Lauren Chaphe's Geographic Information Science (GIS) Portfolio

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...