Spatio-temporal modeling of parcel-level land-use changes using machine learning methods
Spatio-temporal modeling of parcel-level land-use changes using machine learning methods
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DOI：
10.1016/j.scs.2023.104390
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发表时间：
2023-03
影响因子：
11.7
通讯作者：
Emre Tepe;Abolfazl Safikhani
中科院分区：
文献类型：
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作者：
Emre Tepe;Abolfazl Safikhani
块地级土地发展动态的时空建模对于维持城市的可持续发展至关重要。由于数据规模的快速增长超出了传统的基于统计的空间模型的能力，因此控制当代和历史条件的地块级城市发展建模涉及计算挑战。机器学习（ML）方法为大规模数据集提供了计算上可行的方法。本文介绍了新的机器学习应用，使用先进的算法和GPU并行处理来模拟大规模城市土地开发。特别关注的是加速空间权重矩阵的构建和ML模型的训练。具体来说，人工神经网络和随机森林应用于佛罗里达州的土地利用数据，该数据包含近900万个地块，根据历史和社区数据预测地块的土地利用变化。自适应哈希算法与GPU并行处理相结合，将识别用于计算空间自相关的固定数量的最近邻的平均处理时间提高了近16,000倍。使用GPU， ML模型的训练时间缩短了49-547倍。此外，我们最好的机器学习模型达到了大约92%的准确率，同时优于一些竞争方法，包括逻辑回归。如此高的预测精度有助于决策者调整预算拨款，以满足地方土地利用变化预测。
Spatio-temporal modeling of parcel-level land development dynamics is essential to maintain sustainable urban growth. Modeling parcel-level urban development controlling contemporaneous and historical conditions involve computational challenges since data sizes grow quickly beyond the capabilities of conventional statistical-based spatial models. Machine Learning (ML) methods provide computationally feasible methods for large-scale data sets. This paper introduces new ML applications using advanced algorithms and GPU parallel processing to model large-scale urban land developments. Special attention is given to accelerating the construction of spatial weight matrices and training ML models. Specifically, artificial neural networks and random forests are applied to the state of Florida’s land-use data, which contains nearly 9 million parcels, to predict parcels with changes in their land use based on historical and neighborhood data. The adaptive Hashing algorithm coupled with GPU parallel processing accelerates the average processing time for identifying the fixed number of nearest neighbors used for accounting spatial autocorrelation, by almost 16,000 times. Also, ML model training times are shortened by 49–547 times using GPU. Further, our best ML model achieves approximately 92% accuracy while outperforming some competing methods, including logistic regression. Such a high prediction accuracy helps policymakers adjust budget allocations to meet local land-use change projections.