A. Starling's GIS Blog

       An important consideration in mapping to take into account is scale. The larger the scale value, the more detailed a map may be (showing a smaller area). For example, if we were to make a map of airports with airports represented as points (vector data) and we zoomed in significantly, we would be able to see many more details (polygon features indicating building, lines indicating runways). As a note, vector data consists of points, lines, and polygons.      Raster data, on the other hand, is comprised of cells. The smaller the cells, the more details/higher resolution. The bigger the cells, the lower the resolution. The level of detail relates to the processing time of the data cells, so the higher the resolution, the more processing time is necessary.      Gerrymandering is manipulation of districts to give an advantage to one political party over another. One way to determine if a district is undergoing gerrymandering is to...

     In this lab, we sought to use different interpolation methods to determine which would be the best to most accurately depict the BOD (Biochemical Oxygen Demand) within the Tampa Bay area in Florida. We explored Thiessen, Inverse Distance Weighted (IDW), and Spline (Regularized and Tension) methods. Thiessen interpolation advantages include the interpolated surface equaling the values of each given sample point, which means there isn’t a difference between the true and interpolated values. Disadvantages include the abrupt change of between values at the edges of the polygons this interpolation method outputs. While Thiessen interpolation is good for sampled locations, it may not be as accurate around unsampled locations. IDW averages values and determines the weight based off point cell proximity. This method assumes points closer to one another are more alike and is good for more evenly distributed, uniform, dense points. Spline interpolation will estimate va...

                In this lab, we explored two types of elevation data models - Digital Elevation Model (DEM) and Triangulated Irregular Network (TIN). The DEM is raster-based and information is stored in grid format, while TIN is vector-based and uses irregularly spaced points that were calculated according to the connections of sets of triangles for contouring. Because of this, the DEM has smoother curves in its contour lines, while TIN has pointed/jagged contour lines. The DEM contour lines in this instance also have a few more lines due to the data parameters.                 The TIN contour lines are likely more accurate, as TIN can differentiate between more complex terrain than DEM contour typically can (depending on resolution). TIN uses irregularly placed elevation points that vary based on data density, but DEM is a grid with elevation values stored within the grid cel...

     The goal of the analysis for this week’s study on accuracy assessment was to determine which dataset, TIGER Roads or Street Centerlines, were more complete for Jackson County, Oregon.  In this lab, we are measuring completeness using the assumption that the more road there is, the more complete it is.      We could use the length of the roads to determine completeness. After ensuring data was in the same projection, we looked at the statistics (Attribute Table -> Visualize Statistics) to determine the sum of the road segment lengths: Street Centerline : 10873.3 km TIGER Roads : 11382.7 km Because this lab measures completeness based on road length (the more road there is, the more complete it is) – in this case, TIGER Roads is more complete than Street Centerline. We went a little further in this lab to determine length of roads with each grid of Jackson County, Florida – see Map 1: Map 1: Percent Difference of Road Length between TI...

     Within this study analysis, I compared different sets of street/road intersection data for Alburquerque, New Mexico that was collected separately by the City of Alburquerque and StreetMaps USA . Using an orthophoto base layer as the reference for this analysis, I then compared the accuracy of both datasets using the procedure set out by the National Standard for Spatial Data Accuracy (NSSDA).      I started off by sectioning the study area into four quadrants, which allowed for each section to have 20% of the points needed to determine data accuracy and I ensured that points were at least 10% of diameter apart. Per NSSDA (1999), “Twenty or more test points are required to conduct a statistically significant accuracy evaluation regardless of the size of the data set or area of coverage. Twenty points make a computation at the 95 percent confidence level reasonable.”     There were 208 sub-quadrants, and these were evenly divided to create fo...

 This lab provided us with the opportunity to compare accuracy and precision of (50) points that were collected using a GPS unit.     Accuracy is the difference between the reference point/true value and the measured point(s)/value(s), while precision refers to how close measured  point(s)/ value(s) are in relation to one another.  Horizontal accuracy is determined by calculating the difference in distance between the reference point and (in this case) average waypoint. We used the Measure tool in ArcGIS Pro to determine the distance between these two points (3.27 meters apart). Horizontal precision in this lab focused on determining waypoints that feel within the 68 th percentile from the average waypoint. We determined vertical (elevation) and horizontal po ints  that were within the 68 th percentile, as the 68 th percentile is the most commonly used measure of precision and indicates the distance within which 68% of observations (points) are....