ERROR PROPAGATION IN OVERLAY ANALYSIS

GISs give users complete freedom to combine, overlay and analyze data from many different sources, regardless of scale, accuracy, resolution and quality of the original data. The mixing of geographical information from different map scales and sources is a key aspect of GIS functionality, but it does raise the question as to what effects the combination of different levels of data uncertainty has on both the output maps and on the data derived from spatial query and analysis. It must be recognized that there are many good reasons for wishing to combine data in these ways, but a major problem arises because GIS packages fail to offer any means of keeping track of the effects of error propagation and how it affects the results. This paper is concerned with developing methods able to estimate the confidence regions of GIS outputs by taking into account certain selected sources of uncertainty affecting spatial databases. A Monte Carlo simulation-based method is used as a general means of estimating the effects of input data uncertainty on the map outputs after an arbitrary sequence of overlay analysis. The objective is to identify and handle the effects of data uncertainty in GIS by defining uncertainty envelopes to create ‘credibility regions’ around the results. This is considered the minimum need to allow a GIS to function in a mixed data environment.