Developing a web-based cellular automata model for urban growth simulation

Cellular automata as an emerging technology have been adapted increasingly by geographers and planners to simulate the spatial and temporal processes of urban growth. While the literature reports many applications of cellular automata models for urban studies, in practice, the operation of the models as well as the configuration and calibration of relevant parameters used in the models were only known to the model builders. This is largely due to the constraint that most cellular automata models were developed based on desktop computer programs, either by incorporating the model within a desktop GIS environment, or developing the model independent of a desktop GIS. Consequently, there is little input from the user to test or visualise the actual operation or evaluate the applicability of the model under different conditions. This paper presents a methodology to implement a fuzzy constrained cellular automata model of urban growth within a web-based GIS environment, using the actual urban growth of Metropolitan Sydney, Australia from 1976 to 2006 as a case study. With the web-based cellular automata model, users can visualise and test the operation of the model; they can also modify or calibrate the model's parameters to evaluate its simulation accuracies, or even feed the model with various 'what-if' conditions to generate alterative outcomes. Such a web-based modelling platform provides a useful and effective channel for government authority and stakeholders to evaluate different urban growth scenarios. It also provides an interactive environment that can foster public participation in urban planning and management

An optimised cellular automata model based on adaptive genetic algorithm for urban growth simulation

This paper presents an improved cellular automata (CA) model optimized using an adaptive genetic algorithm (AGA) to simulate the spatiooral process of urban growth. The AGA technique can be used to optimize the transition rules of the CA model defined through conventional methods such as logistic regression approach, resulting in higher simulation efficiency and improved results. Application of the AGA-CA model in Shanghai's Jiading District, Eastern China demonstrates that the model was able to generate reasonable representation of urban growth even with limited input data in defining its transition rules. The research shows that AGA technique can be integrated within a conventional CA based urban simulation model to improve human understanding on urban dynamics

Linking Climate Change and Socio-economic Impact for Long-term Urban Growth in Three Mega-cities

Urbanization has become a global trend under the impact of population growth, socio-economic development, and globalization. However, the interactions between climate change and urban growth in the context of economic geography are unclear due to missing links in between the recent planning megacities. This study aims to conduct a multi-temporal change analysis of land use and land cover in New York City, City of London, and Beijing using a cellular automata-based Markov chain model collaborating with fuzzy set theory and multi-criteria evaluation to predict the city\u27s future land use changes for 2030 and 2050 under the background of climate change. To determine future natural forcing impacts on land use in these megacities, the study highlighted the need for integrating spatiotemporal modeling analyses, such as Statistical Downscale Modeling (SDSM) driven by climate change, and geospatial intelligence techniques, such as remote sensing and geographical information system, in support of urban growth assessment. These SDSM findings along with current land use policies and socio-economic impact were included as either factors or constraints in a cellular automata-based Markov Chain model to simulate and predict land use changes in megacities for 2030 and 2050. Urban expansion is expected in these megacities given the assumption of stationarity in urban growth process, although climate change impacts the land use changes and management. More land use protection should be addressed in order to alleviate the impact of climate change

Urban land cover change detection analysis and modeling spatio-temporal Growth dynamics using Remote Sensing and GIS Techniques: A case study of Dhaka, Bangladesh

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Dhaka, the capital of Bangladesh, has undergone radical changes in its physical form, not only in its vast territorial expansion, but also through internal physical transformations over the last decades. In the process of urbanization, the physical characteristic of Dhaka is gradually changing as open spaces have been transformed into building areas, low land and water bodies into reclaimed builtup lands etc. This new urban fabric should be analyzed to understand the changes that have led to its creation. The primary objective of this research is to predict and analyze the future urban growth of Dhaka City. Another objective is to quantify and investigate the characteristics of urban land cover changes (1989-2009) using the Landsat satellite images of 1989, 1999 and 2009. Dhaka City Corporation (DCC) and its surrounding impact areas have been selected as the study area. A fisher supervised classification method has been applied to prepare the base maps with five land cover classes. To observe the change detection, different spatial metrics have been used for quantitative analysis. Moreover, some postclassification change detection techniques have also been implemented. Then it is found that the ‘builtup area’ land cover type is increasing in high rate over the years. The major contributors to this change are ‘fallow land’ and ‘water body’ land cover types. In the next stage, three different models have been implemented to simulate the land cover map of Dhaka city of 2009. These are named as ‘Stochastic Markov (St_Markov)’ Model, ‘Cellular Automata Markov (CA_Markov)’ Model and ‘Multi Layer Perceptron Markov (MLP_Markov)’ Model. Then the best-fitted model has been selected based on various Kappa statistics values and also by implementing other model validation techniques. This is how the ‘Multi Layer Perceptron Markov (MLP_Markov)’ Model has been qualified as the most suitable model for this research. Later, using the MLP_Markov model, the land cover map of 2019 has been predicted. The MLP_Markov model shows that 58% of the total study area will be converted into builtup area cover type in 2019. The interpretation of depicting the future scenario in quantitative accounts, as demonstrated in this research, will be of great value to the urban planners and decision makers, for the future planning of modern Dhaka City

