Human-Computer Interfaces and Identifying User Groups

Haklay and Tobon stress the need to design both software and hardware that is most convenient for an identified user group and their goals. Cases such as Braille displays for computers are clear examples of a positive, improved human-computer interface. However, when applied to a complex analytical field such as GIS, HCI studies run into the issue of defining the user group. There is no immediate common attribute shared amongst all GIS users unlike the condition of being blind for Braille-users.

The authors are instructive when they indicate that identified difficulties with GIS are more human-based rather than technology-based. The challenge with users of GIS is that experience with the software varies wildly, and some may be unaware that they are a part of a GIS analysis (as a source of information or doing it themselves). It is tempting to simplify displays to extend the potential audience of GIS, and we have seen this in many GeoWeb 2.0 apps and platforms such as Geocommons. Geocommons’ site boasts that users can “Easily create rich interactive visualizations to solve problems without any experience using traditional mapping tools”. Web 3.0 continues in this direction by offering semantic searches and increasingly mobile applications and devices. In the drive to eliminate the distance between computers and humans however, it becomes easier for others to manipulate and use naïve users as data sources. The increased digitization of our world has sparked debates about privacy and perceived privacy issues.

A question HCI studies could ask then is how to best segment GIS users into groups. Can the semantic intelligence of Web 3.0 be used to map common thought processes/links to better identify common goals? This understanding can be used to direct intermediate GIS users to resources that explain the basics of analytic functions, while underweighting papers that describe advanced processes (I frequently ran into advanced “Petri Net” algorithms when searching for the general history of temporal GIS). Are there alternatives to segmenting users by “skill”, which is a vague measure and largely prescriptive?  


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