文献类型：期刊文献

英文题名：Integration of GP and GA for mapping population distribution

作者：Liao, Yilan[1];Wang, Jinfeng[1];Meng, Bin[2];Li, Xinhu[3]

第一作者：Liao, Yilan

通讯作者：Wang, JF[1]

机构：[1]Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Coll Arts & Sci, Beijing 100083, Peoples R China;[3]Chinese Acad Sci, Inst Urban Environm, Xiamen 361003, Peoples R China

第一机构：Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China

通讯机构：[1]corresponding author), Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, 11 Datun Rd, Beijing 100101, Peoples R China.

年份：2010

卷号：24

期号：1

起止页码：47-67

外文期刊名：INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE

收录：;Scopus(收录号：2-s2.0-76649132656);WOS:【SSCI(收录号:WOS:000274511100003)，SCI-EXPANDED(收录号:WOS:000274511100003)】；

基金：This work was supported by the Project of the National Natural Science Foundation of China (70571076 and 40471111), the Hi-Tech Research and Development Program of China (2006AA12Z15), the National Basic Research Priorities Program (2001CB5103) of the Ministry of Science and Technology of China and Knowledge Innovation Program of the CAS (KZCX2-YW-3-8). In addition, Professor Brian Lees (UNSW@ ADFA, Australia) and six anonymous reviewers gave the useful comments on the drafts during the submission.

语种：英文

外文关键词：Mapping population distribution; Surface modelling; GIS; GP; GA

摘要：Mapping population distribution is an important field of geographical and related research because of the frequent need to combine spatial data representing socio-demographic information across various incompatible spatial units. However, the research may become very complex and difficult when a population in multiple places is estimated by various factors. Previous efforts in the field have contributed to the selection of appropriate independent variables and the creation of different population models. However, the level of accuracy obtainable with these studies is limited by the spatial heterogeneity of population distribution within the individual census districts, particularly in large rural areas. A high-accuracy modelling method for population estimation based on integration of Genetic Programming (GP) and Genetic Algorithms (GA) with Geographic Information Systems (GIS) is presented in this paper. GIS was applied to identify and quantify a set of natural and socioeconomic factors which contributed to population distribution, and then GP and GA were used to build and optimise the population model to automatically transform census population data to regular grids. The study indicated that the proposed method performed much better than the stepwise regression analysis and adapted gravity model methods in estimating the population of both urban and rural areas. More importantly, this proposed method could provide a single, unified approach to mapping population distribution in various areas because the paradigms of these algorithms are general.