mappiness (noun): the pursuit of spatial understanding, usually with data and spatial analytic methods, that is creative, useful, and fulfilling. Could be individual or collective.

Stuff about our quest (i.e., the structure of this talk)

• Your guide (that’s me!)
• The mission
• Optional side-quests
• Helpers along the way
• The fellowship of the map

The Center for Geographic Analysis (CGA) at Harvard

Requires “spatial practice”

1. Working with, managing, transforming, visualizing, communicating, and teaching about spatial information, data, and questions.

2. Actual practice doing the thing (mapping, analyzing, interpreting).

And also “communities”

1. Requires multiple types of expertise and backgrounds—disciplinary, but also faculty, staff, students, etc.

2. Building networks, increasing visibility, and expanding capacity.

Our mission fits within a wider, dynamic landscape

Exciting times for spatial data and methods—new data! new methods!

“Interesting” times for applications—there’s never been more need for spatial expertise

Side-quest 1: Data

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

Side-quest 2: Methods

• “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
  
  
  

Side-quest 3: Demand

• Actually tackling wicked social, environmental, and health challenges

• Increasing understanding of the world around us

• Doing really interesting and innovative research

• And training the next generation to do even better

Side-quest 4: An applied adventure

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
• 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

• 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 task

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)

Geography of hot Tube transport

Extreme Degree Minutes

An exemplary example of spatial research and community

1. Neutral role for AI

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

And it’s all changing so fast—a brave new world, in many ways

Opportunity costs are a real thing

2. Bigger data, bigger problems

Exciting and cool, but also potentially problematic when it comes to issues of bias, representation, and privacy.

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

Most novel data sources still rely on traditional data for validation.

Example: socio-economic and demographic characteristics for mobility data

3. Money, money, money

Tighter university budgets, shifting research funding priorities

Building communities of spatial practice requires resources and working across disciplines sometimes means no one claims ownership

4. Communication, communication, communication

How do people (students, researchers, general public) find out what we’re doing and how we can help?

Highlighting the importance of a spatial perspective, as well the quotidian tasks of where to find data, how to answer spatial questions, etc.

Shout outs

• Open source software, but especially tutorials and training

• Open data and robust data infrastructure

• University centers that serve as spatial community glue

A personal favorite: The 30 Day Map Challenge

Because what is mappiness without maps?

Also a great example of spatial practice and community

Takeaway thoughts

• It takes a village to make a map
• Community operates at multiple scales and each is valuable
• We shouldn’t ignore the side-quests—importance of data and methods
• Life-long learning for life-long mappiness