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This article reports on the 4th ACM SIGSPATIAL Workshop on Geospatial Humanities, held in conjunction with the 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. The article outlines the objectives of the workshop, and briefly describes the technical program.

This article reports on the 3rd ACM SIGSPATIAL Workshop on Geospatial Humanities, held in conjunction with the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. The article outlines the objectives of the workshop, and briefly describes the technical program.

Scholars in the humanities have a long tradition in the investigation of spatial theory and methods, as well as on the analysis of space and place within its multiple disciplines. Although technolo...

The field of Spatial Humanities has advanced substantially in the past years. The identification and extraction of toponyms and spatial information mentioned in historical text collections has allowed its use in innovative ways, making possible the application of spatial analysis and the mapping of these places with Geographic Information Systems. For instance, automated place name identification is nowadays possible with Named Entity Recognition (NER) systems. Statistical NER methods based on supervised learning, in particular, are highly successful with modern datasets. However, there are still major challenges to address when dealing with historical corpora. These challenges include language changes over time, spelling variations, transliterations, OCR errors, and sources written in multiple languages among others. In this article, considering a task of place name recognition over two collections of historical correspondence, we report an evaluation of five NER systems and an approach that combines these through a voting system. We found that although individual performance of each NER system was corpus dependent, the ensemble combination was able to achieve consistent measures of precision and recall, outperforming the individual NER systems. Additionally, the results showed that these NER system are not strongly dependent on pre-processing and translation to modern English.

Several tasks related to geographical information retrieval and to the geographical information sciences involve toponym matching, that is, the problem of matching place names that share a common referent. In this article, we present the results of a wide-ranging evaluation on the performance of different string similarity metrics over the toponym matching task. We also report on experiments involving the usage of supervised machine learning for combining multiple similarity metrics, which has the natural advantage of avoiding the manual tuning of similarity thresholds. Experiments with a very large dataset show that the performance differences for the individual similarity metrics are relatively small, and that carefully tuning the similarity threshold is important for achieving good results. The methods based on supervised machine learning, particularly when considering ensembles of decision trees, can achieve good results on this task, significantly outperforming the individual similarity metrics.

This article reports on the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities, held in conjunction with the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. The article outlines the objectives of the workshop, and briefly describes the technical program.

High-resolution population grids built from historical census data can ease the analyses ofgeographical population changes, at the same time also facilitating the combination of populationdata with other GIS layers to perform analyses on a wide range of topics. This article reports onexperiments with a hybrid spatial disaggregation technique that combines the ideas of dasymetricmapping and pycnophylactic interpolation, using modern machine learning methods to combinedifferent types of ancillary variables, in order to disaggregate historical census data into a 200 mresolution grid. We specifically report on experiments related to the disaggregation of historicalpopulation counts from three different national censuses which took place around 1900, respectively inGreat Britain, Belgium, and the Netherlands. The obtained results indicate that the proposed methodis indeed highly accurate, outperforming simpler disaggregation schemes based on mass-preservingareal weighting or pycnophylactic interpolation. The best results were obtained using modernregression methods (i.e., gradient tree boosting or convolutional neural networks, depending on thecase study), which previously have only seldom been used for spatial disaggregation.

In order to better support the text mining of historical texts, we propose a combination of complementary techniques from Geographical Information Systems, computational and corpus linguistics. In previous work, we have described this as `visual gisting' to extract important themes from text and locate those themes on a map representing geographical information contained in the text. Here, we describe the steps that were found necessary to apply standard analysis and resolution tools to identify place names in a specific corpus of historical texts. This task is seen as an initial and prerequisite step for further analysis and comparison by combining the information we extract from a corpus with information from other sources, including other text corpora. The process is intended to support close reading of historical texts on a much larger scale by highlighting using exploratory and data-driven approaches which parts of the corpus warrant further close analysis. Our case study presented here is from a corpus of Lake District travel literature. We discuss the customisations that we have to make to existing tools to extract placename information and visualise it on a map.

Drawing on the expertise of leading researchers from around the globe, this pioneering collection of essays explores how geospatial technologies are revolutionizing the discipline of literary studies. The book offers the first intensive examination of digital literary cartography, a field whose recent and rapid development has yet to be coherently analysed. This collection not only provides an authoritative account of the current state of the field, but also informs a new generation of digital humanities scholars about the critical and creative potentials of digital literary mapping. The book showcases the work of exemplary literary mapping projects and provides the reader with an overview of the tools, techniques and methods those projects employ.