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Characterizing the Heterogeneity of the OpenStreetMap Data and Community
Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad, GIS.
Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för bygg- energi- och miljöteknik, Energisystem.
Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Samhällsbyggnad, GIS.ORCID-id: 0000-0002-2337-2486
2015 (engelsk)Inngår i: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 4, nr 2, s. 535-550Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

OpenStreetMap (OSM) constitutes an unprecedented, free, geographical information source contributed by millions of individuals, resulting in a database of great volume and heterogeneity. In this study, we characterize the heterogeneity of the entire OSM database and historical archive in the context of big data. We consider all users, geographic elements and user contributions from an eight-year data archive, at a size of 692 GB. We rely on some nonlinear methods such as power law statistics and head/tail breaks to uncover and illustrate the underlying scaling properties. All three aspects (users, elements, and contributions) demonstrate striking power laws or heavy-tailed distributions. The heavy-tailed distributions imply that there are far more small elements than large ones, far more inactive users than active ones, and far more lightly edited elements than heavy-edited ones. Furthermore, about 500 users in the core group of the OSM are highly networked in terms of collaboration.

sted, utgiver, år, opplag, sider
2015. Vol. 4, nr 2, s. 535-550
Emneord [en]
OpenStreetMap, big data, power laws, head/tail breaks, ht-index
HSV kategori
Identifikatorer
URN: urn:nbn:se:hig:diva-20223DOI: 10.3390/ijgi4020535ISI: 000358987600006Scopus ID: 2-s2.0-84948967039OAI: oai:DiVA.org:hig-20223DiVA, id: diva2:852398
Tilgjengelig fra: 2015-09-09 Laget: 2015-09-09 Sist oppdatert: 2018-12-03bibliografisk kontrollert
Inngår i avhandling
1. Topological and Scaling Analysis of Geospatial Big Data
Åpne denne publikasjonen i ny fane eller vindu >>Topological and Scaling Analysis of Geospatial Big Data
2018 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Geographic information science and systems face challenges related to understanding the instinctive heterogeneity of geographic space, since conventional geospatial analysis is mainly founded on Euclidean geometry and Gaussian statistics. This thesis adopts a new paradigm, based on fractal geometry and Paretian statistics for geospatial analysis. The thesis relies on the third definition of fractal geometry: A set or pattern is fractal if the scaling of far more small things than large ones recurs multiple times. Therefore, the terms fractal and scaling are used interchangeably in this thesis. The new definition of fractal is well-described by Paretian statistics, which is mathematically defined as heavy-tailed distributions. The topology of geographic features is the key prerequisite that enables us to see the fractal or scaling structure of the geographic space. In this thesis, topology refers to the relationship among meaningful geographic features (such as natural streets and natural cities).

The thesis conducts topological and scaling analyses of geographic space and its involved human activities in the context of geospatial big data. The thesis utilizes the massive, volunteered, geographic information coming from LBSM platforms, which are the global OpenStreetMap database and countrywide, geo-referenced tweets and check-in locations. The thesis develops geospatial big-data processing and modeling techniques, and employs complexity science methods, including heavy-tailed distribution detection and head/tail breaks, along with some complex network analysis. Head/tail breaks and the induced ht-index are a powerful tool for geospatial big-data analytics and visualization. The derived scaling hierarchies, power-law metrics, and network measures provide quantitative insights into the heterogeneity of geographic space and help us understand how it shapes human activities at city, country, and world scales. 

sted, utgiver, år, opplag, sider
Gävle: Gävle University Press, 2018. s. 73
Serie
Studies in the Research Profile Built Environment. Doctoral thesis ; 7
Emneord
Third definition of fractal, scaling, topology, power law, head/tail breaks, ht-index, complex network, geospatial big data, natural cities, natural streets
HSV kategori
Identifikatorer
urn:nbn:se:hig:diva-26197 (URN)978-91-88145-24-6 (ISBN)978-91-88145-25-3 (ISBN)
Disputas
2018-05-16, Lilla Jadwiga-salen, Kungsbäcksvägen 47, Gävle, 10:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-04-24 Laget: 2018-03-04 Sist oppdatert: 2018-04-25

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