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 However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered.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and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents.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.

最近的文献使用历史视角来更好地理解城市经济学的基本问题。然而，大量高质量的历史文件仍未得到充分利用：它们的使用受到原始格式或需要恢复的大量信息的阻碍。在本文中，我们描述了机器学习的灵活性和预测能力如何以及何时可以帮助研究人员利用这些历史文献的潜力。我们首先讨论城市经济学的重要问题如何依赖于对历史数据源的分析以及与此类数据的转录和协调相关的挑战。然后我们解释机器学习方法如何解决其中一些挑战，并讨论可能的应用。