scholarly journals Analysis of the Driving Forces of Urban Expansion Based on a Modified Logistic Regression Model: A Case Study of Wuhan City, Central China

2019 ◽  
Vol 11 (8) ◽  
pp. 2207 ◽  
Author(s):  
Ti Luo ◽  
Ronghui Tan ◽  
Xuesong Kong ◽  
Jincheng Zhou
Keyword(s):  
Logistic Regression ◽  
Spatial Analysis ◽  
Field Strength ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Urban Expansion ◽  
Central China ◽  
Driving Mechanism ◽  
Wuhan City ◽  
Methodological Innovation

Urban development policies and planning schemes are essential drivers of urban expansion in the contemporary world. However, they are usually investigated by qualitative analysis and it is difficult to use them in spatial analysis models. Within the advancement of technology regarding the geostatistical dataset, this study uses a field strength model to quantify policy-oriented factors and designs a modified logistic regression model to analyze the main drivers of urban expansion by selecting natural environment, socioeconomic development, and especially policy-oriented variables. Wuhan City in central China is taken as an example: the modified model is applied and compared with the classical model, and the driving mechanism of urban expansion in Wuhan from 2006 to 2013 is determined through spatial analysis. The results show that the urban system planning in combination with various anthropologic and environmental factors can be comprehensively quantified and described by the urban field strength. The methodological innovation of the classical logistic regression model is tested by statistical and spatial analysis methods, and the results verify that the modified regression model can be used more accurately to investigate the driving mechanism of urban expansion in the past and simulate the spatial pattern of urban evolution in the future.

scholarly journals Dynamic Simulation of Urban Expansion Based on Cellular Automata and Logistic Regression Model: Case Study of the Hyrcanian Region of Iran

2016 ◽  
Vol 8 (8) ◽  
pp. 810 ◽  
Author(s):  
Meisam Jafari ◽  
Hamid Majedi ◽  
Seyed Monavari ◽  
Ali Alesheikh ◽  
Mirmasoud Kheirkhah Zarkesh
Keyword(s):  
Logistic Regression ◽  
Cellular Automata ◽  
Regression Model ◽  
Dynamic Simulation ◽  
Logistic Regression Model ◽  
Urban Expansion ◽  
Model Case

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.

scholarly journals Knowledge, attitudes and willingness to organ donation among the general public: A cross-sectional survey in China

2020 ◽  
Author(s):  
Xiaojing Fan ◽  
Meng Li ◽  
Heike Rolker ◽  
Jiaoyang Du ◽  
Duolao Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Organ Donation ◽  
General Public ◽  
Logistic Regression Model ◽  
Central China ◽  
Western China ◽  
Knowledge And Attitudes ◽  
Willingness To Donate ◽  
The Mean

Abstract Background The purpose of this study is to assess the level of knowledge, attitudes, and willingness to organ donation among the general public in China. Methods The study population consisted of 4274 participants from Eastern, Central and Western China. The participants’ knowledge, attitudes and willingness to organ donation were collected by a self-designed questionnaire consisting of 30 items. Knowledge is measured by 10 items and presented as a 10 point score, attitudes is measured by 20 items using a 5-step Likert scale and total score ranged between 0 and 80; while the willingness to donate is assessed as binary variable (0 = No; 1 = Yes). A logistic regression model was used to assess the association of knowledge and attitudes with willingness to organ donation, controlling for demographic and socioeconomic confounders. Results The questionnaire response rate was 94.98%. The mean score (± SD) of the general public’s knowledge to organ donation was 6.84 ± 1.76, and the mean score (± SD) of attitudes to organ donation was 47.01 ± 9.07. The general public’s knowledge and attitudes were the highest in Eastern China, followed by West and Central China. The logistic regression model indicated a positive association between knowledge and the willingness to organ donation (OR = 1.12, 95%CI: 1.08, 1.17; P < 0.001); attitudes were also positively potential determinant of more willingness to organ donation (OR = 1.08, 95%CI: 1.07, 1.09; P < 0.001). Conclusions Knowledge and attitudes were found to be positively associated with the Chinese general public’s willingness to organ donation. Knowledge about the concept of brain death and the transplant procedure may help raise the rate of willingness to organ donation.

scholarly journals REMOTE SENSING AND MULTIVARIATE LOGISTIC REGRESSION MODEL FOR THE ESTIMATION OF URBAN EXPANSION (CASE OF DARKHAN CITY, MONGOLIA)

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 721-726
Author(s):  
P. Myagmartseren ◽  
D. Ganpurev ◽  
I. Myagmarjav ◽  
G. Byambakhuu ◽  
G. Dabuxile
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Regression Model ◽  
Urban Area ◽  
Urban Growth ◽  
Logistic Regression Model ◽  
Urban Expansion ◽  
Landsat Tm ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression

Abstract. Urban expansion and land use and land cover change (LUCC) studies are a key knowledge of efficient local governance and urban planning and a lot contributing to the future sustainable development of the city. The main goal of the paper is to model a future urban spatial expansion by 2029 and 2039 of Darkhan city using Landsat TM satellite imagery (land use and cover change map of 1999, 2009, and 2019) and multivariate logistic regression model. Clark Lab’s (Clark University) IDRISI &amp; TerrSet software applied for the urban expansion prediction and the correlation between expansion and driving factors. On account of multivariate logistics regression modelling, eight physical factors influencing urban expansion identified to predict urban expansion based on USGS Landsat TM imageries (Landsat Multispectral Scanner with 60 m resolution). The regression statistic accounted for the probability of future urban expansion was positive. Overall, the LUCC has estimated the transition of natural cover to the impervious surface in Darkhan city. Our result estimates an increase in the built-up area and slum area during the period 1999–2009 and 2009–2019, represents LUCC was characterized by an external transformation from natural to urban area. According to the future urban growth prediction, the urban area would be significantly spread into the open space and natural vegetation area. The main findings stated here are that Darkhan city is expanding in an unsystematic way, even though the urban growth has not been analysed in detail and has a bad case of urban unregulated sprawl.

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