An improved assessment method for urban growth simulations across models, regions, and time

AbstractFor urban growth modeling, assessment metrics derived from cell-by-cell comparisons are mainly related to the size of the study area and the urban growth rate. Non-urban areas always occupy an important part of the city to which cellular automata (CA) models do not contribute much, so the simulation accuracy is often exaggerated when this part is included. To enable comparing simulation results across models, regions, and time, we developed an improved equivalent area-based assessment (EQASS) method using cell-by-cell comparison metrics. As against existing assessment methods, EQASS is computed by including the same area of urban and suburban areas (i.e., equivalent areas). EQASS was tested in three Chinese coastal cities using a heuristic CA model and two spatial statistical CA models to simulate urban growth. The results show that EQASS can exclude correct rejections that are not attributable to CA models; these correct rejections have a significant impact on the model assessment. The improved assessment can better evaluate the performance of CA models across regions and over time than the conventional assessment method that accounts for the full study area. This study extends the simulation assessment method and provides a good solution for selecting the best CA model from many candidate models.Keywords: Model assessmentcellular automatabuffer analysisurban growthaccuracy comparison Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe software, codes and input datasets involved in this study are available at https://doi.org/10.6084/m9.figshare.21203147.Additional informationFundingSupported by the National Natural Science Foundation of China (42071371) and the National Key R&D Program of China (2018YFB0505400).Notes on contributorsChen GaoChen Gao received the M.S. degree in marine sciences from Shanghai Ocean University, Shanghai, China, in 2021. She is currently working toward the Ph.D. degree in surveying and geoinformation with Tongji University, Shanghai, China.Yongjiu FengYongjiu Feng received the Ph.D. degree in geomatics from Tongji University, Shanghai, China, in 2009. He is currently a Professor and Associate Dean with the College of Surveying and Geo-Informatics, Tongji University. His research interests include spatial modeling, synthetic aperture radar, and radar detection of the moon and deep space.Mengrong XiMengrong Xi received the B.E. degree in geomatics engineering from Tongji University, Shanghai, China, in 2022. He is currently working toward the Ph.D. degree in surveying and geoinformation with Tongji University, Shanghai, China.Rong WangRong Wang received the M.S. degree in marine sciences from Shanghai Ocean University, Shanghai, China, in 2022. She is currently working toward the Ph.D. degree in artificial intelligence with Tongji University, Shanghai, China.Pengshuo LiPengshuo Li received the B.E. degree in geomatics engineering from Tongji University, Shanghai, China, in 2021. He is currently working toward the M.S. degree in surveying and geoinformation with Tongji University, Shanghai, China.Xiaoyan TangXiaoyan Tang received the M.S. degree in cartography and geographical information engineering from Chang’an University, Xi’an, China, in 2013. She is currently working toward the Ph.D. degree in surveying and geoinformation with Tongji University, Shanghai, China.Xiaohua TongXiaohua Tong received the Ph.D. degree in geomatics from Tongji University, Shanghai, China, in 1999. He is currently a Professor with the College of Surveying and GeoInformatics, Tongji University. His research interests include photogrammetry and remote sensing, trust in spatial data, and image processing for high-resolution satellite images.