Street-Scape is a contextual visualisation of an urban environment.
Our surrounding environment is increasingly mapped and analysed using discrete sensorial information (such as noise levels or air pollution) resulting in a simplified representation of the complex world we inhabit. We use sensors which are cheap to deploy and measure only discrete characteristics that can be measured by the means of these sensors. Furthermore, there is yet another selection by choosing how to label, represent and store the gathered data. Databases alone are useless without a further translation making the stored information human readable or analysable. This involves discarding some aspects while amplifying the others to create analysis or visualisations that prove some arguments and not others.
Street-Scape utilises a technique where gathering data and creating the depiction is simultaneous. Through minimising distillation and translation these visualisations thus result in a more natural and contextual representation of the environment. They display multivariate content such as the density of the people, their demographic features and movement speed in space.
The walking direction of people in the street is plotted in one direction on the 5 minute timeline to make their relative distances between each-other more apparent. All static objects are blurred creating an ambience of the environment while making the moving ones more apparent. The visualisations are rendered in a way that produces people walking 5km/h (average walking speed) with original proportions, everyone moving faster thinner and everyone slower respectively wider.
Street-Scape excludes the unnecessary personal information rendering the captured people anonymous while revealing their demographic qualities such as their approximate age and gender. Thus, presenting the relative amount of children, grown-ups, older people, bikers, etc in a particular location during the visualised time.