Next to the Visual analytics page, the platform provides dedicated pages for specific spatio-temporal analytics. The first one is Trend analytics.
This page focuses on how things change over time.
Let’s click on Trend analytics in the navigation bar on the left. The page opens and loads some default statistics:
By default it focuses on the last day and the day before in the data set.
It compares the
Number of trips between these two days and shows the trend during these days, the average, and the difference. Including an indication of how this number increased or decreased.
It shows the trend statistics for the entire data set under Global Trend.
It also shows the statistics for local areas. By default, the page selects 3 areas from your drawings and/or your uploaded Areas of interest GeoJSON files.
Let’s modify some of these settings, and look at the number of trips by hour of the day for the last week:
On the TIME RANGE panel:
Click on the number 1 to look at just one time period.
Then select Last Week from the dropdown box next to it.
On the AREAS OF INTEREST panel click on the CLEAR ALL button to remove all selected areas.
Finally, on the PROPERTY panel click on By hour to see the number of trips aggregated by hour of day.
You should now see that most trips happen around 4pm (in UTC time) and that there is also a peak around 7am (in UTC time).
Let’s also look at how busy it is in the
In the AREAS OF INTEREST panel, search for
Steinwerder in the auto-complete text field.
Click on the ADD button.
Notice the very different profile compared to the global trend, with busy hours as early as 4am and a busy afternoon as a whole.
Let’s save our analysis by clicking on the bookmark icon in the top-right corner of the page and name the bookmark
All analyses on the xyzt.ai platform can be downloaded and saved to a
Right-click on the widget and select Save as CSV.
Or click on the 3 dots (…) in the top-right corner of the widget and select Save as CSV.
A CSV file with the statistics used to render the widget will be downloaded. You can then use this information for example in Excel, or for further data processing, for example using Python.
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