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 However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered.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and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents.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.
|Regional Science and Urban Economics
|Early online date
|13 Jul 2021
|E-pub ahead of print - 13 Jul 2021
Bibliographical noteFunding Information:
The authors have been funded with research grants only: ORA grant ES/V013602/1 (MAPHIS: Mapping History) and EUR grant ANR-17-EURE-0001 . There is thus no conflict of interest.
We are grateful to Clément Gorin for very helpful discussions, as well as to Gilles Duranton and two anonymous reviewers for useful comments. The authors acknowledge the support of the ANR/ESRC/ SSHRC , through the ORA grant ES/V013602/1 (MAPHIS: Mapping History). Laurent Gobillon acknowledges the support of the EUR grant ANR-17-EURE-0001 .
© 2021 Elsevier B.V.