Urban Expansion Simulation of Southeast England Using Population Surface Modelling and Cellular Automata

10.1068/a3520 ◽  
2002 ◽  
Vol 34 (10) ◽  
pp. 1855-1876 ◽  
Author(s):  
Fulong Wu ◽  
David Martin
Keyword(s):  
Cellular Automata ◽  
Urban Growth ◽  
Urban Expansion ◽  
Empirical Relationship ◽  
Land Development ◽  
Population Projections ◽  
Surface Modelling ◽  
Commuting Time ◽  
Transition Rules ◽  
Proper Balance

The question of where to accommodate future urban expansion has become a politically sensitive issue in many regions. Against the backdrop of ‘urban compaction’ policy, this study uses population surface modelling and cellular automata (CA) to conduct an empirical urban growth simulation for Southeast England. This implementation leads to a consideration of the proper balance between the theoretical abstraction of self-organised growth and empirical constraints to land development. Specifically, we use 1991 and 1997 postcode directories to construct population surfaces. From these, the distributions of developed and vacant (rural) land are derived. Development potential is assessed through accessibility surfaces, which are constructed from the travel/commuting time to major London rail termini, to motorway junctions, and to principal settlements. Through investigating the frequencies of land development in relation to the accessibility surfaces, we can begin to understand the distribution of land development in this region. Based on this empirical relationship, the transition rules of a CA simulation of future urban expansion are constructed. In addition, government population projections at the county level are used to constrain simulation to the year 2020. The study demonstrates the utility of empirical CA in urban growth modelling; in particular the importance of empirically informed CA simulation rules in characterising the distribution of land development.

scholarly journals Modeling of Urban Growth Using Cellular Automata and GIS Case of Benslimane in Morocco

2020 ◽  
Author(s):  
BENCHELHA MOHAMED ◽  
Benzha Fatiha ◽  
Rhinane Hassan ◽  
BENCHELHA SAID ◽  
BENCHELHA TAOUFIK ◽  
...  
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Urban Growth ◽  
Urban Expansion ◽  
Confusion Matrix ◽  
Spatial Transformation ◽  
Urban Growth Modeling ◽  
Main Component Analysis ◽  
Spatio Temporal ◽  
Main Component

In this study, our goal was to research land-use change by combining spatio–temporal land use/land cover monitoring (LULC (1989–2019) and urban growth modeling (1999–2039) in Benslimane, Morocco, to determine the effect of urban growth on different groups based on cellular automata (CA) and geospatial methods. A further goal was to test the reliability of the AC algorithm for urban expansion modeling. To do this, four years of satellite data were used at the same time as population density, downtown distance, slope, and ground road distance. The LULC satellite reported a rise of 3.8 km2 (318% variation) during 1989–2019. Spatial transformation analysis reveals a good classification similarity ranging from 89% to 91% with the main component analysis (PCA) technique. The statistical accuracy between the satellite scale and the replicated built region of 2019 gave 97.23 %t of the confusion matrix overall accuracy, and the region under the receiver operational characteristics (ROC) curve to 0.94, suggesting the model's high accuracy. Although the constructed area remains low relative to the total area of the municipality's territory, the LULC project shows that the urban area will extend to 5,044 km2 in 2019, principally in the western and southwestern sections. In 2019–2039, urban development is expected to lead to a transformation of the other class (loss of 1,364 km2), followed by vegetation cover (loss of 0.345 km2). In spatial modeling and statistical calculations, the GDAL and NumPy Python 3.8 libraries were successful.

scholarly journals Using building types and demographic data to improve our understanding and use of urban sprawl simulation

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Mojtaba Eslahi ◽  
Rani El Meouche ◽  
Anne Ruas
Keyword(s):  
Cellular Automata ◽  
Urban Growth ◽  
Urban Areas ◽  
Geographical Information Systems ◽  
Dynamic Models ◽  
Urban Expansion ◽  
Demographic Data ◽  
Population Data ◽  
Geographical Information ◽  
Sleuth Model

<p><strong>Abstract.</strong> Many studies, using various modeling approaches and simulation tools have been made in the field of urban growth. A multitude of models, with common or specific features, has been developed to reconstruct the spatial occupation and changes in land use. However, today most of urban growth techniques just use the historical geographic data such as urban, road and excluded maps to simulate the prospective urban maps. In this paper, adding buildings and population data as urban fabric factors, we define different urban growth simulation scenarios. Each simulation corresponds to policies that are more or less restrictive of space considering what these territories can accommodate as a type of building and as a global population.</p><p>Among the urban growth modeling techniques, dynamic models, those based on Cellular Automata (CA) are the most common for their applications in urban areas. CA can be integrated with Geographical Information Systems (GIS) to have a high spatial resolution model with computational efficiency. The SLEUTH model is one of the cellular automata models, which match the dynamic simulation of urban expansion and could be adapted to morphological model of the urban configuration and fabric.</p><p>Using the SLEUTH model, this paper provides different simulations that correspond to different land priorities and constraints. We used common data (such as topographic, buildings and demography data) to improve the realism of each simulation and their adequacy with the real world. The findings allow having different images of the city of tomorrow to choose and reflect on urban policies.</p>

scholarly journals Urban Growth Modeling and Future Scenario Projection Using Cellular Automata (CA) Models and the R Package Optimx

