scholarly journals Dynamic Vulnerability Analysis of Mountain Settlements Exposed to Geological Hazards: A Case Study of the Upper Min River, China

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Mingtao Ding ◽  
Chuan Tang ◽  
Tao Huang ◽  
Zemin Gao
Keyword(s):  
Population Density ◽  
Dynamic Change ◽  
Evaluation Index System ◽  
Geological Hazards ◽  
Road Density ◽  
Important Indicator ◽  
Min River ◽  
Vulnerability Evaluation ◽  
Mountain Settlements ◽  
Economic Density

The upper reaches of Min River (The upper Min River) is located in Southwest China with significant mountain settlements, which are vulnerable to frequent geological hazards. Based on a field investigation, collation of yearbook data, and analysis through the use of SPSS statistical software, a vulnerability evaluation index system of geological hazards was devised. According to the actual field situation and the acquired data of the study area in 2006, 2009, and 2015, 16 indicators were selected as settlement vulnerability evaluation indexes of geological hazards. The indexes included population density, building coverage, and economic density. Based on the comprehensive evaluation model of entropy value, the dynamic change in the settlement vulnerability of geological hazards was analyzed. The results showed that population density, building coverage, economic density, and road density were the factors that affected the settlement vulnerability of geological hazards the most—Wenchuan earthquake caused considerable damage to the upper Min River, making the area the most vulnerable in 2009. However, its vulnerability decreased in 2015, which indicated that postearthquake reconstruction achieved significant results. Thus, the vulnerability has emerged as an important indicator reflecting the safety and healthy development of mountain settlements.

Spatial Model Approach for Deforestation

2013 ◽  
pp. 1901-1912
Author(s):  
Lilik B. Prasetyo ◽  
Chandra Irawadi Wijaya ◽  
Yudi Setiawan
Keyword(s):  
Population Density ◽  
Rural Areas ◽  
Forest Cover ◽  
Positive Impact ◽  
Forest Loss ◽  
Road Density ◽  
Policy Makers ◽  
Explanatory Variables ◽  
The Government ◽  
Negative Impacts

Java is very densely populated since it is inhabited by more than 60% of the total population of Indonesia. Based on data from the Ministry of Forestry, forest loss between 2000-2005 in Java was about 800,000 hectares. Regardless of the debate on whether the different methodologies of forest inventory applied in 2005 have resulted in an underestimation of the figure of forest loss or not, the decrease of forest cover in Java is obvious and needs immediate response. Spatial modeling of the deforestation will assist the policy makers in understanding this process and in taking it into consideration, when decisions are made on the issue. Moreover, the results can be used as data input to solve environmental problems resulting from deforestation. The authors of this chapter modeled the deforestation in Java by using logistic regression. Percentage of deforested area was considered as the response variable, whilst biophysical and socioeconomic factors, that explain the current spatial pattern in deforestation, were assigned as explanatory variables. Furthermore, the authors predicted the future deforestation process, and then, for the case of Java, it was validated with the actual deforestation derived from MODIS satellite imageries from 2000 to 2008. Results of the study showed that the impacts of population density, road density, and slope are significant. Population density and road density have negative impacts on deforestation, while slope has positive impact. Deforestation on Java Island tends to occur in remote areas with limited access, low density population and relatively steep slopes. Implication of the model is that the government should pay more attention to remote rural areas and develop good access to accelerate and create alternative non agricultural jobs in order to reduce pressure on the forest.

Spatial Model Approach for Deforestation

2011 ◽  
pp. 376-387 ◽  
Author(s):  
Lilik B. Prasetyo ◽  
Chandra Irawadi Wijaya ◽  
Yudi Setiawan
Keyword(s):  
Population Density ◽  
Rural Areas ◽  
Forest Cover ◽  
Positive Impact ◽  
Forest Loss ◽  
Road Density ◽  
Policy Makers ◽  
Explanatory Variables ◽  
The Government ◽  
Negative Impacts

