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Karst environmental issues have become one of the hot spots in contemporary international geological research. The same problem of water shortage is one of the hot spots of global concern. The peak-cluster depression basins in southwest of Guangxi is an important water connotation and ecological barrier areas in the Pearl River Basin of China. Thus, studying the spatial and temporal variations and the influencing factors of its water yield services is critical to achieve the sustainable development of water resources and ecological environmental protection in this region. As such, this paper uses the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to assess the spatial and temporal variabilities of water yield services and its trends in the peak-cluster depression basins in southwest of Guangxi from 2000 to 2020. This work also integrates precipitation (Pre), reference evapotranspiration (ET), temperature (Tem), digital elevation model (DEM), slope, normalized difference vegetation index (NDVI), land use/land cover (LULC) and soil type to reveal the main factors that influence water yield services with the help of Geodetector. Results show that: 1) in time scale, the total annual water yield in the study area show a fluctuating and increasing trend from 2000 to 2020, with a growth rate of 7.3753 × 108 m3/yr, and its multi-year average water yield was 538.07 mm; 2) in spatial pattern, with high yield areas mainly distributed in the south of the study area (mainly including Shangsi County, Pingxiang City, Ningming County, Longzhou County and Jingxi County), and low yield areas mainly distributed in Baise City and Nanning City; 3) the dominant factor of water yield within karst and non-karst landforms is not necessarily controlled by precipitation, and the explanation degree of DEM factors in karst areas is significantly higher than that in non-karst areas; 4) amongst the climatic factors, Pre, ET and Tem are dominant in the spatial pattern of region water yield capacity. among which Pre has the highest explanatory power for the spatial heterogeneity of annual water production, with q values above 0.8, and each driver showed a significant interaction on the spatial distribution of water yield, with Pre exhibiting the strongest interaction with LULC. [ABSTRACT FROM AUTHOR]

Characterizing the availability of opportunities to residents has been a long-term aim in health care geographic investigation. It is important to measure the degree of inequity in health care accessibility and to identify underserved areas, due to the uneven distribution of health care services. In this study, JavaScript was used to calculate travel time based on Amap, as this can provide a more reliable data support to measure the health care accessibility in Xi'an communities, China. Based on the overall equity, herein, an attempt was made to quantify the equity of health care accessibility, and to identify health care underserved areas inside the different communities. Results show that the accessibility to low-level health care services is high in the northern areas and low in the southern areas, while the accessibility to high-level and comprehensive health care services shows a clear core-periphery spatial structure. Moreover, the overall equity of the health care accessibility is relatively low, and the inequity of high-level health care accessibility is further aggravated. Furthermore, the quantified equity of accessibility to high-level and comprehensive health care services in the central urban areas is better; however low-level health care services are relatively inadequate. There are significant differences among health care underserved areas, in particular, for the worst equity and the lowest accessibility areas (A1) and the worse equity and the lowest accessibility areas (B1) in high-level underserved areas. Notably, the sharing of health care services and the reasonable flow of health technical personnel among different levels of health institutions can make the high-level health care services in the central urban areas have a greater trickle effect on the surrounding areas. [ABSTRACT FROM AUTHOR]