Academic literature on the topic 'Cellular automaton SLEUTH'
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Journal articles on the topic "Cellular automaton SLEUTH"
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Rienow, Andreas, and Roland Goetzke. "Supporting SLEUTH – Enhancing a cellular automaton with support vector machines for urban growth modeling." Computers, Environment and Urban Systems 49 (January 2015): 66–81. http://dx.doi.org/10.1016/j.compenvurbsys.2014.05.001.
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Mallouk, A., H. Elhadrachi, M. E. I. Malaainine, and H. Rhinane. "USING THE SLEUTH URBAN GROWTH MODEL COUPLED WITH A GIS TO SIMULATE AND PREDICT THE FUTURE URBAN EXPANSION OF CASABLANCA REGION, MOROCCO." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W12 (February 26, 2019): 139–45. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w12-139-2019.
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<p><strong>Abstract.</strong> The rapid and sometimes uncontrolled acceleration of urban growth, particularly in developing countries, places increasing pressure on environment and urban population well-being, making it a primary concern for managers. In Casablanca city, Morocco’s economic capital, the rapid urbanization was a result of population explosion, rural exodus and the emergence of new urban centers. Therefore, a system for urban growth simulation and prediction to anticipate infrastructural needs became indispensable to optimize urban planning. The main aim of this work is to study the urban extension of the Grand Casablanca region from 1984 to 2022 and to predict urban growth in 2040 using the SLEUTH cellular automaton model. The methodology consists of calibrating the model using data extracted from a time series of satellite images with a resolution of 30 m acquired between 1984 and 2018, as well as vector data relating to the urban projects planned on the horizon of 2022. The supervised classification and digitization of these images, together with a DEM of the study area, provided the input data required by the model, including Slope, Land use, Exclusion, Transportation and Hillshade. This data was introduced into the model using ArcSLEUTH, a custom extension of ArcGIS to compile the SLEUTH model. The result is synthetic maps of urban growth in the study area up to 2040, as well as the expected percentage indicators of change. The result is an effective decision-support tool for decision-makers and planners to develop more informed development strategies for the region and its people.</p>
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Song, Jie, Xinyu Fu, Yue Gu, Yujun Deng, and Zhong-Ren Peng. "An examination of land use impacts of flooding induced by sea level rise." Natural Hazards and Earth System Sciences 17, no. 3 (March 7, 2017): 315–34. http://dx.doi.org/10.5194/nhess-17-315-2017.
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Abstract. Coastal regions become unprecedentedly vulnerable to coastal hazards that are associated with sea level rise. The purpose of this paper is therefore to simulate prospective urban exposure to changing sea levels. This article first applied the cellular-automaton-based SLEUTH model (Project Gigalopolis, 2016) to calibrate historical urban dynamics in Bay County, Florida (USA) – a region that is greatly threatened by rising sea levels. This paper estimated five urban growth parameters by multiple-calibration procedures that used different Monte Carlo iterations to account for modeling uncertainties. It then employed the calibrated model to predict three scenarios of urban growth up to 2080 – historical trend, urban sprawl, and compact development. We also assessed land use impacts of four policies: no regulations; flood mitigation plans based on the whole study region and on those areas that are prone to experience growth; and the protection of conservational lands. This study lastly overlaid projected urban areas in 2030 and 2080 with 500-year flooding maps that were developed under 0, 0.2, and 0.9 m sea level rise. The calibration results that a substantial number of built-up regions extend from established coastal settlements. The predictions suggest that total flooded area of new urbanized regions in 2080 would be more than 25 times that under the flood mitigation policy, if the urbanization progresses with few policy interventions. The joint model generates new knowledge in the domain between land use modeling and sea level rise. It contributes to coastal spatial planning by helping develop hazard mitigation schemes and can be employed in other international communities that face combined pressure of urban growth and climate change.
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Varquez, Alvin Christopher G., Sifan Dong, Shinya Hanaoka, and Manabu Kanda. "Improvement of an Urban Growth Model for Railway-Induced Urban Expansion." Sustainability 12, no. 17 (August 21, 2020): 6801. http://dx.doi.org/10.3390/su12176801.
