6 Matching Annotations
  1. Jan 2022
    1. Elliott, P., Eales, O., Bodinier, B., Tang, D., Wang, H., Jonnerby, J., Haw, D., Elliott, J., Whitaker, M., Walters, C., Atchison, C., Diggle, P., Page, A., Trotter, A., Ashby, D., Barclay, W., Taylor, G., Ward, H., Darzi, A., … Donnelly, C. (2022). Post-peak dynamics of a national Omicron SARS-CoV-2 epidemic during January 2022 [Working Paper]. http://spiral.imperial.ac.uk/handle/10044/1/93887

  2. Apr 2020
  3. Mar 2020
    1. Not only are public transport datasets useful for benchmarking route planning systems, they are also highly useful for benchmarking geospatial [13, 14] and temporal [15, 16] RDF systems due to the intrinsic geospatial and temporal properties of public transport datasets. While synthetic dataset generators already exist in the geospatial and temporal domain [17, 18], no systems exist yet that focus on realism, and specifically look into the generation of public transport datasets. As such, the main topic that we address in this work, is solving the need for realistic public transport datasets with geospatial and temporal characteristics, so that they can be used to benchmark RDF data management and route planning systems. More specifically, we introduce a mimicking algorithm for generating realistic public transport data, which is the main contribution of this work.
  4. Aug 2018
    1. Although the spatio-temporal variation in rumor quantity and content has long been of interest to thefield, collecting data that accounts for temporalandspatial characteristics of rumoring has been extraordinarily difficult to dowith any degree of precision. Some have been able to capture rumoring data with some degree of temporal precision (Bordiaand Rosnow, 1998; Danzig, 1958; Greenberg, 1964) or with some spatial precision (Larsen, 1954), but bridging the two hasbeen difficult. Synthesizing temporal and spatial rumoring data across a wide variety of events had long been beyond thecapabilities of researchers. Simply gathering reliable data on rumoring was already fraught with challenges.

      Check these citations on difficulty of temporal data capture. Since they are all quite old studies (between 20-60 years old), I question how relevant they are to current behavior -- either offline or online.