SLEUTH Model Research Articles

A comprehensive review of urban growth studies and predictions using the Sleuth model

Urban growth is a complex phenomenon It has been the subject of in-depth research for the last few years. There are various models used to measure and simulate urban growth. Most of these methods are founded on GIS & RS techniques coupled with the CA algorithm, as only these tools and techniques have the capabilities to conduct spatiotemporal studies, manage spatiotemporal dynamics, and provide a detailed depiction and modeling from the bottom-up tactic. Recently, the slope, land use, exclusion, urbanization, transportation, and hill shade (SLEUTH) model has been the most commonly used model. It is easily accessible because it is open source; moreover, its source code is also easily accessible. The SLEUTH model’s name alludes to the necessary inputs —slope, land use, excluded area, urban extension, transportation, network and hillshade. The model has been used in many cities and has proven to be efficient. The present review paper reviews the past literature pertaining to urban development and prediction to further support the research on urban planning, urban growth and prediction.

Modeling urban expansion in Zahedan’s dry climate: insights from the SLEUTH model

IntroductionRapid uncontrolled growth of build-up areas has increasingly challenged the sustainable use of urban area simulating the growth patterns of fastest-growing cities is more necessary in dry climates, due to low ecological suitability for urban development and meeting the needs of citizens. Therefore, this research conducted aiming at predicting the expansion of urban land use in Zahedan City, Iran, which has a dry climate with an evenness landscape.MethodsUrban Expansion in Zahedan Modeled using SLEUTH (slope, land use, exclusion, urban extent, transportation and hillshade) in two historical and environmental scenarios until 2050. The input data were extracted from processing on DEM and remote sensing data and the SLEUTH model was calibrated in four stages from 1990 to 2020.Results and discussionThe results showed that the increase in Ahead extent in 2050 is more than twice as much as in 2020, and this increase was associated with a less dispersion of urban patches in the environmental scenario compared to the historical scenario. Also, the results clarified that the developable spaces are saturated in terms of slope in the east and there is the lack of urban green spaces. These results reveal the need for the attention of city managers in predicting the urban green space in the expected growth areas and compensating for the lack of vegetation cover in the former urban areas. Geographic extension of predicted urban land can be used in future environmental planning and urban developing strategies, as well as it is suggested to adopt this approach as a plan for urban planning in dry climates.

Urban sprawl prediction in Batna City, Eastern Algeria, Using the SLEUTH model

As a developing country, Algeria has experienced urban extensions, often poorly managed, which have generated numerous social, urban, and economic problems. In this context, this work aims to present an integrated approach combining spatial modeling, geographic remote sensing, and geographic information system to plan sustainable urban growth and mitigate the aforementioned issues. This work introduces urban sprawl simulation using the SLEUTH model, based on the cellular automata method. SLEUTH, implemented with a open-source code, facilates the simulation and prediction of urban sprawl. Applied to the Batna metropolitan area, SLEUTH is calibrated using four chronological series of data extracted from satellite images spanning from 1986 to 2020, with approximately 2,000 hectares transformed into urban land, representing an increase of about 180%. Future scenarios simulations were conducted for a 50-year period up to 2072, revealing two growth stages of urbanized areas. Beyond 2048, population density experiences a constant increase, marking the point at which Batna city reaches its urbanization limits. These findings highlight the necessity for urban planners to prepare an appropriate urban policy within a suitable timeframe. The integration of the SLEUTH model into the decision-making process is recommended to enhance urban policy management.

Advancing urban planning in arid agricultural-urbanized landscapes of Iran: Spatial modeling evidence from a rapidly developing region

Binary agricultural-urbanized landscapes in the central arid zone of Iran are under the imminent threat of fragmentation and low crop yields due to uncontrolled low-density urban expansion. To characterize the current process in a case study area in Central Isfahan (1550 km2), two scenarios of Business As Usual (BAU) and Agricultural Land Protection (ALP) were simulated using the SLEUTH model and compared statistically to provide relevant urban planning insights. A time-series (1992- 2022) Landsat NDVI-based procedure was developed to characterize the annual cropping intensity of land parcels as a surrogate for agricultural land suitability, showing that only 16.7% of the region (303 parcels with a mean cropping NDVI of 0.184) has a high agricultural suitability. Landsat-derived urban areas also showed a significant road-influenced outlying pattern during the study period, especially in high cropping intensity areas with the SLEUTH Breed and Road Gravity scores of 100 and 60, respectively. By reducing these scores to half, the ALP scenario was developed and together with the BAU was projected up to the year 2045. Results showed significant restrictive growth of new road-influenced urban patches under the APL scenario which also targeted least-suitable agricultural areas, especially in high cropping intensity parcels (z= -6.657, p=0.000, sig≤ 0.05, 2-tailed).

