Interpreting map usage patterns using geovisual analytics and spatio-temporal clustering

Extracting meaningful information from the growing quantity of spatial data is a challenge. The issues are particularly evident with spatio-temporal data describing movement. Such data typically corresponds to movement of humans, animals and machines in the physical environment. This article considers a special form of movement data generated through human–computer interactions with online web maps. As a user interacts with a web map using a mouse as a pointing tool, invisible trajectories are generated. By examining the spatial features on the map where the mouse cursor visits, a user's interests and experience can be detected. To analyse this valuable information, we have developed a geovisual analysis tool which provides a rich insight into such user behaviour. The focus of this paper is on a clustering technique which we apply to mouse trajectories to group trajectories with similar behavioural properties. Our experiments reveal that it is possible to identify experienced and novice users of web mapping environments using an incremental clustering approach. The results can be used to provide personalised map interfaces to users and provide appropriate interventions for completing spatial tasks.Science Foundation Irelan

Kinetic data by nonisothermal reaction calorimetry: a model-assisted calorimetric evaluation

The use of a reaction calorimeter in combination with kinetic modeling software to obtain nonisothermal kinetic data is presented. The Diels−Alder reaction of maleic anhydride and isoprene in DMF was used as a model to demonstrate the feasibility of the method. The Arrhenius A factor and the activation energy could be achieved from a single experiment with a reaction calorimeter (Mettler RC1) by fitting the experimental heat generation curve to a second-order kinetic model using commercially available software packages. The use of fitting software revealed a discrepancy between the experimental reaction heat and the heat calculated from the rate parameters. This discrepancy could be resolved by reintegration of the heat generation curve using an adjusted baseline derived from the rate data. The methodology was applied by varying reaction conditions (starting concentrations, heat rate, temperature range), and the results appeared to be independent of these variations within the experimental errors (Ea = 58.5 ± 2.0 kJ mol-1, A factor 4.02 × 106 L mol-1 s-1). It is shown by comparison with literature data that this fast method generates kinetic parameters with a sufficient reliability to be used with process-modeling tools for computer-supported scale-up