Intensity Analysis and the Figure of Merit’s components for assessment of a Cellular Automata – Markov simulation model

Varga, Orsolya; Pontius, Robert Gilmore; Singh, Sudhir Kumar; Szabó, Szilárd

doi:10.1016/j.ecolind.2019.01.057

Cited by 101 publications

(46 citation statements)

References 56 publications

(58 reference statements)

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“…It refers to the fact that the simulation model in the case of the L1 group explains less change. While the FOM is not enough to qualify model performance, as stated in Varga et al 2019 [37], four FOM components-Wrong Hits, False Alarms, Misses and Hits-return lower values in L1 group, where lower ratios of observed changing areas were also present in either calibration or validation intervals. However, we do not know about any papers focusing on the statistical relationship between FOM components and observed changes.…”

Section: Effects Of Fom and Fom Componentsmentioning

confidence: 91%

“…If the FOM is 100%, then there is a complete overlap between predicted and observed change. The FOM components provide a deeper insight into the similarity of changes [37]. The FOM components show all types of incorrectly and correctly predicted change as a percentage of the size of the combination of the extent and zoom level, as follows:…”

Section: Metrics Concerning Calibration and Validation Intervalsmentioning

confidence: 98%

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Effects of Category Aggregation on Land Change Simulation Based on Corine Land Cover Data

et al. 2020

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Several factors influence the performance of land change simulation models. One potentially important factor is land category aggregation, which reduces the number of categories while having the potential to reduce also the size of apparent land change in the data. Our article compares how four methods to aggregate Corine Land Cover categories influence the size of land changes in various spatial extents and consequently influence the performance of 114 Cellular Automata-Markov simulation model runs. We calculated the reference change during the calibration interval, the reference change during the validation interval and the simulation change during the validation interval, along with five metrics of simulation performance, Figure of Merit and its four components: Misses, Hits, Wrong Hits and False Alarms. The Corine Standard Level 1 category aggregation reduced change more than any of the other aggregation methods. The model runs that used the Corine Standard Level 1 aggregation method tended to return lower sizes of changing areas and lower values of Misses, Hits, Wrong Hits and False Alarms, where Hits are correctly simulated changes. The behavior-based aggregation method maintained the most change while using fewer categories compared to the other aggregation methods. We recommend an aggregation method that maintains the size of the reference change during the calibration and validation intervals while reducing the number of categories, so the model uses the largest size of change while using fewer than the original number of categories.

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Section: Effects Of Fom and Fom Componentsmentioning

confidence: 91%

Section: Metrics Concerning Calibration and Validation Intervalsmentioning

confidence: 98%

Effects of Category Aggregation on Land Change Simulation Based on Corine Land Cover Data

et al. 2020

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“…The figure of merit (FOM)-which consists of misses, hits, wrong hits and false alarms-is a popular metric for model validation by using three-map comparison, whose range is from 0 to 1. The closer the value is to 1, the more similar the simulated change is to the actual changes [56,57]. In this experiment, we used FOM to determine parameters and judge the simulation results.…”

Section: Model Parameter Estimation and Accuracy Verificationmentioning

confidence: 99%

A Cellular Automata Model Constrained by Spatiotemporal Heterogeneity of the Urban Development Strategy for Simulating Land-use Change: A Case Study in Nanjing City, China

et al. 2019

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While cellular automata (CA) has become increasingly popular in land-use and land-cover change (LUCC) simulations, insufficient research has considered the spatiotemporal heterogeneity of urban development strategies and applied it to constrain CA models. Consequently, we proposed to add a zoning transition rule and planning influence that consists of a development grade coefficient and traffic facility coefficient in the CA model to reflect the top-down and heterogeneous characteristics of spatial layout and the dynamic and heterogeneous external interference of traffic facilities on land-use development. Testing the method using Nanjing city as a case study, we show that the optimal combinations of development grade coefficients are different in different districts, and the simulation accuracies are improved by adding the grade coefficients into the model. Moreover, the integration of the traffic facility coefficient does not improve the model accuracy as expected because the deployment of the optimal spatial layout has considered the effect of the subway on land use. Therefore, spatial layout planning is important for urban green, humanistic and sustainable development.

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“…The allocation disagreement measures the mismatch in the spatial allocation. Equation 6shows how the quantity disagreement is derived from the FOM components, while Equation 7shows how the allocation disagreement is derived from the FOM components [49,50]. The total disagreement can be calculated using Equation (8).…”

Section: Validation Of the Modelmentioning

confidence: 99%

Investigation of Future Land Use Change and Implications for Cropland Quality: The Case of China

et al. 2019

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Cropland loss resulting from land use change has drawn great attention in China due to the threat to food security. However, little is known about future magnitude and quality of cropland of China. In this study, the dynamic conversion of land use and its effects model (Dyna-CLUE) together with the Markov model and the potential yield data were used to simulate the influence of land use change on cropland quality in the next two decades under three scenarios. The results indicate that, under the trend scenario, the high-yield and medium-yield cropland would decrease and the low-yield cropland would increase between 2015 and 2030. The crop yield would decrease by 1.3 × 109 kg. Under planned scenario, high-yield and medium-yield cropland would decrease and the low-yield cropland would increase, and total crop yield would stay almost unchanged. Under the cropland protection scenario, the high-yield cropland would reduce slightly, and the medium-yield and low-yield cropland would increase substantially. The crop yield would increase by 5.36 × 1010 kg. The result of this study will help decision-makers to develop reasonable land use policies to achieve the goals of harmonious development between food security, economic growth, and environmental protection.

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Intensity Analysis and the Figure of Merit’s components for assessment of a Cellular Automata – Markov simulation model

Cited by 101 publications

References 56 publications

Effects of Category Aggregation on Land Change Simulation Based on Corine Land Cover Data

Effects of Category Aggregation on Land Change Simulation Based on Corine Land Cover Data

A Cellular Automata Model Constrained by Spatiotemporal Heterogeneity of the Urban Development Strategy for Simulating Land-use Change: A Case Study in Nanjing City, China

Investigation of Future Land Use Change and Implications for Cropland Quality: The Case of China

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