Simulation of the potential impact of urban expansion on regional ecological corridors: A case study of Taiyuan, China

• We found suitable location for potential corridors by adopting high resolution spatial data. • We predicted the expansion of urban built-up area in Taiyuan from 2015 to 2035. • The impact of urban expansion on regional ecological corridors has been analyzed. • We identified the corridors that should be enhanced for conservation and restoration. • Our results can provide planners insightful suggestions for spatial planning. Urbanization caused by intensive land-use change is the main driving force of environment degradation. The development and protection of ecological corridors to connect green natural features has been recognized as an effective means for improving the resilience of cities. It is especially important for the cities which are in the rapid urbanizing process. In this paper, we used high-resolution spatial data to extract the distribution of ecological corridors by using the Least Cost Path model for the city of Taiyuan, China. Then, a prediction of the urban area for the year 2035 was achieved by using the SLEUTH model. Finally, we analyzed the negative impact of urban expansion on the corridors under current urbanization trends. Our results show that the regional corridors are mostly distributed in the western mountainous area, connecting large natural areas. There are also three ecological corridors crossing the city from the east to west. According to our prediction, Taiyuan will mainly grow southward along the urban edge and road network. As a result, two ecological corridors in the central and southern parts of Taiyuan will be largely occupied, especially the corridors in Xiaodian district which account for 72.51% of the total occupied corridor area. To avoid further conflict, the urbanization intensity along the sides of the corridors should be restricted to maintain corridors of a certain width. Our research findings can provide urban planners insightful suggestions for conservation and restoration of ecological networks and assist in spatial planning.

Urban modelling and forecasting of landuse using SLEUTH model.

The present study performs landcover modelling using the SLEUTH model. The urban land use changing factors are calibrated to predict the Land Use Land Cover (LULC) for a densely populated and developing smart city, Prayagraj, India. This research aims to use the SLEUTH model for simulating the future urban growth with the help of historical LULC (1990–2020), road network and elevation data. The influence of road gravity and slope resistant coefficients is very significant in this study's outcome. The built-up area of the region increased from 40.22 km2 (5.10% of total area) in 1990 to 85.89 km2 (10.89%) in 2020. According to prediction results, in the next 20 years, the built-up growth rate would be 1.9% and approximated built-up area would be 118.66 km2 (14.98%) in the year 2040. The quality of the result has been quantified in terms of best fit value of Optimal SLEUTH Metric (OSM) and validated against the existing LULC. The study utilises a spatially explicit urban growth model with 30 m resolution remote sensing data and provides future landuse of Prayagraj city.

Urban growth modelling and social vulnerability assessment for a hazardous Kathmandu Valley

In 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.

Using the T‐EFA method in a cellular automata‐based urban growth simulation's calibration step

AbstractChanges in land cover driven by urban sprawl increase the threat of urbanization of forests and agricultural lands. Therefore, monitoring urban sprawl by creating simulation models is frequently carried out to understand sustainable city management. Cellular automata‐based models are mostly preferred to reduce the damage led by urban sprawl, and the SLEUTH model is the most well known. Several methods have been developed for the SLEUTH model calibration step, such as optimum SLEUTH metrics and total exploratory factor analysis (T‐EFA), to improve the model accuracy. This study aims to create a high‐accuracy urban growth simulation model using low‐resolution data, investigate the T‐EFA method's success in the calibration step, and find the urban sprawl effects on land cover change. Istanbul was selected as our study area due to witnessing its tremendous urban sprawl since the 1950s. According to our results, the urban growth that occurred between 2000 and 2018 could be defined more closely to reality using the T‐EFA method, and Istanbul will continue to grow until 2040, with approximately 428.7 km2 of agricultural lands, 553.4 km2 of forests, and 0.1 km2 of wetlands being transformed to urban. In addition, the geologically risky areas under threat of urbanization will increase by 60% between 2018 and 2040.

