An agent-integrated irregular automata model of urban land-use dynamics

Dahal, Khila R.; Chow, Theodore

doi:10.1080/13658816.2014.917646

Cited by 38 publications

(28 citation statements)

References 30 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…O'Sullivan, 2001b;Dahal and Chow, 2015) and there have been successful attempts to employ irregular space representation in CA-based models (e.g. Hu and Li, 2004;Moreno et al, 2008;Dahal and Chow, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…As the aim of CA models is often to aid or improve urban planning, it would seem appropriate to adopt the same space representation as that used by urban planners. Early attempts to develop irregular CAbased models of land use change and urban growth have used cadastral parcels or similar types of space representation (Moreno et al, 2008;Pinto and Antunes, 2010;Lugo and Valdivia, 2012;Dahal and Chow, 2014).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Configuring the neighbourhood effect in irregular cellular automata based models

González

Barros

2016

International Journal of Geographical Information Science

View full text Add to dashboard Cite

Abstract:Cellular automata (CA) models have been widely employed to simulate urban growth and land use change. In order to represent urban space more realistically, new approaches to CA models have explored the use of vector data instead of traditional regular grids. However, the use of irregular CA-based models brings new challenges as well as opportunities. The most strongly affected factor when using an irregular space is neighbourhood. Although neighbourhood definition in an irregular environment has been reported in the literature, the question of how to model the neighbourhood effect remains largely unexplored.In order to shed light on this question, this paper proposed the use of spatial metrics to characterise and measure the neighbourhood effect in irregular CA-based models. These metrics, originally developed for raster environments, namely the enrichment factor and the neighbourhood index, were adapted and applied in the irregular space employed by the model. Using the results of these metrics, distance-decay functions were calculated to reproduce the push-and-pull effect between the simulated land uses. The outcomes of a total of 55 simulations (five sets of different distance functions and eleven different neighbourhood definition distances) were compared with observed changes in the study area during the calibration period. Our results demonstrate that the proposed methodology improves the outcomes of the urban growth simulation model tested and could be applied to other irregular CA-based models.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Configuring the neighbourhood effect in irregular cellular automata based models

González

Barros

2016

International Journal of Geographical Information Science

View full text Add to dashboard Cite

show abstract

“…The sitespecific assessment of the simulation results showed that the integration of the extended RL model is helpful for improving the predictive power of an ABM for residential land growth simulation. To evaluate the model performance further, the predicted pixels were categorized into perfect match, close match, and poor match with reference to the study by Dahal and Chow (2014). The perfect match category includes converted pixels in the predicted map that spatially agree with the developed area in the reference map.…”

Section: Experiments Results and Analysismentioning

confidence: 99%

An agent-based procedure with an embedded agent learning model for residential land growth simulation: The case study of Nanjing, China

Xie

Clarke

et al. 2019

Cities

View full text Add to dashboard Cite

A B S T R A C TThe agent-based modelling (ABM) is commonly used to simulate urban land growth. A key challenge of ABM for the simulation of urban land-use dynamics in support of sustainable urban management is to understand and model how human individuals make and develop their location decisions that then shape urban land-use patterns. To investigate this issue, we focus on modelling the agent learning process in residential location decisionmaking process, to represent individuals' personal and interpersonal experience learning during their decisionmaking. We have constructed an extended reinforcement learning model to represent the human agents' learning when they make location decisions. Consequently, we propose and have developed a new agent-based procedure for residential land growth simulation that incorporates an agent learning model, an agent decision-making model, a land use conversion model, and the impacts of urban land zoning and the developers' desires. The proposed procedure was first tested by using hypothetical data. Then the model was used for a simulation of the urban residential land growth in the city of Nanjing, China. By validating the model against empirical data, the results showed that adding agent learning model contributed to the representation of the agent's adaptive location decision-making and the improvement of the model's simulation power to a certain extent. The agentbased procedure with the agent learning model embedded is applicable to studying the formulation of urban development policies and testing the responses of individuals to these policies. .cn (Z. Chen).Cities xxx (xxxx) xxx-xxx 0264-2751/

show abstract

“…With respect to cell-focused research, attempts have been made to adjust the regular grid. Researchers have applied the Voronoi and Delaunay methods to segment the research area, proposed the 'point-line plane' model to conceptualize CA, and used entities such as parcels, streets, and buildings to define cells (Perzy et al 2014, Dahal and Chow 2014. Finally, improvements in neighborhood-focused research have also gained popularity in recent years (Van Vliet et al 2009.…”

Section: Introductionmentioning

confidence: 98%

A partitioned and asynchronous cellular automata model for urban growth simulation

Zeng

2015

International Journal of Geographical Information Science

View full text Add to dashboard Cite

Cellular automata (CA) models are used to analyze and simulate the global phenomenon of urban growth. However, these models are characterized by ignoring spatially heterogeneous transition rules and asynchronous evolving rates, which make it difficult to improve urban growth simulations. In this paper, a partitioned and asynchronous cellular automata (PACA) model was developed by implementing the spatial heterogeneity of both transition rules and evolving rates in urban growth simulations. After dividing the study area into several subregions by k-means and knn-cluster algorithms, a C5.0 decision tree algorithm was employed to identify the transition rules in each subregion. The evolving rates for cells in each regularly divided grid were calculated by the rate of changed cells. The proposed PACA model was implemented to simulate urban growth in Wuhan, a large city in central China. The results showed that PACA performed better than traditional CA models in both a cell-to-cell accuracy assessment and a shape dimension accuracy assessment. Figure of merit of PACA is 0.368 in this research, which is significantly higher than that of partitioned CA (0.327) and traditional CA (0.247). As for the shape dimension accuracy, PACA has a fractal dimension of 1.542, which is the closest to that of the actual land use (1.535). However, fractal dimension of traditional CA (1.548) is closer to that of the actual land use than that of partitioned CA (1.285). It indicates that partitioned transition rules play an important role in the cell-to-cell accuracy of CA models, whereas the combination of partitioned transition rules and asynchronous evolving rates results in improved cell-to-cell accuracy and shape dimension accuracy. Thus, implementing partitioned transition rules and asynchronous evolving rates yields better CA model performance in urban growth simulations due to its accordance with actual urban growth processes. ARTICLE HISTORY

show abstract

An agent-integrated irregular automata model of urban land-use dynamics

Cited by 38 publications

References 30 publications

Configuring the neighbourhood effect in irregular cellular automata based models

Configuring the neighbourhood effect in irregular cellular automata based models

An agent-based procedure with an embedded agent learning model for residential land growth simulation: The case study of Nanjing, China

A partitioned and asynchronous cellular automata model for urban growth simulation

Contact Info

Product

Resources

About