Comparing support vector machines with logistic regression for calibrating cellular automata land use change models

Mustafà, Ahmed; Rienow, Andreas; Saadi, Ismaïl; Cools, Mario; Teller, Jacques

doi:10.1080/22797254.2018.1442179

Cited by 76 publications

(33 citation statements)

References 59 publications

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“…A negative correlation with the proximity drivers from roads is also expected, because the probability of expansion in the vicinity of existing roads, is generally higher (Hu & Lo, 2007;Luo & Wei, 2009;Mustafa, Rienow, et al, 2018).…”

Section: Builtup Causative Factorsmentioning

confidence: 99%

“…The ( ) , the neighborhood effect, was evaluated through a 3x3 moving window. It will be determined using the CA approach, with the method proposed by White &Engelen (1997, andalready applied in Mustafa, Heppenstall, et al (2018), expressed with Eq c. Mustafa, Rienow, et al, (2018), Chen et al, (2014) and Poelmans & Van Rompaey, (2009) examined several square sizes and found that the model runs with the 3x3 neighborhood moving window and produced a land-use pattern that best fits the actual pattern. Furthermore, Mustafa, Rienow, et al, (2018) used a 100 m resolution, similar to that of this research, and found that a 3x3 neighborhood moving window outperformed other window sizes (5x5, 7x7, 9x9, and 11x11).…”

Section: Transition Potential and Calibration Processmentioning

confidence: 99%

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Modeling urban sprinkling with cellular automata

Saganeiti

Mustafà

Teller

et al. 2021

Sustainable Cities and Society

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This paper presents a spatiotemporal analysis to simulate and project urban sprinkling with coupled cellular automata (CA) and multinomial logistic regression (MLR) model. Our case study, the Basilicata region, south of Italy, is characterized by urban sprinkling-literally "a small amount of urban territory distributed in scattered particles". The region is witnessing a decoupled growth between demographic trend and urban expansion. We applied a coupled approach based on CA and MLR for urban sprinkling modeling and simulation. From three regional building datasets (1989, 1998 and 2013) building density maps were created and used to calibrate and validate the model and to project future urban expansion. Built-up causative factors were identified through an analysis of 19 articles that were compared and discussed according to their main features (methods, case studies, drivers, urbanization dynamics and demographic growth). The transition probability for the first period (1989-1998) was calibrated with MLR for built-up causative factors and with the multiobjective genetic algorithm (MOGA) for CA neighborhood effects. The calibrated model was used to simulate the 2013 urban pattern which was compared with the actual map of 2013 (validation). We then used our calibrated model to simulate urban expansion in 2030. The results of the 2030 forecast show the largest variations in class 1 (low density built-up patches) that correspond to urban sprinkling.

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Section: Builtup Causative Factorsmentioning

confidence: 99%

Section: Transition Potential and Calibration Processmentioning

confidence: 99%

Modeling urban sprinkling with cellular automata

Saganeiti

Mustafà

Teller

et al. 2021

Sustainable Cities and Society

Self Cite

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“…CA is a spatial pattern of each landscape unit each land as a cell, and its specific evolutionary rules determine how it self-organizes in space and its spatial dependence to simulate changes in land use [55]. The evolution rules of CA depend on the influence of the grids and their neighbors, so the attributes and locations of the grid are factors that influence land use [56,57].…”

Section: Autocorrelationmentioning

confidence: 99%

An Application of the Spatial Autocorrelation Method on the Change of Real Estate Prices in Taitung City

Wang

Chang

Wang

2019

IJGI

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The main purpose of this paper is to use regression models to explore the factors affecting housing prices as well as apply spatial aggregation to explore the changes of urban space prices. This study collected data in Taitung City from the year 2013 to 2017, including 3533 real estate transaction price records. The hedonic price method, spatial lag model and spatial error model were used to conduct global spatial self-correlation tests to explore the performance of house price variables and space price aggregation. We compare the three models by R² and Akaike Information Criterion (AIC) to determine the spatial self-correlation ability performance, and explore the spatial distribution of prices and the changes of price regions from the regional local indicators of spatial association spatial distribution map. Actual analysis results show an improvement in the ability to interpret real estate prices through the feature price mode from the R² value assessment, the spatial delay model and the spatial error model. Performance from the AIC values show that the difference of the spatial delay model is smaller than that of the feature price model and the spatial model, demonstrating a better performance from the space delay model and the spatial error model compared to the feature price model; improving upon the estimation bias caused by spatial self-correlation. For variables affecting house pricing, research results show that Moran’s I is more than 0 in real estate price analysis over the years, all of which show spatial positive correlation. From the LISA analysis of the spatial aggregation phenomenon, we see real estate prices rise in spaces surrounded by high-priced real estate contrast with the scope of space surrounded by low-cost real estate shifting in boundary over the years due to changes in the location and attributes of real estate trading transactions. Through the analysis of space price aggregation characteristics, we are able to observe the trajectory of urban development.

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“…In addition, machine learning algorithms have been used extensively to explain LULCs. Several researches had combined Cellular automata (CA) with a plethora of modeling frameworks such as Markov chains [10], neural networks [11] support vector machines [12] and kernel-based methods [13] among others. More recently, CA have been successfully combined with Random Forest [14,15].…”

Section: Introductionmentioning

confidence: 99%

A Random Forest-Cellular Automata Modeling Approach to Predict Future Forest Cover Change in Middle Atlas Morocco, Under Anthropic, Biotic and Abiotic Parameters

Legdou

Chafik

Amine

et al. 2020

Lecture Notes in Computer Science

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This study aims to predict forest species cover changes in the Sidi M'Guild Forest (Mid Atlas, Morocco). Used approach combines remote sensing and GIS and is based on training Cellular Automata and Random Forest (RF) regression model for predicting species cover transition. Five covariates that precludes such transition have been chosen according to Pearson's test. The model was trained and validated based on the use of forest cover stratum transition probabilities between 1990 and 2004 and then validated using 2018 forest species cover map. Validation of the predicted map with that of 2018 shows an overall agreement between the two maps (72%) for each number of RF's trees used. The 2032 projected forest species cover map indicate a strong regression of Cedar atlas and thuriferous juniper cover and a medium regression of mixture holm oak and thuriferous juniper, mixture of atlas cedar and thuriferous juniper, and sylvatic and asylvatic vacuums, a very strong progression of holm oak, and of mixture atlas cedar, holm oak and thuriferous juniper and medium progression of mixture of atlas cedar and holm oak. These findings provide important insights to planners, natural resource managers and policy-makers to reconsider their strategies to ensure the sustainability goals.

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Comparing support vector machines with logistic regression for calibrating cellular automata land use change models

Abstract: Cools & Jacques Teller (2018) Comparing support vector machines with logistic regression for calibrating cellular automata land use change models,

Cited by 76 publications

References 59 publications

Modeling urban sprinkling with cellular automata

Modeling urban sprinkling with cellular automata

An Application of the Spatial Autocorrelation Method on the Change of Real Estate Prices in Taitung City

A Random Forest-Cellular Automata Modeling Approach to Predict Future Forest Cover Change in Middle Atlas Morocco, Under Anthropic, Biotic and Abiotic Parameters

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