Carbonate bedrock control of soil Cd background in Southwestern China: Its extent and influencing factors based on spatial analysis

Since the 1990s, the notion of a circular economy has been developing globally; countries all over the world have been considering the development of a circular economy as an important means of achieving sustainable development. As the development of an industrial circular economy can help promote the efficient recycling of resources, it is an important starting point for industrial transformation and upgrading, and represents a key factor that will lead to the development of a circular economy in China. China’s varying provinces (municipalities and autonomous regions) have successively implemented circular economy practices in the industrial field. The research object of the present study is 30 provinces, autonomous regions, and municipalities directly under the control of central government (Hong Kong, Macao, Taiwan, and Tibet were not included owing to lack of data). Through the integration of geographic information systems (GIS) technology and the spatial analysis model, data envelopment analysis (DEA) model, and Tobit regression model, a measure model and index system are constructed, in order to carry out a multi-angle comprehensive study integrating the efficiency evaluation, spatial analysis, and influencing factors analysis of China’s industrial circular economy. It is an important innovation, and an important contribution to the existing research system. The conclusions are as follows: (1) In general, the overall level of China’s industrial circular economy’s efficiency was not high, and there was still a lot of room for improvement. The integrated efficiency of the industrial circular economy in the eastern region was relatively high, followed by that in the western region, and the lowest level in the middle region. (2) The efficiency of China’s industrial circular economy displayed obvious spatial aggregation characteristics at the provincial level, including clear spatial dependence and spatial heterogeneity. High-value aggregation areas were mainly distributed in the eastern coastal areas, and low-value aggregation areas were concentrated and contiguously distributed in the middle and western inland areas. (3) The four elements of economic level, openness to the outside, government regulation, and industrialization aggregation each impose a significant positive impact on the efficiency of China’s industrial circular economy, which can promote its efficiency. The level of industrialization exerts a significant negative impact on the efficiency of the industrial circular economy, which hampers its improvement. The impact of technological innovation on the efficiency of the industrial circular economy is not statistically significant.

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Spatial analysis of logistics ecological efficiency and its influencing factors in China: based on super-SBM-undesirable and spatial Dubin models

Environmental Science and Pollution Research ◽

10.1007/s11356-021-16323-x ◽

2021 ◽

Author(s):

Dongling Bai ◽

Qianli Dong ◽

Syed Abdul Rehman Khan ◽

Yan Chen ◽

Dongfang Wang ◽

...

Keyword(s):

Spatial Analysis ◽

Influencing Factors ◽

Ecological Efficiency

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Prevalence and influencing factors of depression of caregivers in children with epilepsy in southwestern China: a cross-sectional study

Medicine ◽

10.1097/md.0000000000023571 ◽

2021 ◽

Vol 100 (10) ◽

pp. e23571

Author(s):

Chunsong Yang ◽

Tang Yao ◽

Yuanlong Huang ◽

Li Zhao ◽

Lingli Zhang

Keyword(s):

Influencing Factors ◽

Cross Sectional Study ◽

Southwestern China ◽

Sectional Study ◽

Cross Sectional

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Building a historical flooding map through spatial analysis

10.5194/egusphere-egu2020-1183 ◽

2020 ◽

Author(s):

Priscila Barros Ramalho Alves ◽

Iana Alexandra Alves Rufino ◽

Slobodan Djordjévic ◽

Akbar Javadi

Keyword(s):

Spatial Analysis ◽

Influencing Factors ◽

Flow Direction ◽

Policy Makers ◽

Model Builder ◽

Flood Susceptibility ◽

Multiple Datasets ◽

Gis Environment ◽

Flood Susceptibility Mapping ◽

The City

<p>Due to the increase of flooding cases around the world, there is a need for producing even more accurate flood susceptibility mapping. For this, different models, software and frameworks have been developed for many years to assist local authorities and policy-makers for forecasting hazards and mitigating flooding impacts. However, spatially model flooding in real-world systems remains considered as a difficult task. Forecasting flooding requires knowledge from past events and mapping flood locations is crucial to explain the correlation among the flooding and the influencing factors as well as model calibration and validation. In developing countries, a collection of flooding records and inventories remains challenging, either because the data is not available, or because it is not in the suitable scale and resolution. Building historical flooding map is considered a time-consuming process with multiple datasets and normally with costly field surveys. Besides, acquiring this data can be harder due to the inexistence of flooding insurance or civil protection agencies support. This work aims to contribute to this context by developing and assessing a GIS-based framework to map historical flooding cases through the use of spatial analysis. In this study, we used ArcGIS Pro software to construct a historic flooding map for Campina Grande, Brazil. The city faces recurrent flooding episodes, but there is not an available official map with flooding locations to guide decisions for mitigation. The GIS-based framework allows analysing and better understand the interactions of flooding locations and geographic features. The analysis obtained 230 flooding locations in different scales (buildings, streets and neighbourhoods) and sources for the period from 2004 to 2018. Topographic and hydrologic flood influencing factors (altitude, slope, distance to rivers and lakes, flow direction and accumulation) were selected and combined as layers in the GIS environment. Further, criteria were modelled based on spatial analysis and relations to estimate proximity areas around flood occurrence points with high probability of flooding conditions. These tools allowed to compare visual and data patterns of features and surfaces. All locations and factors were then integrated through Model Builder in order to generate a surface with flooding locations within the city. The final historical flooding map was evaluated and validated with 172 points of confirmed flood cases in the city. The GIS-based framework represents a way of analysing and producing historical inventory maps for flooding management using spatial analysis.</p>

