Quality Evaluation and Obstacle Identification of Human Settlements in the Qinghai–Tibet Plateau Based on Multi-Source Data

Abstract

The unique high/cold environment of the Qinghai–Tibet Plateau (QTP) limits the natural distribution of the population living there and threatens local residents’ health. Thus, exploring the quality of human settlements in this area is of great significance. In this study, 5 first-level indicators and 25 second-level indicators were initially selected, and the entropy TOPSIS method was used to determine the weight of each indicator and evaluate the quality of the human settlements in each county of the QTP. Then, the coefficient of variation and spatial autocorrelation were used to analyze the spatial differences in human settlement quality. Finally, the obstacle degree model was used to identify those obstacles that affect the quality of the human settlements in the QTP. This study has gathered important findings. (1) The human settlement quality in these counties can be divided into 18 high-level areas, 45 mid- and high-level areas, 44 mid-level areas, 79 mid- and low-level areas, and 28 low-level areas. (2) In terms of spatial patterns, the north is higher than the south, the east is slightly higher than the west, and the surrounding area is higher than the interior. (3) In the clustering model, the high–high clustering trend is mainly concentrated in the north of the QTP, whereas the south-central part of the QTP and the zone where Tibet, Qinghai, and Sichuan meet exhibit obvious low–low clustering. (4) The variability of human settlement quality occurs in the order of Sichuan < Yunnan < Gansu < Xinjiang Autonomous Region < Tibet Autonomous Region < Qinghai. (5) The main first-level obstacles affecting human settlement quality in the counties of the QTP are living conditions, construction level of public service facilities, and infrastructure. The main second-level obstacles are the number of living service facilities, the number of residential districts, and the density of the road networks.

Comprehensive Evaluation and Analysis of Human Settlements’ Suitability in the Yangtze River Delta Based on Multi-Source Data

“…In future research, the residents' subjective perceptions of human settlements could be explored through questionnaires and other means and combined with the objective environment to explore the suitability of human settlements. Furthermore, human settlements are a dynamic system [10], and exploring their temporal evolution features is vital to improving human settlements. However, this study is only a static study on the suitability of human settlements.…”

“…The human settlement is a surface space that is closely related to people's daily lives [9]. With the acceleration of urbanization and industrialization, the population has gradually increased, accompanied by an increasing demand for resources and increasing environmental demands, leading to the gradual prominence of the problems of human settlements [10] and impacting on the quality of residents' lives. Human settlements' suitability affects the distribution of regional populations [11].…”

Adaptability Evaluation of Human Settlements in Chengdu Based on ArcGIS

“…In this study, we combine the actual situation of Chengdu and the standards of park city construction to establish an evaluation index system for the suitability of Chengdu's human settlement. Based on relevant studies, the natural elements include climate, topography, soil and water resources, water and heat conditions, atmosphere, land use, land cover, and natural disasters [27,28]. These factors collectively influence human settlement, although not all of them are representative or practical in practice.…”

Spatial Heterogeneity of Watershed Ecosystem Health and Identification of Its Influencing Factors in a Mountain–Hill–Plain Region, Henan Province, China

“…Spatial autocorrelation is a mathematical representation of spatial dependence that describes the correlation between a variable at a location in space and the same variable at its neighboring location and has been widely used in spatial dependence analyses [82][83][84]. This study aims to evaluate the spatial aggregation pattern of ecosystem health in the YRB by using spatial autocorrelation analysis.…”