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Increasing population in urban areas drives urban cover expansion and spatial growth. Developing urban growth models enables better understanding and planning of sustainable urban areas. The SLEUTH model is an urban growth simulation model which uses the concept of cellular automata to predict land cover change using six spatial inputs of historical data (slope, land use, exclusion, urban, transportation, and hill-shade). This study investigates the potential of SLEUTH to capture railway-induced urban growth by testing methods that can consider railways as input to the model, namely (1) combining the exclusion layer with a station map; (2) creating a new input layer representing stations in addition to the default six inputs. Districts in Tsukuba, Japan and Gurugram, India which historically showed evidence of urban growth by railway construction are investigated. Results reveal that both proposed methods can capture railway impact on urban growth, while the former algorithm under the right settings may perform better than the latter at finer resolutions. Coarser resolution representation (300-m grid-spacing) eventually reduces the differences in accuracy among the default SLEUTH model and the proposed algorithms.
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Ayazli, I. E. "USING THE TOTAL EXPLORATORY FACTOR ANALYSIS (T-EFA) AS A CALIBRATION TECHNIQUE FOR SLEUTH MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W3-2020 (November 23, 2020): 85–88. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w3-2020-85-2020.
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Abstract. Developments in information technologies (IT) allow to modelling dynamic and complex form of cities and several studies have been implemented since 1990s. The cellular automata based urban growth simulation model, SLEUTH is the most well-known one among the simulation models. Calibration is the most important stage of the model created in three stages such as test, calibration, prediction. The more precise the calibration is completed, the more accurate the model generates. Several methods have been developed for the calibration step in which growth coefficients values are calculated by metrics. The study aims to investigate success of the Total Exploratory Factor Analysis (T-EFA) technique, which provides using the 13 metrics all together, in rapid grown settlement areas using high resolution data. In this context, the Sancaktepe district of Istanbul was selected as the study area and a simulation model was generated for the year 2050. The obtained results are promising to apply the T-EFA method in different studies.
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Eslahi, Mojtaba, Rani El Meouche, and Anne Ruas. "Using building types and demographic data to improve our understanding and use of urban sprawl simulation." Proceedings of the ICA 2 (July 10, 2019): 1–8. http://dx.doi.org/10.5194/ica-proc-2-28-2019.
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<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>
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Abubakar, Ghali Abdullahi, Jiexia Wu, Amir Reza Shahtahmassebi, and Ke Wang. "Necessity of a Multifaceted Approach in Analyzing Growth of Impervious Surfaces." Sustainability 12, no. 10 (May 18, 2020): 4109. http://dx.doi.org/10.3390/su12104109.
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While substantial efforts have been devoted to the remote sensing of impervious surfaces, few studies have developed frameworks to connect impervious surfaces’ growth with spatial planning decisions. To this end, this paper develops a multifaceted approach with three components: Visualization, numerical analysis, and simulation at the sub-pixel level. First, the growth of impervious surfaces was visualized through write function memory (WFM) insertion for the period of 1974–2009 of Cixi County in Zhejiang Province, China. Second, anomaly detection, statistical analysis, and landscape metrics were used to quantify changes in impervious surfaces over time. Finally, a slope, land use, exclusion, urban extent, transportation, and hill shade (SLEUTH) cellular automata model was employed to simulate the impervious surface growth until 2015 under four specific spatial decision scenarios: Current trends, environmental protection growth, business growth, and Chinese policy for protecting rural regions. The results show that Cixi County experienced compact growth due to expansion and internal intensification. Interestingly, the SLEUTH reveals that the projected space of impervious surfaces’ growth was consistent with reality in 2015. The framework established in this study holds considerable potential for improving our understanding of the interaction between impervious surfaces’ growth and planning aspects.
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Harb, Mostapha, Matthias Garschagen, Davide Cotti, Elke Krätzschmar, Hayet Baccouche, Karem Ben Khaled, Felicitas Bellert, et al. "Integrating Data-Driven and Participatory Modeling to Simulate Future Urban Growth Scenarios: Findings from Monastir, Tunisia." Urban Science 4, no. 1 (February 27, 2020): 10. http://dx.doi.org/10.3390/urbansci4010010.
