Integration of GP and GA for mapping population distribution

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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.

Keywords: GA; GIS; GP; Mapping population distribution; Surface modelling

Document Type: Research Article


Affiliations: 1: Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China 2: College of Arts and Science, Beijing Union University, Beijing 100083, China 3: Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361003, China

Publication date: January 1, 2010

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