Coordinate Projections

This module was extremely helpful, as we dived deep into projection systems. Through the lab, we analyzed UTM, State Plane, Albers and determined when they are the best fit, as well as how to project them properly.

A handful of tools we utilized are listed below:

Projection Wizard

Geodetic Parameter Search

For our final map, we choose a state in the USA, and determined which projection to use; UTM vs State Plan.

I choose the state Utah because I have always wanted to go to Zion! I decided not to use the State Plane because there are three different ones that subdivide the state. Since state plane’s do not work here, I opted for UTM over Albers because we are looking at a relatively smaller area not close to the poles. I tried a couple of different UTM NAD83 Zones and landed on UTM NAD83 Zone 12. In this zone, Utah completely lies within one UTM Zone.

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Positional Accuracy: NSSDA

In this analysis, I compared the street and road intersect data collected for Alburquerque, NM by the City of Alburquerque and the application StreetMaps. I used an orthophoto base layer as the reference for this analysis, to compare and determine the accuracy of both the City and Streetmap layers using NSSDA procedures. The most difficult part of this analysis for me was how to determine what 20% per quadrant looks like. Because the reference map was divided into 208 quadrants, I had to determine how to subdivide all the quadrant's equality into 20%. After multiple trials and error, I decided to subdivide the entire area (208 sub-quadrants) into 4 equal-area subsections. In this way, I could do 5 random right intersection points per subsection or 20% per subsection. Map 1: City of Albuquerque city map data. Map 2: City of Alburquerque SteetMap data When selecting a random intersection to place the points within each quadrant, I choose a location that had data f...

Isarithmic Mapping

Map 1: Annual Precipitation, Washington State Map 1 is an Isarithmic map that follows the continuous phenomenon of rainfall in Washington state over a 30 year period. The data was created by the PRISM group at the Oregon State University in 2006, and then downloaded and amended by the U.S. Department of Agriculture, Natural Resources Conservation Service, National Geospatial Management Center in 2012. Eden Santiago Gomez, analyzed the data on 5/2/2021, to create the map above. Santiago Gomez created continuous tones for the data, also adding a hillshade effect. She then converted the floating raster data into Integer data via the geoprocessing tool Int (Spatial Analyst Tool) to bring out hypsometric tinting. Lastly, she added contours of the data via the Contour List tool. How the precipitation data was derived and interpolated? The PRISM system has been continually developed over the past couple decades, utilizing physiographical maps and climate fingerprints as its ...

Supervised Classification

This week we learned how to perform supervised and unsupervised classification on ERDAS Imagine. On the figure 1 map below, you can see my attempt at classifying land use/cover of Germantown, Maryland. This week my map falls short of the assignment goals, as I was not able to get ArcGIS pro to replicate the recoded feature classification outputs I performed in ERDAS. I am not sure what I did wrong, but in the future, I will make sure to analyze signature points more carefully via their histograms and mean plots to identify potential errors. Figure 1: Supervised classification of Germantown, Maryland with a smaller distance map of the main feature map.

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