Java is very densely populated since it is inhabited by more than 60% of the total population of Indonesia. Based on data from the Ministry of Forestry, forest loss between 2000-2005 in Java was about 800,000 hectares. Regardless of the debate on whether the different methodologies of forest inventory applied in 2005 have resulted in an underestimation of the figure of forest loss or not, the decrease of forest cover in Java is obvious and needs immediate response. Spatial modeling of the deforestation will assist the policy makers in understanding this process and in taking it into consideration, when decisions are made on the issue. Moreover, the results can be used as data input to solve environmental problems resulting from deforestation. The authors of this chapter modeled the deforestation in Java by using logistic regression. Percentage of deforested area was considered as the response variable, whilst biophysical and socioeconomic factors, that explain the current spatial pattern in deforestation, were assigned as explanatory variables. Furthermore, the authors predicted the future deforestation process, and then, for the case of Java, it was validated with the actual deforestation derived from MODIS satellite imageries from 2000 to 2008. Results of the study showed that the impacts of population density, road density, and slope are significant. Population density and road density have negative impacts on deforestation, while slope has positive impact. Deforestation on Java Island tends to occur in remote areas with limited access, low density population and relatively steep slopes. Implication of the model is that the government should pay more attention to remote rural areas and develop good access to accelerate and create alternative non agricultural jobs in order to reduce pressure on the forest.

scholarly journals Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Shaohua Wang ◽  
Yanyan Chen ◽  
Jianling Huang ◽  
Ning Chen ◽  
Yao Lu
Keyword(s):  
Population Density ◽  
Spillover Effects ◽  
Positive Influence ◽  
Least Square ◽  
Spatial Autoregression ◽  
Road Density ◽  
Traffic Crashes ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Spatial Weight

This study presents a spatial approach for the macrolevel traffic crashes analysis based on point-of-interest (POI) data and other related data from an open source. The spatial autoregression is explored by Moran’s I Index with three spatial weight features (i.e., (a) Rook, (b) Queen, and (c) Euclidean distance). The traditional Ordinary Least Square (OLS) model, the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and the Spatial Durbin Model (SDM) were developed to describe the spatial correlations among 2,114 Traffic Analysis Zones (TAZs) of Tianjin, one of the four municipalities in China. Results of the models indicated that the SDM with the Rook spatial weight feature is found to be the optimal spatial model to characterize the relationship of various variables and crashes. The results show that population density, consumption density, intersection density, and road density have significantly positive influence on traffic crashes, whereas company density, hotel density, and residential density have significant but negative effects in the local TAZ. The spillover effects coefficient of population density and road density are positive, indicating that the increase of these variables in the surrounding TAZs will lead to the increase of crashes in the target zone. The impacts of company density and hotel density are just the opposite. In general, the research findings can help transportation planners and managers better understand the general characteristics of traffic crashes and improve the situation of traffic security.

Gridded Emission Inventory of Criteria Air Pollutants for National Capital Territory, Delhi

2020 ◽  
Author(s):  
Rahul Chaurasia ◽  
Manju Mohan
Keyword(s):  
Air Pollution ◽  
Spatial Distribution ◽  
High Resolution ◽  
Power Plant ◽  
Population Density ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Road Density ◽  
Criteria Air Pollutants ◽  
National Capital

<p>The megacities of the world are experiencing a punishing level of air pollution where primary sources of emissions are industrial, residential and transportation. Delhi is also no exception and had been worst performing in terms of air quality and air pollution. In this backdrop, a high-resolution emission inventory becomes an essential tool to predict and forecast pollutant concentration along with the assessment of the impact of various government policies. This study aims to prepare a high-resolution gridded emission inventory (1km*1km) of criteria air pollutants (PM10, PM2.5, NO<sub>2</sub>, SO<sub>2 </sub>and CO) for Delhi-NCT (National Capital Territory).  The bottom-up gridded emission inventory has been prepared taking account of population density, land use pattern and socio-economic status. The emission from all the primary sectors has been taken into accounts such as transport, residential burning, industries, power plants, and municipal solid waste burning.  The emissions are estimated using emission factors and activity data for each sector. The emission factor for various fuel type burning is taken from CPCB (Central Pollution Control Board) reports and previous literature. Data corresponding to various sectors such as the amount of fuel consumed, population density, road density, traffic congestion points, industrial location, unauthorized colonies, slums, and total solid waste generation has been acquired from various government bodies, reports, and literature. The result reveals that the total estimated emissions from transportation, industries and domestic sector contribute nearly 72%, 60%, 52% of NOx, SO2 and PM10 emission respectively.  The transport sector has been found as the bulk contributor towards CO and NOx emissions. Domestic sector and Power plant emission have been found to be a bulk contributor of CO and SO2. Later, the spatial distribution of the emission is done using GIS technique (Arc-GIS). For spatial distribution of emission, district-wise population data, road density data, power plant location and digitization of the road network was carried out.</p>

