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Visualising Spatial Dispersion in Cultural Heritage Data

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Опубліковано в::ISPRS International Journal of Geo-Information vol. 14, no. 7 (2025), p. 267-292
Автор: Laya, Targa
Інші автори: Villuendas Esperanza, Portalés Cristina, Sebastián, Jorge
Опубліковано:
MDPI AG
Предмети:
Accuracy
Scientific visualization
Museums
Linked Data
Datasets
Culture
Ontology
Public access
Dispersion
Spatial data
Cultural resources
Heterogeneity
Representations
Case studies
Data science
Collections
Information processing
Digital humanities
Qualitative research
Digitization
Visualization
Cultural heritage
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Короткий огляд:The digitisation of cultural heritage has transformed how GLAM (Galleries, Libraries, Archives and Museums) institutions manage and share collections. Digital catalogues are indispensable for documenting and granting public access to cultural assets. However, integrating spatial data remains challenging due to the ambiguity, uncertainty, granularity, and heterogeneity of historical data. This study addresses these issues through a case study on the Museo de América’s “Place of Provenance” data, proposing a methodology for data cleaning and evaluating geocoding accuracy using Nominatim, ArcGIS, and GeoNames APIs. We assess these APIs by quantifying geocoding errors through a “balance sheet” method, identifying instances of over-representation, under-representation, or neutral results for geographical regions. The effectiveness of each API is analysed using confusion matrices and interactive cartograms, offering insights into misallocations. Our findings reveal varying accuracy among the APIs in processing heterogeneous historical spatial data. Nominatim achieved a 40.91% neutral result in correctly geocoding countries, underscoring challenges in spatial data representation. This research provides valuable methodological experiences and insights for researchers and GLAM institutions working with cultural heritage datasets. By enhancing spatial dispersion visualisation, this work contributes to understanding cultural circulations and historical patterns. This interdisciplinary work was developed as part of the ClioViz project, integrating Data Science, data Visualisation, and art history.
ISSN:2220-9964
DOI:10.3390/ijgi14070267
Джерело:Advanced Technologies & Aerospace Database