Hierarchical modeling of urban growth across the conterminous USA: developing meso-scale quantity drivers for the Land Transformation Model

Tayyebi, Amin; Pekin, Burak K.; Pijanowski, Bryan C.; Plourde, James D.; Doucette, Jarrod; Braun, David

doi:10.1080/1747423x.2012.675364

Cited by 64 publications

(42 citation statements)

References 57 publications

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“…This includes unprecedented land surface changes such as increasing urbanization (Tian et al, 2011;Tayyebi et al, 2013;Pijanowski et al, 2014) and deforestation (Mas, 2004;Pineda Jaimes et al, 2010;Tayyebi et al, 2015). Areas of current scientific concern include understanding the global impacts of land use / land cover (LULC) changes on multiple ecosystem services (Meehan et al 2013;Vaz 2016), analyzing landscape changes at the global scale (Tayyebi et al, 2014) and improving estimates of greenhouse gas emissions from the LULUCF (land use, land-use change and forestry) sectors (Schlamadinger et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Arsanjani

See

Tayyebi

2016

International Journal of Digital Earth

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Global land cover maps have been widely employed as the base layer for a number of applications including climate change, food security, water quality, biodiversity, change detection and environmental planning. Due to the importance of land cover, there is a pressing need to increase the temporal and spatial resolution of global land cover maps. A recent advance in this direction has been the GlobeLand30 dataset derived from Landsat imagery, which has been developed by the National Geomatics Center of China (NGCC). Although overall accuracy is greater than 80%, the NGCC would like help in assessing the accuracy of the product in different regions of the world. To assist in this process, this study compares the GlobeLand30 product with existing public and online datasets, i.e. CORINE, Urban Atlas, OpenStreetMap and ATKIS for Germany in order to assess overall and per class agreement. The results of the analysis reveal high agreement of up to 92% between these datasets and GlobeLand30 but that large disagreements for certain classes are evident, in particular wetlands. However, overall, GlobeLand30 is shown to be a useful product for characterizing land cover in Germany, and paves the way for further regional and national validation efforts.

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Section: Introductionmentioning

confidence: 99%

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Arsanjani

See

Tayyebi

2016

International Journal of Digital Earth

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“…Generally, a Kappa value between 0.01 and 0.20 indicates slight agreement, and PCM results from 40% to 60% are interpreted as acceptable models [23,25,71,75]. Thus, while the Kappa value indicates that agreement would be too poor to accept, if this was an actual model, this example model might be acceptable when considering the PCM.…”

Section: Accuracy and Reliabilitymentioning

confidence: 99%

Forecasting Urban Vacancy Dynamics in a Shrinking City: A Land Transformation Model

Lee

Newman

2017

IJGI

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Abstract:In the past two centuries, many American urban areas have experienced significant expansion in both populating and depopulating cities. The pursuit of bigger, faster, and more growth-oriented planning parallels a situation where municipal decline has also been recognized as a global epidemic. In recent decades many older industrial cities have experienced significant depopulation, job loss, economic decline, and massive increases in vacant and abandoned properties due primarily to losses in industry and relocating populations. Despite continuous economic decline and depopulation, many of these so-called 'shrinking cities' still chase growth-oriented planning policies, due partially to inabilities to accurately predict future urban growth/decline patterns. This capability is critical to understanding land use alternation patterns and predicting future possible scenarios for the development of more proactive land use policies dealing with urban decline and regeneration. In this research, the city of Chicago, Illinois, USA is used as a case site to test an urban land use change model that predicts urban decline in a shrinking city, using vacant land as a proxy. Our approach employs the Land Transformation Model (LTM), which combines Geographic Information Systems and artificial neural networks to forecast land use change. Results indicate that the LTM is a good resource to simulate urban vacant land changes. Mobility and housing market conditions seem to be the primary variables contributing to decline.

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“…The nature of landscape transformations is described in Pijanowski and Robinson [2]. Description of the LTM model is also provided in Pijanowski et al [13,54,55].…”

Section: Case Study Descriptionmentioning

confidence: 99%

Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models

Alexandridis

Pijanowski

2013

Entropy

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Assessing spatial model performance often presents challenges related to the choice and suitability of traditional statistical methods in capturing the true validity and dynamics of the predicted outcomes. The stochastic nature of many of our contemporary spatial models of land use change necessitate the testing and development of new and innovative methodologies in statistical spatial assessment. In many cases, spatial model performance depends critically on the spatially-explicit prior distributions, characteristics, availability and prevalence of the variables and factors under study. This study explores the statistical spatial characteristics of statistical model assessment of modeling land use change dynamics in a seven-county study area in South-Eastern Wisconsin during the historical period of . The artificial neural network-based Land Transformation Model (LTM) predictions are used to compare simulated with historical land use transformations in urban/suburban landscapes. We introduce a range of Bayesian information entropy statistical spatial metrics for assessing the model performance across multiple simulation testing runs. Bayesian entropic estimates of model performance are compared against information-theoretic stochastic entropy estimates and theoretically-derived accuracy assessments. We argue for the critical role of informational uncertainty across different scales of spatial resolution in informing spatial landscape model assessment. Our analysis reveals how incorporation of spatial and landscape information asymmetry OPEN ACCESSEntropy 2013, 15 2481 estimates can improve our stochastic assessments of spatial model predictions. Finally our study shows how spatially-explicit entropic classification accuracy estimates can work closely with dynamic modeling methodologies in improving our scientific understanding of landscape change as a complex adaptive system and process.

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Hierarchical modeling of urban growth across the conterminous USA: developing meso-scale quantity drivers for the Land Transformation Model

Cited by 64 publications

References 57 publications

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Forecasting Urban Vacancy Dynamics in a Shrinking City: A Land Transformation Model

Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models

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