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Urban Start-up Agglomeration and Venture Capital Investment (view source)
Revision as of 19:03, 16 November 2020
, 19:03, 16 November 2020→Heuristic Layer
I also calculated an '''inflectionlayer''' (as opposed to the heurflhlayer, where flh stands for fraction of locations in hulls, described above). This inflectionlayer is '''the first time''' that the second central difference in the '''share of startups in economic clusters''' switches sign. It is only possible to calculate this when there are at least 4 data points, as the central difference requires data from layer-1, layer and layer+1, and we need two central differences. The variable is included in dataset (and so do files, etc.) version 3-4 forwards.
However, the inflectionlayer is really meaningless. The sign of the second central switches back and forward due to integer effects and I can't find a straight forward algorithm to pick the "correct" candidate from the set of results. Picking the '''first one''', which I currently pick, is completely arbitrary. There are a bunch of examples of the curves and the issue(s) in Results3-34.xlsx sheet 'Inflection'. I expect that if I put a bunch of time into this I could come up with some change thresholds to rule candidate answers in or out, but even then this isn't a good method. The individual curves are just way too noisy. Using the heuristic result above solve this noise problem.
One complaint made about the heuristic results is that it is near the middle (i.e., it's 48.7717%, which happens to be near 50%). Although the nature of any HCA on geographic coords implies that the result is unlikely to the close to the bounds (0 or 100%) and more likely to be near the middle (50%), it could be in an entirely different place. '''This result (48.7717%) characterizes the agglomeration of venture-backed startup firms'''. You'd get a very different number if you studied gas stations, supermarkets, airports, or banana plantations!
