The use of geographic information systems in determining transit service areas has not progressed far beyond simple buffering operations even though there is widespread capability to analyze network walking distances in conjunction with demographic, cadastral, and land-use data sets. This article presents a method for determining the population with walking access to bus stop locations using the spatial and aspatial attributes of parcels and the network distances from parcels to bus stop locations. This parcel-network method avoids the well-known and unrealistic assumptions associated with the existing methods and reduces overestimation of the population with access to transit, resulting in improved spatial precision and superior inputs to transit service decision-making processes. Comparisons of the parcel-network method, the buffer method, and the network-ratio method are made in a study area within the Dallas metropolitan area. The novel integration of cadastral data with network analysis in our method holds promise for research in many areas of geographic information science.

Network data structures were one of the earliest representations in geographic information systems (GIS), and network analysis remains one of the most significant and persistent research and application areas in geographic information science (GIScience). Network analysis has a strong theoretical basis in the mathematical disciplines of graph theory and topology, and it is the topological relationships inherent in networks that led to revolutionary advances in GIS data structures. Networks can represent an alternative datum for geo-location in the context of linear referencing and support a set of tools for graphical display known as dynamic segmentation. Many network location problems are among the most difficult to solve in terms of their combinatorial complexity and, therefore, provide both a challenge and an opportunity for GIScience researchers. Because elements of network analysis appear in a wide range of academic disciplines—from physics, to sociology, to neurobiology—there are ample opportunities...