A segment derived patch-based logistic cellular automata for urban growth modeling with heuristic rules

Li, Xuecao; Gong, Peng; Yu, Le; Hu, Tengyun

doi:10.1016/j.compenvurbsys.2017.06.001

Cited by 56 publications

(24 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regression analysis of a model to verify the effect of the final choice of the regression model is a logistic regression analysis and weight regression analysis. The regression coefficients R 2 were 0.679, 0.742, 0.727, 0.714, and 0.696, and the statistics p were 0.042, 0.038, 0.044, 0.030, and 0.045 [81,82]. From the viewpoint of statistics, each regression equation is reasonable, and the regression effect is significant.…”

Section: Influence Factormentioning

confidence: 84%

Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian

Yang

Weiling

et al. 2018

Complexity

View full text Add to dashboard Cite

The spatial evolution of land use in Jinzhou area was simulated using fuzzy cellular automata to determine all factors influencing urban land use change. For this purpose, land use data of multiple sources and remote sensing images from 2003 to 2015 were analyzed. The following results were obtained: (1) real land use data and simulation data for 2015 were tested using four indices. Under the 5 m × 5 m scale, the model shows good performance for simulation of areas with various land use types. (2) Among simulations under four scenarios, the effect of traffic guidance on the development of construction land was more distinct under the economic development mode, which clearly showed the phenomenon of “agglomeration” along major traffic lines. (3) Jinshitan Street is adjacent to the sea, and land use changes are significant under the 4th scenario, and thus related departments should pay more attention. (4) Shortcomings of conventional cellular automata model in processing complex systems could be mitigated through the integration of fuzzy sets.

show abstract

Section: Influence Factormentioning

confidence: 84%

Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian

Yang

Weiling

et al. 2018

Complexity

View full text Add to dashboard Cite

show abstract

“…Eleven different models were used for this comparison: i.e. CLUE, CLUES-S, Geomod, Environment Explorer, Land Use Scanner, Logistic regression, LTM, SAMADA, SLEUTH (Pontius et al, 2008), Patchlogistic-CA and cell/pixel based logistic-CA (Chen, 2016;Li, Gong, Yu, & Hu, 2017) and SLEUTH (Yin, Kong, Yang, James, & Dronova, 2018). Also, the fuzzy kappa value of SUSM simulation is slightly higher than the reported values in other studies (Long, Han, Lai, & Mao, 2013;Vliet et al, 2009;Wu et al, 2010) (refer to table-2 in supplement).…”

Section: Performance Of Susmmentioning

confidence: 92%

SUSM: a scenario-based urban growth simulation model using remote sensing data

Kantakumar

Kumar

Schneider

2019

European Journal of Remote Sensing

View full text Add to dashboard Cite

The introduction of the Foreign Direct Investment (FDI) policy in 1991 made India one of the fastest growing economies in the world. This has had a profound effect on India's urbanization. The rapid urbanization of Indian cities poses a threat to natural and social environments, as expansion of the cities often outpaces the urban planning process. Thus, smart and strategic planning processes that use current and easily available datasets in combination with future urbanization scenarios are needed. To this end, we developed the scenario-based urban growth simulation model (SUSM), which can be used for impact analysis of different planning measures in both spatial and temporal contexts. SUSM uses remote sensing derived inputs, such as land use maps, slope, roads and centres of urban areas along with urban development scenarios. It uses logistic regression for calibration and a constrained stochastic cellular automaton for simulation of urban growth.SUSM is tested in one of the fastest growing urban agglomerations of India: The Pune metropolis, which covers an area of 1642 km 2 . SUSM is calibrated using urban growth maps derived from LANDSAT satellite images from 1992 to 2001. Subsequently, SUSM was used to simulate urban growth of Pune for 2013. A comparison of the SUSM simulation result with the actually measured urban growth derived from a LANDSAT 8 scene from 2013 is used to validate SUSM and to assess the effect of urban plans upon the growth of Pune. Our results show that: (i) SUSM is capable of predicting the location of future urbanization with an accuracy of 79% and a fuzzy kappa index of agreement 0.81; (ii) inclusion of official urban development plans as input for SUSM did not provide a better agreement with the observed growth; (iii) SUSM, parameterized with remote sensing data, can be used effectively to understand urban growth and assess the effects of alternative urban development plans in terms of the spatial expansion of cities. ARTICLE HISTORY

show abstract

“…Cellular Automata (CA) model has been increasingly used to simulate urban growth and land use dynamics (Li et al, 2002;He et al, 2006;Ahmed et al 2018;Li et al 2017;Maher et al 2016;Süha et al 2016). However, there are some limitations because they cannot explicitly consider the influences of social and human factors in urban expansion simulation.…”

Section: Introductionmentioning

confidence: 99%

Urban Expansion Modeling Approach Based on Multi-Agent System and Cellular Automata

Zeng

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Urban expansion is a land-use change process that transforms non-urban land into urban land. This process results in the loss of natural vegetation and increase in impervious surfaces. Urban expansion also alters the hydrologic cycling, atmospheric circulation, and nutrient cycling processes and generates enormous environmental and social impacts. Urban expansion monitoring and modeling are crucial to understanding urban expansion process, mechanism, and its environmental impacts, and predicting urban expansion in future scenarios. Therefore, it is important to study urban expansion monitoring and modeling approaches. We proposed to simulate urban expansion by combining CA and MAS model. The proposed urban expansion model based on MSA and CA was applied to a case study area of Changsha-Zhuzhou-Xiangtan urban agglomeration, China. The results show that this model can capture urban expansion with good adaptability. The Kappa coefficient of the simulation results is 0.75, which indicated that the combination of MAS and CA offered the better simulation result.

show abstract

A segment derived patch-based logistic cellular automata for urban growth modeling with heuristic rules

Cited by 56 publications

References 46 publications

Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian

Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian

SUSM: a scenario-based urban growth simulation model using remote sensing data

Urban Expansion Modeling Approach Based on Multi-Agent System and Cellular Automata

Contact Info

Product

Resources

About