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 stu...

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 p...

The purpose of this lab was to perform a post Hurricane Sandy damage assessment on structures within a study area in New Jersey. I began by performing a raster mosaic with pre- and post-Sandy imagery. With these mosaics added to a map, the Flicker and Swipe tools could be used to examine the structures pre- and post-Sandy.  I created a new point feature class for the damage assessment and created attribute domains for the analysis. Using domains helps assessments such as this one by ensuring data integrity because they limit value choices for each field. In addition, once the domains are created, a form can be created to use with ArcGIS Collector which is an app can be used in the field for a thorough damage assessment. Below is a screen capture of the domains I created with the Codes and Descriptions of the Structure Damage domain visible. I then performed my damage assessment by locating and identifying attributes based on storm damage.  I zoomed into the Ocean Cou...

The purpose of this module was to explore procedures for coastal flooding and storm surge analyses using elevation models, overlay analyses for vectors and rasters, and spatial queries. The first data set was for an area in New Jersey that was impacted by Hurricane Sandy. I converted LAS files from pre- and post- Sandy for the coastline area were to TINs and then rasters. I subtracted the two rasters from each other using the Calculate Raster tool and analyzed the resulting shapefile was for damage with a 2019 building overlay. There are several areas in the study area that show significant erosion (red areas) that have not been rebuilt. The map below shows the overall results of this analysis. The second data set was also for New Jersey. A DEM was provided and I reclassified it into areas that would flood based on the Hurricane Sandy storm surge of 2 meters. I then converted the raster to a polygon and examined the result for Cape Map County. Based on the analysis, about 52% of C...

The purpose of this week's lab was to explore 3 selected hotspot mapping techniques for crime analysis for 2017 homicides in the Chicago area. The results of each technique were compared against 2018 homicide data to assess each technique's reliability for predicting crime. The first technique was Grid Overlay Hotspot Analysis. The goal was to determine the number of 2017 homicides in each grid cell and select the cells with the highest count. This was accomplished by first performing a spatial join between the 1/2 mile grid cells and the 2017 homicide data which added a field representing the number of homicides in each grid. I then used the Select by Attributes tool to select all counts greater than 0 and saved the selection as a separate feature class. I selected grids with the top 20% manually from the attribute table - the total number selected was calculated by dividing the total by 5 - and saved the selection as a separate feature class. To dissolve this feature class,...