Lauren Chaphe's Geographic Information Science (GIS) Portfolio

To see my official portfolio, please see the link below:  https://docs.google.com/document/d/16GaLTTi5j0aiEpk0gDzoSS3IbkwO24_96i_VH5hEkIw/edit?usp=sharing  Thank you,  Lauren Chaphe

My LinkedIn profile (Lauren Chaphe) is below. Please feel free to send a connection request! https://www.linkedin.com/in/laurenchaphe/  My profile is mostly geared towards highlighting my background, education, and skills. I wanted to show that I have the skills and knowledge required to be in the GIS field, but also wanted to show that I can use those skills towards the environmental field, such as ecology and marine biology. My education at Georgia College & State University, Florida State University, and the University of West Florida are listed along with some of the course titles I have taken for each educational achievement. My skills include common GIS keywords, such as GIS analysis, spatial data, ArcGIS Pro, and spatial databases, in addition to common environmental keywords such as marine and freshwater biology, environmental permitting, and wildlife assessments. Each skill is linked to my employment or educational experience as well to establish competency. I also inc...

The Modifiable Area Unit Problem (MAUP) essentially says that there are various results for data that is created at different scales. Specifically, scales that are smaller (i.e ratios of 1:100000) have different vector and raster results than at scales that are larger (i.e ratios of 1:1200). An example showing water features at different scales are shown below:  Each line color represents the same water feature that was created at different scales. The red line contains more details than the pink, which is more generalized and does not include the smaller tributary lines.  The same idea also applies to raster data. Examples showing a 1m resolution LiDAR DEM (Digital Elevation Model) (top) shows much more detail than a raster with a 90m resolution (bottom) Gerrymandering is one example that takes advantage of the MAUP effect. Gerrymandering manipulates the boundaries of voting districts to favor one political party or reduces the voting power of ethnic or minority groups. The r...

Interpolation involves estimating unknown data based on measured or known collection data points that are located nearby. There are multiple methods of interpolation, but this module evaluated the following methods: Thiessen Spline Inverse Distance Weighing (IDW)  In this scenario, we wanted to interpolate water quality data for the entire Tampa Bay area. Water quality data included measured biochemical oxygen demand (BOD) in milligrams per liter at 41 sampling points distributed within Tampa Bay. This same dataset was used in all three methods and the results were compared.  The CREATE THIESSEN POLYGONS tool was used to create the Thiessen method interpolation results. This method essentially assigns a value based on the closest sample point. This is a simple method and easy to interpret, but the boundaries are too abrupt to accurately estimate the natural flow of water in Tampa Bay.  The IDW method assumes that values that are closer have more "weight" or influence than...

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:...