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 Analyzing the Driving Factors Causing Urban Expansion in the Peri-Urban Areas Using Logistic Regression: A Case Study of the Greater Cairo Region

2019 ◽  
Vol 4 (1) ◽  
pp. 4 ◽  
Author(s):  
Muhammad Salem ◽  
Naoki Tsurusaki ◽  
Prasanna Divigalpitiya
Keyword(s):  
Logistic Regression ◽  
Population Density ◽  
Urban Areas ◽  
Urban Expansion ◽  
Driving Factors ◽  
Relative Operating Characteristic ◽  
Probability Map ◽  
Greater Cairo ◽  
Cell Window ◽  
Greater Cairo Region

The peri-urban area (PUA) of the Greater Cairo Region (GCR) in Egypt has witnessed a rapid urban expansion during the last few years. This urban expansion has led to the loss of wide, areas of agriculture lands and the annexation of many peripheral villages into the boundary of the GCR. This study analyzed the driving factors causing the urban expansion in the GCR during the period 2007–2017 using the logistic regression model (LRM). Eight independent variables were applied in this model: distance to the nearest urban center, distance to the nearest center of regional services, distance to water streams, distance to the main agglomeration, distance to industrial areas, distance to nearest road, number of urban cells within a 3 × 3 cell window and population density. The analysis was conducted using LOGISTICREG module in Terrset software. This research showed that the population density and distance to the nearest road have the highest regression coefficients, 0.540 and 0.114, respectively, and were the most significant driving factors of urban expansion during the last 10 years (2007–2017). Moreover, based on the results of the LRM, a probability map of urban expansion in the PUA was created, which shows that most urban expansion would be around the existing urban areas and near roads. The relative operating characteristic (ROC) value of 0.93 indicates that the probability map of urban expansion is valid.

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 Negative Impact of Amphetamine-Type Stimulant Use on Opioid Agonist Treatment Retention in Ontario, Canada

2021 ◽  
Vol 12 ◽  
Author(s):  
Kristen A. Morin ◽  
Frank Vojtesek ◽  
Shreedhar Acharya ◽  
David C. Marsh
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Drug Screening ◽  
Urban Areas ◽  
Logistic Regression Model ◽  
Opioid Agonist ◽  
Stimulant Use ◽  
Urine Drug ◽  
The Impact ◽  
Urine Drug Screening

Objective: The objective of this study was to evaluate epidemiological trends of co-use patterns of amphetamine-type stimulants and opioids and the impact of co-use patterns on Opioid Agonist Treatment (OAT) retention in Ontario, Canada. The secondary objective was to assess geographical variation in amphetamine-type stimulant use in Northern Rural, Northern Urban, Southern Rural and Southern Urban Areas of Ontario.Methods: A retrospective cohort study on 32,674 adults receiving OAT from ~70 clinics was conducted between January 1, 2014, and December 31, 2020, in Ontario, Canada. Patients were divided into four groups base on the proportion of positive urine drug screening results for amphetamine-type stimulants during treatment: group 1 (0–25%), group 2 (25–50%), group 3 (50–75%), and groups 4 (75–100%). A Fractional logistic regression model was used to evaluate differences over time in amphetamine-type stimulant use with urine drug screening results. A Cox Proportional Hazard Ratio model was used to calculate the impact of amphetamine-type stimulant use on retention in OAT and adjusted for sociodemographic characteristics, drug use and clinical factors. Lastly, a logistic regression model was used on a subgroup of patients to assess the impact of geography on amphetamine-type stimulant use in Northern Rural, Northern Urban, Southern Rural and Southern Urban Areas of Ontario.Results: There were significant differences in amphetamine-type stimulant positive urine drug screening results year-over-year from 2015 to 2020. Significant differences were observed between amphetamine-type stimulant groups with regards to sociodemographic, clinical and drug use factors. Compared to those with no amphetamine-type stimulant use, the number of days retained in OAT treatment for amphetamine-type stimulant users was reduced (hazard ratio 1.19; 95% confidence interval = 1.07–1.17; p < 0.001). Lastly, an adjusted logistic regression model showed a significant increase in the likelihood of amphetamine-type stimulant use in Northern Rural regions compared to Southern Urban areas.Conclusion: There was a significant increase in amphetamine-type stimulant use among individuals in OAT from 2014 to 2020, associated with decreased OAT retention. Research is required to determine if tailored strategies specific to individuals in OAT who use amphetamine-type stimulants can improve OAT outcomes.

