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BreATHe Data Dive
26th
September 2018
Mark Owen
BATH: HACKED
mark@bathhacked.org
Hacking the
traffic in Bath
Important Dates
26th
September - Data Dive
10th
October – Community Introduction
17th
October – Preflight
20th
October – Hack Day
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.
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
ANPR Camera Sites
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
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
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)
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?
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?
What are we doing tonight?
○
Get your happy data paws on the data at
bit.ly/banesanprdata
○
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?
bit.ly/banesanprdata
Questions?
mark@bathhacked.org
@azazell0
Join us on Slack
bit.ly/bathhackedslack

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Breathe date dive

  • 4. Important Dates 26th September - Data Dive 10th October – Community Introduction 17th October – Preflight 20th October – Hack Day
  • 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. 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
  • 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. 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. 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. 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. 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. What are we doing tonight? ○ Get your happy data paws on the data at bit.ly/banesanprdata ○ 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?