Make no little plans.
They have no magic to stir men's blood and probably themselves will not be realized.
~Daniel H. Burnham

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Vision and large-scale infrastructure built our cities. For example, New York City owes much of its prominence over Philadelphia to the Erie Canal. San Francisco is knitted together through the Golden Gate Bridge, and London enjoys safe harbour because of the two billion dollar Thames Barrier. Toronto Urban Region’s economy can be traced back to two key infrastructure projects: Highway 401 and Adam Beck power station (Hoornweg, 2015).

For cities in advanced economies, where economic growth is likely to remain an anemic few percentage points, the challenge however is no longer to develop grand plans and build large-scale infrastructure. Rather plans are about better understanding a more complex set of circumstances. Big data is emerging as a powerful and necessary tool to enhance this understanding.

Cities need to build resilience, improve existing infrastructure, reduce carbon emissions, and provide jobs and livability. Stirring men’s blood to meet this more complex challenge is difficult. In most large cities, trust is eroding and fear increasing as citizens adapt to local and global changes. Again, big data can provide an opportunity to engage the public more in planning processes as well as provide critical public assurances on levels of service delivery and their cost effectiveness.

Building cities in the New Normal

The Toronto Urban Region is a typical large urban agglomeration in a high-income country. Our peers include cities like Montreal, Chicago, Boston, Sydney, Sao Paulo, Tokyo and Paris. Toronto is now at another fork in the road as congestion strains the economy and the patience of commuters (2). Toronto Urban Region’s most intractable challenge is mobility and livability (with a commensurate supportive economy).

Urban areas like Toronto Urban Region strive for mixed use, transit oriented walkable communities, but spend little time in policy and design exercises that consider future infrastructure changes, or the impact or rapid modernization and digital infrastructure. Service delivery needs to be planned within shifting economies, climate change (the need for low carbon and adaptive planning), changes to demographics, and a rapidly emerging digital information network.  How can these new realities be leveraged to produce the meaningful plans we need for the future.

The approach that is needed is more than the clamour for new roads and subway lines. This time a project like a new Highway 401 will not stir our blood, as any potential benefits will be fleeting. We face a planning and civil engineering challenge where the priority is not to build some new large-scale infrastructure or turn over yet more farmland to suburban houses. The numerous ‘little things’ and uncertainty needs to be addressed on a grand scale, even if the individual actions seem small.

Little, little – Big, big. Cities Respond

Successful urban areas focus on the ‘small’ things that collective make up the ‘big’ things
Coordinating service delivery and social cohesion across large urban agglomerations is the largest challenge facing urban planners and engineers. The current Coordinated Land Use Planning Review, by the Province, highlights this well. The Plan is a credible and important document receiving global commendations. Yet it is the day-to-day implementation where land developers and municipalities often vie for local initiatives that may help a few, but weaken the overall fabric.

Data provides an opportunity to support the various planning tools and processes that we use in city building.  It directly affects how we innovate across sectors, affecting how people move, where employment is cultivated and how clusters locally and beyond interact.  When we think about strategic planning and how it affects future uncertainties of innovation, we need to consider development plans and integration of the opportunities accruing from new technologies. This needs to be pursued from a systems perspective, that considers the ‘big picture’ and develops key inter-connections and system integration. The most progressive communities will evaluate overall infrastructure systems; increasingly taking advantage of circular systems and the benefits of data driven decisions.  

While the focus in planning must include design and function, it must also encompass how people behave and how their behavior could be influenced. Both physical and social infrastructure are required components. For instance, a plan may include: easy access to bicycle networks and transit.  We need to understand what opportunities there are to encourage people to get out of their cars and complete the connections; What is the future role for ride sharing and micro-transit apps and data driven services and how do we plan for it.

The access to data and the use of data in defining plans provides an opportunity to engage the community more actively in the process.  The future will mean we can move to real time data to support traditional modeling.  This creates an active engagement in processes that most, other than planners, don’t even pay attention to.  Residents will be able to see the relationship between data and the decisions.  This is significant.

The Role of Big Data

‘Big data’ is loosely defined as large amounts of sensor-driven information (customers and installed sensors). Perhaps the qualifying ‘big’ is intended to stir men’s imaginations, hoping to bring about grand changes. Improved IT and digital data management certainly has a role to play in improved service delivery. Data analytics can support operational efficiencies, ensuring that buildings are designed to high-energy performance standards, and are operating to this potential.

Likely the most compelling role for big data is in the transportation sector, especially in the Toronto Urban Region. Cities like Sydney, Australia and Singapore make extensive use of dynamic tolling systems (with large-scale improvements in traffic flow). Autonomous vehicles are the next area of big data expansion. Cars will ‘communicate’ with each other, generating large amounts of data. By 2050 100 percent of vehicles are expected to be electric (or fuel cell) and fully autonomous. Building cities to not only adapt to these evolving circumstances, but to actually encourage them, and even design them (e.g. ‘learning communities’), is the goal of today’s urban planners.  

As cities rely more on data systems and IT integrated management, a key vulnerability also arises. Cyber-security and data management is one of the fastest evolving issues for cities. System redundancy, protection of privacy, and maintenance of data quality is critical. New challenges will certainly arise: can autonomous vehicles drive in blizzards, will people embrace ride-sharing and micro-transit, how much data privacy do we need (are we willing to have a private corporation know all our travel habits for example). Can we successfully integrate data into the planning process?  

The Role of Collaboration

The recent unexpected disallowance by the Province of Ontario of tolling by the City of Toronto on Don Valley and Gardiner Expressways is illustrative. Responding to dissent in the ‘905-region’ the Premier of Ontario rescinded expected approvals. Challenges associated with saying no to the proposed tolls are problematic for how this exacerbates a growing rift between city core (416 area code) and suburbs (905 area code) and makes collaboration more difficult.  Dynamic congestion pricing, as shown in London, Singapore and Stockholm, is a powerful tool to reduce congestion (as discussed in the 2009 OECD study for Toronto transportation).  Dynamic pricing (tolls) plays a critical role in providing both the data and funding needed to improve overall mobility.

Big data can enhance collaboration across large urban regions. For example, typical daily commute times and costs should be provided; perhaps through a simple phone-application (similar to gridwatch), which then encourages commuters to change their behaviours to improve overall mobility. Provision of credible ‘costs of congestion’ would also help promote collaboration and prioritize interventions, as people would have a better understanding of potential benefits associated with recommended (newly regulated) behaviours.

Promoting collaboration likely stirs little blood and passion. Building something, investing in a new system and new technology – this tends to dominate the news cycle. Yet to address congestion and other important planning issues in regions like Toronto, strategic use of big data, collaboration, and applying many ‘tweaks’ to the existing system are most promising.

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This article was published in Ontario Planning Journal 2017 (now Y Magazine).

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End Notes

1) Burnham (1907) quoted in: Charles Moore (1921) Daniel H. Burnham, Architect, Planner of Cities.
2) Traffic congestion in Toronto Urban Area (Greater Golden Horseshoe) is at least $3.3 billion/year (OECD, 2009)