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- handle: 10670/1.s4oqkp
- halshs: halshs-03231786
info:eu-repo/semantics/OpenAccess
Mots-clés
Urban economics History Machine learning 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 AccessSujets proches
Economics of cities City economics Cities and towns--Economic aspects Learning, MachineCiter ce document
Pierre-Philippe Combes et al., « Urban economics in a historical perspective: Recovering data with machine learning », HAL-SHS : économie et finance, ID : 10670/1.s4oqkp
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Résumé
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 However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered.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and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents.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.