基于GIS的小流域人口数据空间化研究——以梅江流域为例

发布时间：2018-05-07 21:58

本文选题：梅江流域 + 人口　；参考：《地理与地理信息科学》2013年06期

【摘要】：以江西省梅江流域为例,基于GIS空间分析技术和多源数据融合技术对小流域的人口数据空间化方法进行了探究。为使结果更精确,根据小流域的特征选取地形、道路、河流作为主影响因子,并将主因子分成若干类子因子。选取居民点为影响人口分布的指引性因子,以体现人口的空间分布状况;选取土地利用中居民地面积作为居民点的指数,以体现居民点的人口数量。引入居民地指数密度变量作为各因子影响居民点分布的定量指标。在模拟过程中:首先,计算出子因子的居民地指数密度,将其作为子因子对居民点分布的影响权值(子因子居民点权值),再融合得出主因子对居民点分布的影响权值(主因子居民点权值);其次,将整个研究区分为城镇居民点、农村居民点与远离居民点3个区域,以其人口密度为各居民点类型对人口分布的影响权值(居民点人口权值);再次,融合各主因子居民点权值和居民点人口权值得到单因子人口权值,再融合单因子人口权值得到多因子人口权值,即人口密度系数;最后,将各乡镇人口根据人口密度系数生成100m×100m分辨率的人口密度图。
[Abstract]:Taking Meijiang River Basin in Jiangxi Province as an example, the spatial method of population data in small watershed was studied based on GIS spatial analysis technology and multi-source data fusion technology. In order to make the result more accurate, the terrain, road and river are selected as the main influencing factors according to the characteristics of the small watershed, and the main factors are divided into several kinds of sub-factors. Choosing residential land as the guiding factor to influence the population distribution to reflect the spatial distribution of the population and selecting the land area of the residents in land use as the index to reflect the population of residential areas. The index density variable is introduced as a quantitative index to influence the distribution of residential areas. In the simulation process: firstly, the population exponential density of the subfactor is calculated. Using it as a sub-factor to influence the distribution of residential areas (sub-factor of settlement weight value), and then fusing the main factors on the distribution of settlements weight value (the main factor of residential weight value); secondly, the whole study is divided into urban settlements, Rural settlements and remote settlements in 3 regions, with their population density as the impact of the population distribution of each settlement type weight value (residential population weight value); third, The population weight value of single factor and population weight value of residential area are fused to obtain single factor population weight value, and then the population weight value of single factor is obtained, that is, population density coefficient; finally, the population density coefficient is obtained by combining the population weight value of each main factor and population weight value of residential area. The population density maps with a resolution of 100m 脳 100m were generated according to the population density coefficient of each township.
【作者单位】：江西师范大学地理与环境学院;鄱阳湖湿地与流域研究教育部重点实验室;
【基金】：国家自然科学基金项目(40971266、41171393)
【分类号】：C922;K901

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