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An ArcGIS add-in for spatiotemporal data mining in climate data
Earth Science Informatics ( IF 2.7 ) Pub Date : 2019-08-29 , DOI: 10.1007/s12145-019-00404-0
Jisheng Xia , Jinne Li , Pinliang Dong , Kecheng Yang

Spatiotemporal data mining has many important applications in environmental modelling. This paper introduces a modified algorithm for spatiotemporal data mining based on density-based spatial clustering of applications with noise (DBSCAN). Based on the modified algorithm, a geographic information system (GIS) add-in was developed using ArcObjects and C#, and is freely available for download. Compared with some existing methods, the new method can perform automated detection of spatiotemporal clusters using limited user input parameters. The application of the add-in was demonstrated using summer temperature data collected from 104 weather stations in southwest China from 1961 to 2011. The results suggest that the modified algorithm can provide more detailed partition of temperature zones compared with some existing methods.
更新日期:2019-08-29
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