Of cells and cities: a comparative Econometric and Cellular Automata approach to Urban Growth Modeling

This paper presents a comparative assessment of two distinct urban growth modeling approaches. The first urban model uses a traditional Cellular Automata methodology, based on Markov transition chains to prospect probabilities of future urban change. Drawing forth from non-linear cell dynamics, a multi-criteria evaluation of known variables prospects the weights of variables related to urban planning (road net- works, slope and proximity to urban areas). The latter model, frames a novel approach to urban growth modeling using a linear Logit model (LLM) which can account for region specific variables and path depen- dency of urban growth. Hence, the drivers and constraints for both models are used similarly and the same study area is assessed. Both models are projected in the segment of Faro-Olh ̃ao for 2006 and a comparative assessment to ground truth is held. The calculation of Cohenââ¬â¢s Kappa for both projections in 2006 allows for an assessmentof both models. This instrumental approach illuminates the differ- ences between the traditional model and the new type of urban growth model which is used. Both models behave quite differently: While the Markov Cellular Automata model brings an over classification of ur- ban growth, the LLM responds in the underestimation of urban sprawl for the same period. Both excelled with a Kappa calculation of over 89%, and showed to have fairly good estimations for the study area. One may conclude that the Markov CA Model permits a riper un- derstanding of urban growth, but fails to analyze urban sprawl. The LLM model shares interesting results within the possibility of identi- fying urban sprawl patterns, and is therefore an interesting solution for some locations. Another advantage of the LLM is directly linked to the possibility of establishing probability for urban growth. Thus, while the traditional methodology shared better results, LLM can be also an interesting estimate for urban patterns from an econometric perspective. Hence further research is needed in exploring the utility of spatial econometric approaches to urban growth.

Urban sprawl analysis and modeling in Asmara, Eritreia: Application of Geospatial Tools

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Urbanization pattern of Greater Asmara Area for the last two decades (1989 to 2009) and a prediction for the coming ten years was studied. Satellite images and geospatial tools were employed to quantify and analyze the spatiotemporal urban land use changes during the study periods. The principal objective of this thesis was to utilize satellite data, with the application of geospatial and modeling tools for studying urban land use change. In order to achieve this, satellite data for three study periods (1989, 2000 and 2009) have been obtained from USGS. Object-Based Image Analysis (OBIA); and image classification with Nearest Neighbor algorithm in eCognition Developer 8 have been accomplished. In order to assess the validation of the classified LULC maps, Kappa measure of agreement has been used; results were above minimum and acceptable level. ArcGIS and IDRISI Andes have been employed for LUCC quantification; spatiotemporal analysis of the urban land use classes;to examine the land use transitions of the land classes and identify the gains and losses in relation to built up area; and to characterize impacts of the changes. Since, the major concern of the study was urban expansion, the LULC classes were reclassified in to built up and non-built up for further analysis. Urban sprawl has been measured using Shannon Entropy approach; results indicated the urban area has undergone a considerable sprawl. Finally, LCM has been used to develop a model, asses the prediction capacity of the developed model and predict future urban land use change of the GAA. Multi-layer perceptron Neural Network has been used to model the transition potential maps, results were successful to make ‘actual’ prediction with Markov Chain Analyst.Despite the GAA is center of development and its regional economic and social importance, its trend of growth remains the major factor for diminishing productive land and other valuable natural resources. The findings of the study indicated that, in the last twenty years the built up area has tripled in size and impacted the surrounding natural environment. Thus, the findings of this study might support decision making for sustainable urban development of GAA

Revealing the evolution of spatiotemporal patterns of urban expansion using mathematical modelling and emerging hotspot analysis

Peer reviewe

Dynamics of Land Use and Land Cover Changes in Harare, Zimbabwe: A Case Study on the Linkage between Drivers and the Axis of Urban Expansion

With increasing population growth, the Harare Metropolitan Province has experienced accelerated land use and land cover (LULC) changes, influencing the city’s growth. This study aims to assess spatiotemporal urban LULC changes, the axis, and patterns of growth as well as drivers influencing urban growth over the past three decades in the Harare Metropolitan Province. The analysis was based on remotely sensed Landsat Thematic Mapper and Operational Land Imager data from 1984–2018, GIS application, and binary logistic regression. Supervised image classification using support vector machines was performed on Landsat 5 TM and Landsat 8 OLI data combined with the soil adjusted vegetation index, enhanced built-up and bareness index and modified difference water index. Statistical modelling was performed using binary logistic regression to identify the influence of the slope and the distance proximity characters as independent variables on urban growth. The overall mapping accuracy for all time periods was over 85%. Built-up areas extended from 279.5 km2 (1984) to 445 km2 (2018) with high-density residential areas growing dramatically from 51.2 km2 (1984) to 218.4 km2 (2018). The results suggest that urban growth was influenced mainly by the presence and density of road networks

Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model

Rapid urbanization is occurring throughout China, especially in megacities. Using a land use model to obtain future land use/cover conditions is an essential method to prevent chaotic urban sprawl and imbalanced development. This study utilized historical Landsat images to create land use/cover maps to predict the land use/cover changes of Tianjin city in 2025 and 2035. The cellular automata–Markov (CA–Markov) model was applied in the simulation under three scenarios: the environmental protection scenario (EPS), crop protection scenario (CPS), and spontaneous scenario (SS). The model achieved a kappa value of 86.6% with a figure of merit (FoM) of 12.18% when compared to the empirical land use/cover map in 2015. The results showed that the occupation of built-up areas increased from 29.13% in 2015 to 38.68% (EPS), 36.18% (CPS), and 47.94% (SS) in 2035. In this context, current urbanization would bring unprecedented stress on agricultural resources and forest ecosystems, which could be attenuated by implementing protection policies along with decelerating urban expansion. The findings provide valuable information for urban planners to achieve sustainable development goals

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

Simulating future land use/cover changes is of great importance for urban planners and decision-makers, especially in metropolitan areas, to maintain a sustainable environment. This study examines the changes in land use/cover in the Tokyo metropolitan area (TMA) from 2007 to 2017 as a first step in using supervised classification. Second, based on the map results, we predicted the expected patterns of change in 2027 and 2037 by employing a hybrid model composed of cellular automata and the Markov model. The next step was to decide the model inputs consisting of the modeling variables affecting the distribution of land use/cover in the study area, for instance distance to central business district (CBD) and distance to railways, in addition to the classified maps of 2007 and 2017. Finally, we considered three scenarios for simulating land use/cover changes: spontaneous, sub-region development, and green space improvement. Simulation results show varied patterns of change according to the different scenarios. The sub-region development scenario is the most promising because it balances between urban areas, resources, and green spaces. This study provides significant insight for planners about change trends in the TMA and future challenges that might be encountered to maintain a sustainable region

Urban land expansion model based on SLEUTH, a case study in Dongguan city, China

The SLEUTH urban model is developed with sets of predefined growing rules involving Spontaneous Growth, New Spreading Center Growth, Edge Growth, Road Influenced Growth and Self-modification. They are applied continuously to lead the urban simulation to a specific morphology. A SLEUTH land use model was set up to simulate urban growth trajectory of Dongguan city from 1997 to 2009. The accuracy of localized parameters was evaluated to illuminate the growth pattern of Dongguan. Two different scenarios were set to predict the urban development from 2022 to 2030. Edge Growth is the dominant force of Dongguan's urbanization: regions adjacent to growth centers are more likely to be urbanized than remote area in general. Rapid urban expansion takes up large amount of other land types, around 2030, urbanization will reach the critical state in spatial. Unlike excessive growth rate in scenario 1, the urbanization speed is obviously more reasonable and sustainable in scenario 2, which confirms SLEUTH urban model is a good assistant of urban planning to avoid willful expansion with a scenario forecast. To protect ecological environment and promoting sustainable development of the region, relevant decision makers should take effective strategies to control urban sprawl. By the set of forecast scenarios, SLEUTH can certainly predict future urban development as an auxiliary to urban planners and government.Dongguan is under rapid urbanization in these decades. SLEUTH is an urban land use model named after the six input layers (Slope, Land use, Excluded, Urban, Transportation and Hill shade), and it is applied for simulating how surrounding land use changes due to urban expansion. A SLEUTH model was coupled with multi-source GIS (Geographic Information Systems) and RS (Remote Sensing) data to simulate urban growth trajectory of Dongguan city from 1997 to 2009. The accuracy of localized parameters was evaluated to illuminate the growth pattern of Dongguan. Based on the hypothesis that the urbanization process is as fast as before, a historical scenario from 2010 to 2050 was built up to choose the suitable study periods. In order to prove SLEUTH is able to offer reasonable outcomes for urban plan, two different scenarios were set to predict the urban development from 2022 to 2030, which shows SLEUTH is able to offer reasonable outcomes to government policy makers. Finally, the dynamic mechanism of urban growth combined with local characteristics was discussed. Some suggestions were also proposed for future urban planning and policy making in this study

Simulating the Impact of Urban Sprawl in Spatiotemporal Variation of Air Pollution in Bangalore Region using ML and GIS.

The rapid expansion of urban areas due to rise in population and economic growth is increasing the additional demand on natural resources thereby causing land-use changes especially in megacities. Bengaluru, being a city of India’s high-tech industry has been facing deteriorating environmental conditions. This paper deals with studying the problems by designing the Machine Learning (ML) model which helps in detecting the Green Cover Change, Urban sprawl, Air Quality Parameters concentration in Bangalore region for the future and analyse them with Geographic Information System (GIS) data’s using Remote Sensing (RS) satellite images of various years of Bangalore city along with some mitigatio

The global issue 'mega-urbanization': An unsolvable challenge for stakeholders, researchers and residents?

This study aims at discussing the complex, multi-dimensional issue of the global phenomenon of urbanization. Based on a theoretical review and discussion on the situation of cities, the causes, dimensions and consequences of urban growth the idea is to raise the main questions for future activities to meet this challenge. For it a pragmatic and holistic framework is proposed to systematize the manifold approaches and to stimulate discussions on this issue addressing inter- and transdisciplinary thinking