Research Articles

Estimation of rural housing structure and its vulnerability in China

  • 1. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2012-05-04

  Revised date: 2012-06-20

  Online published: 2013-02-07


The damage of dwelling houses constitutes the primary cause of casualties and asset loss in seismic disasters that occurred in Chinese rural areas. The structure of houses is crucial for assessing the vulnerability of rural houses. However, at present, available data on rural housing structure are incomplete and their spatial scales are inconsistent. This paper estimated the amount and ratio of rural houses in five structures, namely ‘wood’, ‘brick’, ‘mixed’, ‘reinforced concrete’, and ‘other’, for 2380 counties across China. With the percentage sampling census data in 2005, four accuracy levels were specified. Then, a set of down-scaling models were established, where the impact of climate, economic development level and ethnic minority cultural factors on rural housing structure, as well as the spatial autocorrelation of neighboring spatial units were considered. Based on the estimation results, a database of county-level rural housing structure was established, based on which the vulnerability of rural houses in different areas was clarified.

Cite this article

GAO Xiaolu, JI Jue . Estimation of rural housing structure and its vulnerability in China[J]. Journal of Geographical Sciences, 2013 , 23(1) : 179 -191 . DOI: 10.1007/s11442-013-1002-1


Barlow L, Westergren K, Holmberg L et al., 2009. The completeness of the Swedish Cancer Register: A sample survey for year 1998. Acta Oncologica, 48(1): 27-33.
Carter T R, Fronzek S, B?rlund I, 2004. A framework for developing consistent global change scenarios for Finland in the 21st century. Boreal Environment Research, 9(2): 91-107.
Dehn M, Bürger G, Buma J et al., 2000. Impact of climate change on slope stability using expanded downscaling . Engineering Geology, 55(3): 193-204.
Dong W, Wei Z L, 2006. Forecasting of ozone level in time series using MLP model with a novel hybrid training algorithm. Atmospheric Environment, 40(5): 913-924.
Gaffin S R, Rosenzweig C R, Xing X et al., 2004. Downscaling and geo-spatial girding of socio-economic projections from the IPCC special report on emissions scenarios (SRES). Global Environmental Change, 14(2): 105-123.
Ge Q S, Zou M, Zheng J Y et al., 2008. Integrated Assessment of Natural Disaster Risks in China. Beijing: Science Press. (in Chinese)
Gong P, Howarth P J, 1990. The use of structural information for improving land-cover classification accuracies at the rural-urban fringe. Photogrammetric Engineering and Remote Sensing, 56: 67-73.
Hall F, Townshend J, Engman T, 1995. Status of remote sensing algorithms for estimation of land surface state parameters. Remote Sensing of Environment, 51: 138-156.
Herold M, Scepan J, Clarke K C, 2002. The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environment and Planning A, 34: 1443-1458.
Hessami M, Gachon P, Ouarda T B M J et al., 2008. Automated regression based statistical downscaling tool. Environmental Modelling & Software, 23(6): 813-834.
Hewitson B C, Crane R G, 1996. Climate downscaling: Techniques and application. Climate Research, (7): 85-95.
Loibl W, Toetzer T, 2003. Modeling growth and densification processes in suburban regions: Simulation of landscape transition with spatial agents. Environmental modeling & Software, (8): 553-563.
Ministry of Construction of the People’s Republic of China, 1993. GB50178-93, Standard of Architectural Climate and Thermo Zones in China.
National Bureau of Statistical of China (NBSC), 2006. China Population Statistics Yearbook. Beijing: China Statistics Press. (in Chinese)
Shen Z J, Kawakam M, 2009. Geosimulation model using geographic automata for simulating land-use patterns in urban partitions. Environment and Planning B: Planning and Design, 36: 802-803.
Wang J F, Wu J L, Sun Y J et al., 2005. Techniques of spatial data analysis. Geographical Research, 24(3): 464-472. (in Chinese)
Wang W Y, Wang J A, 2001. The distributive pattern of hail disasters based on three data sources in China. Geographical Research, 20(3): 380-387. (in Chinese)
Wang Y, Shi P J, Wang J A, 2005. Impact of earthquake disaster on rural residents: A case study on Dayao County of Yunnan Province. Journal of Natural Disasters, 14(6): 110-115. (in Chinese)
Wilby R L, Wigley T M L, Conway D et al., 1998. Statistical downscaling of general circulation model output: A comparison of methods. Water Resour. Research, 34: 2995-3008.
Xia Z G, Clarke K C, 1997. Approaches of scaling geo-spatial data. In: Quattrochi D A, Goodchild M F (eds.). Scale in Remote Sensing and GIS. Lewis publisher, Boca Raton, FL: 309-360.
Yeh G O, Li X, 2001. A constrained CA model for the simulation and planning of sustainable urban forms by using GIS. Environment and Planning B: Planning and Design, 28(5): 733-753.