Defining goals

e.g., equity, wellbeing, health, sustainability

Understanding why things are the way they are

(this is complicated!)

Being deliberate with data

moving beyond convenient, found data

Matching methods and data to the problem

(this is not always complicated!)

Thinking about the ways in which health, climate change, and transportation intersect

Case in point: The London Tube

• 272 stations
• 11 lines, covering 402 kilometres
• More than five million passengers on the busiest days

Future heat on the London Tube

• Only 4 out of 11 London underground lines have air conditioning systems

• The average summer temperature in London is expected to increase by 2.7 degrees Celsius by the 2050s

• The probability of heatwaves could also increase five-fold and they’re expected to occur every other year

• By 2070, the mean maximum air temperature in the UK in August is projected to increase by up to 6 °C in summer compared to 2018

Hot is already here

Average station temperatures in London (2019)

The big idea

How can we estimate current and future heat exposure on the Tube and who (where) is most affected?

Simple (and important) question with complex data requirements

• Who’s travelling? (demographic and health characteristics)

• Where are they going? (origins and destinations)

• What do journey temperatures look like? (estimated from known station-surface differentials)

Exciting data and analytical tools at our disposal

• Synthetic Population Catalyst (SPC)–A synthetic population that simulates individual-level travel behaviour (homeplace and workplace), travel mode, person and socio-economic factors that allow us to explore heat vulnerability at the individual level

• Tube operation timetable–For travel times and route estimation. Provides accurate estimation whether travellers for each OD-pair will take air-conditioned Tube lines.

• Clim-recal–Estimates weather and heat wave days in the past and future on a daily basis in 2.2 km*2.2 km cells covering the entire UK. Local variation in the dataset is used to estimate heat exposure more accurately.

A preliminary sense of findings

• The inequality of heat exposure risk is significant in spatial terms

• Trickiness of estimation but lots of useful data that can be brought to bear

• But also some of this should be being measured directly!