The spatial distribution of urban service facilities is largely constrained by the road network. In this study, network point pattern analysis and correlation analysis were used to analyze the relationship between road network and healthcare facility distribution. The weighted network kernel density estimation method proposed in this study identifies significant differences between the outside and inside areas of the Ming city wall. The results of network K-function analysis show that private hospitals are more evenly distributed than public hospitals, and pharmacy stores tend to cluster around hospitals along the road network. After computing the correlation analysis between different categorized hospitals and street centrality, we find that the distribution of these hospitals correlates highly with the street centralities, and that the correlations are higher with private and small hospitals than with public and large hospitals. The comprehensive analysis results could help examine the reasonability of existing urban healthcare facility distribution and optimize the location of new healthcare facilities.
Flood disasters from dam breaks cause serious loss of human life and immense damage to infrastructure and economic stability. The application of Geographic Information System technology integrated with hydrological modeling for mapping flood-inundated areas and depth can play a momentous role in further minimizing the risk and possible damage. In the present study, base terrain data, hydrological data, and dam engineering data were integrated using the MIKE-21 dam-break model to analyze flood routing under the most serious scenarios. A deterministic approach was used to calculate the hydraulic elements of dam breakage during a flood. Additionally, the hydraulic elements generated by the MIKE-21 dam-break model (a modelling system for estuaries, coastal waters, and seas)-including flood depth, submersion time, and flow direction-were integrated with a digital elevation model of the site downstream of the dam in order to map the possible affected areas. Using an empirical model in addition to using the superimposition of dam flood calculation results and the social and economic survey data, dam damage assessment was implemented. In accordance with a relevant standard, the flood risk mapping guidelines and a set of client/server structures were developed for a management system for dam-break hazard mapping of the Foziling reservoir. The simulation data and the study results can provide a scientific basis for emergency management of the reservoir and provide a socio-economic framework for downstream areas.
The survival and distribution of animals cannot be separated from a certain environment. How patterns in mammalian species depend on the environment remain unclear. This study incorporating spatial data on climate, precipitation, topography, and vegetation quantitatively analyzed the influence of specific geographical factors on the spatial distribution of terrestrial mammalian richness using the Geodetector model. We used the spatial analysis method of geographical information systems (GIS), separating the mammalian distribution of 621 species into 10 by 10 km grids to measure spatial richness. Our results showed that there were significant spatial differences in terrestrial mammalian richness in China. There was a low richness in the east and west, but high richness in the south. Individual factor detection results showed that annual precipitation (AP) and the minimum temperature of the coldest month (MTCM) were the dominant factors affecting the spatial pattern of mammal richness in China. Patterns in the distribution of species richness had distinct characteristics for different mammalian orders and were influenced by different environmental factors. The richness distribution of most orders was mainly affected by MTCM and AP. Interactive detection results showed that interacting factors in pairs play much bigger roles in the spatial distribution of species richness than individual factors. The synergistic effect of elevation with AP and MTCM best explained the distribution differences of species richness. We found that the Geodetector model is a valuable tool, hoping to be more widely used in biogeography.
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