Welcome to my GIS blog! I will be posting module content for peer review and constructive criticism. I will also be reviewing my workflow and any issues I had with the coursework.
Hi! I'm Chris. Please see my story map here ! I am a USAF veteran utilizing the VA VR&E program. I am a graduate student looking to expand on my GIS knowledge. I served as a Geospatial Analyst for 12.5 years and currently hold a B.S. in Geography and B.S. in GIS. This seemed the next logical step to utilize my education benefits. Upon completion, I hope to utilize my skills to improve my current companies GIS practices.
This week's lab was about utilizing different spectral combinations to identify features. Different combinations can be utilized to help identify very specific features or phenomena. Analysts in the wild use these different combos to help solve problems such as deforestation, flooding, or change detection. My personal experience is in the military intelligence world, where we utilized these different bands to help us identify or expose dangerous operations. We could use these different combinations to identify changes in equipment location, homemade explosives/IED identification, as well as verifying if a site is operational or not. One example, closely related to this assignment, would be to verify if powerplants were operating. The easiest and quickest way to determine if they were operational would be to look at the stacks/flumes to identify smoke or vapor, or to identify changes in water temperature in the cooling ponds.
This week's module focused on Data Classification and the different methods used to manipulate the information. We were tasked to identify the age 65+ population in Miami-Dade County. Our goal with the task was to create two layouts with four separate map frames showing different methods of classification. These methods were Quantile, Equal Interval, Standard Deviation, and Natural break. Layout number one utilized the percentage of population over Age 65. Layout number two is the count of population normalized by square mile.
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