2018 ◽  
Vol 7 (10) ◽  
pp. 387 ◽  
Author(s):  
Yongjiu Feng ◽  
Zongbo Cai ◽  
Xiaohua Tong ◽  
Jiafeng Wang ◽  
Chen Gao ◽  
...  
Keyword(s):  
Cellular Automata ◽  
Urban Growth ◽  
Growth Dynamics ◽  
R Package ◽  
Future Scenarios ◽  
Good Ability ◽  
Gradient Based ◽  
Transition Rules ◽  
Alternative Scenarios ◽  
Spatially Explicit Modeling

Cellular automata (CA) is a spatially explicit modeling tool that has been shown to be effective in simulating urban growth dynamics and in projecting future scenarios across scales. At the core of urban CA models are transition rules that define land transformation from non-urban to urban. Our objective is to compare the urban growth simulation and prediction abilities of different metaheuristics included in the R package optimx. We applied five metaheuristics in optimx to near-optimally parameterize CA transition rules and construct CA models for urban simulation. One advantage of metaheuristics is their ability to optimize complexly constrained computational problems, yielding objective parameterization with strong predictive power. From these five models, we selected conjugate gradient-based CA (CG-CA) and spectral projected gradient-based CA (SPG-CA) to simulate the 2005–2015 urban growth and to project future scenarios to 2035 with four strategies for Su-Xi-Chang Agglomeration in China. The two CA models produced about 86% overall accuracy with standard Kappa coefficient above 69%, indicating their good ability to capture urban growth dynamics. Four alternative scenarios out to the year 2035 were constructed considering the overall effect of all candidate influencing factors and the enhanced effects of county centers, road networks and population density. These scenarios can provide insight into future urban patterns resulting from today’s urban planning and infrastructure, and can inform future development strategies for sustainable cities. Our proposed metaheuristic CA models are also applicable in modeling land-use and urban growth in other rapidly developing areas.

scholarly journals Modelling and simulating ‘informal urbanization’: An integrated agent-based and cellular automata model of urban residential growth in Ghana

2022 ◽  
pp. 239980832110688
Author(s):  
Felix S. K. Agyemang ◽  
Elisabete Silva ◽  
Sean Fox
Keyword(s):  
Cellular Automata ◽  
Growth Model ◽  
Urban Growth ◽  
Urban Expansion ◽  
City Region ◽  
African City ◽  
Local Data ◽  
Support Tool ◽  
Agent Based ◽  
Urban Growth Model

The global urban population is expected to grow by 2.5 billion over the next three decades, and 90% of this growth will occur in African and Asian countries. Urban expansion in these regions is often characterised by ‘informal urbanization’ whereby households self-build without planning permission in contexts of ambiguous, insecure or disputed property rights. Despite the scale of informal urbanization, it has received little attention from scholars working in the domains of urban analytics and city science. Towards addressing this gap, we introduce TI-City, an urban growth model designed to predict the locations, legal status and socio-economic status of future residential developments in an African city. In a bottom-up approach, we use agent-based and cellular automata modelling techniques to predict the geospatial behaviour of key urban development actors, including households, real estate developers and government. We apply the model to the city-region of Accra, Ghana, drawing on local data collection, including a household survey, to parameterise the model. Using a multi-spatial-scale validation technique, we compare TI-City’s ability to simulate historically observed built-up patterns with SLEUTH, a highly popular urban growth model. Results show that TI-City outperforms SLEUTH at each scale, suggesting the model could offer a valuable decision support tool in similar city contexts.

