TY - JOUR
T1 - The land transformation model-cluster framework: applying k-means and the Spark computing environment for large scale land change analytics
AU - Omrani, H
AU - Parmentier, B
AU - Helbich, M
AU - Pijanowski, B
PY - 2019/1
Y1 - 2019/1
N2 - This study introduces a novel framework for land change simulation that combines the traditional Land Transformation Model (LTM) with data clustering tools for the purposes of conducting land change simulations of large areas (e.g., continental scale) and over multiple time steps. This framework, called “LTM-cluster”, subsets massive land use datasets which are presented to the artificial neural network-based LTM. LTM-cluster uses the k-means clustering algorithm implemented within the Spark high-performance compute environment. To illustrate the framework, we use three case studies in the United States which vary in simulation extents, cell size, time intervals, number of inputs, and quantity of urban change. Findings indicate consistent and substantial improvements in accuracy performance for all three case studies compared to the traditional LTM model implemented without input clustering. Specifically, the percent correct match, the area under the operating characteristics curve, and the error rate improved on average of 9%, 11%, and 4%. These results confirm that LTM-cluster has high reliability when handling large datasets. Future studies should expand on the framework by exploring other clustering methods and algorithms.
AB - This study introduces a novel framework for land change simulation that combines the traditional Land Transformation Model (LTM) with data clustering tools for the purposes of conducting land change simulations of large areas (e.g., continental scale) and over multiple time steps. This framework, called “LTM-cluster”, subsets massive land use datasets which are presented to the artificial neural network-based LTM. LTM-cluster uses the k-means clustering algorithm implemented within the Spark high-performance compute environment. To illustrate the framework, we use three case studies in the United States which vary in simulation extents, cell size, time intervals, number of inputs, and quantity of urban change. Findings indicate consistent and substantial improvements in accuracy performance for all three case studies compared to the traditional LTM model implemented without input clustering. Specifically, the percent correct match, the area under the operating characteristics curve, and the error rate improved on average of 9%, 11%, and 4%. These results confirm that LTM-cluster has high reliability when handling large datasets. Future studies should expand on the framework by exploring other clustering methods and algorithms.
KW - Clustering Parallel processing
KW - Spark environment
KW - Land use change
U2 - 10.1016/j.envsoft.2018.10.004
DO - 10.1016/j.envsoft.2018.10.004
M3 - Article
SN - 1364-8152
VL - 111
SP - 182
EP - 191
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
ER -