Indirect connections could help recommend to a person, how she/he could get the goods by connecting her/him with other people nearby who bought the desired item. At the same time, if the person cannot directly get that article, he/she can still obtain related items. Indeed, all blue nodes are part of a basket (orange nodes) that are somehow similar.
The blocking pattern is maybe the most interesting one. It reveals that some people (vendors or even affected persons) may be blocking certain items based on localization or wholesale purchases.
In humanitarian actions, there is always a big issue regarding access, either for an organization to access affected places or for affected populations to access emergency goods. With graph visualization and analysis, we open a new window of opportunity to understand who (people, organizations, or providers) has access to certain goods, as well as through whom we can get that transaction done.
How do people move during natural disasters or armed conflict situations is also one of the most crucial questions for humanitarian organizations. Thanks to Linkurious, it is possible to follow those movements. Although KACHE’s localization dataset is not the greatest one, we can represent transactions based on where people completed them (nodes are blurred for privacy reasons).