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Breathe date dive


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Notes from BreATHe Data Dive, 26/09/2018

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Breathe date dive

  1. 1. BreATHe Data Dive 26th September 2018
  2. 2. Mark Owen BATH: HACKED
  3. 3. Hacking the traffic in Bath
  4. 4. Important Dates 26th September - Data Dive 10th October – Community Introduction 17th October – Preflight 20th October – Hack Day
  5. 5. Hack Day Goals Help residents and organisations better understand how traffic impacts pollution. Highlight how changes in behaviour patterns might help make Bath a healthier place to live. Find creative solutions and insightful answers to residents’ problems and questions. Do something amazing that changes how we all think about the problems we face. Let the world know we’re trying to make our city a better place. Have fun, share and make useful things.
  6. 6. The Data Approx. 4 million ANPR camera observations Two week survey from 31st October 2017 to 13th November 2017 67 camera sites Enhanced characteristics for approx. 400,000 vehicles 3 CSV files – ~300MB unzipped, ~98MB zipped
  7. 7. ANPR Camera Sites
  8. 8. ANPR Camera Sites Data sites.csv ○ id ○ name – original name ○ location_id – camera location id ○ direction – direction of travel ○ longitude ○ latitude One site location (ANPR 31c) provided but not found in observations
  9. 9. ANPR Camera Observations Data observations.csv ○ id ○ t – timestamp of observation ○ site_id – site of observation ○ vehicle_id – vehicle observed Some vehicles are unmatched i.e. number plate not found Vehicles are anonymised
  10. 10. Vehicle Data vehicles.csv ○ id- UUID for anonymisation purposes ○ make – e.g. Ford, Ferrari ○ type – e.g. CARS, PSVs ○ subtype – e.g. Moped, Fare Stage Buses ○ intro_date – date vehicle was introduced ○ euro_status – Euro status e.g. Euro 5 ○ engine_capacity – capacity of engine in cm3 ○ gross_vehicle_weight – weight in kg ○ fuel_type – e.g. Diesel, Petrol ○ co2 – CO2 emmissions in g/km ○ fc_combined – fuel consumption combined across usage in L/100km ○ fc_extra_urban – fuel consumption outside of urban area in L/100km ○ fc_urban_cold – fuel consumption in urban area in L/100km ○ bus – is this a bus? (possibly to come) ○ taxi – is this a taxi? (possibly to come)
  11. 11. Vehicle Data ○ Some vehicle makes have been “Suppressed” where 5 or less vehicles of that make were found ○ Some data values maybe null where information is not available for a vehicle ○ Some values may need consistency improvements e.g. Petrol and PETROL may both appear ○ Not normalised to keep files to a minimum ○ We need some way to apportion pollutants (e.g. NOX, PMx) to each vehicle based on vehicle characteristics. Any ideas?
  12. 12. Some initial questions Can we apportion pollution to vehicles by make / type / age etc.? Can we compare ANPR data to actual air quality readings? How do bus journey times compare to cars? Are bus lanes helping? Where might we put electric vehicle charging points? How can we help people explore the data & understand impact? Can we spot anything about bus & taxi behaviour?
  13. 13. What are we doing tonight? ○ Get your happy data paws on the data at ○ Get stuck in & work together! ○ Anything dumb we’ve done with the data? ○ What formats would be most useful? ○ How to handle data size ○ Where should we put the data? Socrata, Github, Database server? ○ Ideas about routing? ○ Volunteer data ninjas? ○ Any ideas on how to use data with GIS tools? ○ What tools will we all be using? ○ Can we find some methodology to calculate pollutants? ○ Already have an idea for hack but need the data shaping in some way? ○ Do you have experience of slinging this kind of data? ○ How can we make this data useable by non-techy people?
  14. 14. Questions? @azazell0 Join us on Slack