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Quantitative Analysis of the Influencing Factors and Their Interactions in Runoff Generation in a Karst Basin of Southwestern China

Water ◽

10.3390/w12102898 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2898

Author(s):

Wenjuan Hou ◽

Jiangbo Gao ◽

Shaohong Wu

Keyword(s):

Quantitative Analysis ◽

Influencing Factors ◽

Southwestern China ◽

Dominant Factor ◽

Comprehensive Treatment ◽

Runoff Generation ◽

Mountain Regions ◽

Rocky Desertification ◽

Factors Influencing ◽

Geological Conditions

The unique geological conditions of karst regions create highly heterogeneous habitat characteristics, and the addition of human disturbance results in rocky desertification. Water and soil loss are the core questions, and moreover, runoff is the key factor in this process. To further investigate these problems, a typical karst peak cluster depression in southwestern China was selected for this study. Based on the optimal simulation of the runoff yield and flow in this area, the factor detectors and interaction detectors in the geographical detector method were used to quantitatively analyze the factors influencing runoff and their interactions for different geomorphic types. The results show that: (1) the three main factors influencing the total river runoff, surface runoff, and groundwater are landscape fragmentation, land use type, and precipitation, but the ranking of these main influencing factors in each geomorphic type region exists different; (2) the dominant factor in the relatively higher elevation regions is precipitation; (3) the interaction detector results reveal that the interactions between factors enhance the overall influence of a single factor on the runoff generation in all of the geomorphic type regions, including two interaction types of nonlinear enhancement and bifactor enhancement; and (4) the interactions between the factors in the middle elevation plain, middle elevation terrace, and middle relief mountain regions are stronger than those in the middle elevation hill and small relief mountain regions. Quantitative analysis of the factors influencing runoff in karst areas cannot only promote optimization of the water and soil services, but it also provides a scientific basis for improving the comprehensive treatment of rocky desertification.

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Using Gis-based Spatial Analysis To Determine Factors Influencing the Formation of Sinkholes in Greene County, Missouri

Environmental and Engineering Geoscience ◽

10.2113/eeg-2014 ◽

2018 ◽

Vol 24 (3) ◽

pp. 251-261 ◽

Cited By ~ 2

Author(s):

Shishay T. Kidanu ◽

Neil L. Anderson ◽

J. David Rogers

Keyword(s):

Spatial Analysis ◽

Influencing Factors ◽

Ground Surface ◽

Ordinary Least Squares ◽

Hazard Mapping ◽

Infrastructure Development ◽

Least Squares Regression ◽

Karst Terrain ◽

Gwr Model ◽

Greene County

Abstract Sinkholes are inherent features of the karst terrain of Greene County, Missouri, that present hazards and engineering challenges to construction/infrastructure development. Analysis of relationships between the spatial distribution of sinkholes and possible influencing factors can help in understanding the controls involved in the formation of sinkholes. The spatial analysis outlined herein can aid in the assessment of potential sinkhole hazards. In this research, Geographic Information System–based ordinary least squares regression (OLS) and geographically weighted regression (GWR) methods were used to determine and evaluate principal factors appearing to influence the formation and distribution of karst sinkholes. From the OLS result, seven out of 12 possible influencing factors were found to exert significant control on sinkhole formation processes in the study area. These factors are overburden thickness, depth to groundwater, slope of the ground surface, distance to the nearest surface drainage line, distance to the nearest geological structure (such as faults or folds), distance to the nearest road, and distance to the nearest spring. These factors were then used as independent variables in the GWR model. The GWR model examined the spatial non-stationarity among the various factors and demonstrated better performance over OLS. GWR model coefficient estimates for each variable were mapped. These maps provide spatial insights into the influence of the variables on sinkhole densities throughout the study area. GWR spatial analysis appears to be an effective approach to understand sinkhole-influencing factors. The results could be useful to provide an objective means of parameter weighting in models of sinkhole susceptibility or hazard mapping.

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