Concept

The LBSN Structure presented here directly reflects the following components:

  1. Facets: Any information on Location Based Networks (LBSN) can be broadly organized in 4 Facets, the Social Facet (Who?), the Topical Facet (What?), the Spatial Facet (Where?) and the Temporal Facet (When?).

  2. Objects: Various distinct entities exist on LBSN that can be referenced and distinguished such as a Post, a User, a Place, or an Event.

  3. Relationships: Objects on LBSN relate to each other in various ways. For example, several Posts are related to a particular User. Or, two users are related by being connected, e.g. as Friends. These relationships are organized under Interlinkage, which can also be considered as an additional, 5th Facet.

  4. Bases: Objects can be broken down further in bases. A Post, for example, consists of several attributes, which we consider bases, such as its title, the post_body (the content and description), or other information automatically added (the timestamp of publication).

  5. Metrics (Overlays): Any base can become the context of analysis, which is expressed in the task matrix presented by Dunkel et al. 1. Typical metrics (overlays) in visual analytics include:

    • postcount: The number of posts for a particular context ("PC")
    • usercount: The number of users for a particular context ("UC")
    • userdays: The number of cumulative distinct user count per day for a particular context ("PUD", as coined by Wood et al. 2)

Note the difference

The difference between a base and an overlay may not be immediately obvious. A base defines the underlying context that is explored. For example, a specific region (spatial facet), or a distinct temporal window (temporal facet) would be considered a base. A metric (or overlay) reflects what is measured, e.g. the number of posts (postcount), the number of users (usercount) or the number of distinct user days (userdays). Metrics only apply to the visual analytics part of the process.

These were the most frequent metrics that we observed in practice. However, many other metrics exist and this list is not exhaustive. See also Dunkel et al. 3, which includes references to research that currently make use of these metrics.

Immediately, the connection between bases, metrics, and privacy becomes obvious. To measure "postcounts", one needs to count distinct number of posts. Posts are typically referenced by an ID, a unique identifier. Each of these IDs is a reference to a person in a specific situation. Similarly, user IDs allow to identify users across several posts. Such unique identifiers are therefore the primary cause of privacy conflicts.

  1. Dunkel, A., Andrienko, G., Andrienko, N., Burghardt, D., Hauthal, E., & Purves, R. (2018). A conceptual framework for studying collective reactions to events in location-based social media. International Journal of Geographical Information Science, 33(4), 780--804. https://doi.org/10.1080/13658816.2018.1546390 ::: ↩

  2. Wood, S. A., Guerry, A. D., Silver, J. M., & Lacayo, M. (2013). Using social media to quantify nature-based tourism and recreation. Scientific Reports, 3(1), 2976. https://doi.org/10.1038/srep02976 ::: ↩

  3. Dunkel, A., Löchner, M., & Burghardt, D. (2020). Privacy-aware visualization of volunteered geographic information (VGI) to analyze spatial activity: A benchmark implementation. ISPRS International Journal of Geo-Information, 9(10). https://doi.org/10.3390/ijgi9100607 ::: ↩

Last update: September 28, 2021
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