人口空间数据误差分析研究

Spatial population data, obtained through the pixeling method, makes many related researches more convenient. However, the limited methods of precision analysis prevent the spread of spatial distribution methods and cumber the application of the spatial population data. This paper systematically analyzes the different aspects of the spatial population data precision, and re-calculates them with the reformed method, which makes breakthrough for the spread of the pixeling method and provides support and reference for the application of spatial population data. The paper consists of the following parts: (2) characters of the error; (2) origins of the error; (3) advancement on the calculating methods of the spatial population data. In the first place, based on the analysis of the error trait, two aspects of the spatial population data precision are characterized and analyzed: numerical character and spatial distributing character. The later one, placed greater emphasis on in this paper, is depicted in two spatial scales: county and town. It is always essential and meaningful to the research in this paper that spatial distribution is as important as numerical value in analyzing error of the spatial distributed data. The result illustrates that the spatial population data error appears spatially in group, although it is random in the aspect of data statistics, all of that shows there lies spatial systematic error. Secondly, this paper comes to conclude and validate the lineal correlation between the residential land area (from 1:50000 map and taken as real area) and population. Meanwhile, it makes particular analysis on the relationship between the residential land area, which is obtained from the land use map and the population in three different spatial scales: village, town and county, and makes quantitative description of the residential density variation in different topological environment. After that, it analyzes the residential distributing traits and precision. With the consideration of the above researches, it reaches the conclusion that the error of the spatial distributed population is caused by a series of factors, such as the compactness of the residents, loss of the residential land, the population density of the city. Eventually, the paper ameliorates the method of pixeling the population data with the help of the analysis on error characters and causes. It tests 2-class regionalization based on the 1-class regionalization of China, and resorts the residential data from the land use map. In aid of GIS and the comprehensive analysis of various data source, it constructs models in each 2-class district to calculate spatial population data. After all, LinYi Region is selected as the study area. In this area, spatial distributing population is calculated and the precision is analyzed. All it illustrates is that new spatial distributing population has been improved much. The research is fundamental work. It adopts large amounts of data in different types and contains many figures to make convincing and detailed conclusions.