Setting the stage (liner notes)

Red herring: a distraction from the real problem(s) at hand

Track 1: Our data ecosystem is diverse and growing

And so much more than “new and emerging forms of data”

• Historical – maps, diaries, inventories, censuses, surveys, archeological sites

• Big and smart – sensors, social media, internet, mobility, street view

• Government and administrative – census, medical, vital records, administrative

• Linked – joining or nesting information across locations, time, or individual

• From space and from the air – satellite imagery, aerial photography, drones, LiDAR

Data need infrastructure

• Not just technical – also social

• Requires investment and maintenance

• Crumbles with lack of attention

• Easily taken for granted

Track 2: Our breadth of methods has never been greater

• “Traditional” methods to ascertain relationships and associations across variables and places, collapse data dimensionality, compare groups and outcomes

• Data science and visualization

• Machine learning

• AI: computing and analytical innovations that facilitate data discovery and manipulation, text analysis, feature extraction, data creation, and analytics at scale
  
  
  

Track 3: Unprecedented potential

(and challenges)

• To tackle wicked social, environmental, and health challenges

• Increase understanding of the world around us

• Do really interesting and innovative research

• And train the next generation to do even better

But…

As spatial and social scientists we can occasionally get distracted by the shiny data and methods objects. The attraction of the novel.

(This track segues seamlessly to the next.)

Track 4: Red herrings on the path to spatial greatness

The ways in which data and methods can distract us from the real problem of improving well-being and reducing spatial inequalities.

This is the “tortured geographer” part of the talk.

1. AI

There’s no such thing as AI, as in “let’s use AI for this”

Also: the problem at hand should determine the method.

(which may often be “AI”!)

A collective challenge for us is to identify the ways in which AI, machine learning, and data science can advance knowledge and actually help make the world a better place

Keeping in mind that opportunity costs are a real thing

2. Big data

Exciting and cool, but also potentially problematic when it comes to issues of bias, representation, and privacy. We should be more open about this.

Examples: mobile phone data, social media, even satellite imagery

Most novel data sources still rely on traditional data for validation. We need to be careful not to throw the baby out with the bathwater.

Example: socio-economic and demographic characteristics for mobility data

I wish we talked more about the real potential to revolutionize how we collect traditional forms of data, given the technology we have access to.

Data and data infrastructure can be lost.

3. AI and big data together

A match made in heaven and so much potential. But also so much temptation to put the cart before the horse.

Track 5: Spatial inequality and the smart city

Thinking about how emerging technologies intersect with the spatial demography of cities to exacerbate, reproduce, and generate inequalities across areas or groups.

Especially placement of sensors in the urban landscape

• Sensor coverage and “sensor deserts”
• Who’s in the “gaps”
• Promoting equitable decision-making

Even with good intentions infrastructures miss people and places

The big idea

How can we support informed and equitable decision-making around sensor placement, especially what criteria ideal networks might satisfy and the inevitable trade-offs involved?

We’ve got good optimization algorithms for this

• What is the ”best” allocation of n sensors, given a particular goal?

  • Single objective greedy algorithm: Place sensors one by one and maximize coverage of one sub-group only.
  • Multi-objective genetic algorithm (NSGA2): Generate a spectrum of networks representing the coverage trade-offs between different sub-groups.

• Decision support tools that visualize options and trade-offs

Coverage for older residents (>65)

Coverage for place-of-work population

Making it more user-friendly

No fancy data here and well-known methods

But should make us think:

1. how are our (air quality) data produced?

2. how can we do better?

Track 6: Public transportation heat exposure in a warming world

Thinking about how health, climate change, and transportation intersect

Case in point: The London Tube

• The first Tube line opened in 1863 and is still running as part of the Metropolitan line
• 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 doing the travelling? (demographic and health characteristics)

• Where are they going and how do they get there? (origin and destination flows)

• What’s the temperature at the station and on board? (estimated from known station-surface differentials)

Exciting geographical data

• Synthetic Population Catalyst (SPC)–A synthetic population that simulates individual-level travel behavior (home and workplace), travel mode, demographic and socio-economic characteristics that allow us to explore heat vulnerability at the individual level

• Tube operation timetable–For travel times and route estimation, we use the timetable provided by Transport for London (TfL) APIs. Provides accurate estimation whether travelers 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.

Geography of hot Tube transport

Extreme Degree Minutes

Inequity of impacts aren’t only spatial

• More vulnerable populations are (slightly more) impacted

• Non-white ethnicities also (slightly more) impacted

• Unable to really look at age, which is a big deal

What if we fix it?

• A cool thing is that we can test policy changes: what if AC is installed on certain lines?

• 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

• An exemplary example of where geographers have a lot to contribute

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

Track 7: Spatial data infrastructure for the 21st century

Making satellite imagery data usable, useful, and used in the social sciences, health, and policy

The big idea

We’ve now got the compute, methods, and sensor quality for satellite imagery to be a game-changer for social science and health research and policy making

The ESRC SDR Imagery Data Service*

1. Imagery innovation–research-ready imagery-based data products, building off and developing innovative computing and AI methods that facilitate efficient automated workflows for measures and indicators

2. Data for all–data distribution channels that meet researchers and policymakers where they are

3. Capability and community–building capacity for understanding and working with imagery and imagery-derived data

Track 8: Tying it all together

What’s the big deal

AI and novel data are true game changers

• Novel insights and applications

• Data (and methods to exploit that data) that give us completely new views into human behavior and preferences

• At scale

• Useful but also fun

What keeps me up at night

• Preserving the production and availability of publicly-owned data

• Lack of investment into novel forms of public data

• Preoccupation with fast, new, shiny instead of slow, theoretically grounded and perhaps actually impactful

• Bandwagons (AI or otherwise)

• Spillover effects: on publication, hiring, external research funding…

Complex spatial inequality challenges require complex approaches¹

• Inter-disciplinarity
• Data integration
• Theory
• A lot of social science under the hood ²