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Urban Start-up Agglomeration and Venture Capital Investment (view source)
Revision as of 14:53, 3 September 2019
, 14:53, 3 September 2019→Approach
For us:
#Discarding outliers
#Elbow on fraction of points locations in hulls
#Chosen by the researcher
#Maximum R-Squared
*Elbow on fraction of maximum hull area in hulls
====Discarding Outliers==== We don't need to discard outliers, per se, just find a layer where outliers are singletons. One way to do this is to take the highest layer with a single hull (or two hulls or three hulls, etc.) If a layer never has a hull, then presumably it only has a single location or a line of locations (note that it is possible for a line to have more than 2 locations both because of multitons and because of perfect alignment) ====Elbow on fraction of points locations in hulls====
[[File:Elbowdataresult.png|right]] The '''elbowcalc''' and '''elbowdata''' queries provide the data. '''elbowdata''' takes layer/finallayer (i.e., fraction unclustered, as the layer 1 is the all encompassing hull and final layer is the raw locations), rounds it to two digits, and then calculates the average fraction of locations in hulls and the average hull area fraction of all encompassing hull area. The former gives a nice curve with an elbow (found by taking the second derivative and setting it equal to zero) at x=0.40237.
We then identify the layer that is closest to having a fraction of locations in hulls of 0.40237, taking the lower level (i.e., the more clustered level) whenever there is a tie. The resulting indicator variable is called '''elbowflhlayer''' and is made in table '''Elbowflh'''.
===Image Analysis===
