陕西省人口分布影响因素的空间计量分析

人口分布影响因素研究有利于揭示人口分布规律,预判人口分布趋势。基于陕西省区县级人口、经济社会、自然地理等数据,通过因子分析方法和空间计量建模解析人口分布的影响因素。研究发现,人口地域别比率不仅取决于一个特定区县内可观测的经济社会、历史基础、自然地理等外在特征,还取决于该区县不可观测的、模型遗漏的其他共有特征,其中经济与公共服务因子、人口基底因子对人口分布具有最显著的正向解释力,其他因素影响较弱或统计不显著;城市等级可显著强化产业结构、人均收入和地形因素对人口分布的影响。研究认为,经济与公共服务因素是优化人口分布的关键,同时需考虑自然地理因素的限制作用。研究对人口分布优化政策的制定具有参考价值。

Factors influencing population distribution in ShaanxiProvinceusing spatial econometric analysis

When conducting empirical research on the factorsinfluencing population distribution,it is helpful to understand populationdistribution and its evolutionary trends.This study considers all the 107counties in Shaanxi Province,northwest China as research objects and attemptsto use demographic,socioeconomic,and natural geographic statistical data for2015 to fit an ordinary least squares (OLS)regression and spatial econometric model to analyze the factorsinfluencing the Shaanxi Province’s population distribution.Thedemographic and socioseconomic data were extracted from the 2016 ShaanxiRegional Statistical Yearbook,by the Statistics Bureau of Shaanxi Province inDecember 2016.The Shaanxi Province county leveladministrative map was drawn using the National Earth System Science DataSharing Infrastructure,National Science & Technology Infrastructure ofChina (http://www.geodata.cn).Natural geographical data wereextracted,calculated,and analyzed from one kilometer (km) Resolution DigitalElevation Model Data Set of China from the Scientific Data Center of Cold andArid Regions in China (http://westdc.westgis.ac.cn).The population spatialdatabase was established using the ArcGIS 10.0 software program populated withthe aforementioned data.The explanatory variable in our models is the RegionalProportion of Population (RPP),which isa proportion calculated by dividing the population of one county by the totalpopulation of the region.The RPP can effectively avoid the heteroscedasticitythat may be caused by large differentials between the areas of the ShaanxiProvince counties.The independent variables(economic and publicservice,population base,industrial structure,per capita Income,topography,andaverage elevation)were obtained through factor analysis to avoid overlookingvariables and issues of multicollinearity.The OLS regression model demonstratesthe fact that there is a significant relationship between RPP and theexplanatory variables,as well as the dummy variables,defined in the model.Theadministration rank variable might play an important role in influencing thecoefficient.However,the dependent variable and its residuals cannot satisfy theno spatial autocorrelation assumption,although they can satisfy the other Gauss Markov assumptions.Therefore,we also attempt to fit the spatial lagmodel and the spatial error model (SEM).The SEM is the best model based onLagrange multiplier (LM),Robust LM,and Akaikeinformation criterion tests.The SEM reveals that the RPP of a particular countyin Shaanxi Province depends on not only the characteristics of observablevariables but also other characteristics that may be unobservable or omittedfrom the model.Among the model〖JP8〗’〖JP〗s independent variables,the economic andpublic service factor exhibits the most significant positive explanatory poweron the RPP.The population base factor,which represents basic agriculturalconditions,and the population in the year 2000 demonstrate positive explanatorypower.The industrial structure factor is negatively correlated with RPP,whichindicates that the second industry has limited absorptive capacity in terms ofincreasing employment compared with the third industry.Average elevation hasnegative explanatory influence on RPP.The effects of per capita income andtopography are insignificant,possibly because some counties with higher percapita incomes have economies based on natural resources or minerals and areoften located in remote mountain areas.Nonetheless,topography exhibits acomplex spatial coupling relationship withclimate,precipitation,temperature,and humidity.The administration rank of acounty influences population distribution significantly.Our main conclusion isthat the key,controllable determinative factors for optimizing population distributionare socioeconomic factors,although the restrictive role of natural geographicalfactors should not be overlooked.By considering the spatial interactionsbetween the explanatory variables and error terms,this study corrects thebiased estimations of the OLS model and provides a scientific analysis of thefactors influencing population distribution,which is of great reference valuein terms of projecting as well as optimizing population distribution trends.