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However, we rely on [[https://www.python.org/ python]] scripts to retrieve addresses from Google Maps, as well as compute the [https://en.wikipedia.org/wiki/Hierarchical_clustering Hierarchical Cluster Analysis (HCA)] itself, and estimate a cubic to determine the HCA-regression method agglomeration count for an [https://en.wikipedia.org/wiki/Metropolitan_statistical_area MSA]. We also use two [https://www.stata.com/ Stata] scripts: one to compute the HCA-regressions, and another to estimate the paper's summary statistics and regression specifications. Finally, we use QGIS to construct the map images based on queries to our database. These images use a [https://maps.google.com Google Maps] base layer.

The script [[:File:Agglomeration_CBSA.sql.pdf|Agglomeration_CBSA.sql]] provides the processing steps within the PostgreSQL database. We first load the startup data, add in the longitudes and latitudes, and combine them with the [[https://en.wikipedia.org/wiki/Core-based_statistical_area CBSA]] boundaries. Startups in our data our keyed by a triple (coname, statecode, datefirstinv) as two different companies can have the same names in different states, or within the same state at two different times.