Implementation of a prototype toolbox for communicating spatial data quality and uncertainty using a wildfire risk example

Current GIS are often described as rich in functionality but poor in knowledge content and transfer. This paper presents a prototype for communicating data quality in spatial databases using a hybrid design between data-driven and user-driven factors based upon traditional communication and cartographic concepts. The prototype aims to give data users a better understanding of the uncertainty that affects their information by utilizing a knowledge-based method where they can choose from multiple visualizations to represent the uncertainty in their data, as well as access information about why a particular visualization has been proposed. In doing so, decisions become more transparent to data users, which increases the capability of the prototype to act as a training aid. The example case study examines the data quality in a source dataset and illustrates how the concepts apply in an operational environment at different levels of communication.