Previous studies have highlighted the importance of having long term data for the study of cities, but such sources are relatively scarce. This is especially the case for data about relations between cities, which is a crucial aspect of urban dynamics. Over the last two decades, many efforts have been made to digitalize texts, including books and newspapers, which are primary sources on most of our societies. Researchers have shown that these massive digital archives can be used to identify macroscopic trends related to historical and cultural changes. The wealth of geographic information in such digital archives has not been used much, while they are very valuable for the study of cities. In this paper, we present DIGGER, a newly developed dataset that we built on Delpher, the digital archive of historical newspapers of the National Library of the Netherlands, by extracting geographical information from a selection of 102 million of news items. This dataset allowed us to study the spatial diffusion of information on and between the Dutch cities from a corpus of 81 newspapers published in 29 different cities between 1869 and 1994. This paper presents the method developed to build the dataset as well as the validation steps for the accuracy of the place name recognition. This dataset can be used to study the evolution of the Dutch urban system as well as aspects related to the spatial diffusion of information and geographical bias in media coverage.

While there is consensus that network embeddedness of cities is of great importance for their development, the precise effect is difficult to assess because of a lack of consistent information on relations between cities. This paper presents, applies and evaluates a rather novel method to establish the strength of relationships between places, a method we refer to as ‘the toponym co-occurrence method’. This approach builds the urban system on the basis of co-occurrences of place names in a text corpus. We innovate by exploiting a so far unparalleled amount of data, namely the billions of web pages contained in the commoncrawl web archive, and by applying the method also to small places that tend to be ignored by other methods. The entire settlement system of the Netherlands is consequently explored. In addition, we innovatively apply machine learning techniques to classify these relations. Much attention is paid to solving biases deriving from place name disambiguation. Gravity modelling is employed to assess the resulting spatial organization of the Netherlands. It turns out that the gravity model fits very well with the pattern of relationships between places as found in digital space, which contributes to our assessment that the toponym co-occurrence method is a solid proxy for relationships in real space. Using the method, it is established that the relationships in the Randstad region, by many considered a coherent metropolitan entity, are actually somewhat less strong than expected. In contrast, historically important, but nowadays small cities in the periphery tend to have maintained their prominent position in the pattern of relationships. Suburban, relatively new places in the shadow of a larger city tend to be weakly related to other places. Several suggestions to further improve the method, in particular the classification of relationships, are discussed.

The study of relations between cities has long been a major focus in urban research. For decades, this field has grown integrating contributions from many disciplines. But today, the field appears rather fragmented. This study analyses the body of literature that has developed over the last 23 years to identify schools of thought on interurban relationships and to see to what extent these interact with each other. It does so by innovatively employing bibliometric analysis to the study of systems of cities, which allows a bottom-up identification of five schools of thought: one predominantly focusing on the regional or intra-metropolitan scale and centred on concepts of polycentricity; one addressing the global scale with a focus on world city networks; one employing simulation and complexity theories to understand behaviour of agents building the urban system bottom-up; one rooted in (new) economic geography and focusing on growth and decline in the urban system; and, one seeking regularities with respect to city size distributions. The conceptual, methodological and empirical aspects of these different schools are discussed by means of a ‘semantic map’ derived from the vocabulary of titles and abstracts of papers. The coupling of the semantic map with the citation networks of these schools of thought confirms the increasing fragmentation of the field over the last decades. However, in the most recent years, the different schools of thought start to interact slightly more. The desirability and feasibility of a move from multidisciplinarity to interdisciplinarity in urban systems research needs further exploration.