A review of wildland fire spread modelling, 1990-present 3: Mathematical analogues and simulation models

In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behvaiour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of a simulation or mathematical analogue nature. Most simulation models are implementations of existing empirical or quasi-empirical models and their primary function is to convert these generally one dimensional models to two dimensions and then propagate a fire perimeter across a modelled landscape. Mathematical analogue models are those that are based on some mathematical conceit (rather than a physical representation of fire spread) that coincidentally simulates the spread of fire. Other papers in the series review models of an physical or quasi-physical nature and empirical or quasi-empirical nature. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.Comment: 20 pages + 9 pages references + 1 page figures. Submitted to the International Journal of Wildland Fir

Modelling urban spatial change: a review of international and South African modelling initiatives

August 2013Urban growth and land use change models have the potential to become important tools for urban spatial planning and management. Before embarking on any modelling, however, GCRO felt it was important to take note of, and critically assess lessons to be learnt from international experience and scholarship on spatial modelling, as well as a number of South African experiments that model future urban development. In 2012, GCRO initiated preliminary research into current international and South African modelling trends through a desktop study and telephone, email and personal interviews. This Occasional paper sets out to investigate what urban spatial change modelling research is currently being undertaken internationally and within South Africa. At the international level, urban modelling research since 2000 is reviewed according to five main categories: land use transportation (LUT), cellular automata, urban system dynamics, agent-based models (ABMs) and spatial economics/econometric models (SE/EMs). Within South Africa, urban modelling initiatives are categorised differently and include a broader range of urban modelling techniques. Typologies used include: provincial government modelling initiatives in Gauteng; municipal government modelling initiatives; other government-funded modelling research; and academic modelling research. The various modelling initiatives described are by no means a comprehensive review of all urban spatial change modelling projects in South Africa, but provide a broad indication of the types of urban spatial change modelling underway. Importantly, the models may form the basis for more accurate and sophisticated urban modelling projects in the future. The paper concludes by identifying key urban modelling opportunities and challenges for short- to long-term planning in the GCR and South Africa.Written by Chris Wray, Josephine Musango and Kavesha Damon (GCRO) Koech Cheruiyot (NRF:SARChI chair in Development Planning and Modelling at Wits

Exploring the potential climate change impact on urban growth in London by a cellular automata-based Markov chain model

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Urbanization has become a global trend under the combined influence of population growth, socioeconomic development, and globalization. Even though recent urban planning in London has been more deliberate, the relationships between climate change and urban growth in the context of economic geography are still somewhat unclear. This study relies on rainfall prediction with the aid of the Statistical DownScaling Model (SDSM), which provides the statistical foundation for future flooding potential within the urban space of London while considering major socioeconomic policies related to land use management. These SDSM findings, along with current land use policies, were included as other factors or constraints in a cellular automata-based Markov Chain model to simulate and predict land use changes in London for 2030 and 2050. Two scenarios with the inclusion and exclusion of flood impact factor, respectively, were applied to evaluate the impact of climate change on urban growth. Findings indicated: (1) mean monthly projected precipitation derived by SDSM is expected to increase for the year 2030 in London, which will affect the flooding potential and hence the area of open space; (2) urban and open space are expected to increase > 16 and 20km 2 (in percentage of 1.51 and 1.92 compared to 2012) in 2030 and 2050, respectively, while agriculture is expected to decrease significantly due to urbanization and climate change; (3) the inclusion of potential flood impact induced from the future precipitation variability drives the development toward more open space and less urban area.The research is supported by the Global Innovation Initiative (British Council Grant No. Gll206), funded by the British Council and the Department for Business, Innovation and Skills

