Understanding the potential relationship between the socio-economic variables and contributions to OpenStreetMap
OpenStreetMap (OSM) has seen an exponential increase in the last few years and large volumes of geodata have been received from volunteered individuals. The collected geodata are heterogeneous in terms of different dimensions such as spatial patterns of contributions, quality, patterns of contributi...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2015
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| In: |
International journal of digital earth
Year: 2014, Jahrgang: 8, Heft: 11, Pages: 861-876 |
| ISSN: | 1753-8955 |
| DOI: | 10.1080/17538947.2014.951081 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1080/17538947.2014.951081
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| Verfasserangaben: | J. Jokar Arsanjani, Mohammed Bakillah |
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| 520 | |a OpenStreetMap (OSM) has seen an exponential increase in the last few years and large volumes of geodata have been received from volunteered individuals. The collected geodata are heterogeneous in terms of different dimensions such as spatial patterns of contributions, quality, patterns of contributing individuals, and type of contributions. Because contributors’ personal information is anonymously stored by the OSM administrators, alternative methods are needed to investigate the role of contributors’ characteristics on their mapping behavior. This study is intended to explore the potential socio-economic characteristics of contributors in highly contributed areas to have better insights about the latent patterns of involved individuals in a highly dynamic state of the most active country in OSM, Germany. A logistic regression model (LRM) is applied to discover the potential correlations between dependent and independent variables. The findings explain that the areas with high population density, middle level of education, high income, high rate of overnight stays, high number of foreigners, and residents aged from 18 to 69 are more likely to be involved in OSM. Furthermore, the degree of dynamism in OSM is a function of proximity to built-up areas. Finally, concluding remarks concerning the independent variables and model sensitivity are presented. | ||
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| 650 | 4 | |a logistic regression analysis | |
| 650 | 4 | |a OpenStreetMap | |
| 650 | 4 | |a social science | |
| 650 | 4 | |a spatial analysis | |
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