scholarly journals The Application of a Logistic Regression Model for Predicting Preferences of Transport System Users

2018 ◽  
Vol 64 (4) ◽  
pp. 145-159
Author(s):  
A. Brzeziński ◽  
K. Brzeziński ◽  
T. Dybicz ◽  
Ł. Szymański
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Transport System ◽  
Urban Areas ◽  
Logistic Regression Model ◽  
Probabilistic Approach ◽  
The Impact ◽  
Selection Of ◽  
Made In

AbstractWithin the INMOP 3 research project, an attempt was made to solve a number of problems associated with the methodology of modelling travel in urban areas and the application of intermodal models. One of these is the ability to describe the behaviour of transport system users, when it comes to making decisions regarding the selection of means of transport and searching for relationships between travel describing factors and the decisions made in regard of means of transport choice.The paper describes a probabilistic approach to the determination of modal split, and the application of a logistic regression model to determine the impact of variables describing individual and mass transport travels on the probability of selecting specific means of transport. Travels in local model of Warsaw city divided into 9 motivation groups were tested, for which ultimately 8 models were developed, out of which 7 were deemed very well fitted (obtained pseudo R2 was well above 0.2).

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.

Do farmer and credit specific characteristics matter in microfinance programmes’ participation? Evidence from smallholder farmers in Ada west and east districts

2019 ◽  
Vol 79 (3) ◽  
pp. 353-370 ◽  
Author(s):  
Emmanuel Tetteh Jumpah ◽  
Yaw Osei-Asare ◽  
Emmanuel Kodjo Tetteh
Keyword(s):  
Logistic Regression ◽  
Interest Rate ◽  
Regression Model ◽  
Urban Areas ◽  
Logistic Regression Model ◽  
Smallholder Farmers ◽  
Household Head ◽  
Sampling Technique ◽  
Content Type ◽  
Factors Affecting

Purpose Users of smallholder farmer microfinance are able to make enough returns to repay credits advanced to them. However, they are in dire need of financial capital such that they are inconsiderate of farmer- and credit-specific characteristics when participating in a microfinance programme. This study analyses perceptions of stakeholders regarding select farmer and credit characteristics within the microfinance industry. The study identifies and analyses the factors that influence participation in a microfinance programme by farmers using the logistic regression model. The purpose of this paper is to widen the knowledge base of rural agricultural finance, including factors that influence participation in microfinance intervention(s) thereof. Design/methodology/approach A total of 104 participants and 120 non-participant farmers in microfinance programmes were interviewed using a semi-structured questionnaire by applying the multistage sampling technique. The paper applied the logistic regression model in which farmer- and credit-specific characteristics were used to estimate the probabilities of participation. Findings The logistic regression results showed that distance, interest rate, experience, membership of farmer-based organisation, number of dependants, household, gender and age were statistically significant farmer- and credit-specific characteristics that influence participation in microfinance programmes. Interest rate and distance exact negative significance influence on participation, whereas membership of farmer-based organisations, experience, gender, household head and age influence participation positively. Reduction in the interest rate and expansion of microfinance to very remote areas rather than locations in urban areas are crucial in terms of improving participation. Research limitations/implications The paper used data from only farmers so there is a limit to which the results can be generalised for all microfinance users. It may be relevant to undertake a study that considers non-farm enterprises. Practical implications This paper brings to light the need to develop well-structured microfinance facilities that meet the specific needs of the rural poor in transitioning economies while taking into consideration critical factors affecting participation before the establishment of such programmes. Originality/value This paper provides empirical evidence to show that farmer- and credit-specific characteristics are essential to ensure participation and success of microfinance programmes thereof.

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 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 & 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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