographic factors,including architectural climate zone and architectural thermo zone,and economic and social factors,for example,average relative GDP per capita,minority counties,were considered in the models,and also spatial autocorrelation was considered in prefecture-level models.Linear regression models and MLP models were compared with Rsqure and RMSE when making spatial down-scaling models,and linear regression models were adopted.Finally,models' Rsqures reached 70% and 40% corresponding to prefecture-level and province-level models,which seemed acceptable facing the incomplete and inconsistent data at present.
County-level housing structure data,especially those in rural areas,are critical for evaluating regional seismic vulnerability and making anti-disaster planning.The data open to the public,however,are incomplete and inconsistent in scale.This paper addressed this issue and estimated the county-level rural housing structure data across the country,including rural and urban housing number and housing ratios in five structures,including wood,brick,mixed,and reinforced concrete.The national 1/100 census data conducted in 2005 were taken as the basic data.Based on an evaluation of the original data,four spatial accuracy levels were specified according to their statistical scales.Separation method was used to identify rural and urban housing data individually from total housing data.Besides,spatial down-scaling models were established to transfer housing data from province-level or prefecture-level to county-level.The influences of geographic factors,including architectural climate zone and architectural thermo zone,and economic and social factors,for example,average relative GDP per capita,minority counties,were considered in the models,and also spatial autocorrelation was considered in prefecture-level models.Linear regression models and MLP models were compared with Rsqure and RMSE when making spatial down-scaling models,and linear regression models were adopted.Finally,models' Rsqures reached 70% and 40% corresponding to prefecture-level and province-level models,which seemed acceptable facing the incomplete and inconsistent data at present.
引文
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[2]王文宇,王静爱,基于三种信息源的中国冰雹灾害区域分异研究.地理研究,2001,20(3):380～387.
[3]王静爱,史培军,朱骊,等.中国自然灾害数据库的建立与应用.北京师范大学学报:自然科学版,1995,31(1):121～126.
[4]方伟华,王静爱.中国历史水灾案例数据库的建立及相关问题探讨.北京师范大学学报:自然科学版,1998,34(2):269～275.
[5]刘新立,史培军.空间不完备信息在区域自然灾害风险评估中的处理与应用——理论部分.自然灾害学报,1999,8(4):1～9.
[6]徐霞,王静爱,王文宇.自然灾害案例数据库的建立与应用——以中国98洪水灾害案例数据库为例.北京师范大学学报:自然科学版,2000.36(2):274～280.
[7]Shi P J.Du J.Ji M X,et al.Urban risk assessment research of major natural disasters in China.Advances in EarthScience,2006,21(2):170～177.
[8]薛哗,黄崇福.自然灾害风险评估模型的研究进展.应用基础与工程科学学报,2006,14(12):1～10.
[9]孟斌,王劲峰.地理数据尺度转换方法研究进展.地理学报,2005,60(2):277～288.
[10]Wu J.Hierarchy and scaling:Extrapolating information along a scaling ladder.Canadian Journal of Remote Sens-ing,1999,25(4):367～380.
[11]Nordhaus W.Creation and analysis of a geophysically scaled economic dataset.http://www.econ.yale.edu/～nordhaus/homepage/,2001.
[12]Hessami M,Gachon P,Ouarda T B M J,et al.Automated regression-based statistical downscaling tool.EnvironmentalModelling&Software,2008,23(6):813～834.
[13]Dehn M,Bürger G,Buma J,et al.Impact of climate change on slope stability using expanded downscaling.Engi-neering Geology,2000,55(3):193～204.
[14]Wilby R L.Downscaling,general circulation model output:A review of methods and limitations.Progress inPhysical Geography,1997,21(4):530～548.
[15]中华人民共和国建设部.中国计划出版社.GB50178-93.中国建筑气候区划标准1994.
[16]张晓旭,冯宗宪.中国人均GDP的空间相关与地区收敛:1978～2003.经济学,2008,(1):399～414.
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