B.S. in Geography, Penn State.
I’ve always been fascinated by studying data, specifically patterns in human behavior.
With so much data collected today, we need outlets for showcasing that data, particularly in visual ways, such as showing patterns on maps.
I have experience with ArcGIS Pro, ArcGIS Online, R Studio, and Stata for manipulating data and mapping patterns.
Table of Contents
Raster and Terrain Analysis
Geofencing and Heads-up Digitizing
Site Suitability Locating
Graduated Circles and Bivariate Maps
Interpolation of Precipitation
Cluster Outlier Analysis (Local Moran’s I)
Raster and Terrain Analysis
Use Cases: Raster and terrain analysis uses data to model the Earth’s surface to calculate things such as slope, hillshade, ruggedness, and more.
Lab Example: Here, I used raster data for Joshua Tree National Park, CA and mapped it in Esri’s ArcGIS Pro. I manipulated the data to fit certain criteria that would show me park locations to view the perfect sunset.
The criteria included having a southwest-facing location (ideal for sunsets), an elevation of 1800-3000 meters, and a relatively steep but hikeable slope of approximately 20-40% gradient. Once the map was configured, the user could interact with the map and select different areas within the park that would show the observable area from that specific vantage point.
2. Georeferencing & Heads-up Digitizing
Use Cases: Georeferencing means assigning geographic coordinates to a scanned image or map. Since working as a law enforcement investigator, I see the value of this type of analysis. If during a cold-case investigation, a person provides an image of a street from long ago but cannot positively identify the actual geographic location, georeferencing may be an option to match the location with a modern-day image. This can be used in historic contexts as well.
Lab Example: In this lab, I used georeferencing techniques for Allegheny County, PA to overlay satellite imagery from 1938 and line up with the modern day.
3. Site Suitability Locating
Use Cases: Site suitability is the process of identifying the best location using certain criteria for a specific purpose such as where to establish a location for a new business or school.
Lab Example: In this example, I wanted to find the best location to put a new pizza shop in Westmoreland County, MI. I didn’t want to put my new shop too close to other pizza shops, and I wanted to make sure there were enough people in the area who may be interested in buying pizza. Specifically, I visualized areas that have greater than 400 people living nearby who live at least 10 km from the nearest pizza shop and created a buffer to show the area that the new pizza shop would reach.
4. Graduated Circles and Bivariate Maps
Use Cases: Graduated circles can be used in GIS to visualize where events happen and the magnitude of the event based on specific variables. Bivariate maps are used to show relationships between variables.
Lab Example: In this lab, we received a lot of data from a database that I visualized in the following two maps.
In the first one, I analyzed fatalities related to conflict in Africa in 2015 using graduate circles to show which areas experienced fatalities because of conflict, how many fatalities happened, and where those fatalities were located.
The second map analyzed the percentage of women who have completed secondary education or higher compared with the percentage of households with electricity.
5. Interpolation and Kriging
Use Cases: Kriging and other interpolation techniques are used to “smooth out the data” and predict amounts of a variable for where data lacks. While there is a lot of data constantly collected, there will always be unreached areas or places where data is missing, which is why interpolation techniques are essential.
Lab Example: In this lab, I chose the interpolation technique of Kriging for annual precipitation across the United States. Different techniques of interpolation can be used and will yield slightly different results. As you can see in the maps, the original raster has a lot more blank areas where data was not collected. In the kriging map, the data was spread out and predicted approximately how much rainfall occurred in areas that did not originally have data associated.
6. Cluster Outlier Analysis (Local Moran’s I)
Use Cases: Cluster analysis showcases groups of similar data points. Moran’s I allows analysts to determine if data is clustered geographically.
Lab Example: In this lab, I analyzed e-cigarette prices in Alameda SF County, CA for clustering and outliers at gas stations and convenience stores within the county. Oakland had significantly high prices surrounded by other high prices.
