Key Driving Factors Influencing Urban Growth: Spatial-Statistical Modelling with CLUE-s

Ahmed, Sohel; Bramley, Glen; Verburg, Peter H.

doi:10.1007/978-94-007-6735-5_7

Cited by 11 publications

(9 citation statements)

References 56 publications

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“…This research utilized a rich combination of data sources, including the following: (1) The value of influence factors in the equation was extracted mainly through the CLUE-S model, which was developed by Verburg [68]. This model is widely used in studies on land use and land cover change [69][70][71][72]. Firstly, the relevant variable data were defined into a unified coordinate system of the 30 × 30 m grid layer in ArcGIS platform.…”

Section: Data Sampling and Processmentioning

confidence: 99%

Inside or Outside? The Impact Factors of Zoning–Land Use Mismatch

Zhang

et al. 2019

Sustainability

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A land-use plan is a core policy tool to curb excessive non-agriculturalization of agricultural land. The effect of plan implementation can affect sustainable land use and regional development. Empirical studies have shown that land development commonly and frequently fails to conform to land-use plans. However, neither qualitative nor quantitative studies are conducted to comprehensively explore the reasons for zoning-land use mismatch. To help bridge this gap, this study explored to what extent a plan has been implemented and what factors have affected zoning-land use mismatch. A new deviation discriminant framework of planning implementation was presented. Moreover, the logistics model was applied to discuss which factors substantially affect the zoning-land use mismatch. The plan implementation results were divided into the conformed, exceeded and unused areas. The general land-use plan failed in its spatial control over rural settlements and other built-up lands, with both more than 90% of the newly added construction land beyond zoning. In addition, the newly added construction land of rural settlements, other built-up lands, and transportation lands all exceeded the quota control. Furthermore, the physical factors of distance from the river, the elevation, the slope and the level, and the social-economic factors of the gross domestic product, the fiscal revenue, the fixed assets investments, and the rank of town have prominent effects on zoning-land use mismatch. Enhancing the flexibility of the land-use plan and strengthening the relationship between planning quotas and spatial zoning in the future are necessary to promote the effect of plan implementation.

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Section: Data Sampling and Processmentioning

confidence: 99%

Inside or Outside? The Impact Factors of Zoning–Land Use Mismatch

Zhang

et al. 2019

Sustainability

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“…According to previous studies of urban growth (e.g., [36,61,79]) the urban growth driving forces used to predict future urban growth; the relevant data are shown in Table 3. The slope and elevation maps with resolution of 30 × 30 m are derived from the digital elevation model extracted from topographic maps produced by National Cartographic Center of Iran.…”

Section: Study Area and Datasetmentioning

confidence: 99%

Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm

Naghibi

Delavar

Pijanowski

2016

Sensors

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Cellular Automata (CA) is one of the most common techniques used to simulate the urbanization process. CA-based urban models use transition rules to deliver spatial patterns of urban growth and urban dynamics over time. Determining the optimum transition rules of the CA is a critical step because of the heterogeneity and nonlinearities existing among urban growth driving forces. Recently, new CA models integrated with optimization methods based on swarm intelligence algorithms were proposed to overcome this drawback. The Artificial Bee Colony (ABC) algorithm is an advanced meta-heuristic swarm intelligence-based algorithm. Here, we propose a novel CA-based urban change model that uses the ABC algorithm to extract optimum transition rules. We applied the proposed ABC-CA model to simulate future urban growth in Urmia (Iran) with multi-temporal Landsat images from 1997, 2006 and 2015. Validation of the simulation results was made through statistical methods such as overall accuracy, the figure of merit and total operating characteristics (TOC). Additionally, we calibrated the CA model by ant colony optimization (ACO) to assess the performance of our proposed model versus similar swarm intelligence algorithm methods. We showed that the overall accuracy and the figure of merit of the ABC-CA model are 90.1% and 51.7%, which are 2.9% and 8.8% higher than those of the ACO-CA model, respectively. Moreover, the allocation disagreement of the simulation results for the ABC-CA model is 9.9%, which is 2.9% less than that of the ACO-CA model. Finally, the ABC-CA model also outperforms the ACO-CA model with fewer quantity and allocation errors and slightly more hits.

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“…It evaluates the capability of the model in producing the best suitability maps independent of applying the threshold values to create a binary change map [75,76]. However, according to Pontius and Si (2014), several concerns exist regarding the use of ROC and the area under the curve of the ROC as an accuracy measure showing the summarization of the strength of the model [75].…”

Section: Fom = B/(a + B + C + D)mentioning

confidence: 99%

Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling

Naghibi

Delavar

2016

IJGI

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This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA) using the artificial bee colony (ABC) optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO) as intelligent approaches were performed to evaluate the potential of the proposed methods. According to previous studies, swarm intelligence algorithms for solving optimization problems such as discovering transition rules of CA in land use change/urban growth modeling can produce reasonable results. Modeling of urban growth as a dynamic process is not straightforward because of the existence of nonlinearity and heterogeneity among effective involved variables which can cause a number of challenges for traditional CA. ABC algorithm, the new powerful swarm based optimization algorithms, can be used to capture optimized transition rules of CA. This paper has proposed a methodology based on remote sensing data for modeling urban growth with CA calibrated by the ABC algorithm. The performance of ABC-CA, PSO-CA, and CA-logistic models in land use change detection is tested for the city of Urmia, Iran, between 2004 and 2014. Validations of the models based on statistical measures such as overall accuracy, figure of merit, and total operating characteristic were made. We showed that the overall accuracy of the ABC-CA model was 89%, which was 1.5% and 6.2% higher than those of the PSO-CA and CA-logistic model, respectively. Moreover, the allocation disagreement (simulation error) of the simulation results for the ABC-CA, PSO-CA, and CA-logistic models are 11%, 12.5%, and 17.2%, respectively. Finally, for all evaluation indices including running time, convergence capability, flexibility, statistical measurements, and the produced spatial patterns, the ABC-CA model performance showed relative improvement and therefore its superiority was confirmed.

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Key Driving Factors Influencing Urban Growth: Spatial-Statistical Modelling with CLUE-s

Cited by 11 publications

References 56 publications

Inside or Outside? The Impact Factors of Zoning–Land Use Mismatch

Inside or Outside? The Impact Factors of Zoning–Land Use Mismatch

Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm

Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling

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