Format du dépôt |
Fichier |
Type de dépôt |
Pré-publication, Document de travail |
Titre |
en
Urban economics in a historical perspective: Recovering data with machine learning
|
Résumé |
en
A recent literature has used a historical perspective to better understand fundamental questions of urban economics. However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered. In this paper, we describe how and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents. We first discuss how important questions of urban economics rely on the analysis of historical data sources and the challenges associated with transcription and harmonisation of such data. We then explain how machine learning approaches may address some of these challenges and we discuss possible applications.
|
Auteur(s)
|
Pierre-Philippe Combes
1, 2
, Laurent Gobillon
3, 4
, Yanos Zylberberg
5
1
Institut d'Études Politiques [IEP] - Paris
( 300161 )
-
- France
2
CNRS -
Centre National de la Recherche Scientifique
( 441569 )
- France
3
PSE -
Paris School of Economics
( 301309 )
- 48 boulevard Jourdan 75014 Paris
- France
-
Université Paris 1 Panthéon-Sorbonne ( 7550 )
;
-
École normale supérieure - Paris ( 59704 )
;
-
Université Paris Sciences et Lettres ( 564132 )
;
-
École des hautes études en sciences sociales ( 99539 )
;
-
École des Ponts ParisTech ( 301545 )
;
-
Centre National de la Recherche Scientifique ( 441569 )
;
-
Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement ( 577435 )
4
PJSE -
Paris Jourdan Sciences Economiques
( 578027 )
- 48 boulevard Jourdan 75014 Paris
- France
-
Université Paris 1 Panthéon-Sorbonne UMR8545 ( 7550 )
;
-
École normale supérieure - Paris ( 59704 )
;
-
Université Paris Sciences et Lettres ( 564132 )
;
-
École des hautes études en sciences sociales ( 99539 )
;
-
École des Ponts ParisTech ( 301545 )
;
-
Centre National de la Recherche Scientifique ( 441569 )
;
-
Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement UMR1393 ( 577435 )
5
University of Bristol [Bristol]
( 220393 )
- Senate House, Tyndall Avenue, Bristol BS8 1TH
- Royaume-Uni
|
Langue du document |
Anglais
|
Date de production/écriture |
2021-05
|
Page/Identifiant |
30 p.
|
Public visé |
Scientifique
|
Domaine(s) |
-
Sciences de l'Homme et Société/Economies et finances
|
Mots-clés (JEL) |
-
R - Urban, Rural, Regional, Real Estate, and Transportation Economics/R.R1 - General Regional Economics/R.R1.R11 - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
-
R - Urban, Rural, Regional, Real Estate, and Transportation Economics/R.R1 - General Regional Economics/R.R1.R12 - Size and Spatial Distributions of Regional Economic Activity
-
R - Urban, Rural, Regional, Real Estate, and Transportation Economics/R.R1 - General Regional Economics/R.R1.R14 - Land Use Patterns
-
N - Economic History/N.N9 - Regional and Urban History/N.N9.N90 - General, International, or Comparative
-
C - Mathematical and Quantitative Methods/C.C4 - Econometric and Statistical Methods: Special Topics/C.C4.C45 - Neural Networks and Related Topics
-
C - Mathematical and Quantitative Methods/C.C8 - Data Collection and Data Estimation Methodology • Computer Programs/C.C8.C81 - Methodology for Collecting, Estimating, and Organizing Microeconomic Data • Data Access
|
Référence interne |
-
PSE Working Papers n°2021-33
|
Projet(s) ANR |
|
Mots-clés |
en
Urban economics, History, Machine learning
|