and from that set of options a decision-making entity can select the final route alignment. These applications typically require generating a set of non-inferior options that balance numerous competing interests such as economic cost, environmental impact, maintenance accessibility, visual pollution, etc. Raster-based shortest path analysis is the predominantly used method for locating linear features over terrain, such as new transmission line corridors, pipelines, roadways, as well as analyzing the connectivity of a landscape for habitat analysis and urban systems. While GIS analysis techniques are numerous and broad, and an entire book could be written covering all impacts of data representation the main objective of this article is to focus on the impacts of two common data transformations when representing terrain as a raster network for locating a linear feature using shortest path analysis: 1) defining the network generated by connecting raster cells to their neighbors, and 2) the range of the attribute scale that represents the costs to locate the feature at each raster cell. Thus, it is important to be aware of the effects of spatial data representation, and to establish guidelines that help to ensure that GIS analyses accurately represent real-world conditions and provide impartial solutions. While misinformation through cartographic manipulations have been well documented, if the GIS user has a desired outcome from the analysis they may even use data manipulations to covertly drive the solutions toward a desired goal. Otherwise, the outcome of a spatial analysis may inadvertently be distorted. A GIS user must almost always prepare and manipulate spatial data in order to make it suitable for use in analysis, and thus it is imperative that the user understand the impacts that these manipulations may have on the final results. As with any sort of analysis, the results from spatial analysis are highly dependent on the quality of the data provided, as well as the understanding that the GIS user has with respect to the methods used. Spatial analysis is used to bring meaning and insights out of spatially referenced data, and the set of methods that are identified as spatial analysis tend to be some of the most heavily used in geographic information system (GIS) software. įunding: Argonne National Laboratories, grant number 1F-32422 The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: which is an archived snapshot of the following open source repository. Received: DecemAccepted: MaPublished: April 15, 2021Ĭopyright: © 2021 F. Matisziw, University of Missouri Columbia, UNITED STATES Based on these outcomes, we outline recommendations for ensuring geographic information system (GIS) data representations maintain analysis results that are accurate and unbiased.Ĭitation: Medrano FA (2021) Effects of raster terrain representation on GIS shortest path analysis. Experiments in solving biobjective shortest paths show that results are highly dependent on the parameters of the data representations, with exceedingly variable results based on the choices made in reclassifying attributes and generating networks from the raster. Such analysis is commonly used to locate transmission lines, where the results could have major implications on project cost and its environmental impact. We study the consequences of two common data preparations when locating a linear feature performing shortest path analysis on raster terrain data: 1) the connectivity of the network generated by connecting raster cells to their neighbors, and 2) the range of the attribute scale for assigning costs. Users should understand the impacts that data representations may have on their results in order to prevent distortions in their outcomes. Spatial analysis extracts meaning and insights from spatially referenced data, where the results are highly dependent on the quality of the data used and the manipulations on the data when preparing it for analysis.
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