FUTURES: Multilevel Simulations of Emerging Urban–Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm

Meentemeyer, Ross K.; Tang, Wenwu; Dorning, Monica A.; Vogler, John; Cunniffe, Nik J.; Shoemaker, Douglas A.

doi:10.1080/00045608.2012.707591

Cited by 142 publications

(141 citation statements)

References 66 publications

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“…Whereas the outcomes of deterministic models is invariable, stochastic models attempt to capture variability in human agency and environmental processes and, therefore offer a means of addressing simulation uncertainty. Stochastic models of land change are gaining popularity among LULC researchers because of their capability to account for the randomness and use in being extrapolated to scenarios of alternative futures [39]. By routinely running numerous simulations, stochastic models facilitate exploration of a range of outcomes possibly attributable to the complexity of coupled human-natural systems (e.g., [41]).…”

Section: Land Change Modelsmentioning

confidence: 99%

“…Charlotte is a major economic center that has experienced substantial urban development in the past four decades, increasing from two percent of the total study area in 1976 to approximately 30 percent in 2016. Located within the Southern Piedmont physiographic province, the region's mild topography has allowed for some of the densest road networks within the southeastern United States [39]. Zoning plays a major role in shaping the location of new urban developments as there are few environmental constraints to construction [40].…”

Section: Study Locationmentioning

confidence: 99%

“…Urban transitions are based on three sub-models: the DEMAND module controls total urban growth, new urban locations are controlled by a module that considers local site suitability factors (POTENTIAL), and a stochastic patch growing algorithm (PGA). The DEMAND sub-model is a user defined quantity of land converted to developed, based on the relationship between population change and the amount of land developed during the calibration period [39]. To determine the location for POTENTIAL urban development to occur, FUTURES requires a probability surface generated using any site suitability modeling technique [39,58].…”

Section: Future Urban-regional Environment Simulationmentioning

confidence: 99%

“…FUTure Urban-Regional Environment Simulation (FUTURES [39]) is an open source model [57] developed to project regional scale urban change at the pixel level. Urban transitions are based on three sub-models: the DEMAND module controls total urban growth, new urban locations are controlled by a module that considers local site suitability factors (POTENTIAL), and a stochastic patch growing algorithm (PGA).…”

Section: Future Urban-regional Environment Simulationmentioning

confidence: 99%

“…Therefore, a consistent methodology for assessing model accuracy would offer land change scientists a reproducible means to perform cross model comparisons and elucidate trade-offs among quantity, allocation, and configuration. In this study, we build upon the work of Pontius et al (2008) by comparing four inductive pattern-based land change models-SLEUTH [36], GEOMOD [37], Land Change Modeler (LCM) [38], and FUTURES [39]. We hypothesized differences in quantity, allocation, and configuration accuracy would arise from differences in model characteristics.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Land Change Modelsmentioning

confidence: 99%

Section: Study Locationmentioning

confidence: 99%

Section: Future Urban-regional Environment Simulationmentioning

confidence: 99%

Section: Future Urban-regional Environment Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Pickard

Gray

Meentemeyer

2017

Land

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Dynamic Coastal Flood Risk Assessment of a Coastal Island in West Bengal, India

Praneta,

Aishwarya,

Bharath

2024

Sustainable Development Using Geospatial Techniques

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Accounting for residential propagule pressure improves prediction of urban plant invasion

et al. 2016

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Abstract. Plant invasions substantially impact the ecosystem services provided by forests in urbanizing regions. Knowing where invasion risk is greatest helps target early detection and eradication efforts, but developing an accurate predictive model of invasive species presence and spread on the basis of habitat suitability remains a challenge due to spatial variation in propagule pressure (the number of individuals released) which is likely conflated with suitability. In addition to neighborhood propagule pressure that originates with propagules dispersing from naturalized populations within invaded habitats, we expect residential propagule pressure arising from the widespread use of exotic plants in the yards of single-family residences to be an important driver of invasions, and to notably improve the predictive accuracy of species distribution models (SDMs). To this end, we collected presence/absence data for a widespread forest invader, Ligustrum sinense (Chinese privet), from 400 stratified random plots located along an urban gradient across the Charlotte, North Carolina metropolitan area. We assessed the relative contribution of residential propagule pressure and neighborhood propagule pressure to improving the predictive performance of a probit SDM for Chinese privet that only contains environmental predictors. Our results indicate that, although the environment-only model predicted the highest geographic area to be at risk of invasion by privet, it also had the highest rate of failure to accurately predict observed privet occurrences as indicated by the omission (incorrectly predicted absence) and commission (incorrectly predicted presence) error rates. Accounting for residential propagule pressure substantially improved model performance by reducing the omission error by nearly 50%, thereby improving upon the ability of the model to predict privet invasion in suboptimal habitat. Given that this increase in detection was accompanied by a decrease in the geographic area predicted at risk, we conclude that SDMs for invasive exotic shrubs and potentially for other synanthropic generalist plants may be highly inefficient when residential propagule pressure is not accounted for. Accounting for residential propagule pressure in models of invasive plants results in a more focused and accurate prediction of the area at risk, thus enabling decision makers to feasibly prioritize regional scale monitoring and control efforts.

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FUTURES: Multilevel Simulations of Emerging Urban–Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm

Cited by 142 publications

References 66 publications

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

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

Dynamic Coastal Flood Risk Assessment of a Coastal Island in West Bengal, India

Accounting for residential propagule pressure improves prediction of urban plant invasion

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