A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
How do contributions of organizations impact data inequality in OpenStreetMap?
Abstract Despite the rapid advancement and extensive applications of online Volunteered Geographical Information (VGI) projects such as OpenStreetMap (OSM), the persistence of data inequality remains a significant challenge, compromising the global reliability of their data products. This study examines the influence of contributions made by organizations, which have notably risen within the OSM community, on data inequality. The Gini coefficient is utilized to quantify data inequality, while a suite of statistical methods, including spectral analysis and robust correlation analysis, is applied to evaluate the distribution and impact of organizational efforts across various nations. Our findings indicate that organizations predominantly allocate their resources to nations with less complete data and surpass collective efforts of average contributors in mitigating OSM data inequality. Furthermore, the phenomena appears to be particularly significant for NGOs or corporations with humanitarian visions.
Highlights OpenStreetMap data exhibit significant inequality among countries, with a reduction within the span of our study (2015–2020). Organizations contribute more to countries with incomplete data, reducing data inequality more effectively than the crowd. Organizations with humanitarian visions prioritize countries with the most substantial data gaps.
How do contributions of organizations impact data inequality in OpenStreetMap?
Abstract Despite the rapid advancement and extensive applications of online Volunteered Geographical Information (VGI) projects such as OpenStreetMap (OSM), the persistence of data inequality remains a significant challenge, compromising the global reliability of their data products. This study examines the influence of contributions made by organizations, which have notably risen within the OSM community, on data inequality. The Gini coefficient is utilized to quantify data inequality, while a suite of statistical methods, including spectral analysis and robust correlation analysis, is applied to evaluate the distribution and impact of organizational efforts across various nations. Our findings indicate that organizations predominantly allocate their resources to nations with less complete data and surpass collective efforts of average contributors in mitigating OSM data inequality. Furthermore, the phenomena appears to be particularly significant for NGOs or corporations with humanitarian visions.
Highlights OpenStreetMap data exhibit significant inequality among countries, with a reduction within the span of our study (2015–2020). Organizations contribute more to countries with incomplete data, reducing data inequality more effectively than the crowd. Organizations with humanitarian visions prioritize countries with the most substantial data gaps.
How do contributions of organizations impact data inequality in OpenStreetMap?
Yang, Anran (author) / Fan, Hongchao (author) / Jia, Qingren (author) / Ma, Mengyu (author) / Zhong, Zhinong (author) / Li, Jun (author) / Jing, Ning (author)
2024-01-23
Article (Journal)
Electronic Resource
English
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