陕西省人口资源分布格局变化趋势及驱动力研究

基于两期人口普查数据,运用ESDA方法,分析陕西省2000年至2010年人口分布变化空间格局与自然、经济、社会驱动力。将驱动力进行多元回归并进行空间栅格拟合,得出不同尺度的人口变化趋势。结果表明:陕西省人口变化北部快南部慢,全局集聚程度提高,人口变化率局部集聚中部高北部低,人口重心北移,在人口变化趋势与植被变化的关系上多数地区人口对植被有正向影响但南部少数区县为负向;建立多元回归方程能近似的模拟人口变化规律,人口的增长多在河谷地带与高速公路沿线,地势平坦的区域经济中心与交通便利区域。趋势拟合显示,自然因素本身并不在短时间内决定人口迁移,但受到经济拉力作用时,自然条件较差地区往往人口减少会较快。根据不同驱动因子的作用,将其按主成分分析法和系统聚类法进行分类,为后续的因子选取提供依据。

Tendency of population distribution pattern change and driving forces in Shaanxi province

The population distribution pattern changes and natural-economy-social driving force in Shaanxi province from 2000 to 2010 were analyzed by census data and ESDA method.A multiple regression was taken on the driving force and then the different scales of population change trends were drawn.The results show: population changed fastly in the northern while slowly in the southern in Shaanxi province,and the rate was high in the middle and relatively low in the western and eastern;the Global Gathering Index showed more unbalanced population pattern,and the Local Cluster Index showed the population change rate was high in the central while low in the northern;the population center of gravity moved to north which might be suffered by the pull of economy;The relationship between population change and ecology reflected a positive ecological impact on most regions,especially in the northern.The correlation between population change rate and natural factors showed the relation degree was significant but not high enough.The correlation between cultural factors and population change rate was high.The growth of the population were more in the valley area and along the highway,where are the regional economic centers and were provided with convenient transportation.Trend fitting displayed that natural factors were not decided to migration in a short time,but by the economic pull force,areas where natural conditions relatively poor were often suffered quickly population decline.Different divisions could be taken to composite the trend by towns and which was helpful to took regionalization and regional optimization.