Regional Vulnerability Evaluation Index System of Environmental Emergencies in Petrochemical Industry

2014 ◽  
Vol 1073-1076 ◽  
pp. 400-404 ◽  
Author(s):  
Hong Tian ◽  
Jian Zhang
Keyword(s):  
Index System ◽  
Petrochemical Industry ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Vulnerability Factors ◽  
Economic Vulnerability ◽  
Vulnerability Evaluation ◽  
The People ◽  
Regional Vulnerability ◽  
Constituent Elements

This paper introduces regional vulnerability of environmental emergencies in petrochemical industry based on the analysis of the vulnerability and the theory of the constituent elements. And it builds the regional vulnerability evaluation index system of environmental emergencies in the petrochemical industry from analyzing the people vulnerability factors, economic vulnerability factors, environment vulnerability factors and regional respond capability.

Research on Water Resources Vulnerability Evaluation Model Based on Extension Theory

2013 ◽  
Vol 726-731 ◽  
pp. 4123-4130
Author(s):  
Hai Jiao Liu ◽  
Yu Zhi Shi ◽  
Ming Yuan Fan ◽  
Ji Wen Huang ◽  
Hua Wei Chen ◽  
...  
Keyword(s):  
Water Resources ◽  
Index System ◽  
Evaluation Model ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Entropy Weight ◽  
Vulnerability Evaluation ◽  
Efficient Performance ◽  
Proposed Model ◽  
Water Resources Vulnerability

Water resources vulnerability evaluation has important significance to guide the water resources management and water ecological environment protection. This paper builds the water resources vulnerability evaluation index system from three aspects of natural, human and bearing capacity and integrates matter-element theory with entropy weight to construct the matter-element extension evaluation model of water resources vulnerability. The area of Shandong Province is selected for study, and the proposed model and evaluation index system are applied to respectively evaluates the water resources vulnerability of each city, the results show that the proposed model has a efficient performance, and water resources vulnerability evaluated is higher in whole study area and varies significantly in space, moreover, the northwestern cities are higher, on the contrary, the southeastern cities are lower.

scholarly journals Urban-rural gradients of ecosystem services and the linkages with socioeconomics

2015 ◽  
Vol 39 ◽  
pp. 1-31 ◽  
Author(s):  
Yin Hou ◽  
Felix Müller ◽  
Bo Li ◽  
Franziska Kroll
Keyword(s):  
Ecosystem Services ◽  
Population Density ◽  
Habitat Quality ◽  
Unemployment Rate ◽  
Rural Areas ◽  
Road Density ◽  
Urban Fabric ◽  
Housing Area ◽  
Urban Rural

The principle of urban-rural gradients can reveal the spatial variations of ecosystem services and socioeconomic dimensions. The interrelations between ecosystem services and socioeconomics have scarcely been considered in the context of urban-rural areas. We investigated the spatial gradients and the mutual linkages of several ecosystem services and socioeconomic variables in the urban-rural areas of Leipzig, Germany, and Kunming, China. The results showed some regularities in the spatial patterns of ecosystem services and socioeconomic dimensions in both study areas. Habitat quality and f-evapotranspiration of Leipzig and habitat quality of Kunming demonstrated apparent increasing trends along all gradient patterns. However, the other ecosystem services presented divergent spatial variabilities in different gradient patterns. Road density, urban fabric and population density showed identical declining trends in both case study areas. Differently, household size, housing area as well as unemployment rate in Leipzig presented inconsistent spatial dynamics with considerable fluctuations. With regard to the gradient interrelations, road density, urban fabric and population density were strongly correlated with most ecosystem services in both case study areas. In contrast, the gradients of household size, housing area and unemployment rate of Leipzig showed inconsistent correlations with the ecosystem services gradients. Our study provides evidence to the argument that typical patterns of ecosystem service gradients do not exist in different urban-rural areas.

scholarly journals Assessment of Wetland Ecosystem Health Using the Pressure–State–Response (PSR) Model: A Case Study of Mursidabad District of West Bengal (India)

2020 ◽  
Vol 12 (15) ◽  
pp. 5932 ◽  
Author(s):  
Subhasis Das ◽  
Biswajeet Pradhan ◽  
Pravat Kumar Shit ◽  
Abdullah M. Alamri
Keyword(s):  
Population Density ◽  
Ecosystem Health ◽  
Census Data ◽  
West Bengal ◽  
Health Conditions ◽  
Wetland Ecosystem ◽  
Road Density ◽  
Rapid Urbanization ◽  
State Response ◽  
Pressure State

