Cassiano Bastos Moroz, Tobias Sieg, Annegret H. Thieken
{"title":"Spatial constraints in cellular automata-based urban growth models: A systematic comparison of classifiers and input urban maps","authors":"Cassiano Bastos Moroz, Tobias Sieg, Annegret H. Thieken","doi":"10.1016/j.compenvurbsys.2024.102118","DOIUrl":null,"url":null,"abstract":"<div><p>Spatial constraints are fundamental to integrating the spatial suitability to urbanization into Cellular Automata-based (CA) urban growth models, but there is a lack of consensus on the optimal methods for this purpose. This study compared the performance of three probabilistic classifiers to generate suitability surfaces for CA-based urban growth models: Logistic Regression using Generalized Linear Model (LR-GLM), Logistic Regression using Generalized Additive Model (LR-GAM), and Random Forest (RF). The study also evaluated the sensitivity of these classifiers to the input urban map adopted as a dependent variable. For this analysis, seven maps were tested: the historical urban map containing the entire extent of the urban footprint, and six additional maps containing only the recently urbanized areas over timeframes ranging from one year up to two decades. The comparison evaluated the goodness of fit of the suitability surfaces and the spatial accuracy of the urban growth simulations, using five large Brazilian cities as case study areas. The results revealed that the RF classifier significantly outperformed the LR-based classifiers. However, this overperformance was more prominent when incorporating the new urban cells over the last one to two decades of growth as input urban maps. In addition, the sensitivity analysis of the input urban maps emphasized the benefits of calibrating the classifier using the recently urbanized cells rather than the historical urban extent. We consistently observed these results concerning classifiers and input urban maps across all five case study areas. Thus, the RF classifier combined with a training dataset containing the newly urbanized areas over at least the last 10 years systematically resulted in the suitability surfaces with the highest predictability among all tested scenarios.</p></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"110 ","pages":"Article 102118"},"PeriodicalIF":7.1000,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers Environment and Urban Systems","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0198971524000474","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 0
Abstract
Spatial constraints are fundamental to integrating the spatial suitability to urbanization into Cellular Automata-based (CA) urban growth models, but there is a lack of consensus on the optimal methods for this purpose. This study compared the performance of three probabilistic classifiers to generate suitability surfaces for CA-based urban growth models: Logistic Regression using Generalized Linear Model (LR-GLM), Logistic Regression using Generalized Additive Model (LR-GAM), and Random Forest (RF). The study also evaluated the sensitivity of these classifiers to the input urban map adopted as a dependent variable. For this analysis, seven maps were tested: the historical urban map containing the entire extent of the urban footprint, and six additional maps containing only the recently urbanized areas over timeframes ranging from one year up to two decades. The comparison evaluated the goodness of fit of the suitability surfaces and the spatial accuracy of the urban growth simulations, using five large Brazilian cities as case study areas. The results revealed that the RF classifier significantly outperformed the LR-based classifiers. However, this overperformance was more prominent when incorporating the new urban cells over the last one to two decades of growth as input urban maps. In addition, the sensitivity analysis of the input urban maps emphasized the benefits of calibrating the classifier using the recently urbanized cells rather than the historical urban extent. We consistently observed these results concerning classifiers and input urban maps across all five case study areas. Thus, the RF classifier combined with a training dataset containing the newly urbanized areas over at least the last 10 years systematically resulted in the suitability surfaces with the highest predictability among all tested scenarios.
空间约束是将城市化空间适宜性纳入基于蜂窝自动机(CA)的城市增长模型的基本要素,但对于实现这一目的的最佳方法还缺乏共识。本研究比较了三种概率分类器的性能,以便为基于蜂窝自动机的城市增长模型生成适宜性曲面:使用广义线性模型的逻辑回归(LR-GLM)、使用广义加法模型的逻辑回归(LR-GAM)和随机森林(RF)。研究还评估了这些分类器对作为因变量的输入城市地图的敏感性。在这项分析中,测试了七张地图:包含整个城市足迹范围的历史城市地图,以及另外六张仅包含最近城市化地区的地图,时间范围从一年到二十年不等。比较以巴西五个大城市为案例研究区域,评估了适宜性表面的拟合度和城市增长模拟的空间准确性。结果显示,射频分类器的性能明显优于基于 LR 的分类器。然而,当将过去一二十年发展中的新城市单元作为输入城市地图时,这种超常表现更为突出。此外,对输入城市地图的敏感性分析强调了使用最近城市化的小区而不是历史城市范围来校准分类器的好处。在所有五个案例研究区域中,我们始终观察到这些有关分类器和输入城市地图的结果。因此,射频分类器与包含至少过去 10 年新城市化区域的训练数据集相结合,系统地生成了所有测试方案中预测性最高的适宜性表面。
期刊介绍:
Computers, Environment and Urban Systemsis an interdisciplinary journal publishing cutting-edge and innovative computer-based research on environmental and urban systems, that privileges the geospatial perspective. The journal welcomes original high quality scholarship of a theoretical, applied or technological nature, and provides a stimulating presentation of perspectives, research developments, overviews of important new technologies and uses of major computational, information-based, and visualization innovations. Applied and theoretical contributions demonstrate the scope of computer-based analysis fostering a better understanding of environmental and urban systems, their spatial scope and their dynamics.