Abstract
BackgroundThe appropriate resolution of a zone system is key to the development of any transport model, as well as other spatial analyses. The number and shape of zones directly impacts the effectiveness of any further modeling steps, with the trade-off between computation time and model accuracy being a particularly important consideration. Currently, zone systems are often designed by hand. The gradual rasterization zoning algorithm produces good empirical results by computationally generating raster cells of varying area, but similar population and employment.MethodsWe address several limitations of the original algorithm in this paper. Firstly, the allocation of employment to raster cells is weighted by land use instead of by area percentage. Secondly, the algorithm is extended to respect municipal delineations. Raster cells are split along these delineations, with any undesirably small zones created by this process re-merged with neighbors in the same municipality. Aligning the generated cells to municipalities simplifies and improves the disaggregation of socioeconomic data. An iterative algorithm has been developed to automatically determine the threshold that results in the zone system of the desired size. Raster cells are split along these delineations, with any undesirably small zones created by this process re-merged with neighbors in the same municipality.Results and ConclusionAligning the generated cells to municipalities simplifies and improves the disaggregation of socioeconomic data. Using this algorithm, a zone system has been generated for the Munich metropolitan region. Only 13 iterations were needed to converge within 5% of target of 5000 delineated zones. The improved algorithm maintains the advantages of the original algorithm and adds several important improvements that are useful when creating a zone system.
Highlights
The appropriate resolution of a zone system is key to the development of any transport model, as well as other spatial analyses
While regular raster cells are very efficient for certain spatial analysis, including Cellular Automata, they are inefficient for transport modeling and other computing-intensive analyses dealing with complicated zonal interactions [4, 5]
In conclusion, our improved algorithm maintains the advantages of the original algorithm and adds several important improvements that are useful when creating a zone system
Summary
The appropriate resolution of a zone system is key to the development of any transport model, as well as other spatial analyses. Eidlin [10] for example, showed that New York City is the densest place in the U.S if city boundaries are chosen as the unit of analysis, whereas the selection by metropolitan areas, in contrast, would assign this title to Los Angeles. This issue was labelled by Openshaw [20] as the Modifiable Area Unit Problem (MAUP), expressing that results of spatial. While regular raster cells are very efficient for certain spatial analysis, including Cellular Automata, they are inefficient for transport modeling and other computing-intensive analyses dealing with complicated zonal interactions [4, 5]. This way, most resources are allocated to areas that deserve most attention by the analyst
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