GIS Blog: Melanie A. Sutton

Raster-based digital elevation models (DEMs) and vector-based triangulated irregular networks (TINs) are two alternative methods to represent digital topography. A DEM is sampled at a particular resolution and can only be made more accurate if the raster is resampled to a higher resolution. Alternatively, to make a TIN more accurate, additional points and breaklines can be added. For example, in the image below, to make the sharp boundaries around a lake more accurate, the TIN has been forced to use the exact boundaries and elevation of the lake polygon (shown in red): In the enhanced TIN above, notice how the TIN grid aligns perfectly along the shoreline with the addition of many more triangular cells in this area. Additionally, more triangles appear near the bends in the shoreline to more accurately capture these details. In this lab, we also learned how contour lines differ when derived from DEMs versus TINs. We observed that contour lines generated from a DEM are much smoother in

  The goal of the accuracy assessment summarized in the map above was to determine quality of road networks provided by Jackson County GIS compared to a data set provided by the U.S. Census Bureau. One measure of such quality is the completeness of the data set, or how comprehensive the coverage of the data set is. As noted by Haklay (2010, pp. 690-692), one method to make this comparison between two data sets of road networks involves overlaying a grid over the study area and then calculating the difference in the total length of line segments in each cell. To get a percentage, one of the data sets serves as the base (in this case, the Jackson County GIS data set served this purpose). The calculated percentage can then be positive or negative based on this formula:      % difference = 100% * (Jackson County GIS - U.S. Census Bureau)/Jackson County GIS A positive value indicates better coverage by Jackson County GIS in the underlying grid cell (noted by shades of blue). Alternative

For ongoing professional engagement I have selected GISCorps , a volunteer-based group which operates under the Urban and Regional Information Systems Association, an international non-profit that promotes "the effective and ethical use of spatial information and information technologies for the understanding and management of urban and regional systems." Becoming a "Friend" to GISCorps is free and allows you to receive their quarterly newsletter and announcements about developments within GISCorps, including the amazing work of their volunteers. Alternatively, if you are ready to use your GIS and geospatial expertise to help communities in need, you can register for free as a "Volunteer". Joining GISCorps with this level means you’ll be notified of volunteer opportunities that match your skills, and then you follow-up with an application. This level of participation includes agreeing to a GIS Service Pledge that includes a commitment from Esri for a d

  The image above shows 20 test point locations across a street map for the city of Albuquerque. In selecting these test points to report the horizontal accuracy of two alternative geographic data sources providing street centerlines, the following constraints were applied, in compliance with using the National Standard for Spatial Data Accuracy to measure and report the associated quality (Minnesota Planning Land Management Information Center, 2019): Locations were selected using street intersections derived from orthophotos of the study area - these images contained detailed imagery of street centerlines and nearby features (e.g., medians, sidewalks) that could be readily used to accurately determine intersection points. Given the street uniformity throughout the region, approximately 25% of the points were derived from each quadrant of the overall study area, and all quadrants were equally represented in this way (meeting the requirement to draw at least 20% of test points from each

  Precision provides an assessment of the consistency of a measurement method (Bolstad, 2016, p. 624), or how close the measured values are to each other. The most commonly used measurement of precision is the distance that includes 68% of the repeated observations. For this dataset, as noted in the map legend above, the 68% horizontal precision value is 4.5 m. This means that 68% of independent observations are within 4.5 m of the average location. Alternatively, accuracy provides an assessment of how close a value is to the true value (Bolstad, 2016, p. 624). In this lab, horizontal accuracy was estimated as the distance between the average location and a known reference point, and was determined to be approximately 3.25 m.  With horizontal accuracy estimated as 3.25 m and 68% horizontal precision estimated as 4.5 m, we note a significant difference of 1.25 m between these values. Using a 68% horizontal precision value estimate means there are still 32% of the observations that excee