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Current rapid urbanization trends in developing countries present considerable challenges to local governments, potentially hindering efforts towards sustainable urban development. To effectively anticipate the challenges posed by urbanization, participatory modeling techniques can help to stimulate future-oriented decision-making by exploring alternative development scenarios. With the example of the coastal city of Monastir, we present the results of an integrated urban growth analysis that combines the SLEUTH (slope, land use, exclusion, urban extent, transportation, and hill shade) cellular automata model with qualitative inputs from relevant local stakeholders to simulate urban growth until 2030. While historical time-series of Landsat data fed a business-as-usual prediction, the quantification of narrative storylines derived from participatory scenario workshops enabled the creation of four additional urban growth scenarios. Results show that the growth of the city will occur at different rates under all scenarios. Both the “business-as-usual” (BaU) prediction and the four scenarios revealed that urban expansion is expected to further encroach on agricultural land by 2030. The various scenarios suggest that Monastir will expand between 127–149 hectares. The information provided here goes beyond simply projecting past trends, giving decision-makers the necessary support for both understanding possible future urban expansion pathways and proactively managing the future growth of the city.
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Sekovski, I., C. Armaroli, L. Calabrese, F. Mancini, F. Stecchi, and L. Perini. "Coupling scenarios of urban growth and flood hazards along the Emilia-Romagna coast (Italy)." Natural Hazards and Earth System Sciences 15, no. 10 (October 14, 2015): 2331–46. http://dx.doi.org/10.5194/nhess-15-2331-2015.
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Abstract. The extent of coastline urbanization reduces their resilience to flooding, especially in low-lying areas. The study site is the coastline of the Emilia-Romagna region (Italy), historically affected by marine storms and floods. The main aim of this study is to investigate the vulnerability of this coastal area to marine flooding by considering the dynamics of the forcing component (total water level) and the dynamics of the receptor (urban areas). This was done by comparing the output of the three flooding scenarios (10, 100 and > 100 year return periods) to the output of different scenarios of future urban growth up to 2050. Scenario-based marine flooding extents were derived by applying the Cost–Distance tool of ArcGIS® to a high-resolution digital terrain model. Three scenarios of urban growth (similar-to-historic, compact and sprawled) up to 2050 were estimated by applying the cellular automata-based SLEUTH model. The results show that if the urban growth progresses compactly, flood-prone areas will largely increase with respect to similar-to-historic and sprawled growth scenarios. Combining the two methodologies can be useful for identification of flood-prone areas that have a high potential for future urbanization, and is therefore crucial for coastal managers and planners.
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Sekovski, I., C. Armaroli, L. Calabrese, F. Mancini, F. Stecchi, and L. Perini. "Coupling scenarios of urban growth and flood hazard along the Emilia-Romagna coast (Italy)." Natural Hazards and Earth System Sciences Discussions 3, no. 4 (April 1, 2015): 2149–89. http://dx.doi.org/10.5194/nhessd-3-2149-2015.
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Abstract. The extent of coastline urbanization reduces their resilience to flooding, especially in low lying areas. The study site is the Emilia-Romagna Region coastline (Italy), historically affected by marine storms and floods. The main aim of this study is to investigate the vulnerability of this coastal area to marine flooding by considering the dynamics of the forcing component (Total Water Level) and the dynamics of the receptor (urban areas). This was done by comparing the output of the three flooding scenarios (10, 100 and >100 year return periods) to the output of different scenarios of future urban growth up to 2050. Scenario-based marine flooding extents were derived by applying the Cost-Distance tool of ArcGIS® to a high resolution Digital Terrain Model. Three scenarios of urban growth (similar-as-historic, compact and sprawled) up to 2050 were estimated by applying the cellular automata based SLEUTH model. The results show that, if the urban growth is compact-like, flood-prone areas will largely increase with respect to similar-as-historic and sprawled growth scenarios. Combining the two methodologies can be useful for identify flood-prone areas that have a high potential for future urbanization, and is therefore crucial for coastal managers and planners.
