It has been shown that the coronavirus dissipates from the air in around 15 minutes under normal conditions. Our IT team wanted to look at providing a scalable solution that would be unobtrusive and allow us to minimize physical and temporal overlap in the library, while still providing some required social distancing improvements.
Traccar is an open source GPS monitoring tool. Users download an app that connects to a central server; gathering and displaying their location in real time on a web-based map. Users may set up notifications for when someone enters or leaves a region, and may thus coordinate physical activities and minimize risk.
Also Traccar provides an API and file export options for data science and data visualization. Currently the library is looking at how this data might be useful for helping in wayfinding tools for ADA accommodations, as well as improving the efficiency of housekeeping by discovering and grouping high traffic areas.
Using the App
Adding a user in the library server requires downloading the Traccar app for either android or ios
Once you have that downloaded and installed the server we are using is http://18.104.22.168:8082/
You will need to provide your Device Identifier (located in the app) and once that's ready you can begin starting and stopping tracking within the app
Above is a picture of me sitting at home. As mentioned Traccar web exports are in xlsx format (there is a JSON api and a python library for that, and haven't had time to dig in there yet) anyhow - here's a jupyter notebook with the Traccar -> geojson converter
once the conversion is done the geojson can be used to do some mapping = here's the last month or so of data.
This is the big picture, and includes my recent trip to grab donuts
Once we zoom in it's kinda cool - you can see there are some point cloud densities near what turn out to be the stairs and also a fair bit of wandering behind the service desk...
Notes, issues, things to do:
Overall resolution - probably accurate within ~15-20 feet on the top floor, not nearly as accurate going in the bottom floor... across the board floor level detection is not really a doable thing... elevation data is an open problem space, and getting the interior maps availble for research is an ongoing issue.
https://osminedit.pavie.info is a website that aims to democratize this process by allowing floorplans to be imported, georeferenced, and tagged with information that would be **really** useful for ADA accommodations.
understanding motion and trajectory data - I used the vector point to path plugin to make path files from the paths - http://movingpandas.org/ has some interesting takes on grouping trajectory data to highlight most common routes and https://scikit-mobility.github.io/scikit-mobility/ may be helpful if we are able to parse the floor level data and get that mess sorted out
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