This was a multi-subject lab week. For the first part, it combined some physical activity in and around my local neighborhood for in situ data point acquisition. Then the second part looked at what and how projections affect the presentation of data and maps.
Part I. Field Mapping and Data Collection
The goal: collect data on safety features in my local neighborhood.
My target: fire hydrants in and around the common areas of my neighborhood.
The process: build a feature layer shell to house the collected information, host and sync the shell with ArcGIS Online, collect real-world point data with Arc Field Map from my phone, present.
The brief process above makes it seen simpler than it was. This multi-software, multi-step process, played to the strengths of several different programs and methods. First, ArcGIS Pro was used to build the feature layer shell that would essentially be the data base organization tool for the collected data. then this was hosted on ArcGIS online in order to be able to sync both the computer software with the phone application used for collecting and storing the points, Arc Field Map. This was a particularly useful tool for collecting data.
While I did all of my data collection connected to cell signal, it can be done offline instead. This is particularly useful for inside buildings or in other areas with limited or no signal. For the collection, 3 classes or condition categories were established. They are; excellent for like new condition, fair for some damage but overall functional, and poor being broken, damaged, in need of replacement.
Once this was established, I walked all the way around the neighborhood and through the central common area. I identified 20 fire hydrants along this path. I identified 3 hydrants in the excellent category with brand-new fresh and shiny yellow paint with no cracking. Oppositely, there was only 1 poor-condition hydrant which had significant rusting, pitting, and worn hydrant nuts which could prevent them from being unscrewed in a timely manner during an emergency. The other 16 were in fair shape with some rust spots and cracking and peeling of paint, but otherwise, they should be completely functional.
After collecting the points and reconnecting to Arc Online and ArcGIS Pro, the goal was to develop several different methods for sharing the collected data. These were through a Map Package from ArcGIS Pro, through Arc Onlines web maps, and also as an exported KML for use in Google Earth. The deliverable below is an example of the upload into Google Earth. Which KML sharing is one of the most useful and easiest sharing methods. However, for more interaction with the data itself, I would rely upon ArcGIS Pro or the Online Webmaps.
Part II. Projections
Map projections are the methods in which the spherical earth is transposed to a 2d or flat map. Different projections seek to retain different qualities of a map, such as the shape presented, or the area of a particular region or shape, or the linear distance and direction from one place to another depending on scale.
In this part of the lab, a basic map of Florida divided into counties was given three different projections to compare differences and similarities. Surprise, Florida has the same shape in all three. However, there are slight differences to its orientation, that the scale below, unfortunately, doesn't highlight well. The UTM projection specifically has a slight skew counter clockwise of the others. The more prominent comparison is in the Area Table which highlights four counties spread across the state. These 4 counties show differences in size (miles squared) for each. You can see that the Albers and State Plane projections lend incredibly similar results. They range from nearly identical out to approx 3-4 square miles separate. Whereas the Albers and UTM projections provide the biggest differences, up to approximately 18 square miles difference for the larger southern counties of Polk and Miami-Dade.
One of the primary goals here is to show the importance of having data sets or products with consistent projections to allow for accurate calculations and analysis. There are two potential projections issues. either having an inaccurate projection to the data, or having no projection information assigned at all. Arc GIS software does a great job at reprojecting on the fly, that is converting new layers to whatever the base projection of the project is. However, if there is no projection associated or Arc can't determine what it is, then it can provide erroneous results.
This was also evidenced with a Raster (picture) dataset which had an 'unknown' projection, which when added to the base map, appeared drastically out of position. That is until the projection was defined for it, to then associate appropriately. Below is the end result map showing the three projections and adding a little of my own desired Red, White, and Blue flair.
Ultimately, this was a fun experience working on many facets of data collection and depiction. While the two parts seem distinctly different, they are apart of the full understanding and generation of a broader project from nothing to full depiction. Onward to the next project.
V/r
Brandon
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