山东省人口空间分布格局的多尺度分析

doi:10.11820/dlkxjz.2012.02.006

Abstract

Abstract: The spatial distribution of population is essential for both environmental and socio-economic researches. Traditional studies on the spatial distribution of population are mostly based on single scale analysis and ignores scale dependence of population distribution. Analysing the spatial distribution of population at different scales is beneficial to more exactly reveal the spatial distribution law of population. This paper analyzed and compared the spatial autocorrelation of population density using spatial autocorrelation method and statistical correlations between population density and environment-economic factors including DEM, slope, annual average sunshine hours, annual rainfall, average temperature, land use data and GDP at city, county and 1 km?1 km grid scale, and further discussed the spatial distribution patterns of population and the effective or indicative factors to reveal the spatial distribution law of the population at multi-scales. Some conclusions can be drawn as follows. (1) The information at different levels of details can be obtained by multi-scale analysis of the spatial distribution of population. At the city scale, the spatial distribution patterns of the population can be derived. High density districts are in the southeast of Shandong Province including Jinan, Liaocheng, Tai'an, Jining, Zaozhuang and Heze. At the county scale, the spatial distribution patterns of population of some cities can be displayed, and the cases are Jinan, Jining and Dongying. The population density in Jinan is high and unevenly distributed. The population density in Jining is high and evenly distributed. The population density in Dongying is low and evenly distributed. At the 1 km?1 km grid scale, the spatial distribution patterns of population of counties can be obtained. (2) Spatial distribution law differs with scales. Environment-economic factors have a greater impact on population distribution than spatial autocorrelation at city and county levels. At the city level, average sunshine hours, percentage of rural land, and average temperature are the main indicators of population distribution. The level of economic development to some extent affects the population distribution, and the percentages of urban land, and rural land are indicative of population distribution.

Key words: population distribution, scale, Shandong Province, spatial autocorrelation, statistical correlation

WANG Jing, YANG Xiaohuan, SHI Ruixiang. Spatial Distribution of the Population in Shandong Province at Multi-Scales[J].PROGRESS IN GEOGRAPHY, 2012, 31(2): 176-182.

References

[1] 王桂新. 中国区域经济发展水平差异与人口迁移关系之研究. 人口与经济, 1997(1): 50-56.
[2] 廖顺宝, 孙九林. 青藏高原人口分布与环境关系的定量研究. 中国人口、资源与环境, 2003, 13(3): 62-67.
[3] 高志强, 刘纪远, 庄大方. 基于遥感和GIS的中国土地资源生态环境质量同人口分布的关系研究. 遥感学报, 1999, 3(1): 66-70.
[4] 孙根年. 国家铁路网密度与人口密度,人均GNP 关系的统计分析. 陕西师范大学学报: 自然科学版, 2000, 28 (4): 98-103.
[5] 韩惠, 刘勇, 刘瑞雯. 中国人口分布的空间格局及其成因探讨. 兰州大学学报, 2000, 84(4): 16-21.
[6] 刘峰, 马金辉, 宋艳华. 基于空间统计分析与GIS 的人口空间分布模式研究: 以甘肃省天水市为例. 地理与地理信息科学, 2004, 20(6): 18-22.
[7] 封志明, 唐焰, 杨艳昭. 中国地形起伏度及其与人口分布的相关性. 地理学报, 2007, 62(10): 1073-1082.
[8] 杜国明, 张树文, 张有全. 城市人口分布的空间自相关分析: 以沈阳市为例. 地理研究, 2007, 26(2): 383-390.
[9] Yang X H, Ma H Q. Natural environment suitability of China and its relationship with population distributions. International Journal of Environmental Research and Public Health, 2009, 6(12): 3025-3039.
[10] Doll C N H, Pachauri S. Estimating rural populations without access to electricity in developing countries through night-time light satellite imagery. Energy Policy, 2010, 38(10): 5661-5670.
[11] 杨小唤, 江东, 王乃斌. 人口数据空间化的处理方法. 地理学报, 2002, 57(增刊): 70-75.
[12] Yang X H, Huang Y H, Dong P. An updating system for the gridded population database of China based on remote sensing, GIS and spatial database technologies. Sensors, 2009, 9(2): 1128-1140.
[13] Deichmann U, Balk D, Yetman J. Transforming population data for Interdisciplinary Usages: From census to grid, 2001. http://sedac.ciesin.columba.edu/plue/gpw/index.html?min.html&2
[14] Guerra C A, Snow R W, Hay S I. Mapping the global extent of malaria in 2005. Trends in Parasitology, 2006, 22 (8): 353-358.
[15] 李双成, 蔡运龙. 地理尺度的转换若干问题的初步探讨. 地理研究, 2005, 24(1): 11-18.
[16] 杜国名, 张树文, 张有全. 城市人口密度的尺度效应: 以沈阳市为例. 中科院研究生院学报, 2007, 24(2): 186- 192.
[17] 刘红辉, 江东, 杨小唤. 基于遥感的全国GDP1km 格网的空间化表达. 地球信息科学, 2005, 7(2): 121-123.
[18] 徐建华. 现在地理学中的数学方法. 北京: 高等教育出版社, 2002: 37-38.
[19] 王劲峰, 廖一兰, 刘鑫. 空间数据分析教程. 北京: 科学出版社, 2010: 101-111.
[20] 杨小唤, 刘业森, 江东. 一种改进人口数据空间化的方法: 农村居住地重分类. 地理科学进展, 2006, 25(3): 62-69.

Spatial Distribution of the Population in Shandong Province at Multi-Scales

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