Exploring Spatial Variations in the Relationships between Landscape Functions and Human Activities in Suburban Rural Communities: A Case Study in Jiangning District, China

Int J Environ Res Public Health. 2021 Sep 17;18(18):9782. doi: 10.3390/ijerph18189782.

Abstract

There is a complicated and contradictory relationship between landscape functions and human activities, especially in the suburban rural communities of metropolises. Previous studies focused on human interference to landscape function, ignoring the impact of landscape functions on human activities. Hence, the present study is focused on the impact of landscape function (based on ecosystem services) on human activities in suburban rural communities of China. The study evaluated the intensity of human activities based on big data; furthermore, the authors analyzed the spatial distribution characteristics through spatial autocorrelation, and probed into the spatial variations in the relationship between human activities and landscape functions using ordinary least squares (OLS) and geographically weighted regression (GWR) models. The result indicates that there are obvious spatial distribution differences in the intensity of human activities in suburban rural communities; that is, the intensity decreases from the inner to the outer suburban areas. Positive influencing factors of human activities are construction area, bus station, road network density, and leisure entertainment, among which, construction area is the principal driver; cultural heritage, hydrological regulation, and provision of aesthetics are negatively or positively correlated with human activities in various regions. The results offer insights for the sustainable development of rural environment in suburban areas and the big data-driven rural research.

Keywords: GWR; OLS; ecosystem services; human activities; landscape function; suburban rural community.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • China
  • Ecosystem*
  • Human Activities
  • Humans
  • Rural Population*
  • Spatial Regression