Angelique's GIS Portfolio

The purpose of this week's lab was to create 3D visualizations of elevations models, create and modify TINs and compare TIN and DEM elevation  models. Digital Elevation Models (DEMs) are raster based models with information stored as a grid array with topography in equally spaced intervals. Triangular Irregular Network (TIN) models are vector based with elevation points (vertices) as a triangulated surface of overlapping triangles. TINs  also include information about altitude, slope and aspect that can be used to extract and analyze study areas. Which model is most useful in GIS analyses depends on the purpose of the analysis. I explored various TINs and DEMs in this lab but the exercise that demonstrates the differences and similarities between the two models is when I compared the contour lines between a TIN and a DEM created from elevation points. To create the TIN, I used the Create TIN tool with the elevation points as the input using the mass points type and the study ar

For this week's lab assignment, we compared the total lengths of roads for two different road networks: TIGER Roads and Street Centerlines The TIGER Roads shapefile came from the US Census Bureau whereas the Street Centerlines shapefile came from Jackson county, Oregon. The objective was to determine the quality and completeness of the road networks. The total length was used as the simple measure of completeness, assuming that more roads means a more complete network. The first step in the analysis was to determine the total lengths of the roads in each network for the entire county. I used the Project tool to project TIGER Roads into the same coordinate systems as the Street Centerlines shapefile. Then I used the Summarize tool  to determine the total length in each county. The TIGER Roads was found to be longer than the Street Centerlines, thus making it the more complete road network. The second step of the analysis was to determine the total length of the roads within each

In this lab, I explored the concept of data accuracy standards by determined the positional accuracy of road networks. The two sets of road networks were for the city of Albuquerque, New Mexico. One was a shapefile of road center lines from the city of Albuquerque itself and the other was a shapefile with streets from StreetMap USA, a TeleAtlas product distributed by ESRI with ArcGIS software. According to National Standard for Spatial Data Accuracy (NSSDA) guidelines, at least 20 reference points within the study area are needed to test the accuracy of each of the road networks. Additionally, no fewer than 20% of the reference points should be located in each quadrant and the distance between each of the points should be at least 10% of the diagonal distance across the study area. Following the NSSDA guidelines, I divided the study area into 4 quadrants measured the diagonal distance of each quadrant to use in my intersection selection process. To select the reference points, I zo

In this lab, we determined the precision and accuracy measurements of provided GPS waypoint data, as well as the root-mean-square error (RMSE) and cumulative distribution function. For geospatial data, precision is how close measurements are to one another, while accuracy is how close the measurement is to the actual - or reference - value. Data can be precise without being accurate and vice versa. GIS data is held to specific accuracy and precision limits and the values are represented as differences, or errors, where accuracy is usually met with the RMSE as a guide.  For this lab assignment, the precision was determined as a distance (in meters) that accounted for 68% of the observations; while the accuracy was determined by measuring the distance between the average and accepted reference points. In both cases, the larger the value, the lower the precision and accuracy.  The result for horizontal precision within 68% of the average waypoint is 4.4 meters, which puts most