Investigating changes in land cover in high-density settlement areas by protected scenario

Uncontrolled urban growth is one of the most prominent problems in modern urbanism and planning. Rapid urbanization and population growth cause changes in land cover. In addition, determining the effects of these changes is essential in terms of sustainable urban management policies. Urban growth is a complex, dynamic structure that initiates changes in land cover. For this reason, simulation models are used extensively in planning studies. In this study, land cover simulation of the Sancaktepe district in Istanbul was carried out with the SLEUTH model based on cellular automata (CA). The study aims to identify the damage caused by uncontrolled urbanization. In this context, a scenario was created based on the assumption that forests will be protected based on the changes in land cover that occurred between 1961-2014. The data used in the model were generated from cadastral maps on a parcel basis. For this purpose, four-period data sets (1961-1992-2001-2014) were prepared between 1961-2014. According to the simulation results, 82% of agricultural land, 2% of forest land and 84% of open land will probably be converted into residential use. According to the results, it has been determined that almost all of the open and agricultural land in the towns of Pasakoy and Samandira in the district of Sancaktepe have been converted into residential areas. According to the prediction that the changes in current land cover will continue, no change is expected in forests, while it has been determined that the potential to transform agricultural land and open land into settlement areas is quite high.

Impacts of spatiotemporal resolution and tiling on SLEUTH model calibration and forecasting for urban areas with unregulated growth patterns

ABSTRACT The SLEUTH model provides a framework for understanding land use evolution around urban areas. Calibration of SLEUTH’s behavioral coefficients can be impacted by scale and nonlinear transitions due to the SLEUTH land use deltatron module’s assumption of linear Markov change probabilities. This study attempted to establish what spatial resolution and temporal scale produces the most accurate forecasts given the linear change assumption. The impact of tiling the input data was also examined. To determine these, SLEUTH was calibrated at four spatial and three temporal scales for Ibadan, Nigeria using both untiled and tiled data. Calibration results were evaluated using accuracy metrics including Figure of Merit (FOM) and mean uncertainty. The best mix of calibration metrics (FOM 0.26) and mean uncertainty (11.64) was achieved at 30 m resolution and an intermediate temporal interval. Tiling input data led to overfitting, allowing good model fit within individual tiles but a reduction in trend recognition across land use types. Subsequently, a 2040 projection that is as accurate as possible, and scientifically justifiable given the available data, was produced. The findings provide a framework for understanding the effect of spatiotemporal scale on SLEUTH inputs that require tiling particularly for urban areas in the global south.

Urban development trend analysis and spatial simulation based on time series remote sensing data: A case study of Jinan, China.

Uncontrolled urban growth detracts from healthy urban development. Understanding urban development trends and predicting future urban spatial states is of great practical significance. In order to comprehensively analyze urbanization and its effect on vegetation cover, we extracted urban development trends from time series DMSP/OLS NTL and NDVI data from 2000 to 2015, using a linear model fitting method. Six urban development trend types were identified by clustering the linear model parameters. The identified trend types were found to accurately reflect the on-ground conditions and changes in the Jinan area. For example, a high-density, stable urban type was found in the city center while a stable dense vegetation type was found in the mountains to the south. The SLEUTH model was used for urban growth simulation under three scenarios built on the urban development analysis results. The simulation results project a gentle urban growth trend from 2015 to 2030, demonstrating the prospects for urban growth from the perspective of environmental protection and conservative urban development.

SLEUTH model sensitivity testing: game of life, cellular neighborhood, and diffusivity

SLEUTH model sensitivity testing: game of life, cellular neighborhood, and diffusivity

Sensitivity analysis and retrieval of optimum SLEUTH model parameters

The Cellular Automata (CA) based SLEUTH model has emerged as a widely applied model to many cities for land use land cover (LULC) change and urban growth modelling due to its simplicity, robustness, and ease of implementation. The present study employed a rigorous sensitivity testing of self-modifying constants, Monte Carlo runs and critical slope to determine their influence on model calibration performance. Calibration performance has been examined in terms of statistical measures i.e., urban area, clusters, edges, mean cluster size, and cluster radius, best model fitness measure (i.e., Optimal SLEUTH Metrics (OSM)), overall accuracy percentage and hit-miss-false alarm method have been used. The sensitivity analysis reveals the optimum values for self-modifying parameters as {1.3, 0.10, 0.90, and 1.25} for boom, bust, critical low and critical high respectively; Monte Carlo runs as sixty (60) and critical slope as 15 to simulate the urban growth of the study area.

