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Tag "urbaDiary"

Some first graphs to analyze the tracking data in some other ways. It is now almost one month (I have not yet the data from all the participants for this time period) of tracking and I think this is gona be the first milestone in the project. At the moment I think that one month could make a pretty good unit as a base to start analyzing the data. A cycle of a four week patterns could provide enough data to paint a rough picture of the activities and range. As it is the first month I m only guessing here and will have to check this assumption as more data will be coming in over the next couple of weeks.

The graph visualization focus on the quantitative aspect of the data together with the time information over the location information. The idea is to look at the schedule information contained in the record. This is of interest as the project is interested to enhance knowledge on personal, spatial routines. The graphs are visualizing the amount of activity over a specific time period. The periods are one day – 24-hours, one week and one month. Using these units of general time frames helps to establish an appropriate framework for the data. Participants are all understood to use these time frames. More specific units could relate to religion, culture or specific responsibility or job. These will be respected on a more individual level of analysis. In the graphs the x-axis represents time were as the y-axis refers to amount of activity. This is measured by the number of log points the GPS device has stored for the time period in question. The graphs do not give information about time spent in one location they solely focus on travel time between destinations.
One month analyzed by day and participant. In total there is four peeks over four weekends. They generally do match, although one peak has slightly moved into week three. This was the UK midterm week, a holiday brake. Participants who have children or work in a school have spent more time traveling during the normal weekdays. Surprisingly the Sunday at the start of this mid term week is very low. All of the participants have recorded little activity. It must have been really bad weather and people stayed indoors. On the contrary, one Saturday pops out extensively. It turns out that one of the participants had an intensive outdoor sports day, during that he generated a large number of points.
Image by UrbanTick for UrbanDiary – Graph UD first month

There is an activity accumulation on Saturdays. This shows up in particular in the week’s graph. Saturday has more than double the amount of points over other days of the week. Not only this one participant who is doing intense sports activity on Saturday, but all of the participants tend to have significantly more activity on Saturdays. Other than that the weekdays are fairly equal in terms of activity with tendency to a low point midweek.

Image by UrbanTick for UrbanDiary – Graph UD week

Compared to the regularity of the week, the 24 hours graph shows a number of peaks. The graph starts at midnight with an expected flat bit representing few activities. In the first hours of the day there is some activity but it reduces to virtually zero in the early hours of the morning. The day then starts with a first peak of the morning rush hour. Around seven participants start leaving the house, but it then really takes off from eight, peaking around nine and coming to a first low point around ten. From this low the second peak starts rising immediately. By looking closely at the participant involved in these first two peaks one can see that actually there is two groups, one generating the first “rush hour peak” and the second group mainly contribute to the second similar peak about one hour later. 
The second peak has a twin peak with a first high point around 10h00 and a second one just before lunch around 13h00. After lunch around two o’clock there is the low point of the day with the least activity during this 24-hour day apart from the early morning hours. 
After the lunch brake, there is a fat afternoon / evening peak. This is representing a number of weekend afternoon activities like the out door sport that was mentioned above. Included into this fat peak are a first evening rush hour high point between five and six and a smaller second peak around eight, probably pointing to the visit of the pub after work.

Image by UrbanTick for UrbanDiary – Graph UD day

Colour correspond with the key on the map here
Generally this reassembles the expected daily routine pattern of a western city. Surprising is more the accuracy the pattern shows up, rather than any unexpected results. Although the sample is not representative this was not expected to find this regularity.

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It has been a busy week, as always. The data collection this week was again good, with some nice tracks. To the disappointment of some participants, the pattern has been VERY similar to last weeks. Unfortunately our lives do not quite cover as much ground in the city as we might like to think, the routines we follow are rather strong. Nevertheless, to find that the perception is different is already a good finding.
But have a look yourself, here is a clip generated from the data.

UrbanDiary_2W_090219 from urbanTick on Vimeo.

A different view gives the following clip. Here, the data is replayed in 24H, so all the records played in one day. The coverage shows that there is activity throughout the day, except the early morning hours. Between 02h00 and 07h00 there is a big gab in activity. The rest of the time 07h00 to midnight and beyond is very active. What is a bit misleading here is that the weekend activities are squeezed in together with the weekday activities. From the clip above we have seen that the activity pattern between the two vary a lot. The next step would be to find a visualization that clearly focuses on this problem.

UD_dayIcon_090220 from urbanTick on Vimeo.

You can see last week’s visuals here.

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A short clip to visualize different peoples movement over the period of one week in London. It is a first test with a number of participants using Garmin GPS devices.
The data returned is actually better than expected, although there is a lot of errors, even in the city centre there is often a signal.
For a better visualization the day and night feature of Google Earth was used to clearly mark the passage of time. It’s sweet how they all rest in their place when it is dark, and then start off early in the morning. The weekend has been used by a number of participants to make trips, sometimes quite far, in most cases to visit relatives or friends.

UDp_090212_GoEa from urbanTick on Vimeo.

Animation produced in Excel and with a converter by Bill Clark brought to Google Earth

Looking forward to get to work with the data from next week.

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