Emily's GIS Blog

 For this blog post, I decided to share and discuss my Linkedin page. You can see my page here . I worked very hard to update my page a few months ago. I went about updating it by doing a lot of research. I found some really helpful sources that provided do's and dont's, as well as examples.  I wanted my page to be professional, organized, clean, and easy to read. To accomplish this I made sure my text was concise and straight to the point. If someone is visiting my page I want them to quickly understand my background and my current status. I also made sure to showcase all of my skills. I added a link to this blog, a list of my skills, and all of my relevant job experience. Another thing I updated and continue to update, is liking organizations that I am interested in. This is a good way to build my connections and stay informed on my discipline. 

 This week was the last module of the Special Topics course! We learned about scale and spatial data. Particularly about the different effects of scale on vector and raster data. With vector data, the scale affects the detail of the data. For example, at a larger scale vector data is much less detailed than the data would be at a smaller scale. The scale of raster data affects the resolution (cell size).  A larger-scale raster (i.e. 90m cell size) is much less detailed than a smaller-scale raster (i.e. 2m cell size). The cell size will also affect the results of any spatial analysis completed, such as slope. Part of the assignment was to determine gerrymandering, which is the manipulating of electoral boundaries to benefit a particular political party. One way to determine it is by measuring the compactness with the Polsby-Popper score (Morgan and Evans 2018). This method creates a score from 0 to 1. In the assignment, I calculated the Polsby-Popper score for the congressional...

 This week we learned about spatial interpolation, specifically Thiessen polygons, inverse distance weighting (IDW), spline, and kriging. Thiessen polygons or nearest neighbor is the simplest interpolation method. It assigns a value to a polygon based on the nearest value. IDW assigns a value based on the closest data point. The spline technique creates a smooth surface by passing through the data set and using a mathematical formula to interpolate. Kriging uses a multivariable formula to interpolate.  We used thiessen polygons, IDW, and spline (regular and tension) techniques to interpolate a data set of water quality in Tampa Bay. Figure 1 shows the results of the regularized spine technique.