Emerging data challenges for next-generation spatial data infrastructure

Abstract

The landscape of spatial data infrastructures (SDIs) is changing. In addition to traditional authoritative and reliably sourced geospatial data, SDIs increasingly need to incorporate data from non-traditional sources, such as local sensor networks and crowd-sourced message databases. These new data come with variable, loosely defined, and sometimes unknown provenance, semantics, and content. The next generation of SDIs will need the capability to integrate and federate geospatial data that are highly heterogeneous. These data comprise a vast observation space: they could be represented in many forms, will have been generated by a variety of producers using different processes and will have originally been intended for purposes that may differ markedly from their later use. There are several discriminative dimensions along which we can describe the properties of the data found in SDIs, such as the data structure, the spatial framework (e.g., field, image, or object-based), the semantics of the attributes, the author or producer, the licensing, etc. These dimensions define a universe of model possibilities for data in an SDI, known as a model space. A core research challenge remains to recognise and resolve - to the degree possible - a comprehensive set of model dimensions that will enable us to characterise the many possible models by which geospatial data can be represented. A second challenge is to describe the transformations within and between models, and the ways in which these transformations change aspects of the underlying model. Despite recent movement toward semantically described services for SDIs, the scope and range of descriptive dimensions for geospatial data are underspecified. In this paper we present a diverse set of important dimensions that point to a series of challenges for data integration and then describe how both traditional and emergent datasets can be characterised within these dimensions, and point to some interesting differences.