• 区域发展 •

### 基于栅格的豫西山区地形起伏特征及其对人口和经济的影响

1. 河南大学环境与规划学院,开封 475004
• 收稿日期:2017-06-24 出版日期:2018-06-10 发布日期:2018-06-04
• 基金资助:
国家自然科学基金项目(41671090)

### Spatial variation of terrain relief and its impacts on population and economy based on raster data in West Henan Mountain Area

ZHANG Jingjing(),ZHU Wenbo,ZHU Lianqi(),CUI Yaoping,HE Shasha,REN Han

1. College of Environment and Planning, Henan University, Kaifeng 475004, Henan, China
• Received:2017-06-24 Online:2018-06-10 Published:2018-06-04
• Supported by:
National Natural Science Foundation of China, No.41671090

Abstract:

Topographic relief can be the constraining factor for the population and economic development in an area. This is especially the case in transitional zones from mountains to plains. In this study, West Henan Mountain Area, situated in the transitional zone from the Qinling Mountains to the Huang-Huai Plain (i.e. the second step to the third step of Chinese macro-topography), was selected as a case study area. Based on the optimal statistical unit (OSU) as determined by the mean turning-point analysis method (MTPAM), a DEM of 200 m resolution was used to extract the relief degree of land surface (RDLS). Integrating the 1:100, 000 land use map, statistical population data at township level and economic data of various industries at county level, raster models of spatial patterns of population and economy were formulated, and then the spatial distributions of population density and economic density at a resolution of 200 m by 200 m were produced using the models. Subsequently, statistical analysis was carried out to reveal the effects that RDLS had on population and economy based on raster data (i.e. RDLS, population density, and economic density), and the differences between the effects of RDLS and those of other terrain factors on the population and economy were also analyzed. The results showed that: (1) the RDLS in the West Henan Mountain Area was prevailed by low value, with 58.6% of the area having the RDLS lower than 0.5 (relative altitude of ≤ 250 m). Spatially, RDLS was higher in the west and lower in the east, higher in the central part and lower in the south and the north. Moreover, there existed strong positive correlations between RDLS and altitude and slope, especially correlated with slope significantly. (2) The relationships between the statistical values (i.e. population density and economic density which were selected to test and verify the models) and the corresponding simulated values were fitted to linear models with 0.943 and 0.909 levels of goodness-of-fit. This fitness indicated that the spatialization results reflected well the actual spatial patterns of population and economy in the study area. (3) The effect of RDLS on population and economy is stronger than that of other terrain factors. RDLS had a good logarithmic fit with population density and economic density, with 0.911 and 0.874 goodness-of-fit, respectively. Specifically, 88.65% of the total population lived in the areas where RDLS was less than 0.5 and 88.03% of the gross regional production was distributed in the areas where RDLS was less than 0.3. It can be clearly seen that economic development was more inclined to agglomerate in areas of lower RDLS values compared with population distribution.