A visual data analysis for determining the geographical extent of the cabreves

Autor: Benito Manuel Zaragozí Zaragozí, Pablo Gimenez-Font
Přispěvatelé: Universidad de Alicante. Departamento de Análisis Geográfico Regional y Geografía Física, Universidad de Alicante. Instituto Interuniversitario de Geografía, Medio, Sociedad y Paisaje (MedSPai)
Rok vydání: 2021
Předmět:
Zdroj: RUA. Repositorio Institucional de la Universidad de Alicante
Universidad de Alicante (UA)
ISSN: 1296-2074
Popis: The historical cadastral archives are an important source of information to help understand our cultural heritage since they contain a trace of the activities, land uses, and buildings developed by people from different periods. However, in the era of Big Data there remain many historical documents of great value that have not been digitized or studied in depth. This is the case of the cabreves, which are precatastral documents used for centuries in several regions of Spain to document those properties that were subject to the payment of taxes to a feudal lord. Rescuing these data would enable studying the landscape structure of relatively recent dates for which there is no cadastral cartography. However, it is difficult to establish the state of conservation, degree of accessibility, content detail, and quality of the archived cabreves. In recent years, progress has been made in digitizing these sources. In Spain, the Spanish Archives Portal (PARES) harmonizes and unifies the efforts of national archives, and a significant number of documents have been archived in recent years. We use text mining techniques to analyze and map the records in which cabreves appear. Out of the 1752 records found, a total of 1408 cabreves have been geocoded and mapped, enabling us to establish which territories and periods can be studied using these sources. From this experience, we request that digital archives maintain a geographical perspective during archival appraisal. This work was supported by grants from the Spanish Ministry of Economy and Competitiveness, project SIOSE-INNOVA (CSO2016-79420-R AEI/FEDER UE).
Databáze: OpenAIRE