Driving Factors of Urban Sprawl in the Romanian Plain. Regional and Temporal Modelling Using Logistic Regression

2021 ◽  
pp. 1-25
Author(s):  
Ines Grigorescu ◽  
Gheorghe Kucsicsa ◽  
Bianca Mitrică ◽  
Irena Mocanu ◽  
Monica Dumitrașcu
Keyword(s):  
Logistic Regression ◽  
Urban Sprawl ◽  
Driving Factors ◽  
Temporal Modelling

What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests

2016 ◽  
Vol 25 (5) ◽  
pp. 505 ◽  
Author(s):  
Futao Guo ◽  
Guangyu Wang ◽  
Zhangwen Su ◽  
Huiling Liang ◽  
Wenhui Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Regional Scale ◽  
Fire Risk ◽  
Driving Factors ◽  
Fire Season ◽  
Fire Occurrence ◽  
Climate Factors ◽  
Local Factors ◽  
Risk Zones

We applied logistic regression and Random Forest to evaluate drivers of fire occurrence on a provincial scale. Potential driving factors were divided into two groups according to scale of influence: ‘climate factors’, which operate on a regional scale, and ‘local factors’, which includes infrastructure, vegetation, topographic and socioeconomic data. The groups of factors were analysed separately and then significant factors from both groups were analysed together. Both models identified significant driving factors, which were ranked in terms of relative importance. Results show that climate factors are the main drivers of fire occurrence in the forests of Fujian, China. Particularly, sunshine hours, relative humidity (fire seasonal and daily), precipitation (fire season) and temperature (fire seasonal and daily) were seen to play a crucial role in fire ignition. Of the local factors, elevation, distance to railway and per capita GDP were found to be most significant. Random Forest demonstrated a higher predictive ability than logistic regression across all groups of factors (climate, local, and climate and local combined). Maps of the likelihood of fire occurrence in Fujian illustrate that the high fire-risk zones are distributed across administrative divisions; consequently, fire management strategies should be devised based on fire-risk zones, rather than on separate administrative divisions.

Incorporating spatial autocorrelation and settlement type segregation to improve the performance of an urban growth model

2019 ◽  
Vol 47 (7) ◽  
pp. 1184-1200 ◽  
Author(s):  
Chao Xu ◽  
Didit O Pribadi ◽  
Dagmar Haase ◽  
Stephan Pauleit
Keyword(s):  
Logistic Regression ◽  
Markov Chain ◽  
Spatial Autocorrelation ◽  
Urban Growth ◽  
Driving Factors ◽  
Autologistic Regression ◽  
Cellular Automata Model ◽  
Urban Growth Model ◽  
Ordinary Logistic Regression ◽  
Settlement Growth

As rapid urbanization and population growth have become global issues, urban growth modeling has become an essential tool for decision-makers to understand how urban growth works in overall dense environments and to assess the sustainability of current urban forms. However, in urban growth models (particularly when incorporating quantitative approaches to include driving factors of urban growth), spatial autocorrelation may influence the overall model performance. In this paper, an empirical study was conducted in the region of Munich, and an integrated urban growth model was tested to explain current urban growth. The modeling contributes to advances in the state of the art by combining a range of driving factors using autologistic regression with a transition probability matrix from the Markov chain method in a cellular automata model simulation. The autologistic regression employed here addresses the impact of spatial autocorrelation compared to ordinary logistic regression. Furthermore, this study compared modeling of overall settlement growth with modeling high- and low-density settlement types separately. Incorporating spatial dependency into the model through application of autologistic regression showed improvements when compared to the ordinary logistic regression model. The Kappa indexes were higher when separately modeling the two types of settlement density compared to modeling overall settlement growth since the driving factors of settlement growth of different densities might be different. From an urban planning perspective, this novel autologistic regression-Markov chain-based cellular automata model is a powerful tool that offers an opportunity for planners and government authorities to gain a more precise understanding of the different urban growth processes that might occur in an urban region similar to the one tested here. It should allow for a better assessment of the potential costs, benefits, and risks of corresponding planning strategies.

scholarly journals Geospatial information on geographical and human factors improved anthropogenic fire occurrence modeling in the Chinese boreal forest

2016 ◽  
Vol 46 (4) ◽  
pp. 582-594 ◽  
Author(s):  
Futao Guo ◽  
Selvaraj Selvalakshmi ◽  
Fangfang Lin ◽  
Guangyu Wang ◽  
Wenhui Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Human Factors ◽  
Boreal Forest ◽  
Forest Fire ◽  
Management Practices ◽  
Explanatory Power ◽  
Driving Factors ◽  
Fire Occurrence ◽  
Geospatial Information ◽  
Anthropogenic Fire

We applied a classic logistic regression (LR) model together with a geographically weighted logistic regression (GWLR) model to determine the relationship between anthropogenic fire occurrence and potential driving factors in the Chinese boreal forest and to test whether the explanatory power of the LR model could be increased by considering geospatial information of geographical and human factors using a GWLR model. Three tests, “all variables”, “significant variables”, and “cross-validation”, were applied to compare model performance between the LR and GWLR models. Our results confirmed the importance of distance to railway, elevation, length of fire line, and vegetation cover on fire occurrence in the Chinese boreal forest. In addition, the GWLR model performs better than the LR model in terms of model prediction accuracy, model residual reduction, and spatial parameter estimation by considering geospatial information of explanatory variables. This indicates that the global LR model is incapable of identifying underlying causal factors for wildfire modeling sufficiently. The GWLR model helped identify spatial variation between driving factors and fire occurrence, which can contribute better understanding of forest fire occurrence over large geographic areas and the forest fire management practices may be improved based on it.

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.

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