In this paper, we present our work on semantic deep mapping at scale by combining information from various sources and disciplines to study historical Amsterdam. We model our data according to semantic web standards and ground them in space and time such that we can investigate what happened at a particular time and place from a linguistics, socio-economic and urban historical perspective. In a small use case we test the spatio-temporal infrastructure for research on entertainment culture in Amsterdam around the turn of the 20th century. We explain the bottlenecks we encountered for integrating information from different disciplines and sources and how we resolved or worked around them. Finally, we present a set of recommendations and best practices for adapting semantic deep mapping to other settings.@en

Over the last two decades, a growing number of databases have been published online that record historical information on the production, distribution and reception of performing arts. The aim of this contribution is to present a starting inventory of European performing arts databases that are available online, since no such overview exists to date.@en

In this article, we demonstrate and argue that one way to acquire a better sense of cinemagoing in the silent film era is to investigate the relations between cinema locations, the socio-economic and demographic profile of their surroundings, and film programming. Driven by the centrality of space as one of the defining traits of new cinema history, we operationalise this inquiry through a data-driven toolkit of interconnected scalable approaches, in order to establish a multilayered contextualisation of Amsterdam’s early cinema landscape. We analyse Amsterdam’s historical cinema market both on the meso level of the city’s overall surroundings and on the micro level of two neighbouring film venues within a specific urban district. By switching between different levels of scale to analyse the cinemas’ programming profiles, we highlight the venues’ positioning within the socio-spatial structure of the city and their neighbourhood community in particular. In the future, the multifaceted analytical exploration enabled by our digital toolkit can be further enhanced by an increased availability of more fine-grained archival and contextual data.@en