A lightweight ensemble spatiotemporal interpolation model for geospatial data

反距离权重法 缺少数据 地理空间分析 数据挖掘 计算机科学 插补(统计学) 插值(计算机图形学) 多元插值 加权 指数平滑 平滑的 空间相关性 人工智能 机器学习 双线性插值 地理 遥感 运动(物理) 计算机视觉 医学 电信 放射科
作者
Shifen Cheng,Peng Peng,Feng Lu
出处
期刊:International Journal of Geographical Information Science [Taylor & Francis]
卷期号:34 (9): 1849-1872 被引量:14
标识
DOI:10.1080/13658816.2020.1725016
摘要

Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations.
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