An iterative road-matching approach for the integration of postal data

The growing demand on geospatial services requires an intensified study of geoinformation from various sources covering the same geographic space. Data matching is one of the fundamental measures that help make different datasets interoperable. This paper reports a special case of data matching between the road layer from the digital landscape model “Basis DLM” maintained by German surveying and mapping agencies and the road database supplied by TeleAtlas Corp. The authors put forward a generic approach which is able to handle three unfavorable matching conditions: (1) missing values of essential road attributes, e.g. street names are only sporadically available in Basis DLM, (2) unsystematic geometric discrepancies between the corresponding road objects, and (3) unknown extent of geometric discrepancies between the two datasets. The proposed approach is implemented in Arc GIS 9.0 as a tool used to match street networks as well as transfer of information between the matched datasets. By means of an innovative matching algorithm termed as “unsymmetrical buffer growing”, this matching approach has gained some self-learning abilities and yielded high matching performance indicated by an average matching rate of 96.1% on large data sets of road networks. Meanwhile, a measurement of matching certainty on the basis of geometrical and topological similarity is introduced, which supports the human operator to judge the quality of matching results and easily detect possible matching errors.