Comparative Evaluation of Open Source Urban Simulation Models Applied to Colombo City and Environs in Sri Lanka

Availability of a variety of urban growth models make model selection to be an important factor in urban simulation studies. In this regard, a comparative evaluation of available urban growth models helps to choose a suitable model for the study area. Thus, we selected three open-source simulation models namely FUTURES, SLEUTH and MOLUSCE to compare in their simplest state to provide a guidance for selection of an urban growth model for Colombo. The urban extent maps of 1997, 2005, 2008, 2014 and 2019 derived from Landsat imageries were used in calibration and validation of models. Models were implemented with the minimum required data with default settings. The simulation results indicate that the estimated quantity of urban growth (148.91 km2) during 2008-2019 by FUTURES model is matching closely with observed urban growth (127.37 km2) during 2008-2019. On the other hand, the SLEUTH model showed an overestimation (250.56 km2) and MOLUSCE showed an underestimation (77.11 km2). Further, the spatial accuracy of urban growth simulation of SLEUTH (Figure of Merit = 0.26) is relatively better in comparison to FUTURES (0.20) and MOLUSCE (0.20). Considering the tradeoff between computational overheads and obtained results, FUTURES could be a good choice over SLEUTH and MOLUSCE, when these models implemented in their simplest form with minimum required datasets. As a future work, we propose the incorporation of exclusion factor for potential surface generation to mitigate the overestimation of urban areas in SLUETH.

Modelling urban expansion of a south-east Asian city, India: comparison between SLEUTH and a hybrid CA model

Accurate identification of the dominant driving factors in the expansion of a city is essential for cellular automata (CA)-based urban expansion modelling. The current study aimed at analyzing the drivers of change for Vijayawada city, a tier-II developing city in south-east Asia, India and then applies them to model the future scenario of the city in a CA-based environment. Furthermore, the study also aims at comparing the efficiency of the renowned CA-based urban growth model, SLEUTH (Slope, Land use, Exclusion, Urban extent, Transportation, Hill shade), and a self-designed CA-based hybrid model developed in combination with genetic algorithm (GA) for developing future scenario of the city. The models were built, calibrated, and validated for the city using land use maps derived from temporal Landsat satellite images. Analysis of the statistical significance of the driving factors shows that the terrain and population density are the two dominant factors influencing expansion of the city. Both the models output on predicting the urban growth indicate that the available open spaces within the existing city extent gets further converted to built-up indicating infill development, and more growth occurs along the fringes of the existing city. Also, emergence of new urban centers merged with the existing city areas for predictions till 2050 and extended into the dense vegetation on the north resulting in further reduction of green cover around the city. Validation of both the models showed overall accuracy higher than 85%. The inclusion of the demographic variable and utilization of the GA ensured that the hybrid model performs better than the SLEUTH model. Analysis on the built-up densification of the cityscape showed a shift in the pattern of built-up dynamics over time. The built urban expansion scenario for Vijayawada informs urban modelers and policymakers the possible evolution of the city.

The future urban growth under policies and its ecological effect in the Jing-Jin-Ji area, China

Since 2016, the Chinese government has invoked some policies to make Jing-Jin-Ji (JJJ) a new urban agglomeration. However, there has been no research to study the effect of these new policies on future urban growth. This study assessed part of these new policies on JJJ urban growth in 2020–2050 using SLEUTH model. Then the ecological effects of the urban growth are evaluated. Results showed the policies had nearly no obvious impact on the whole JJJ urban growth, but affected sub-regional (Beijing, Tianjin and Hebei, respectively) urban growth. Under ecological protection in future, the value of ecological service in JJJ would increase to a maximum of 31.7×108 Yuan/km2 in 2031. The ecological elasticity also increased and the ecological risk was strongly reduced around the present urban area. This ecologically sustainable development is critical to the future urban growth, and should be considered more carefully by urban planners and managers. More policies should be evaluated for JJJ urban growth in future work.

