A framework to enhance semantic flexibility for analysis of distributed phenomena

While some geographic phenomena hold uniform properties, such as land‐use zones, many geographic phenomena are distributed such that their properties vary across an extended area. While such distributed phenomena are best represented as continuous surfaces, individual objects (or features) often emerge among clusters of high or low values in a field. For example, areas of relatively high elevation may be viewed as hills, while flat low‐lying areas are perceived as plains in a terrain. A comprehensive spatial analysis of distributed phenomena should examine both the spatial variance of its attribute surfaces and the characteristics of individual objects embedded in the field. An immediate research challenge to meet such spatial analysis needs is that these emerging features often have vague boundaries that vary according to the use and the user. The nature, and even existence, of these objects depend upon the range of values, or thresholds, used to define them. We propose a representation framework that takes a dual raster‐vector approach to capture both field‐ and object‐like characteristics of distributed phenomena and maintain multiple representations of embedded features delineated by boundaries that are likely to be relevant for the expected uses of the data. We demonstrate how boundaries influence the analysis and understanding of spatiotemporal characteristics of distributed phenomena. Using precipitation as a proof of concept, we show how the proposed framework enhances semantic flexibility in spatiotemporal query and analysis of distributed phenomena in geographic information systems.