首页|An investigation of the temporality of OpenStreetMap data contribution activities
An investigation of the temporality of OpenStreetMap data contribution activities
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OpenStreetMap(OSM)is a dataset in constant change and this dynamic needs to be better understood.Based on 12-year time series of seven OSM data contribution activities extracted from 20 large cities worldwide,we investigate the temporal dynamic of OSM data production,more specifically,the auto-and cross-correlation,temporal trend,and annual seasonality of these activities.Furthermore,we evaluate and compare nine different temporal regression methods for forecasting such activities in horizons of 1-4 weeks.Several insights could be obtained from our analyses,including that the contribution activities tend to grown linearly in a moderate intra-annual cycle.Also,the performance of the temporal forecasting methods shows that they yield in general more accurate estimations of future contribution activities than a baseline metric,i.e.the arithmetic average of recent previous observations.In particular,the well-known ARIMA and the exponentially weighted moving average methods have shown the best performances.
Volunteered geographic informationtime series analysistemporal forecastingOpenStreetMap
Tessio Novack、Leonard Vorbeck、Alexander Zipf
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Centre for Interdisciplinary Methodologies,University of Warwick,Coventry,England
GIScience Research Group,Heidelberg University,Heidelberg,Germany
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