Analysing eye tracking data: From scanpaths and heatmaps to the dynamic visualisation of areas of interest
To understand the visual behaviors of people searching for information on Web pages, heatmaps and Areas Of Interest (AOI) are generally used. These two techniques bring interesting information on how Web pages are scanned by several users. However, two remarks can be expressed: the first one relates to the fact that heatmaps are usually used to represent fixation areas for a given task after it is completed. Thus, it does not represent fixation areas over time. The second remark relates to the use of AOI, which must be defined by the analyst. We present a method, which address these two points. This bottom up approach is based on a mean shift clustering procedure for the identification of areas of interest, which takes into account the temporal aspect. The identification of AOI is thus data driven. This approach allows us to show the evolution of a posteriori AOI both in space and time. The limitations and implications of this new approach are discussed