Wetlands are essential for protein production, water sanctification, groundwater recharge, climate purification, nutrient cycling, decreasing floods and biodiversity preservation. The Mursidabad district in West Bengal (India) is situated in the floodplain of the Ganga–Padma and Bhagirathi rivers. The region is characterized by diverse types of wetlands; however, the wetlands are getting depredated day-by-day due to hydro-ecological changes, uncontrolled human activities and rapid urbanization. This study attempted to explore the health status of the wetland ecosystem in 2013 and 2020 at the block level in the Mursidabad district, using the pressure–state–response model. Based on wetland ecosystem health values, we categorized the health conditions and identified the blocks where the health conditions are poor. A total of seven Landsat ETM+ spaceborne satellite images in 2001, 2013 and 2020 were selected as the data sources. The statistical data included the population density and urbanization increase rate, for all administrative units, and were collected from the census data of India for 2001 and 2011. We picked nine ecosystem indicators for the incorporated assessment of wetland ecosystem health. The indicators were selected considering every block in the Mursidabad district and for the computation of the wetland ecosystem health index by using the analytical hierarchy processes method. This study determined that 26.92% of the blocks fell under the sick category in 2013, but increased to 30.77% in 2020, while the percentage of blocks in the very healthy category has decreased markedly from 11.54% to 3.85%. These blocks were affected by higher human pressure, such as population density, urbanization growth rate and road density, which resulted in the degradation of wetland health. The scientific protection and restoration techniques of these wetlands should be emphasized in these areas.

scholarly journals Examining the Factors Influencing Transport Sector CO2 Emissions and Their Efficiency in Central China

2019 ◽  
Vol 11 (17) ◽  
pp. 4712 ◽  
Author(s):  
Huali Sun ◽  
Mengzhen Li ◽  
Yaofeng Xue
Keyword(s):  
Co2 Emissions ◽  
Carbon Emissions ◽  
Urban Form ◽  
Dynamic Change ◽  
National Level ◽  
High Energy ◽  
Central China ◽  
Transport Sector ◽  
Road Density ◽  
Factors Influencing

The fast development of the transport sector has resulted in high energy consumption and carbon dioxide (CO2) emissions in China. Though existing studies are concerned with the factors influencing transport sector CO2 emissions at the national level (or in megacities), little attention has been paid to the comprehensive impact of socio-economic, urban form, and transportation development on transport sector carbon emissions and emissions efficiency in central China. This paper examines the comprehensive impact of the transport sector’s carbon emissions from six provinces in central China, during the period from 2005 to 2016, based on the panel data model. The dynamic change of CO2 emissions efficiency is then analyzed using the Global Malmquist Luenberger Index. The results indicate that, firstly, economic growth, road density, the number of private vehicles, and the number of public vehicles have caused greater CO2 emissions during the study period, while the freight turnover, urbanization level, and urban population density had repressing effects on CO2 emissions. Secondly, an uneven distribution of CO2 emissions and CO2 emissions efficiency was found among different provinces in central China. Thirdly, changes in CO2 emissions efficiency were mainly due to technical changes. Finally, we present some policy suggestions to mitigate transport sector CO2 emissions in central China.

scholarly journals Research on Equalization of Public Transportation Services Based on Perspective of Groups – A Case Study of Beijing

2021 ◽  
Vol 257 ◽  
pp. 03029
Author(s):  
Ruijiao Liu ◽  
Qiong Tong ◽  
Jin Tian
Keyword(s):  
Public Transportation ◽  
Evaluation Index System ◽  
Survey Method ◽  
Entropy Model ◽  
Important Indicator ◽  
Transportation Services ◽  
Disadvantaged Groups ◽  
Benefit Evaluation ◽  
Transportation Service ◽  
Service Groups

In this paper, by quantifying the value of public transportation travel benefits of ordinary travel groups, economically disadvantaged groups, and physiologically disadvantaged groups, this paper establishes a public transportation travel benefit evaluation index system, uses a questionnaire survey method to collect index data within Beijing, and establishes a TOPSIS model. Based on the travel benefit values of the three groups, the benefit compensation for disadvantaged traffic groups is considered, benefit compensation factors are introduced, the degree of equalization is evaluated based on the Wilson entropy model, and the level of equalization among public transportation service groups in Beijing is empirically studied to find out an important indicator that restricts the level of equalization.

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