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Urban Start-up Agglomeration and Venture Capital Investment (view source)
Revision as of 11:25, 16 December 2020
, 11:25, 16 December 2020→New Work
A new version, written by Jim, is in the works!
=Submission= A revised version of the paper, now co-authored with [[Jim Brander]], was submitted to the Journal of Economic Geography. The paper is now titled: '''A New WorkMethod for Identifying and Delineating Spatial Agglomerations with Application to Clusters of Venture-Backed Startups'''. Files:*Pdf: [[Egan Brander (2020) - A New Method for Identifying and Delineating Spatial Agglomerations (Submitted to JEG).pdf]]*In E:\projects\agglomeration**Last document was Agglomeration Dec 15.docx**Build is Version 3-6-2-2. ==Notes for further improvement== We might want to add some things in/back in. These include technical notes:*To do the HCA we used the AgglomerativeClustering method from the sklearn.cluster library (version 0.20.1) in python 3.7.1, with Ward linkage and connectivity set to none. This method is documented here: https://scikit-learn.org/stable/modules/clustering.html. I checked some of the early results against an implementation of Ward's method using the agnes function, available through the cluster package, in R. https://www.rdocumentation.org/packages/cluster/versions/2.1.0/topics/agnes*The data was assembled and processed in a Postgresql (version 10) database using PostGIS (version 2.4). We used World Geodetic System revision 84, known as WGS1984 (see https://en.wikipedia.org/wiki/World_Geodetic_System), as a coordinate system with an ellipsoidal earth, to calculate distances and areas (see https://postgis.net/docs/manual-2.4/using_postgis_dbmanagement.html). Shapefiles for Census Places were retrieved from the U.S. Census TIGER (Topologically Integrated Geographic Encoding and Referencing) database (see https://www.census.gov/programs-surveys/geography.html).*The statistical analysis was done in STATA/MP version 15.*All maps were made using QGIS v3.8.3. The base map is from Google Maps. City areas are highlighted using U.S. Census TIGER/Line Shapefiles. The methodology has other applications:*Food deserts - one could study the agglomerations of restaurants and other food providers in urban environments.*Airports, cement factories, banana plantations, police/fire stations, hospitals/drug stores, etc.*We could think about commercial applications. Perhaps locating plants/facilities that are/aren't in clusters with a view to buying or selling them? =Version 3 Rebuilt=
===Another round of refinements===
