Journal articles on the topic 'Cellular automaton SLEUTH'

<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>

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.

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.

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.

<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>

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.

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.

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.

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.

With the availability of computational resources, geographical information systems, and remote sensing data, urban growth modeling has become a viable tool for predicting urbanization of cities and towns, regions, and nations around the world. This information allows policy makers, urban planners, environmental and civil organizations to make investments, design infrastructure, extend public utility networks, plan housing solutions, and mitigate adverse environmental impacts. Despite its importance, urban growth models often discard the spatiotemporal uncertainties in their prediction estimates. In this paper, we analyzed the uncertainty in the urban land predictions by comparing the outcomes of two different growth models, one based on a widely applied cellular automata model known as the SLEUTH CA and the other one based on a previously published machine learning framework. We selected these two models because they are complementary, the first is based on human knowledge and pre-defined and understandable policies while the second is more data-driven and might be less influenced by any a priori knowledge or bias. To test our methodology, we chose the cities of Jiaxing and Lishui in China because they are representative of new town planning policies and have different characteristics in terms of land extension, geographical conditions, growth rates, and economic drivers. We focused on the spatiotemporal uncertainty, understood as the inherent doubt in the predictions of where and when will a piece of land become urban, using the concepts of certainty area in space and certainty area in time. The proposed analyses in this paper aim to contribute to better urban planning exercises, and they can be extended to other cities worldwide.

The Pearl River Delta metropolitan region is one of the most densely urbanized megapolises worldwide with high exposure to weather-related disasters such as storms, storm surges and river floods. Shenzhen megacity has been the fastest growing city in the Pearl River Delta region with a significant increase of resident population from 0.32 million in 1980 to 13.03 million in 2018. Being a flood-prone city, Shenzhen’s rapid urbanization has further exacerbated potential flood losses and forthcoming risk. Thus, evaluating the changes in its exposure from present to future is essential for flood risk assessment, mitigation and management purposes. The main objective of this study is to present a methodology to assess the spatio-temporal dynamics of flood exposure from present to future using high-resolution and open-source data with a particular focus on the built-up area. To achieve this, the SLEUTH model, a cellular automata-based urban growth model, was employed for predicting the built-up area in Shenzhen in 2030. An almost threefold increase was observed in total built-up area from 421 km2 in 1995 to 1166 km2 in 2030, with the 2016 built-up area being 858 km2. Built-up areas, both present (2016) and projected (2030), were then used as the land cover input for flood hazard assessment based on a fuzzy comprehensive evaluation model, which classified the flood hazard into five levels. The analysis indicates that the built-up area subjected to the two highest flood hazard levels will increase by almost 88% (212 km2) from present to future. The approach presented here can be leveraged by policymakers to identify critical areas that should be prioritized for flood mitigation and protection actions to minimize potential losses.

Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. Farmlands increased from 2.23 ha to 46.45ha and bared surface increases from 120.00ha to 161.31ha between 1976 to 2016 resulting to decline in vegetation, water body, and wetlands. The Decade of rapid urbanization was found to coincide with the period of increased Public Private Partnership Agreement (PPPA). Increase in farmlands was due to the adoption of urban agriculture which has influence on food security and the environmental sustainability. The observed increase in built up areas, farmlands and bare surfaces has substantially led to reduction in vegetation and water bodies. The oscillatory nature of water bodies LULCC which was not particularly consistent with the rates of urbanization also suggests that beyond the urbanization process, other factors may influence the LULCC of water bodies in urban settlements. Keywords: Minna, Niger State, Remote Sensing, Land Surface Characteristics References Akinrinmade, A., Ibrahim, K., & Abdurrahman, A. (2012). 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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 areas in tropical regions have higher flood risks due to the more frequent occurrence of intense convective rainfalls. The rising urbanization process have caused more surfaces to be covered with impervious materials, resulting in increased runoff. Modelling urban growth and its impact on urban hydrology is essential to ensure informed decision in the sustainable management and planning of cities in developing country like Malaysia. The aim of this research is to develop an integrated system for simulating future flood extents by coupling flood and urban growth models for the Upper Klang Ampang catchment which includes Kuala Lumpurcapital city. HEC-HMS was used for flood modelling while SLEUTH cellular automata model was employed to analyse urban growth in the catchment. The results indicate that using historical satellite images from 1990, 2000, 2010 and 2016 as input data layers alongwith slope, land use, hill shade, road and restricted area layers, a slight increase in urban growth from 2020 until 2050 is predicted which can cause the peak discharge to increase by about 11-15%. The integrated flood estimation-urban growth system can be used as an effective tool in urban planning and management for the city.

AbstractIn our rapidly urbanizing world, many hazard-prone regions face significant challenges regarding risk-informed urban development. This study addresses this issue by investigating evolving spatial interactions between natural hazards, ever-increasing urban areas, and social vulnerability in Kathmandu Valley, Nepal. The methodology considers: (1) the characterization of flood hazard and liquefaction susceptibility using pre-existing global models; (2) the simulation of future urban built-up areas using the cellular-automata SLEUTH model; and (3) the assessment of social vulnerability, using a composite index tailored for the case-study area. Results show that built-up areas in Kathmandu Valley will increase to 352 km2 by 2050, effectively doubling the equivalent 2018 figure. The most socially vulnerable villages will account for 29% of built-up areas in 2050, 11% more than current levels. Built-up areas in the 100-year and 1000-year return period floodplains will respectively increase from 38 km2 and 49 km2 today to 83 km2 and 108 km2 in 2050. Additionally, built-up areas in liquefaction-susceptible zones will expand by 13 km2 to 47 km2. This study illustrates how, where, and to which extent risks from natural hazards can evolve in socially vulnerable regions. Ultimately, it emphasizes an urgent need to implement effective policy measures for reducing tomorrow's natural-hazard risks.