Spatial Inequality and the Smart City

Rachel Franklin

Center for Geographic Analysis | Harvard University

2026-04-27

How do emerging technologies intersect with the spatial demography of cities to exacerbate, reproduce, and generate inequalities across areas or groups?

Especially

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

A Talk in Three Parts:


  1. Why care about sensors and sensor networks?

  2. How to conceptualise and measure coverage?

  3. How to support equitable decision-making around sensor placement?

The ubiquity of sensors

Source: https://arrayofthings.github.io/index.html

Source: https://newcastle.urbanobservatory.ac.uk

Sensors as infrastructure

The perennial challenges of infrastructure

  • Cost

  • Maintenance

  • Engineering and planning

  • Service levels

  • Equity

New forms of infrastructure

(same equity challenges)

  • Digital and internet divides

  • Energy transitions and net zero

  • Smart city infrastructures (sensors, networks, data, etc.)

“New technologies have a tendency to polarise and divide at many levels.”

“smart cities are also internally differentiated … they are geographically uneven at a variety of scales. Whatever it means for a city to be ‘smart’, it is also readily apparent that not all spaces of the city will be equally smart, meaning that smart cities will privilege some places, people and activities over others.”

“Geographies of codified knowledge have always been characterized by stark core–periphery patterns, with some parts of the world at the centre of global voice and representation and many others invisible or unheard.”

“urban futures anticipated by urban big data assemblages are highly uneven, data and algorithms cannot divest themselves of urban inequalities and the persistence of their geographies”

Newcastle upon Tyne

Conceptualizing Coverage

Surveillance technologies

What are we observing + measuring?

Sensor deserts

Areas of the city:

  • not represented by sensors

  • for which knowledge is not produced

  • where modelling uncertainty is higher

How can we measure coverage?

Maximizing Coverage

(one group at a time)

Trade-offs

Over 65s

Trade-offs

Over 65s

Place-of-Work

Managing Coverage Trade-offs

Making Good Decisions is Hard

  • Where do we place sensors (source? exposure?)
  • Sensor quality v quantity
  • Sensors for people versus for models
  • Which groups to prioritize?

Can we identify “compromise” networks that may not be best at coverage of any one sub-group, but are satisfactory across all?

Maximizing Coverage Across Groups

Multi-Objective Optimization

Maximizing Coverage Across Groups

Multi-Objective Optimization

How Does Our Approach Compare to an Actual Existing Network?

146 Urban Observatory sensors, located in 55 output areas


Orange areas: under-served by existing Urban Observatory network

Purple areas: excess coverage or “over-covered” by Urban Observatory network

Supporting Decision-Making

Making Good Decisions is Hard


Developing tools to help create networks that all people and places can benefit equally from

Supporting Decision-Making (and planning!)

Supporting Decision-Making (and planning!)

Supporting Decision-Making (and planning!)

Supporting Decision-Making (and planning!)

Wrapping Up

  • Important: What is the purpose of a given sensor network and what might equitable coverage look like?

  • One size may not fit all

  • Valuable role for optimization tools and decision support interfaces to help work through trade-offs and communicate outcomes.

  • Transparency of decision-making

  • Need for clarity around sensor purpose, surveillance, and bias.

  • Potential for incorporating mobility and time-space trajectories into thinking about exposure and coverage.

Team effort

  • Kate Court, Research Software Engineer, Newcastle University
  • Caitlin Robinson, Lecturer, University of Bristol
  • Jack Roberts, Research Data Scientist, Alan Turing Institute
  • Eman Zied, Post-Doctoral Researcher, Newcastle University

Takeaways

Some final thoughts on:

  • Multi-disciplinarity

  • Lengthy projects

  • The data-equity interface

  • It’s not always about data—with, e.g., sensors, it’s partly about location decisions

  • Easy for inequities to be hard-wired into infrastructures

  • Spatial inequality as an important (the important?) lens

  • New infrastructures…same old challenges

  • Bridging the research-policy divide

Thank you!