Addison's GIS Journey

Spatial interpolation is the technique using points with known values to estimate other areas/points. It is typically used to create maps for precipitation, elevation, and more. In this lab we used four different types of interpolation methods to create a visual of the water quality in Tampa Bay. The four methods used for the estimation of Biochemical Oxygen Demand (BOD) in mg/L were Thiessen, Inverse Distance Weighted(IDW), Spline regularized, and Spline tension.  The Thiessen method proportionally divide and distribute point coverage into polygon regions. Each polygon contains only one point feature. The disadvantages of this include not being suitable for more complex surfaces and it creates sudden boundaries which is unrealistic for most data sets. Thiessen Interpolation The IDW method determines cell values using a linearly weighted combination of sample points. This method assumes the variable being mapped decreases in influence with distance from sampled location. A disadvan...

 This weeks lab focused on triangular irregular networks (TIN) and digital elevation models (DEM). TIN are a form of vector based data and constructed by triangulating a set of points. TIN models are typically used for high precision modeling of smaller areas. DEM is a raster representation of a continuous surface from a grid of squares. DEM models are typically used in land use planning and flow direction studies. While developing elevation models contour lines help visualize the topology of the surface, which can be done using either model.  One aspect of this lab was to develop a ski run suitability map using a DEM. After making the 2D layers visible in 3D the suitability map was able to be developed. From the elevation a slope and aspect raster were created. The elevation, slope, and aspect raster were then reclassified and then combined using the weighted overlay tool. The final suitability raster uses the following weights: 25% aspect, 40% elevation, and 35% slope. The m...

This lab focused on assessing the quality of road networks and determining the completeness of them. Two road networks data sets were given Tiger Roads and Street Centerlines, as well as a county line. Figure 1 shows both road networks within the county line. Figure 1 A grid network containing 1kmx1km polygons were also given. Figure two shows the two road networks in relation to the grid network. Figure 2 The total length of both road networks were found Street Centerlines being 10,805.8km and Tiger Roads being 11,382.7km. The next step was to determine the total length of both road networks within each grid. When needing to determine the total length of roads within each grid, my first thought was using an intersect tool. After some research the pairwise intersect tool seemed to be the best option for the outcome I was looking for. Using the pairwise intersect tool I was able to join each road network data set to the grid feature. This tool eliminated the road lines that were out of ...

  This lab focused on positional accuracy and used procedures given by the National Standard for Spatial Data Accuracy (NSSDA) to assess the data. Within this lab two sets of street data (ABQ cities and StreetMap USA) were given and an accuracy assessment was conducted on both. Using NSSDA guidelines 20 test points of intersections were made as shown above. From these test points reference points of the "true" location of the intersection was determined. Using the statistics from the points made a NSSDA horizontal accuracy statistic worksheet was created for each street data set. Using the NSSDA worksheet the Root Mean Square Error (RMSE) and the NSSDA statistic was found. These are the NSSDA accuracy statements for each data set: Tested 176.28 feet horizontal accuracy at 95% confidence level. (StreetMap USA) Tested 25.56 feet horizontal accuracy at 95% confidence level. (ABQ Cities)