Dissertations / Theses on the topic "Cellular automaton SLEUTH"
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Afonso, Marta Isabel Baptista. "Avaliação de impactes do desenvolvimento urbano sobre a estrutura ecológica da Península de Setúbal: uma aplicação baseada em autómatos celulares utilizando o modelo SLEUTH." Master's thesis, ISA/UL, 2015. http://hdl.handle.net/10400.5/8533.
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Mestrado em Arquitectura Paisagista - Instituto Superior de AgronomiaThe aim of this thesis is to apply a cellular automaton model, SLEUTH, to a 1432,7 km2 area centered in Península de Setúbal to simulate the impacts of different land use policies projected out to 2030. The study area has experienced rapid and disorganized urban growth in the last decades leading to a great loss of natural resources. Therefore the model was calibrated using a historic time series of 1940, 1963, 1990 and 2007 developed areas and the projections were made according to three different policies scenarios: (1) Current Trends, (2) Moderate Ecological Protection and (3) Extreme Ecological Protection. This thesis: Studies the nature conservation history, the evolution of ecological based planning, specifically Ecological Network and the legal framework in Portugal; Studies cellular automaton, highlights the SLEUTH model and reviews the most relevant works based on the model; Analyses the urban growth patterns of the study area since XIX century to the current year; Analyses the components of Ecologic Network of the study area; Tests SLEUTH accuracy; Simulates urban growth out to 2030 with the purpose of understanding how the urban growth affects the Ecological Network’s components and how Ecological Network influences urban growth patterns according to the three scenarios. The results were analyzed with Fragstats 4.2.
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Ven?ncio, Salatiel da Rocha. "Avalia??o do crescimento de ocupa??o da bacia do rio Pitimbu com subs?dios para estudos de poss?veis impactos sobre os recursos hidricos." Universidade Federal do Rio Grande do Norte, 2014. http://repositorio.ufrn.br:8080/jspui/handle/123456789/16009.
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Previous issue date: 2014-03-10Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior
The Pitimbu River Watershed (PRW), belonging to Potiguar capital metropolitan area, State of Rio Grande do Norte, contributes, among other purposes, to human using and animal watering. This watershed is extremely important because, besides filling up with freshwater approximately 30% of the south part of Natal (South, East and West Zones), contributes to the river shore ecosystem equilibrium. Face to the current conjuncture, this study aims to evaluate the urban development dynamics in the PRW, applying Cellular Automata as a modeling instrument, and to simulate future urban scenarios, between 2014 and 2033, using the simulation program SLEUTH. In the calibration phase, urban spots for 1 984, 1992, 2004 and 2013 years were used, with resolution from 100 meters. After the simulation, it was found a predominance of organic growth, expanding the BHRP from existing urban centers. The spontaneous growth occurred through the fullest extent of the watershed, however the probability of effective growth should not exceed 21%. It was observed that, there was a 68% increase for the period between 2014 and 2033, corresponding to an expansion area of 1,778 ha. For 2033, the source of Pitimbu River area and the Jiqui Lake surroundings will increase more than 78%. Finally, it was seen an exogenous urban growth tendency in the watershed (outside-in). As a result of this growth, hydraulics resources will become scarcer
A Bacia Hidrogr?fica do Rio Pitimbu (BHRP), pertencente ? regi?o metropolitana da capital Potiguar, Estado do Rio Grande do Norte (RN), contribui, entre outros fins, para o consumo humano e dessedenta??o animal. Essa bacia ? de suma import?ncia, pois al?m de abastecer com ?gua doce aproximadamente 30% da popula??o da parte sul de Natal (zonas sul, leste e oeste), contribui para o equil?brio do ecossistema ao longo do rio. Diante da conjuntura atual, os objetivos deste estudo foram avaliar a din?mica do desenvolvimento urbano na BHRP, aplicando Aut?matos Celulares como instrumento de modelagem, e simular cen?rios urbanos futuros, entre 2014 e 2033, empregando o programa de simula??o SLEUTH. Na fase de calibra??o, foram utilizadas as manchas urbanas para os anos de 1984, 1992, 2004 e 2013, com resolu??o 100 metros. Ap?s a simula??o, verificou-se que houve uma predomin?ncia do crescimento org?nico, expandindo-se na BHRP, a partir de centros urbanos existentes. O crescimento espont?neo ocorreu por toda extens?o da Bacia, por?m a probabilidade de crescimento efetivo n?o deve ultrapassar 21%. Verificou-se um crescimento de 68% para o per?odo entre 2014 e 2033, correspondendo a uma ?rea de expans?o de 1.778 ha. Para o ano de 2033, a ?rea da nascente do rio Pitimbu e proximidades da lagoa do Jiqui ter?o a possibilidade efetiva de crescimento acima de 78%. Por fim, observou -se uma tend?ncia de crescimento urbano ex?geno (de fora para dentro) na Bacia. Em consequ?ncia desse crescimento, os recursos h?dricos tornar-se-?o cada vez mais escassos
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James, George R. "Predicting the spatial pattern of urban growth in Honolulu county using the cellular automata SLEUTH urban growth model." Thesis, 2005. http://hdl.handle.net/10125/11626.