Urban Growth Modeling Considering Educational Institutions In Qazvin, Iran: An Investigation Into The Performance of Three ANFIS Methods

2021 ◽  
Author(s):  
Yousef Ghobadiha ◽  
Hamid Motieyan
Keyword(s):  
Cellular Automata ◽  
Urban Growth ◽  
Urban Expansion ◽  
Growth Modeling ◽  
Rapid Expansion ◽  
Subtractive Clustering ◽  
Time Interval ◽  
Inference System ◽  
Automatic Production ◽  
Urban Growth Modeling

Abstract Due to increasing urbanization, the rapid expansion of urban spaces has become a major environmental concern over the last few decades. Therefore, modeling the urban expansion as a complex system has been scrutinized in recent years; however, determining the rules that lead to the expansion of urban areas has always been a challenging factor in this field, especially for disaggregated models like cellular automata (CA). To overcome this issue, in this research, an Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed to enhance the simulation of urban growth through the automatic production of transition rules. The ANFIS can be associated with several inputs division methods, such as ANFIS accompanied by grid partitioning (ANFIS-GP), subtractive clustering (ANFIS-SC), and fuzzy c-means clustering (ANFIS-FCM). Hence, twenty-two ANFIS models based on Landsat images for the time interval from 2000 to 2010 and using different division methods were trained to investigate their effect on the efficiency of ANFIS in urban growth modeling. To examine the efficiency, the Cellular Automata-based Markov Chain (CA-MC) as a popular method was developed, and the simulation accuracy of CA-MC and the most accurate ANFIS models were obtained through comparison with observed data. The most accurate ANFIS-SC model had a Kappa of 0.76 and an overall accuracy of 93.41% for the 2019 simulated map. The results from this study reveal that the ANFIS model is effective at simulating urban expansion and the ANFIS-SC is superior to CA-MC, ANFIS-GP, and ANFIS-FCM models in urban expansion modeling.

scholarly journals CELLULAR AUTOMATA MODELING IN THE BUILT UP AREAS WITHIN URBAN DEVELOPMENT AT PONTIANAK, WEST BORNEO, INDONESIA

2017 ◽  
Vol 4 (2) ◽  
pp. 201
Author(s):  
Ely Nurhidayati ◽  
Imam Buchori ◽  
Mussadun Mussadun
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Urban Development ◽  
Image Interpretation ◽  
Land Development ◽  
Landsat Images ◽  
Cellular Automata Model ◽  
Inhibiting Factors ◽  
Transition Rules ◽  
Land Use Development

This research integrated the GIS-Cellular Automata model with the regression model to predict urban development in Pontianak within the built up area change phenomena approach. The research aimed to understand built-up land use development in Pontianak during 1990-2015 and to predict its regional development in 2033. The employed method were satellite the image interpretation approach, hybrid interpretation, and built up land development prediction using transition rules like driving factors and inhibiting factors of urban development. The driving ones are accessibility related to distances to CBD, to main roads, and to the existing built regional areas while the inhibiting ones are peatland and the protected areas. The result showed that the hybrid interpretation, between visual and digital interpretations from the landsat images, can be used to map the built up lands with 94.8% of sampling point’s precision. The non-built up areas in Pontianak during 1990-2015 were 83.52 Ha/year, and the modelling result predicts that non-built regional areas in Pontianak during 2015-2033 will be 80.51 Ha/year heading toward northern and central areas of Pontianak.

scholarly journals Urban Growth Modeling Considering Educational Institutions: An Investigation Into the Performance of Three Anfis Methods

2021 ◽  
Author(s):  
Yousef Ghobadiha ◽  
Hamid Motieyan
Keyword(s):  
Cellular Automata ◽  
Urban Growth ◽  
Urban Expansion ◽  
Growth Modeling ◽  
Rapid Expansion ◽  
Subtractive Clustering ◽  
Time Interval ◽  
Inference System ◽  
Automatic Production ◽  
Urban Growth Modeling

Abstract Due to increasing urbanization, the rapid expansion of urban spaces has become a major environmental concern over the last few decades. Therefore, modeling the urban expansion as a complex system has been scrutinized in recent years; however, determining the rules that lead to the expansion of urban areas has always been a challenging factor in this field, especially for disaggregated models like cellular automata (CA). To overcome this issue, in this research, an Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed to enhance the simulation of urban growth through the automatic production of transition rules. The ANFIS can be associated with several inputs division methods, such as ANFIS accompanied by grid partitioning (ANFIS-GP), subtractive clustering (ANFIS-SC), and fuzzy c-means clustering (ANFIS-FCM). Hence, twenty-two ANFIS models based on Landsat images for the time interval from 2000 to 2010 and using different division methods were trained to investigate their effect on the efficiency of ANFIS in urban growth modeling. To examine the efficiency, the Cellular Automata-based Markov Chain (CA-MC) as a popular method was developed, and the simulation accuracy of CA-MC and the most accurate ANFIS models were obtained through comparison with observed data. The most accurate ANFIS-SC model had a Kappa of 0.76 and an overall accuracy of 93.41% for the 2019 simulated map. The results from this study reveal that the ANFIS model is effective at simulating urban expansion and the ANFIS-SC is superior to CA-MC, ANFIS-GP, and ANFIS-FCM models in urban expansion modeling.

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