Planning for the needs of all in the smart city, starting with better data

Planning for the needs of all in the smart city, starting with better data

About the author: Miranda Sculthorp is an urban research analyst, who is passionate about developing and applying approaches for more gender-equal urban planning and cities. 


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Building cities requires data. When urban planners and municipalities want to develop urban plans, programs and services, they need data on the residents they serve to understand their living conditions, habits and needs. This isn’t anything new, but in today’s world of urban planning, which is increasingly dominated by talk of smart city initiatives, machine learning, and big data analytics, developing and applying a critical lens to urban data collection analysis is essential.

As many cities are looking to use smart sensors to collect real-time information on residents’ use of city services, transit and energy, we need to ask ourselves, whose activity is being captured by urban sensors? I believe we also need to go a step further, to investigate how citizens of different genders, identities and abilities are actually portrayed and represented in urban datasets. When we are looking to build inclusive cities, in which women, girls and marginalized groups can feel safe, engaged and fulfilled, the question of urban data collection, representation and analysis raises challenges regarding the realization of gender equality in our urban environments.

Let’s take one area of interest: urban mobility. Simply put, a resident’s ability to move through the city. Urban mobility is one area of urban planning which has an important gender dimensions because women generally tend to take transit more often than men. They also take transit during different times than men, and for many purposes. Think about the multitude of daytime transit trips that mothers make for doctors appointments, childcare, and for running their daily errands. These trips may be during off-peak hours, and encompass a number of stops during the same outing.

Unfortunately, in the context of Canadian municipalities, this reality continues to be under acknowledged by decision-makers. For instance, a recent controversy arose for the City of Ottawa’s transit commission, when Ottawa City Councillor Shawn Menard argued that public transit is both a gender issue and a climate issue. Following the controversy, Menard defended his views in article in for the Ottawa Citizen. He writes:

“It is willfully blissful to think there are no differences between the transit experience
of women and men. Acknowledging these facts does not mean that the Transit Commission
will have an easy job. Decisions will be difficult and they will require different perspectives.
But if transit commissioners are not prepared to apply a gender lens to transit decisions,
they will be doing a massive disservice to more than half the population.”

This example points to a wider issue: to this day, as urban transit planning continues to put aside the particular needs of women in building transit routes, services and schedules, will these agencies take active measures to apply a gender, and critical lens to sensor-collected data? The repercussions of not doing so, will result in planning and building new transit projects which are primarily designed to meet the needs of those who are most often and most accurately represented in sensor-collected data, which tends to be abled-bodied individuals, who move through transit networks at peak times, and in high-volume areas.

One avenue which can help to better reflect the needs of women and girls in smart-city projects, is collecting and using gender-disaggregated data for urban planning. There are currently a number of initiatives and groups which are working to improve the collection  gender-disaggregated data; however, much of this work is located at the national level, and situated in context of international development programmes. For instance, this is the case with Data2x, which advocates for better disaggregated data, but primarily with the objective of closing global gender gaps. While this is a worthwhile goal, there is a pressing need for these kinds of initiatives coming from communities, women, cities, and their partners at local and regional levels.

Indeed, since its inception in 2002, Women in Cities International (WICI) has been working to fill this gap, by developing tools which give vulnerable city residents self-determination over their own story, feelings and experiences in city life, while also providing mechanisms to integrate and reflect these experiences in city planning.

One of these tools, called the the Women’s Safety Walks, is effectively a data-story telling method for women and girls. In a women’s safety walk, women and girls share their experiences moving around a public space, which can confirm or deny statistical findings of crime and harassment. WICI has adapted and applied the Women’s Safety Walk in number of cities in the Global South, and has also brought the tool to local urban contexts in Montreal and across Canada. Over the last year, WICI has been working with the transit and planning agencies in the city of Laval, Quebec, which has revealed how many women have changed their commuting habits based on their feelings and experiences of safety in certain metro stations.

These observations at the intersection of smart cities, data collection and gender make it clear that there is a need to better understand sensor-collected data, by documenting the ways in which this data collection method can potentially poorly represent women and vulnerable groups. Generating statistical data which broken down by gender, is one way that we can get a fuller picture of the needs of women and girls in their cities. Moreover, we also need to integrate more contextual, on-the-ground knowledge of city life (such as that provided through Women’s Safety Walks) to inform urban policy and planning which is more inclusive, gender-representative and dare I say, smart. Because after all, how can a city really be smart, if it’s primarily planned for and responding to the needs of half of its population?

Planning for the needs of all in the smart city, starting with better data

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