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High-resolution imagery is now routinely utilized to acquire geometric and attribute data for road networks. Frequently, these data must be integrated with data from other sources to meet application requirements. A critical first step in this process of data integration is to identify the correspondences between networks, so that data can be merged or transferred from one network to another. This paper introduces a semiautomated method that combines an automated algorithm and an interactive procedure to match networks that are represented differently. The automated algorithm establishes robust correspondences for nodes, edges, and segments between two networks using a cluster-based matching mechanism. The interactive procedure allows a user to visually check and correct correspondences that are mismatched by the automated algorithm. Two matching examples are presented in the paper to demonstrate the applicability of the proposed method. The results of these two matching examples illustrate that this semiautomated method is highly effective and can meet the needs of real-world applications. The automated algorithm is capable of generating reliable matching measures, resolving difficult matches, and in both cases, correctly identifying the overwhelming majority of the matching counterparts. The interactive procedure, on the other hand, is useful for checking and correcting mismatched counterparts during the early stage of the matching process, which significantly improves performance and reliability. [Copyright &y& Elsevier]

Analyzes the Supreme Court's decision in the case of `Shaw v. Reno.' Court's resolution on the application of strict scrutiny to redistricting undertaken in compliance with the Voting Rights Act; Background of the case; Coverage of the equal protection clause in the constitution; Issue on race-conscious reapportionment.