人口统计数据空间化的一种方法
收稿日期: 2007-01-22
修回日期: 2007-07-02
网络出版日期: 2007-10-25
基金资助
国家973 项目(2001CB5103); 国家863 项目(2006AA12Z15); 国家自然科学基金项目(70571076; 40471111); 中科院知识创新工程(KZCX2-YW-3-8)
A Method of Spatialization of Statistical Population
Received date: 2007-01-22
Revised date: 2007-07-02
Online published: 2007-10-25
Supported by
National "973" Program, No.2001CB5103; National "863" Program, No.2006AA12Z15; National Natural Science Foundation of China, No.70571076; No.40471111; Knowledge Innovation Program of the CAS, No.KZCX2-YW-3-8
人口空间分布信息在环境健康风险诊断、自然灾害损失评估和现场抽样调查比较等地理学和相关学科研究中占有重要的地位。目前随着对地观测技术和地理信息科学的飞速发展, 如何精确地进行人口数据空间化成为了研究的难点和热点。针对采用传统方法解决人口空间化问题所遇到的困难和不足, 设计了遗传规划(genetic programming, GP)、遗传算法(genetic algorithms, GA) 和GIS 相结合的方法, 以GIS 确定量化影响因子权重, 以GP 建立模型结构, 以GA 优化模型参数, 成功建立研究区—山西省和顺县的人口数据格网分布表面。实验证明与传统建模方法(如逐步回归分析模型和重力模型)相比, 所提方法建模过程更为智能化与自动化, 模型结构更为灵活多样, 而且数据拟合精度更高。
廖一兰, 王劲峰, 孟斌, 李新虎 . 人口统计数据空间化的一种方法[J]. 地理学报, 2007 , 62(10) : 1110 -1119 . DOI: 10.11821/xb200710010
Mapping distribution of population has arisen as an important issue in the fields of geographical and relative researches, due to the necessity of combining with spatial data representing socio-graphic information across various spatial units, such as to evaluate the total numbers of people at environmental health risks or died in natural disasters. However, most existing solutions to this problem focus on selection and quantification of influencing factors and rarely take into account the correlation among selected factors. And much expertise is needed in modeling process to formulate the relationships between influencing factors and population data successfully. It usually not only produces information redundancy but increases the complexity of the problem. This paper explores a novel approach to transform population data from census to grid by integrating genetic programming (GP), Genetic Algorithms (GA) and Geographic Information Systems (GIS). A set of natural and socioeconomic factors which contribute to population distribution are identified and quantified under GIS environment. And then GP and GA are severally applied to build and optimize the population model in the hierarchical form, allowing for the computation of the relevant population data error. The experiment proves that the proposed method performs much better than stepwise regression analysis and adapted gravity model approaches. The GP/GA-based method is the first to introduce such computational intelligence techniques as GP and GA to generate gridded population maps, hence it is a methodological innovation in interpolation of population data.
Key words： spatial interpolation; surface modeling; GP; GA; GIS; Shanxi
[1] Wang Xuemei, Li Xin, Ma Mingguo. Advance and case analysis in population spatial distribution based on remote sensing and GIS. Remote Sensing Technology and Application, 2004, 19(5): 320-327.
[王雪梅, 李新, 马明国. 基于遥 感和GIS 的人口数据空间化研究进展及案例分析. 遥感技术与应用, 2004, 19(5): 320-327.]
[2] Tobler W R. Smooth pychophylactic interpolation for geographical regions. Journal of the American Statistical Assoc., 1979, 367(74): 519-530.
[3] Martin D. Mapping population data from zone centroid locations. Transactions of the Institute of British Geographers, 1989, 14(1): 90-97.
[4] Dobson J E, Bright E A, Coleman P R. Landscan: A global population database for estimating populations at risk. Photogrammetric Engineering and Remote Sensing, 2000, 66: 849-857.
[5] Kishore J K, Patnaik L M, Mani V et al. Genetic programming based pattern classification with feature space partitioning. Information Sciences, 2001, 131: 65-86.
[6] Koza J R. A genetic approach to econometric modeling. In: Sixth World Congress of the Econometric Society, Barcelona1, 990c.
[7] Jin Y Q, Wang Y. A genetic algorithm to simultaneously retrieve land surface roughness and soil wetness. International Journal of Remote Sensing, 2001, 22(16): 3093-3099.
[8] Holland J. Adaptation in Natural and Artificial System: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Cambridge, Mass: MIT Press, 1992. 211.
[9] Zhang Liechao, Cai Zhihua, Chen Ansheng. A survey of SGA, GP, GEP. Control & Automation, 2006, 22(2): 185-187, 145.
[ 张烈超, 蔡之华, 陈安升. SGA、GP、GEP 的研究概述. 微计算机信息, 2006, 22(2): 185-187, 145.]
[10] Yun Qingxia, Huang Guangqiu, Wang Zhanquan. Genetic Algorithms and Genetic Programming: An Approach for Search and Optimization. Beijing: Metallurgical Industry Press, 1997.
[云庆夏, 黄光球, 王战权. 遗传算法和遗传规 划: 一种搜索寻优技术北京: 冶金工业出版社, 1997.]
[11] Koza J R. Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems. Stanford University Report STAN-CS-90-1394, 1990, http://www.genetic-programming.com/jkpubs72to93. ht-ml#anchor484765 .
[12] Deichmann U. A review of spatial population database design and modeling. Technical Report 96-3, National Center for Geographic Information and Analysis, USA. 1996.
[13] Yue T X, Wang Y A, Liu J Y. Surface modeling of human population distribution in China. Ecological Modeling, 2005, 181: 461-478.
[14] Li G Y, Weng Q H. Using Landsat ETM+ imagery to measure population density in Indianapolis, Indiana, USA. Photogrammetric Engineering & Remote Sensing, 2005, 71(8): 947-958.
[15] Balk D L, Deichmann U, Yetman G. Determining global population distribution: Methods, applications and data. Advances in Parasitology, 2006, 62: 120-154.
[16] Yue T X, Wang Y A, Chen S P. Numerical simulation of population distribution in China. Population and Environment, 2003, 25(2): 141-163.
[17] Lu Shaohua. Application of genetic programming in China's port throughput prediction. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2006, 30(3): 520-523.
[ 卢少华. 遗传规划在港口吞吐量预测中 的应用. 武汉理工大学学报, 2006, 30(3): 520-523.]
[18] Wang Jiayao, Deng Hongyan. A model of cartographical generalization based on genetic algorithm. Geomatics and Information Science of Wuhan University, 2005, 30(7): 565-569.
[王家耀, 邓红艳. 基于遗传算法的制图综合模型研 究. 武汉大学学报·信息科学报, 2005, 30(7): 565-569.]
[19] Su M C, Chang H T, Application of neural networks incorporated with real-valued genetic algorithms in knowledge acquisition. Fuzzy Sets and Systems, 2000, 112: 85-97.
[20] Dobson J E, Bright E A, Coleman P R et al. Landscan: A global population database for estimating populations at risk. Photogrammetric Engineering and Remote Sensing, 2000, 66: 849-857.
[21] Nelson A, Deichmann U. The African Population Database, Version 4. New York: United Nations Environment Program (UNEP) and the Center for International Earth Science Information Network (CIESIN), Columbia University. 2004. http://www.na.unep.net/datasets/datalist.php3.
/
| 〈 |
|
〉 |