Authors: Shifen Cheng*, State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Feng Lu, State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
Topics: Spatial Analysis & Modeling
Keywords: Spatio-temporal interpolation, Spatio-temporal heterogeneity, Dynamic sliding window, Neural network
Session Type: Paper
Start / End Time: 1:20 PM / 3:00 PM
Room: Proteus, Sheraton, 8th Floor
Presentation File: No File Uploaded
Missing data reconstruction is a critical step in the analysis and mining of spatio-temporal data; however, few studies comprehensively consider missing data patterns, sample selection, and spatio-temporal relationships. As a result, traditional methods often fail to obtain satisfactory accuracy or address high levels of complexity. To combat these problems, this study developed an effective two-step method for spatio-temporal missing data reconstruction (ST-2SMR). This approach includes a coarse-grained interpolation method for considering missing patterns, which can successfully eliminate the influence of continuous missing data on the overall results. Based on the results of coarse-grained interpolation, a dynamic sliding window selection algorithm was implemented to determine the most relevant sample data for fine-grained interpolation, considering both spatial and temporal heterogeneity. Finally, spatio-temporal interpolation results were integrated by using a neural network model. We validated our approach using Beijing air quality data, and found that the proposed method outperforms existing solutions in term of estimation accuracy and reconstruction rate.