Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model

Jantz, Claire A.; Goetz, Scott J.; Donato, David I.; Claggett, Peter R.

doi:10.1016/j.compenvurbsys.2009.08.003

Cited by 184 publications

(132 citation statements)

References 19 publications

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“…The authors evaluated different combinations of the 13 metrics and found that OSM contributes to more accurate and superior predictions than single-metric approaches. Recent studies have furthermore suggested OSM's robustness (Jantz et al, 2010;Sakieh et al, 2015). Hence, it was applied in this work to narrow parameter ranges after each stage.…”

Section: Model Calibrationmentioning

confidence: 99%

“…The first is to adjust one or more of the five parameters (Leao et al, 2004;Rafiee et al, 2009). The second is to modify the growth-resistance levels in excluded layers (Jantz et al, 2010) or apply distinct excluded layers (Akın et al, 2014). The last one, less frequently used than the first two methods, is to alter self-modification parameters which control overall growth rates (Yang and Lo, 2003).…”

Section: Model Predictionmentioning

confidence: 99%

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An examination of land use impacts of flooding induced by sea level rise

Song¹,

Fu²,

Gu³

et al. 2017

Nat. Hazards Earth Syst. Sci.

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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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Section: Model Calibrationmentioning

confidence: 99%

Section: Model Predictionmentioning

confidence: 99%

An examination of land use impacts of flooding induced by sea level rise

Song¹,

Fu²,

Gu³

et al. 2017

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…However, its minimal requirements for input data and socio-ecological knowledge of the system, combined with its automated calibration, likely contribute to SLEUTH's popularity. Calibration of the model aims to determine parameter values which most accurately represent observed land cover change within the study extent [45]. This is achieved using a "brute force" method, where the user defines a range of values and the model iterates combinations of parameter values.…”

Section: Sleuthmentioning

confidence: 99%

“…Urban dynamics are determined by a cellular automata where each cell independently executes state-transition rules [45] including (1) spontaneous growth; (2) new spreading centers; (3) edge growth; and (4) road influenced growth. Each type of growth is controlled by a combination of five coefficients (dispersion, breed, spread, road growth, and slope [36]) that determine the amount, and location of allocated cells.…”

Section: Sleuthmentioning

confidence: 99%

Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models

Pickard

Gray

Meentemeyer

2017

Land

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Abstract:The growing numbers of land change models makes it difficult to select a model at the beginning of an analysis, and is often arbitrary and at the researcher's discretion. How to select a model at the beginning of an analysis, when multiple are suitable, represents a critical research gap currently understudied, where trade-offs of choosing one model over another are often unknown. Repeatable methods are needed to conduct cross-model comparisons to understand the trade-offs among models when the same calibration and validation data are used. Several methods to assess accuracy have been proposed that emphasize quantity and allocation, while overlooking the accuracy with which a model simulates the spatial configuration (e.g., size and shape) of map categories across landscapes. We compared the quantity, allocation, and configuration accuracy of four inductive pattern-based spatial allocation land change models (SLEUTH, GEOMOD, Land Change Modeler (LCM), and FUTURES). We simulated urban development with each model using identical input data from ten counties surrounding the growing region of Charlotte, North Carolina. Maintaining the same input data, such as land cover, drivers of change, and projected quantity of change, reduces differences in model inputs and allows for focus on trade-offs in different types of model accuracy. Results suggest that these four land change models produce representations of urban development with substantial variance, where some models may better simulate quantity and allocation at the trade-off of configuration accuracy, and vice versa. Trade-offs in accuracy exist with respect to the amount, spatial allocation, and landscape configuration of each model. This comparison exercise illustrates the range of accuracies for these models, and demonstrates the need to consider all three types of accuracy when assessing land change model's projections.

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“…(Jantz et al, 2010;Silva and Clarke, 2002;Clarke et al, 1997). Kim et al (2010) 1975, 1980, 1985, 1990, 1995Transportation ITS-SNL Shape file 1975, 1980, 1985, 1990, 1995 Müller and Roeckner, 2008;Roeckner et al, 2003).…”

Section: 이미 국외에서는 미래 토지피복변화를 기후예측에 적용함으로써 예측 결과를 향상시키기 위한 노력이mentioning

confidence: 99%

Analysis of Climate Variability under Various Scenarios for Future Urban Growth in Seoul Metropolitan Area (SMA), Korea

Kim¹,

Jeong²,

Kim³

2012

Journal of Korean Society for Atmospheric Environment

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In this study, climate variability was predicted by the Weather Research and Forecasting (WRF) model under two different scenarios (current trends scenario; SC1 and managed scenario; SC2) for future urban growth over the Seoul metropolitan area (SMA). We used the urban growth model, SLEUTH (Slope, Land-use, Excluded, Urban, Transportation, Hill-Shade) to predict the future urban growth in SMA. As a result, the difference of urban ratio between two scenarios was the maximum up to 2.2% during 50 years (2000~2050). Also, the results of SLEUTH like this were adjusted in the Weather Research and Forecasting (WRF) model to analysis the difference of the future climate for the future urbanization effect. By scenarios of urban growth, we knew that the significant differences of surface temperature with a maximum of about 4 K and PBL height with a maximum of about 200 m appeared locally in newly urbanized area. However, wind speeds are not sensitive for the future urban growth in SMA. These results show that we need to consider the future land-use changes or future urban extension in the study for the prediction of future climate changes.

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Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model

Cited by 184 publications

References 19 publications

An examination of land use impacts of flooding induced by sea level rise

An examination of land use impacts of flooding induced by sea level rise

Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models

Analysis of Climate Variability under Various Scenarios for Future Urban Growth in Seoul Metropolitan Area (SMA), Korea

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