Scenarios of Urban Growth in Kenya Using Regionalised Cellular Automata based on Multi temporal Landsat Satellite Data

The exponential increase of urban areas in Africa during the last decade has become a major concern in the context of local climatic change and the increasing amount of impervious surface. Major African cities such as Nairobi and Nakuru have undergone rapid urban growth in comparison to the rest of the world. In this research we investigated the land-use changes and used the results in urban growth modelling which integrates cellular automata (CA), remote sensing (RS) and geographic information systems (GIS) in order to simulate urban growth up to the year 2030. We used multi-temporal Landsat imageries for the years 1986, 2000 and 2010 to map urban land-use changes in Nairobi and Nakuru. The use of multi-sensor imageries was also explored incorporating World view 2, and Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data for urban land-use mapping in Nakuru. We conducted supervised classification using support vector machine (SVM) which performed better than maximum likelihood classification. Land-use change estimates were obtained indicating increased urban growth into the year 2010. We used the land-use change analysis information to model urban growth in Nairobi and Nakuru. Our urban growth model (UGM) utilised various datasets in modelling urban growth namely urban land-use extracted from land-use maps, road network data, slope data and exclusion layer defining areas excluded from development. The Monte-Carlo technique was used in model calibration. The model was validated using Multiple Resolution Validation (MRV) technique. Prediction of urban land-use was done up to the year 2030 when Kenya plans to attain Vision 2030. Three scenarios were explored in the urban modelling process; unmanaged growth with no restriction on environmental areas, managed growth with moderate protection, and a managed growth with maximum protection on forest, agricultural areas, and urban green. Furthermore, we explored the spatial effects of varying UGM parameters using the city of Nairobi. The objective here was to investigate the contribution of each model parameter in simulating urban growth. The results obtained indicate that varying model coefficients leads to urban growth in different directions and magnitude. However, several model parameters were observed to be highly correlated namely; spread, breed and road. The lowest spatial effect was achieved by at least maintaining spread, breed and road while varying the other parameters. The highest spatial effect was observed by at least keeping slope constant while varying the other four parameters. Additionally, we used kappa statistics to compare the simulation maps. High values of Khisto indicated high similarity between the maps in terms of quantity and location thus indicating the lowest spatial effect obtained. Kenya plans to achieve Vision 2030 in the year 2030 and information on spatial effects of our UGM can help in identifying different scenarios of future urban growth. It is thus possible to discover areas that are likely to experience; spontaneous growth, edge growth, road influenced growth or new spreading centres growth. Policy makers can see the influence of establishing new infrastructure such as housing and road in new areas compared to existing settlements. Moreover, the outcome of this research indicates that Nairobi and Nakuru are experiencing fast urban sprawl with urban land-use consuming the available land. The results obtained illustrate the possibility of urban growth modelling in addressing regional planning issues. This can help in comprehensive land-use planning and an integrated management of resources to ensure sustainability of land and to achieve social equity, economic efficiency and environmental sustainability. Hence, cellular automata are a worthwhile approach for regional modelling of African cities such as Nairobi and Nakuru. This provides opportunities for other cities in Africa to be studied using UGM and its adaptability noted accordingly.Das exponentielle Wachstum afrikanischer Städte im letzten Jahrzehnt ist mit Blick auf die lokalen klimatischen Veränderungen und der zunehmenden Menge an versiegelten Oberflächen von besonderer