Full textConference papers on the topic "Cellular automaton SLEUTH"
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Chandan, M. C., and H. A. Bharath. "Modelling Urban transition using Cellular Automata based Sleuth modelling." In 2018 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2018. http://dx.doi.org/10.1109/ssci.2018.8628940.
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Chanden, Mysore Chandrashekar, J. S. Aadithyaa, P. S. Prakash, and Haridas Bharath. "Machine learning for building extraction and integration of particle swarm optimization with sleuth for urban growth pattern visualization for liveable cities." In 55th ISOCARP World Planning Congress, Beyond Metropolis, Jakarta-Bogor, Indonesia. ISOCARP, 2019. http://dx.doi.org/10.47472/pukd9844.
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Rapidly increasing population and migration from rural areas to nearby urban agglomerations develop tremendous pressure on system of the existing cities without compromising socioeconomic and cultural linkages. Policy interventions, both at global and local scale, have created newer avenues for the researchers to explore real-time solutions for problems world-wide. For instance, the outcome of 2015 United Nations agenda for the achievement of the Sustainable Development Goals (SDGs) by the year 2030 primarily focuses on urbanization issues and probabilistic modelling of future scenarios to obtain a robust alternative for resource utilization and further for maximizing sustainability through land use pattern analysis. This is the clear indication toward the very important role of “ever dormant” urban planning, especially in the case of a rapidly developing country such as India. Remote sensing and geo informatics along with Machine learning can provide extremely relevant information about the pattern change in cities and as input to visualize the future growth pockets. In this context, potential of cellular automata (CA) in urban modelling has been explored by various researchers across the globe. In the recent past, models have been drawing majority of the attention along with geographic CA processes about urban growth and urban sprawl studies. Most recent approaches include optimization of transition rules based on machine learning techniques and evolutionary algorithms that follow nature-inspired mechanism such as Genetic Algorithm, Ant colony optimization, Particle Swarm Optimization (PSO), simulated annealing, Grey Wolf optimizer etc. Irrespective of any modelling technique, model calibration remains one of the challenging and most crucial steps towards obtaining realistic results. This research communication tries to demonstrate a novel idea of integrating PSO with SLEUTH post calibration of the spatial-temporal footprint of urban growth from the year 1990 to 2017 for Kolkata, a historical megacity of Eastern India. Results were evaluated and validated using statistical fit measuresreveals PSO-SLEUTH performed substantially better compared to traditional Brute Force calibration method (BFM). Another significant development was in terms of computation time of optimized values from days (BFM) to hours (PSO). The study identifies Kolkata region to be sensitive to spread and road gravity coefficients during calibration procedure. Results indicate growth along the transport corridors with multiple agents fuelling the growth. Further, with the aid of high spatial resolution data, buildings were extracted to understand the growth parameters incorporating neural networks. Using the results, renewable energy aspects were explored to harness and provide a suitable local solution for energy issues in energy gobbling cities. Pattern of landscape change, development of better process of modeling and extraction of building from machine learning techniques for planning smart cities with self-sustaining energy is presented in this research work.