Topographical relief characteristics and its impact on population and economy: A case study of the mountainous area in western Henan, China

Abstract

Topographical relief is a key factor that limits population distribution and economic development in mountainous areas. The limitation is especially apparent in the mountain-plain transition zone. Taking the transition zone between the Qinling Mountains and the North China Plain (i.e. the mountainous area in western Henan Province) as an example and based on the 200-m resolution DEM data, we used the mean change-point analysis to determine the optimal statistical unit for topographical relief, and thereafter extracted the relief degree. Taking the 1:100,000 land use data, township population and county-level industrial data, population and economic spatial models were constructed, and 200-m resolution grid population and economic density maps were generated. Afterwards, statistical analysis was carried out to quantitatively reveal the impact of topographical relief on population and economy. In addition, the impacts of other topographical factors were discussed. The results showed the following. (1) The relief degree in western Henan is generally low, where 58.6% of the regional topography does not exceed half the height of a reference mountain (relative elevation ≤250 m). Spatially, the relief degree is high in the west while low in the east, and high in the middle while low in the north and south. There is a positive correlation between relief degree and elevation, and a much stronger correlation between relief degree and slope. (2) The linear fitting degree between the population and economic validation data and the corresponding simulation data are 0.943 and 0.909, respectively, indicating that the spatialized results can reflect the actual population and economic distribution. (3) The impact of topographical relief on population and economy was stronger than that of other topographical factors. The relief degree showed a good logarithmic fit relationship with population density (0.911) and economic density (0.874). Specifically, 88.65% of the population lives in areas where the topographical relief is ≤0.5 and 88.03% of the gross regional product was from areas where the relief is ≤0.3. Compared with the population distribution, the economic development showed an obvious agglomeration trend towards low relief areas.

Exploring resources and environmental carrying capacities at the county level: A case study of China’s Fengxian County

“…The industrial structure of different villages and towns have a direct impact on regional development strategy adjustment. Next, according to the relationship between land use types and the development of primary, secondary and tertiary industry, we constructed a multiple regression model [39]. By using the method of regression analysis equation (Table 5), we determined the correlation between the primary industry output value (Y 1 ) and the following land use types: garden (X 11 ), paddy field (X 12 ), dry land (X 13 ), forested land(X 14 ), reservoirs and potholes (X 15 ), rivers and water surfaces (X 16 ), irrigated land (X 17 ), natural reserved areas (X 18 ) and tidal flats (X 19 ).…”

Influence of Relief Degree of Land Surface on Street Network Complexity in China

“…RDLS is frequently used as one of the indicators of habitat suitability evaluation [15] and ecological environment evaluation [16]. It is also widely used in the fields of geological disaster evaluation [17], soil erosion sensitivity evaluation [18], and population distribution and economic development [19]. An urban street network is the basic skeleton of urban spatial form that expresses evident complexity in many aspects, including topology structure [20].…”

Evaluation and analysis on suitability of human settlement environment in Qingdao

“…The Relief Degree of Land Surface (RDLS) index refers to the height difference between the highest point and the lowest point in a certain area [ 17 , 18 ]. The selected area should be able to perfectly express the topographical characteristics of the research area.…”

“…When the value is negative and closer to -1, the negative correlation is stronger and the spatial heterogeneity is stronger. When the value is 0 or close to 0, there is no correlation in the space, or the correlation is smaller [ 18 ]. The calculation formulas are as follows: …”