Exploring spatio-temporal patterns of OpenStreetMap (OSM) contributions in heterogeneous urban areas



Título del documento: Exploring spatio-temporal patterns of OpenStreetMap (OSM) contributions in heterogeneous urban areas
Revista: Boletim de ciencias geodesicas
Base de datos: PERIÓDICA
Número de sistema: 000456634
ISSN: 1413-4853
Autores: 1
1
1
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Instituciones: 1Universidade Federal do Paraná, Programa de Pós-Graduação em Ciências Geodésicas, Curitiba, Paraná. Brasil
2Universidade do Estado do Rio de Janeiro, Departamento de Engenharia Cartográfica, Rio de Janeiro. Brasil
Año:
Volumen: 29
Número: 2
País: Brasil
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Analítico, descriptivo
Resumen en inglés The potential of intrinsic parameters to estimate geospatial data quality on Voluntary Geographic Information (VGI) platforms is a recurrent theme in Cartography. The spatial-temporal distribution in these platforms is very heterogeneous, depending on several factors such as input availability, number, and motivation of volunteers, especially in developing countries. The most recent approaches have been aiming to detail temporal patterns as an additional measure of quality in VGI. This research proposes a methodology to identify and analyze the behavior of the contribution parameters over time (2007-2022) of the OSM platform and differentiates the influences that affect its growth. Part of the Metropolitan region of Curitiba was the study area, subdivided into 1 x 1 km cells. The cumulative growth of contributions was calculated and later adjusted using a Logistic Regression. The obtained parameters made it possible to identify abruptly growing cells caused by external data import, mass contributions, or collective mapping activities. In addition, heterogeneity in the growth of the data available in OSM over time was evident. Furthermore, the proposed methodology promoted the investigation of a new indicator of intrinsic quality based on modelling the spatiotemporal evolution of OSM feature insertions
Disciplinas: Geociencias,
Geografía
Palabras clave: Geodesia,
Cartografía,
Calidad de datos geoespaciales,
Mapas colaborativos,
Parámetros intrínsecos,
Regresión logística
Keyword: Geodesy,
Cartography,
Geospatial Data Quality,
Collaborative Maps,
Intrinsic Parameters,
Logistic Regression
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