An information fusion approach for conflating labeled point-based time-series data

In geographic data analysis, it is often the case that multiple aspects of a single phenomenon are captured by different sources of data. For instance, a storm can be identified based on its precipitation, as well as windspeed, and changes in barometric pressure. It proves beneficial in specific domains to be able to use all available sources of data, and some method must be used to integrate all of these sources of data into a singular decision, often in the form of a classification. This paper proposes the general form of what has been termed the Class Label Conflation Problem – the problem of taking a number of distinct and possibly conflicting sources in the form of spatially-located time series, and using this historical dataset to determine a class label at a new location. In addition to this formalization, this paper details an algorithm (called ACCL) to solve the general case of the problem. This algorithm has its foundations in information theory (specifically Dempster-Shafer Theory), supervised classification, and data fusion. An analysis of the algorithm demonstrates its effectiveness using synthetic datasets as well as the US Drought Monitor as a case study.