Lauren Chaphe's Geographic Information Science (GIS) Portfolio

The topic for this week's lab was working with 3D data in the form of DEMs or TINs. There were several parts to the lab, each with a different set of data and scenario. Local 3D scenes were created for each scenario by setting the raster elevation dataset as the surface source and setting a vertical exaggeration to show the differences in elevation changes better.  Part A simply involved draping a terrain image over a 3D terrain to produce a realistic-looking 3D image.  Part B involved taking a DEM, or Digital Elevation Model, raster and determining the potential suitability for a new ski slope location. The most suitable areas are areas at elevation above 2501 ft, a slope between 30-45 degrees, and the slope is heading in a western direction. In order to determine the suitability, the DEM was reclassified into three categories with rating values. The SLOPE tool was then used to create a slope raster with the elevation DEM as the input feature. This slope raster was also recla...

One of the great things about GIS is that it can be used for many different fields. For example, ESRI lists a few of the major industry areas for GIS as:  Public Safety Health and Human Services Architecture, Engineering & Construction Science State and Local Governments Natural Resources  Conservation Transportation ....And many more! Application areas are pretty much unlimited. For instance, during the pandemic, GIS was used to map and model COVID19 outbreaks, document medical supply inventory, and manage testing sites (https://www.esri.com/en-us/covid-19/response#inventory-and-map-resources). Also, hurricane tracks and storm surge estimates are created with the assistance of GIS modeling and, after the storm passes, it also aids in disaster relief, field data collection, and post-storm damage analysis (https://www.esri.com/en-us/disaster-response/disasters/hurricanes?srsltid=AfmBOoq88lXJN48476zmJwaqINfM0la4c6V2-z5ncUok0g6Yb28vyCzC). Therefore, GIS is an area that will l...

The main goal for this module was to compare the quality of data for two similar road datasets, TIGER Roads (2000) and county Street Centerlines. Both datasets contain polyline street centerlines and attribute information for roads within Jackson County, Oregon. To provide both numerical and visual representations of the analysis, the county was divided into grid cells with equal areas, or 25 square km. In order to compare the two datasets, the length, in kilometers, was calculated for both datasets for each grid cell. The process started by clipping the Grid polygon dataset to remove streets that fall outside the boundaries of the grid cells. Then, the PAIRWISE INTERSECT tool was used, in which the input features were the clipped Street Centerline and the Grid datasets and the output type was set to Line. Then, the DISSOLVE tool was used to dissolve based on the GRIDCODE attribute with multipart features. A new length field was added (double, 1 decimal place) to calculate the new tota...

The goal of this module was to calculate an accuracy estimate for two similar datasets using the methods described in the the National Standard for Positional Accuracy (1999) Positional Accuracy Handbook. Road centerline data created by Street Maps USA and ABQ Streets were provided and viewed using ArcGIS Pro in order to compare to accurate aerial orthophotos. A point feature class was created for the StreetsMaps USA, ABQStreets, and the reference imagery. Then, 20 random sample points were selected within a study area and a point was placed at the center of the mapped road intersection for each dataset and the actual intersection location according to the aerial imagery. Therefore, each sample location had three points. A screenshot of the 20 sample locations is shown below, in which the red lines are the StreetMaps USA data, the green are the ABQ Streets, and the blue are the reference points based on aerial imagery.  A screenshot of a sample intersection is also shown below:...