G.Hudson Mastering GIS

Having moved on from the modules regarding data quality, this week we were introduced to surfaces and surface models, more specifically Triangulated Irregular Networks (TINs) and Demographic Elevation Models (DEMs). TINs are based on vector data and are made up of a series of linked triangles that vary in shape and size, but display a 3D representation of a surface. DEMs are based on raster data, more specifically the spot heights associated with each grid part of a raster.  For this lab we were tasked with creating a TIN and DEM, as well as displaying contours based on both of them. We were then tasked with comparing the contours produced by both. Below I have included images of my results.  (a) Contour lines based on the TIN (b) Contour lines based on the DEM The contours produced as a result of the TIN appear to be more jagged, while the contours produced from the DEM have a smoother appearance. There is also a noticeable difference between the derived contours in areas that hav

Like the second assignment, this module continues to focus on data quality. More specifically, this week focused on data quality of road networks. As such, we were tasked with performing a comparative assessment to determine the completeness of a street centerline shapefile and a Topologically Integrated Geographic Encoding and Referencing (TIGER) road shapefile for Jackson County, Oregon.  To perform this assessment we utilized a non standard method, based on that used by M. Haklay in the "Comparative Study of OpenStreetMap and Ordinance Survey datasets" in 2010. This included using a grid, which encompasses the study area, to clip the road network into smaller sections. These smaller sections give more detailed insight into areas which may be considered complete or incomplete. Once the roads were clipped to each grid cell/section the lengths were calculated and then compared.  My analysis determined that 134 of the grid cells/sections contained parts of the street centerlin

Continuing on from the first week of GIS5935, this week we were introduced to the more common data quality standards used for cartographic and digital geospatial data. These standards were put in place by organizations, such as the United States Geological Survey (USGS) and the Federal Geographic Data Committee (FGDC), to "provide a common language for reporting accuracy to facilitate the identification of spatial data for geographic applications" (FGDC, 1998). In particular, the lab allowed us to perform our own positional accuracy tests using the National Standard for Spatial Data Accuracy (NSSDA) standards.  The NSSDA standard follows a seven step process which includes the following:      1. Determining if the test should be for horizontal accuracy, vertical accuracy or both.      2. Selecting test points from the data set being evaluated      3. Selecting an independent data set of higher accuracy that corresponds to the data set being tested.      4. Collecting measurem