Why is good urban data important?
Good data gives us insights and helps us make good decisions
Good data for urban development and management helps us make our cities and towns better in all sorts of ways.
A few points to consider are as follows:
- Good data should be used in reviews about our urban places that involve everyone.
- Good data can be used to support urban diagnostic reviews and risk and resilience reviews that are held to work out where a city or town stands today, and where everyone wants it to head towards in future.
- Good data should inform strategies and plans, and good investment cases.
- Data about our population and how it is changing is fundamental to strategies and plans, and urban resilience.
- Data about ecology in our urban setting, such as the amount of greenery and the existence of wildlife shows us how "green" our urban places really are and what action we may need to take to improve the situation.
- Data about urban development informs how we need to plan for future infrastructure, amenities and support.
- Data about transport, traffic and active travel (such as walking and cycling) shows us on how our transport and mobility policies are working (or not), and what to do to keep improving.
- Data about the local economy tells us which economic activities are currently working well, and which ones are not, and what to do to keep improving in a sustainable way.
- Data about our exposure and vulnerability to hazards such as flooding and fires can help us agree what to do to improve our resilience.
A few points to be aware of:
- Some data can be obtained for free or at a low cost - we just need to know where to find it, and how to use it.
- Data can support "a smart city" (it is not the entirety of being a smart city).
- Good governance needs to be in place to ensure accurate data collection and good quality analysis, and also data protection and security.
- We should take a systems approach to having a plan to collect and use data.
- A plan to capture and use data needs appropriate investment and funding.
- Whilst we want to collect good data, we must avoid the "data paralysis" trap, in which we continually seek ever more data before taking action.
- AI is a growing field of interest for analysing urban data (see the Urban AI page).
- Data collection and analysis needs to lead to meaningful targets to work towards meaningful benefits.
- The use of data to monitor meaningful targets and meaningful benefits (from local to global) requires us to have a process in place to act on indicators that are under-performing, or thresholds that are reached.
- A good way to either start or refresh what you are doing with data is to see what other cities and towns are doing, and learn from them.
An Urban Future Centre could be used as a forum to display data about your city or town, perhaps linked to an agreed set of indicators that are being monitored (which supports transparent governance). This is one way to allow everyone to review what is happening and ask questions.
How can we think about data to collect?
Layers of data
One way to think about urban data is as a series of interconnected layers, and to see it as (1) structured and (2) unstructured which have different temporal (timescale) uses.
Data can be collected and analysed in different ways. A systems approach can help us to think about how data can be connected together, and what to do with it. AI may be able to support data collection and analysis.
Thinking about layers of data includes examples such as the following:
- Big picture - geography - satellite imagery, meteorological and climate data, ground data at scale, with a certain timescale relevance (the longer-term it is, the greater the uncertainty, usually).
- Medium picture - geography - local aerial photography, drones, LIDAR scanning.
- Detailed picture - geography - specific readings at specific sites/areas.
- Big picture - social - population trends and statistics (health, education etc.).
- Medium picture - social - particular local area demographics, property prices and transport preferences.
- Detailed picture - social - specific population data on a street, for example to inform street energy needs.
- Big picture - physical built environment - urban master planning changes, a city / town-wide urban heat island map.
- Medium picture - physical built environment - street network design and changes, local area planning approvals.
- Detailed picture - physical built environment - street temperature monitoring.
Collecting data for urban indicators
Data collection is at the heart of being able to measure agreed indicators for our city or town. These indicators should cover a range of aspects, with the key ones - key indicators - requiring the most attention and focus. If we have agreed thresholds and tolerances for certain performance levels, we can monitor how well we are doing against our indicators using data.
If we think about it in the right way, data collection can link up through local city / town use through to national monitoring and then through to global frameworks such as the Sustainable Development Goals and the Sendai Framework for Disaster Risk Reduction 2015-2030.
(1) Structured data
Structured data is information (numbers, words, other) that is organised in a pre-defined, consistent type of format which allows computers and humans to access, store, retrieve, and analyse it.
Examples of structured data are as follows:
- Data from monitoring devices.
- Structured surveys.
- Structured and measured urban diagnostic assessments.
(2) Unstructured data
Unstructured data is information that does not have a predefined format or structure. It is not organised and referenced to be easily searchable or analysed using traditional database techniques. Examples are text documents, images, audio and video recordings and social media posts.
Unstructured data is valuable to capture - it can provide important insights into what is happening in a city or a town. Consider the following examples:
- Interviews with people.
- Photos and videos of what is happening in city / town streets.
Tools to capture data
Some data can be collected and analysed in a very basic way. Other data requires certain tools and technology. The tools - software and hardware - vary in cost and coverage of what they can do.
A mapping exercise with the data required and the tools and techniques to capture and analyse it should be part of a Data Management Plan.
Consider the following examples:
- Satellites capture data and make it available in various formats - some of which is free to use.
