Data Collection Using ArcGIS Field Maps
ArcGIS Online map of the point data I collected at subway entrance signs. Green = Excellent condition, Yellow = Fair condition, Red = Poor condition.
For the first part of this lab, we collected public safety data using ArcGIS Field Maps and categorized each of our features' condition as Excellent, Fair, or Poor. I collected data for 10 signs at entrances to three subway stations in Queens and Brooklyn: the Myrtle-Wyckoff L and M station, the Halsey L station, and the Wilson L station. The majority of these entrances were stairs to underground stations and had back-to-back signs on the fence around the stairs. For these, I chose to evaluate only the sign directly above the stairs. Three of the signs were at ground-level station entrances.
Like most non-New Yorkers, I have definitely been confused by subway entrances in the past. However, after living here for a month, I am (usually) confident about the entrances near me and don't rely on the signs as much, so I was interested to see what I would notice if I really looked at them. I was surprised that I rated all four of the entrances to Halsey, my nearest station, as Poor. All of the Halsey signs had traces of spray paint and letters that were missing or eroded, indicating that the signs should probably be replaced. One of the Halsey entrances didn't even have any signs! I rated that one as Poor too, but I think if I designed my own evaluation system, a missing sign might have its own category.
Unfortunately, when I was collecting my data, I wasn’t able to get Field Maps' required accuracy of 30 ft at any of these entrances. Sometimes the range would fluctuate wildly while I stood there, but it would never be under 30 ft, and I'm not sure why. After I collected my data, I used a satellite basemap to find the subway entrances and manually drag each pin as close as I could to their true locations.
Other than that issue, I found collecting the data with ArcGIS Field Maps to be very easy and fun.
This sign for the Canarsie-bound L at Halsey St Station is definitely in Poor condition!
Projections
For the second part of the lab, we explored different map projections and how they impact the calculated areas of four counties in Florida. I knew almost nothing about map projections before starting this lab, so I feel like I learned a lot. I was surprised at how different the counties' areas were between the three projections. I never realized that area would be different between different map projections! It made me wonder which projection is used to calculate area in non-GIS contexts, such as in a list of facts about countries or states.
A comparison of four Florida counties (Alachua, Escambia, Miami-Dade, and Polk) and their areas between the Albers, UTM 16 N, and State Plane N map projections.
It was interesting to realize that different map projections serve different purposes and are suitable for different geographic locations and sizes. For example, there's nothing universal about Universal Transverse Mercator projections. The UTM 16 N projection in particular is not ideal for the state of Florida because half of Florida is in the 17 N zone, so the counties in eastern Florida are distorted and their areas are not accurate. Fortunately, State Plane N projections are designed specifically for each US state, making this one a reliable choice to map just the state of Florida. I can't see the differences between the three projections in the maps, but the table of areas makes their differences clear. I wonder if there's any significant difference between the distances and directions in these projections that would make the scale bar and north arrow not accurate for all of them?
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