Projection Distances

Total measured distances from Washington, D.C. and Kabul, Afghanistan for the 5 different types of map projections shown above:

GCS WGS 1984- (geodesic) 6954.5 miles
Mercator- (planar) 10,112.12 miles
Azimuthal Equidistant- (planar) 8,341.4 miles
Behrmann Equal Area- (planar) 8,763.1 miles
Polyconic- (planar) 7,346.56 miles

Our assignment this week was to take data concerning the geodesic locations of the major cities in the world and locate them on a map using several different types of projections. More specifically we were required to analyze the distance between Washington and Kabul through the various projection styles. The goal was to use several different measuring techniques to show variation and or consistency with the measuring styles. Unfortunately I was unable to get the type of measuring style to change so was only able to report data in the planar measurements.

As can be seen in the above map I chose to put the initial main map with all the data points assigned, note Washington and Kabul called out in blue. From there I chose the mercator, azimuthal, behrmann and polyconic projection styles. As you can see from the recorded data the type of projection changed the measured distance between these two destinations substantially. Here in lies the rub of this capability on ArcGIS, if you are not aware that the measured distance can be variable with the type of projection chosen data you are attempting to extract can be skewed. Although I imagine another style of measurement other than planar may give you more consistent data.

As for the potential of the projections, one is capable of understanding the type of distortion being created under each style and compensating for that in their reported conclusions. You can essentially set the world up into any orientation, whichever best suits the type of reporting one is trying to do. Also the ease at which distances can be measured is quite nice, essentially no measuring has to occur, strictly point and click and record data. As I am learning more about the program it has become possible to see the vast potential one can exploit in delivering data.