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Breadcrumbs
2019
Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - SIGSPATIAL '19
Rich human mobility datasets are fundamental for evaluating algorithms pertaining to geographic information systems. Unfortunately, existing mobility datasets-that are available to the research community-are restricted to location data captured through a single sensor (typically GPS) and have a low spatiotemporal granularity. They also lack ground-truth data regarding points of interest and the associated semantic labels (e.g., "home", "work", etc.). In this paper, we present Breadcrumbs, a
doi:10.1145/3347146.3359341
dblp:conf/gis/MoroKGCHG19
fatcat:spdg5ao5uvcoffkzacxu2dwuvq