Integrated CA model and remote sensing approach for simulating the future development of a city

Population growth and industrial development have resulted in the physical development of cities and transformation of land use, especially in the areas surrounding the City. Reviewing these changes and predicting urban growth through modelling can help environmental managers in making more informed decisions. The purpose of this research was to simulate the expansion of urbanization and also its prediction in the future using the SLEUTH model in Zanjan. In this study, Landsat and IRS images were used 2001, 2002, and 2003. Then, slope data, land use, exclusion, urban development, transportation, and landing were introduced to the Cygwin using the controlled classification method, to implement the SLEUTH model. The results of model prediction indicated a significant increase will happen in urban areas by 38.1% over the next 22 years. The results also showed that with SLEUTH model calibration, the projected growth has a high scattering coefficient, in which case topography limits the development and expansion of the City. In contrast, the coefficients of spread and slope of play with points of 96 and 94 have considerably grown, respectively. The conversion of these lands into urban areas will slow down from now until the next 22 years, and this further development will be achieved along with the main roads of the province, as well as the actual results of the current situation will be the evidence for that. The results of this study can provide further study opportunities for planners and decision makers to consider preventive and controlling factors into strategic decisions.

Spatio-temporal dynamics of flood exposure in Shenzhen from present to future

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.

Development of SLEUTH-Density for the simulation of built-up land density

Urban growth is a complex spatio-temporal phenomenon that includes built-up activities taking place both horizontally and vertically. The built-up land density in a city is a function of land desirability and suitability of a location in terms of the quality of the available public services, access to infrastructure, neighborhood type, vibrancy of socio-economic and cultural characteristics. The simulation of built-up land density may help planning and development authorities in making better land developmental decisions, making appropriate provisions for services, long term land use planning, and allocation of natural resources. In the recent past, various efforts have been made to assess built-up density in terms of the densification of built-up activities at the city scale in terms of floor space indices, building density, residential density (number of housing units per hectare) and urban volume. However, only limited research was reported on the simulation and prediction of spatially distributed built-up land density. In the present study, an algorithm is developed to simulate the spatial distribution of built-up land density as a function of a set of selected urbanization explanatory variables. Its application has been demonstrated using a newly developed version of the SLEUTH model (SLEUTH-Density) to simulating the built-up land density for Ajmer city in India. Development of SLEUTH-Density included designing a density algorithm, writing the programming code, integrating the code with the existing SLEUTH model, and testing the algorithm. The model results were validated indirectly using few built-up land density indices and directly through field verification, which were found to be in good agreement with the simulated built-up land density from SLEUTH-Density

Simulating Urban Growth Scenarios Based on Ecological Security Pattern: A Case Study in Quanzhou, China.

In recent decades, the ecological security pattern (ESP) has drawn increasing scientific attention against the backdrop of rapid urbanization and worsening ecological environment. Despite numerous achievements in identifying and constructing the ecological security pattern, limited attention has been paid on applying ESP to predict urban growth. To bridge the research gap, this paper took Quanzhou, China as a study case and incorporated the identified ESP into an urban growth simulation with three distinct scenarios. Following the “ecological source–ecological corridor–ecological security pattern” paradigm, the ESP identification was carried out from four single aspects (i.e., water, geology, biodiversity, and recreation) into three levels (i.e., basic ESP, intermediate ESP, and optimal ESP). Grounded in an equally weighted superposition algorithm, the four single ESPs were combined as an integrated ESP (IESP) with three levels. Taking IESP as an exclusion element, urban growth simulation in 2030 was completed with thee SLEUTH model. Drawing on the three levels of IESP, our urban growth simulation contained three scenarios. In terms of urban sprawl distribution coupled with urban growth rate, an optimal urban growth scenario is recommended in this paper to balance both urban development and eco-environment protection. We argue that our ESP-based urban growth simulation results shed new light on predicting urban sprawl and have the potential to inform planners and policymakers to contribute to more environmentally-friendly urban development.