7,650
edits
Changes
Jump to navigation
Jump to search
==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?
== R&R ==
Urban Start-up Agglomeration and Venture Capital Investment (view source)
Revision as of 12:33, 10 July 2022
, 12:33, 10 July 2022no edit summary
|Has paper status=Published
}}
=New Submission=
A revised version of the paper, now co-authored with [[Jim Brander]] and based on the version 3 rebuild, was submitted to the Journal of Economic Geography. This is solely a methods paper, and is titled: '''A New Method for Identifying and Delineating Spatial Agglomerations with Application to Clusters of Venture-Backed Startups'''. The policy application would need to be written up as a separate paper.
==Acceptance==
On July 5th 2022, the paper w
* Manuscript ID JOEG-2020-449.R2
== R&R ==
Files:
** Build is Version 3-6-2-2.
** SQL file is: AgglomerationVcdb4.sql
After some inquiries, we heard from Bill Kerr, the associate editor, that the paper had new reviews on Aug 11th. On Aug 23rd, we recieved an email titled "Journal of Economic Geography - Decision on Manuscript ID JOEG-2020-449" giving us an R&R. Overall, the R&R is very positive.
<pdf>File:JOEG1RndReviews.pdf</pdf>
===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?
=SSRN version of the paper (uses v2 build)=