Tragweite. Im Vergleich zu anderen Metropolen erfuhren afrikanische Städte wie Nairobi und Nakuru ein extensives Wachstum der urbanen Flächen. Die vorliegende Arbeit setzt sich mit dem urbanen Landnutzungswandel auseinander und modelliert die Siedlungsflächenausdehnung für das Jahr 2030 mit Hilfe eines Zellulären Automaten (CA), Fernerkundungsdaten (RS) sowie Geographischen Informationssystemen (GIS). Zur Kartierung der Siedlungsflächenausdehnung von Nairobi und Nakuru wurden multitemporale Landsat-Daten der Jahre 1986, 2000 und 2010 verwendet. Zusätzlich wurden multisensorale Daten von World View 2 und ALOS PALSAR für Nakuru eingesetzt. Die Landnutzungsklassifikation erfolgte mit support vector machines (SVM). Dieses Verfahren zeigte bessere Ergebnisse als eine Maximum-Likelihood-Klassifikation. Auf Basis der klassifizierten Satellitendaten erfolgte die Landnutzungsmodellierung für Nairobi und Nakuru. Hierzu wurde die von Goetzke (2011) modifizierte Version von Clarke’s Urban Growth Model (Clarke, Hoppen, & Gaydos, 1997) benutzt. Neben den Landnutzungskarten fungieren Informationen zum Verkehrsnetz, zur Hangneigung und zu Ausschlussflächen als Hauptinputdaten. Die Kalibration erfolgte mit Hilfe von Monte Carlo Iterationen. Zur Validation des Modells wurde eine Multiple Resolution Validation (MRV) durchgeführt. Die Siedlungsflächenausdehnung wurde für das Jahr 2030 simuliert. Zu diesem Zeitpunkt plant das Land Kenia die Umsetzung des Vision 2030 Programmes. Es wurden insgesamt drei Szenarien mit dem Wachstumsmodell gerechnet: (1) Wachstum ohne Planungszwänge, so dass auch Siedlungsflächen in Naturschutzgebieten entstehen dürfen. (2) Siedlungsflächenausdehnung unter moderaten Planungsbedingungen. (3) Wachstum mit sehr restriktiven Planungsbedingungen, unter Einschluss des Schutzes von Wald-, Grün- und- Agrarflächen. Des Weiteren wurde eine Sensitivitätsanalyse der modelleigenen Wachstumsparameter am Beispiel von Nairobi durchgeführt. Es konnte gezeigt werden, welchen Einfluss die Parameter auf die Intensität und das Muster der modellierten Siedlungsflächenausdehnung ausüben. Dabei zeigten die Wachstumskoeffizienten „spread“, „breed“ und „road“ eine signifikante Korrelation. Zur weiteren Analyse der erzielten Modellierungsergebnisse und zum Vergleich der räumlichen Muster wurden Kappa-Statistiken herangezogen. Die Arbeit sieht sich als Beitrag zum Vision 2030 Diskurs der kenianischen Regierung. Die simulierten Szenarien der Siedlungsflächenausdehnung von Nairobi und Nakuru identifizieren die für eine Urbanisierung wahrscheinlich in Frage kommenden Regionen. Die Studie zeigt zudem, dass sich die Siedlungsflächenausdehnung von Nairobi und Nakuru schnell und mit hohen Wachstumsraten vollzieht. Der Einsatz von CA Modellen ist ein wertvoller Ansatz zur regionalen Modellierung nicht nur von kenianischen sondern auch von afrikanischen Städten. Die Arbeit kann somit Entscheidungsträger aus Politik und Verwaltung unterstützen, indem sie die räumlichen Auswirkungen des zukünftigen Ausbaus der Infrastruktur und von Wohnflächen aufzeigt. Eine umfassende Planung von Landnutzungswandel und ein integriertes Management sind essentiell auf dem Weg zu einem bewussteren Umgang mit der Ressource Land sowie zu einer sozialen Gleichheit, wirtschaftlichen Effizienz und einer ökologischen Nachhaltigkeit

Simulating city growth by using the cellular automata algorithm

The objective of this thesis is to develop and implement a Cellular Automata (CA) algorithm to simulate urban growth process. It attempts to satisfy the need to predict the future shape of a city, the way land uses sprawl in the surroundings of that city and its population. Salonica city in Greece is selected as a case study to simulate its urban growth. Cellular automaton (CA) based models are increasingly used to investigate cities and urban systems. Sprawling cities may be considered as complex adaptive systems, and this warrants use of methodology that can accommodate the space-time dynamics of many interacting entities. Automata tools are well-suited for representation of such systems. By means of illustrating this point, the development of a model for simulating the sprawl of land uses such as commercial and residential and calculating the population who will reside in the city is discussed