Driving factors of urban sprawl in Giza governorate of the Greater Cairo Metropolitan Region using a logistic regression model

2016 ◽  
Vol 20 (2) ◽  
pp. 206-225 ◽  
Author(s):  
Taher Osman ◽  
Prasanna Divigalpitiya ◽  
Takafumi Arima
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Urban Sprawl ◽  
Logistic Regression Model ◽  
Driving Factors ◽  
Metropolitan Region ◽  
Greater Cairo

scholarly journals Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

2021 ◽  
Vol 13 (19) ◽  
pp. 10805
Author(s):  
Muhammad Salem ◽  
Arghadeep Bose ◽  
Bashar Bashir ◽  
Debanjan Basak ◽  
Subham Roy ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Urban Areas ◽  
Logistic Regression Model ◽  
Urban Expansion ◽  
Driving Factors ◽  
Sustainable Urban Development ◽  
Landsat Images ◽  
Relative Operating Characteristic ◽  
Simulation Based

During the last three decades, Delhi has witnessed extensive and rapid urban expansion in all directions, especially in the East South East zone. The total built-up area has risen dramatically, from 195.3 sq. km to 435.1 sq. km, during 1989–2020, which has led to habitat fragmentation, deforestation, and difficulties in running urban utility services effectively in the new extensions. This research aimed to simulate urban expansion in Delhi based on various driving factors using a logistic regression model. The recent urban expansion of Delhi was mapped using LANDSAT images of 1989, 2000, 2010, and 2020. The urban expansion was analyzed using concentric rings to show the urban expansion intensity in each direction. Nine driving factors were analyzed to detect the influence of each factor on the urban expansion process. The results revealed that the proximity to urban areas, proximity to main roads, and proximity to medical facilities were the most significant factors in Delhi during 1989–2020, where they had the highest regression coefficients: −0.884, −0.475, and −0.377, respectively. In addition, the predicted pattern of urban expansion was chaotic, scattered, and dense on the peripheries. This pattern of urban expansion might lead to further losses of natural resources. The relative operating characteristic method was utilized to assess the accuracy of the simulation, and the resulting value of 0.96 proved the validity of the simulation. The results of this research will aid local authorities in recognizing the patterns of future expansion, thus facilitating the implementation of effective policies to achieve sustainable urban development in Delhi.

Dynamics of ecosystem services and their driving factors in China’s Beijing-Tianjin-Hebei regional development

2020 ◽  
Author(s):  
Suxiao Li ◽  
Guangchun Lei ◽  
Xiubo Yu
Keyword(s):  
Logistic Regression ◽  
Ecosystem Services ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Arable Land ◽  
Influential Factors ◽  
Driving Factors ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Urbanization Rate

<p><strong>Abstract:</strong> A comprehensive study on the dynamics of ecosystem services and their driving factors is the key prerequisite for enhancing local ecological sustainability. Based on relevant sets of big data, including spatial land data, soil data, DEM, climatic data and social-economic data, using InVEST model and multivariate logistic regression model, the study firstly assessed the spatiotemporal variation of ecosystem services for China’s Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 1990 to 2015. The study then analyzed the natural and socioeconomic factors affecting the ecosystem services. The results show that large spatial and quantitative differences exist in the supply of multiple ecosystem services, and the changes of different types of ecosystem services are driven by different factors. For water yield, the areas of arable land, wetland and built-up land and precipitation are the most influential factors; The areas of arable land, precipitation, temperature, altitude, urbanization rate and amount of nutrient applied per unit area are determinants of changes in nutrient retention; The areas of grassland and forest, temperature, altitude, GDP per capita and urbanization rate affect the soil retention to great extent; for carbon storage, its key influential factors are the areas of different land use types and urbanization rate. The study can facilitate identification of where and how to enhance multiple ecosystem services.</p><p><strong>Keywords:</strong> dynamics of ecosystem services, driving factors, InVEST, multivariate logistic regression model</p>

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

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