- Monitoring devices which may be connected to networks can capture various data, including air quality, noise levels, thermal levels and others.
- Some data can be obtained from free to use commercial services.
Who collects the data we need?
From people to sensors, there are many ways to collect data...
When we know what data we want to collect and analyse, and how to collect and analyse it, we can think about from whom or where the data should come from. Consider the following:
Structured data examples:
- What specialists, consultants and / or municipal employees will be involved for each part?
- Can "citizen data capture" work for any elements (such as biodiversity monitoring)?
- How can businesses in the local community support structured data collection and analysis?
- How much will each data capture and analysis piece cost?
- How will the people working on each part be linked to each other?
Unstructured data examples:
- Are people or is an organisation / specialist employed to interview citizens and businesses about certain topics?
- Are recordings made of town halls and workshops (for example, in Urban Future Centres)?
- Do you need people who know how to use AI to turn unstructured data into structured data and insights?
Budget considerations for data collection:
- Budget determines what you can do.
- Can any funding for one or more data capture elements come partially from a state / national / federal government as part of a particular programme?
- Should private sector support funding in an appropriate way be sought?
What do we do with data?
Inform everyone about what's happening
The collection and analysis of data should be transparently shared with citizens and businesses.
This can be achieved in a wide variety of ways - from sharing it in Urban Future Centres, community meetings and business meetings to showing citizens and businesses what their taxes are being spent on and how their city or town works.
A wide range of data can be used to inform and empower citizens and businesses.
For example, how their actions can make a difference to sustainable development, how vulnerable the place where they live is to hazards such as flooding and wildfire, and how they can make the most out of the circular economy habits.
Display in Urban Future Centres and other discussion arenas
Data can be displayed in Urban Future Centres and in community centres and gathering spaces, for everyone to see and to ask questions about. This can be a valuable way of showing transparent and open governance, and accountability for actions.
Inform our strategies and plans
The collection and analysis of data must take place to an appropriate level of detail to allow decisions to be taken and actions to be agreed (data paralysis must be avoided).
Set key indicators and targets
As mentioned above, the collection and analysis of data should support how we monitor indicators of performance, particularly our key indicators and the thresholds for them, so that if a threshold is getting close to being exceeded, we find out early and can take action before it becomes a problem.
Agree actions required
The collection and analysis of data must help us to make good decisions. There is a linkage here with decision-making techniques, and ensuring that appropriate people are involved in reviewing data and in making the decisions required.
Create compelling investment cases
The importance of investment cases is discussed in the Investment Options section of this website.
Investment cases need to be supported by good data to ensure a solid case is put forward to undertake a particular initiative or project.
Create and maintain city / town "selfies"
How data is effectively communicated is vital. Of course we need the numbers, but we also need to spend enough time presenting them well.
Visual presentations that are skilfully crafted can show us what is happening, and what might need to be done to improve a particular aspect or area of where we live, work or visit.
City / town selfies might help to convey certain messages.
An example of a city / town selfie, of Yerevan, can be seen here.
Who can city / town teams learn from about using data?
Valuable learnings can be gained from other city and town teams that are collecting and analysing data.
Businesses of different types and sizes that operate in the locality can also provide suggestions and learnings. For example, perhaps supermarket retailers can provide advice on structuring people-focused data, and perhaps ride hailing businesses can provide lessons on monitoring transportation data in a protected and de-identified way.
Data and the smart city / town
Smart city / town approaches can use good data...
The Swiss management school IMD, which has a centre for Smart Cities called the Smart City Observatory, defines a smart city as “an urban area that has become more efficient and/or more environmentally friendly and/or more socially inclusive through the use of digital technologies. The goal of a smart city is to improve its attractiveness to citizens and/or businesses by enhancing and/or adding city services.”
Good governance is required to manage smart city initiatives. The protection of all types of data (for the city / town and for its citizens) is paramount, by ensuring it is properly managed and kept secure. This is just one example of the value of applying systems thinking to achieve the best outcomes from any Smart City initiative.
The Suredis Cities website page on Smart Cities provides further context.
Getting started with good data
Think Big, Start Small and Scale...
It may not be possible to collect all the data that you would like to have on Day 1. You probably have to prioritise what is addressed first, and what needs to come later, in a Data Management Plan.
A plan should link to an urban Vision which requires knowing what your current state is and where you want to head with meaningful targets. Then agree how to start in a small way and how to scale up your data (in a data-protected way) over time. Have a flexible plan which can adapt as circumstances change.
Consider an Investment Case to obtain funding for your plan. With careful thought, there could be good opportunities to raise capital and ongoing maintenance funding, as long as you demonstrate value from the proposed investment requirements.
Consider guidance from urban frameworks and international standards such as ISO 37120:2018 (Sustainable cities and communities - Indicators for city services and quality of life).
