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==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 11:24, 10 August 2022
, 11:24, 10 August 2022no edit summary
This paper is published as:
[[Delineating Spatial Agglomerations|Egan, Edward J. and James A. Brander (2022), "New Method for Identifying and Delineating Spatial Agglomerations with Application to Clusters of Venture-Backed Startups.", Journal of Economic Geography, Manuscript: JOEG-2020-449.R2, forthcoming.]]
{{AcademicPaper
|Has title=Urban Start-up Agglomeration and Venture Capital Investment
|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 was accepted to the Journal of Economic Geography:
* Manuscript ID JOEG-2020-449.R2
* Title: A New Method for Identifying and Delineating Spatial Agglomerations with Application to Clusters of Venture-Backed Startups
* Author(s): Edward J. Egan and James A. Brander.
* Editor: Bill Kerr, HBS: wkerr@hbs.edu
* Abstract:
This paper advances a new approach using hierarchical cluster analysis (HCA) for identifying and
delineating spatial agglomerations and applies it to venture-backed startups. HCA identifies
nested clusters at varying aggregation levels. We describe two methods for selecting a
particular aggregation level and the associated agglomerations. The “elbow method” relies
entirely on geographic information. Our preferred method, the “regression method”, also uses
venture capital investment data and identifies finer agglomerations, often the size of a small
neighborhood. We use heat maps to illustrate how agglomerations evolve and indicate how our
methods can aid in evaluating agglomeration support policies.
* Permanent link for code/data: https://www.edegan.com/wiki/Delineating_Spatial_Agglomerations
The paper is now in production. I will build a wiki page called [[Delineating_Spatial_Agglomerations]] that structures the documentation of the build process and shares code and some data or artifacts. Currently, that page redirects here.
== 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)=
=Old Work Using Circles=
See: [[Enclosing Circle Algorithm]]
==Very Old Summary==
