Arcs and nodes are discretely referenced by coordinates. Arcs usually describe the centreline of a network feature, such as a road centreline. In a mixed rural/urban road network in an average Norwegian municipality, with 7000 edges and nodes, there can be as many as 18000 turn possibilities (Husdal, 1998). For a “simple” crossroads with four edges and one node there are as many as 16 possible turns, three directions from each edge to other edges, plus four 180-degree U-turns. This can be quite complex, depending on the amount of travel cost information we want to incorporate in the model: road width, speed limit, road class, delay at traffic lights, delays in taking turns at crossroads, to mention just a few. Consequently, the representation of network elements requires substantial amount of time to be devoted to data preparation and validation. If the directions are derived from digitising a road map, or received as a ready coded network form a data supplier, they may not correspond with the real-world directions and need to be checked. Directions are an explicit part of the vector network topology. All that is needed, simply speaking, is to implement the resistance factors in the attribute tables for the lines or nodes. Since networks utilize the basic arc-node structure, by definition, due to the way the data is stored, the vector network will already have a topological structure, relating all elements. Typical network graph and table structure, listing nodes, connectivity of edges, turn impedance and edge attribute data. For a network to function as a real-world model, an edge will have to be associated with a direction and with a measure of impedance, determining the resistance or travel cost along the network. Nodes can be junctions and edges can be segments of a road or a pipeline. In other words, a network takes the form of edges (or arcs) connecting pairs of nodes (or vertices). Network modeling in generalĪ network model can be defined as a line graph, which is composed of links representing linear channels of flow and nodes representing their connections (Lupien et al.,1987). It will discuss their limitations and advantages, by using a road network as an example. This study will investigate the subject of network analysis in both raster and vector GIS, in order to compare the two spatial models. points, lines and polygons, or raster-based, i.e. Traditionally, a GIS, represents the real world in either one of two spatial models, vector-based, i.e. One major application of network analysis is found in transportation planning, where the issue might be to find paths corresponding to certain criteria, like finding the shortest or least cost path between two or more locations, or to find all locations within a given travel cost from a specified origin. The network data model is an abstract representation of the components and characteristics of real-world network systems. In general, a network is a system of interconnected linear features through which resources are transported or communication is achieved. Whether raster or vector GIS is to be preferred is more a question of choice than of accuracy. Even though the models differ, the solution to different transportation problems in either raster or vector GIS uses the same path finding algorithms. Real world networks, such as a road system, must be modelled appropriately to fit into the different spatial models. In a GIS the real world is represented by either one of two spatial models, vector-based, or raster-based. Network analysis in GIS is often related to finding solutions to transportation problems.
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