So slicing data is something we’ll all have to deal with in our own angles, but how much we need to slice the data will differ between each person. But some things that are very similar is looking at:

1. Dimensions of time

This deals with slicing the data in relation to time. As time is a big factor for each our angles, we need to understand how we can slice and represent the times into something that people can easily manipulate to only see what they want to see from this data.

This means visualising it with two different concepts, relatively short periods and relatively long periods, for example, short periods can account for a whole day, or shorter portions of the day - peak (lunchtimes) and off-peak (late night deadlines) or longer periods can account for weeks or months. So the question we need to think about is how would we slice these times for each portion of the day and how would we combine it for possibly a few days? - overlaying on top of each other, combining the data, heatmaps etc. Or another example is how do we just filter out the data so that we just get the data from only tuesdays? or just the ones from within 1pm to 2pm lunch times? Something to think about is that the answer to this problem can the same or react differently.

2. Dimensions of people

This deals with slicing data in relation to individuals or groups of people that are being tracked. Understanding human behaviour in workplace environments can be really useful but it can also be more useful if we know who or the types of people using it. It can be used to compare similarities and differences between individual and collective behaviours at different times.

Ethics when categorising people:

So there was also a talk about categorising people. Theres two levels to this project - ethics and technological level. In order to analyse spatial movement in workplace environments, I have to look at it from two different perspectives. As I am looking at visualisations, there should be no reason for me to remove the capability of categorising people in random order. It becomes a big issue when real data is being used to categorise or individualise people in the visualisation. There should be the option to take each of our angles and explore the limits of what each of our angles can do even but intentionally using fake/spoofed data to protect the integrity of the participants.