Adventures In Municipal Data Science

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Co-authored with Blair Birdsell

As we deal with the fallout of the big Cs — climate change and the coronavirus — we need to be able to rapidly adapt our urban fabric and buildings. Michael Barnard pointed out late last year in an article, US & Canada Have To Overcome Patchwork Regulations In Low-Carbon Transformation, that the varied regulations across municipalities, provinces, states and countries would slow the necessarily rapid climate transformation. This is more true now that we’ll need to adapt more of our shared spaces to a future that includes physical distancing. 

Graphic with Adventures in Municipal Data Science over brick
Image by Blair Birdsell

Blair Birdsell, co-author of this piece, has a background in architecture and data science. His day job allows him access to the nuts and bolts of sustainable architecture by contributing to the engineering of high-performance, mass-timber buildings. Michael spent a large part of his career to-date as a solution and business architect with a global technology firm. They’ve been collaborating for months on shaping a response to at least part of this challenge, starting with the creation of a normalized repository of municipal regulations. This article focuses on Blair’s technical journey, and is expected to see future installments as the data structures and the repository take shape. We are hoping to gain other collaborators and increase our cities’ resilience in Canada and the US.

Selection of the technical language was straightforward. Python is the pre-eminent language for data science today, used in the majority of machine learning initiatives. One of the main reasons Blair originally picked up the language was to access some of the ocean of data around us, particularly open city data. We see great potential in taking a higher level approach to changing the fabric of our cities through understanding some of this data through math. 

But… baby steps… 

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In this piece we outline the 4 steps Blair took since January to try to integrate some of the Vancouver region’s open data. Initially, he focused on business licenses because it was sufficiently plentiful and complicated enough to represent the scale of effort of the data we really wanted to get to. To even get as far as Blair did – requiring thousands of often complex steps, of which we won’t recount here – was a pain. But in this piece is the germ of an idea we hope connects to other like-minded individuals, and grows over time into something more impactful. Readers interested in learning some of these tools themselves – which are very democratic in our opinion – will find this piece useful in understanding the level of effort required for such an endeavor.

Naturally, being so connected to architecture, Blair couldn’t help but think of this challenge in terms of building a house. 

Foundation of a house
Image by Blair Birdsell

Step 1: Pour the foundation

The foundation of a data science project is much like building a house, which rests on a good foundation. His first task was to laboriously download all the business license data he could. Not particularly glamorous or groundbreaking, he just googled and clicked until he had all the Metro Vancouver Region District data that was available. One thing that is so striking when starting to get involved in smart city data is how fragmented the field is globally. He found it almost incomprehensibly fragmented in regard to data formats and data types when he started researching this project last year, confirming Michael’s initial observation from a bottom-up perspective. This fragmentation necessitates careful thought and a lot of preprocessing to overcome.     

Screen images of Lower Mainland open data web pages
Screen images courtesy of the governments of four British Columbia Lower Mainland cities

Step 2: Frame the house

Currently in the field of data science there is a massive amount of effort and resources being dedicated to reducing or eliminating the preprocessing of data. Blair actually doesn’t mind it as an activity, but, on balance, he also has a lot of other things to do and wants to see the end benefits quickly. There are many parallels with framing a house in his opinion because one really doesn’t know the end form until some preprocessing has been done on the data.  


Initial data structure for municipal regulations
Initial data structure

Step 3: Enclose the frame

Once the project had progressed a bit, and some form established, now one can start to see the problems. In this case, 1 out of the 7 datasets didn’t have any GPS coordinates associated with the data, just street addresses. Consequently, he needed to develop a little side project and create an address parser through Google’s Geocaching API. He successfully added 3700 GPS coordinates to the dataset and this would be good practice for tackling some of the much larger datasets he has his eye on. 


Python geotagging code
Python geotagging code

Step 4: Paint, decorate and move in

With the form complete, some conclusions can be drawn. Mostly, in this case, how incomplete the open data is even regionally within Metro Vancouver’s 21 municipalities, one Electoral Area, and one Treaty First Nation. The patchwork quilt is experienced locally, not at a distance. As a data point, the amalgamation of the City of Toronto which was started in the late 1990s still sees variance in municipal services such as snow plowing more than 20 years later, despite two decades of concerted effort. Bridging this hyper-local regulation across two countries is non-trivial.

This visualization of open data standards across a single metropolitan area starts to show who is lagging, and who is leading. 

Heatmap showing the relative adoption of open data standards by municipalities in the GVRD
Heatmap showing the relative adoption of open data standards by some municipalities in Metro Vancouver by Blair Birdsell

Initially Blair had in mind a fancier, hosted webapp so that readers could play with the data themselves, but that takes resources we don’t have at present. Ideally, if contributing to the unifying of this data or reducing the carbon footprint of buildings whole neighborhoods at a time some interesting, please reach out to Blair on LinkedIn or Instagram

We’re facing an interesting set of challenges. We’re physically distancing due to the coronavirus, but densely populated cities are a requirement for climate action. The combination requires changes to the built environment and new challenges in designing new buildings. Enabling aligned regulatory frameworks across jurisdictions will help us accelerate both. 

Open Government Licence – British Columbia

  1. City of Surrey – Surrey Open Data
  2. City of Maple Ridge – Open Government Portal
  3. City of Vancouver – City of Vancouver Open Data Portal
  4. City of New Westminster – Open Data
  5. City of Burnaby – Open Data Portal
  6. Township of Langley Open Data – Open Data Portal
  7. Maple Ridge – Open Government Portal

Blair Birdsell and Michael Barnard are collaborating with a multi-disciplinary team focused on climate solutions in the urban setting.

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Michael Barnard

is a climate futurist, strategist and author. He spends his time projecting scenarios for decarbonization 40-80 years into the future. He assists multi-billion dollar investment funds and firms, executives, Boards and startups to pick wisely today. He is founder and Chief Strategist of TFIE Strategy Inc and a member of the Advisory Board of electric aviation startup FLIMAX. He hosts the Redefining Energy - Tech podcast ( , a part of the award-winning Redefining Energy team.

Michael Barnard has 732 posts and counting. See all posts by Michael Barnard