Analytical conflation of spatial data from municipal and federal government agencies

As spatial data resources become more abundant, the potential for conflict among them increases. These conflicts can exist between two or many spatial data sets covering the same areas and categories. Therefore, it becomes increasingly important to be able to effectively relate these spatial data sources with others and then create new spatial datasets with matching geography. One extensive spatial dataset is the Census Bureau's TIGER file, which includes Census tracts, block groups, and blocks. At present, however, Census maps often carry information that conflicts with municipally-maintained detailed spatial information. Therefore, in order to fully utilize Census maps and their valuable demographic and economic information, the locational information of the Census maps must be reconciled with the more accurate municipally-maintained reference maps and imagery. Several algorithms for facilitating map conflation already exist. The appropriateness of a map conflation algorithm depends on the quality of the source and reference maps, the level of required accuracy, the scales of the maps, and the availability of auxiliary spatial or non-spatial information. None of the current methods explicitly address topology modification. Therefore, those methods can only merge maps when the two members of a matched pair of common features are topologically equivalent.;We present two different map models corresponding to two different mathematical approaches to map conflation. The first model is based on the cell model of map in which a map is a cell complex consisting of 0-cells, 1-cells, and 2-cells. The second map model is based on a different set of primitive objects that remain homeomorphic even when generalized. The second model facilitates map conflation by guaranteeing the existence of both local and global homeomorphisms everywhere. Matching operations in map conflation are defined in one of two ways, depending on the map model used. The second model allows for the possibility of non-homeomorphic matching. The first model does not. If corresponding matching features within a subregion happen to have the same dimension and be homeomorphic, then more conventional matching strategies may be applied. If corresponding features, however, are not homeomorphic, then an operation called topological surgery may be implemented. A 0-cell conflation test is proposed to deal with the non-homeomorphic case. A new hierarchical based map conflation is also presented to be applied to physical, logical, and mathematical boundaries and to reduce the complexity and computational load. Map conflation techniques developed explicitly for Census maps are formulated and implemented. These new methods guarantee producing a conflated result that is topologically consistent with reference maps. This dissertation also describes how national maps could be updated effectively by exchange of map information between federal and local governments.;Implementation issues are discussed in terms of the theoretical design of map conflation system and results presented for the case study area, Delaware County, OH. We selected three area types, downtown, residential, and water-feature intensive areas, on which to test our conflation strategies. We also present three transformation methods two of which offer new approaches to map conflation. The traditional method is Delaunay Triangulation (DT). The two new methods are Weighted Delaunay Triangulation (WDT) and linear feature based transformation (LineMorp). Our study results indicate that transformed map features approximate ground truth as follows, ordered from best to worst: LineMorp, WDT, and DT. New relationships between map transformation and map generalization or map revision are found from the transformation results.