How Spatial Big Data Underpins Smart Cities



Han Wammes, the Business Development Manager Geospatial Information Management at 1Spatial, writes about how cities can collect and harness the necessary information to create a smart city.  In this piece, Wammes makes the case that “everything happens somewhere and only when you know where everything is can you create the connections that make cities smart. Underpinning any smart city program is trusted geospatial information; one single source of reliable, location-specific data.”

Governments, local councils, utility companies and dog owners; everyone could benefit from their city becoming smarter. Mayors and local councils recognise that a smart city is crucial for a city’s development and improvement in the 21st century. The UN reports that 54% of people worldwide live in a city, half of these in cities which have fewer than 500,000 people. So how can these stakeholders collect and harness the necessary information to create a smart city? Everything happens somewhere and only when you know where everything is can you create the connections that make cities smart. Underpinning any smart city programme is trusted geospatial information; one single source of reliable, location-specific data.

The UN said in its recent report [1] “cities are where the battle for sustainable development will be won or lost”. So how are cities beginning to manage this transformation? Many are starting with isolated, manageable projects. For example, Stockholm fitted sensors into the city’s taxis to provide information on traffic flow which can be converted into news about journey times or recommendations about commuting options. Similarly, data from toll-points can be repurposed in order to manage peak time traffic flow.

Schaerbeek in Belgium is noted for its nineteenth and early twentieth century architecture that, while very valuable and beautiful, can be expensive to heat. Schaerbeek’s innovative project proposed flying drones fitted with thermographic cameras over the city to record heat emissions. The data captured would be analysed using 1Spatial’s Elyx 3D software to create a complete, three-dimensional map of heat emissions across Schaerbeek. The geo-located thermal readings would then be combined with information from the city’s property register to provide its 130,000 residents with personalised, price recommendations for insulating their homes effectively. Accessed through a secure website, the recommended actions (such as loft insulation or double-glazing) would improve insulation whilst respecting the city’s architecture.

Shaarbeek train station
Shaarbeek train station.

In Moorabool Australia, the Shire council uses a system underpinned by geospatial data. For council rangers, the platform offers an animal registration layer that geographically displays 17,000 individual pet registrations. If a stray dog is found, rangers can access the system from a laptop or tablet and search the vicinity by breed or colour to try and locate the owner. Should a local resident contact the council, staff can access the system and potentially reunite pet and owner without the need for a ranger visit at all. The Shire also combines third-party information with its database and allows both council and public access to it.

As we outline in our Little Book of Smart Cities, the challenges facing modern cities is that spatial data gets big very quickly. For example, alongside the location-specific material, spatial data systems may also need to incorporate 3D information, residential records, citizen knowledge and historical data.

Little Book of Smart Cities
Little Book of Smart Cities

Everything has a location, footprint, form and function of a building, field or shoreline. These all interact with each other, affecting and being affected by whatever they happen to be adjacent to. This three-dimensional nature of cities causes data to mount up. On one geographic spot the data may record an underground rail service, offices and homes and perhaps  roof garden.

Geospatial data can also include postcodes, the location of a traffic light or the GPS co-ordinates of a smart phone picture. On top of this, Councils may want to add residential information such as the electoral roll or tax registers. Citizens can also contribute data to councils, for example uses citizens as sensors to provide information on the state of local roads.

Another useful piece of information, but one which contributes to the speed at which spatial data can become unmanageable, is historical data. A building, shoreline or marsh is not transient, it has a history which can be useful to analyse. Historical flood patterns for example, can be very useful to Councils considering how best to spend flood-prevention budgets. But it needs to be accurate, not muddied by additional, non-rules-based, layers of data.

These various forms of data all contribute to the challenge of achieving the required level accuracy in a collaborative environment. One solution is to set a benchmark quality, which is assessed automatically and producing metadata associated with it. If spatial data authorities can publish the known quality of the data then government agencies, industry and consumers will know what they can use it for.

Creating a rules-based data set, a one source of truth, means that contributors, who may be consumers using an app, can also know whether the data they have submitted meets the required standard. This represents a significant step up from acquiring data on an “as is” basis and then discovering later that it isn’t fit for purpose.

However, this demonstrates a need to reduce the people element of data processing, along with levels of latency and problems with human error that come with them. Ultimately, only by employing automated Big Data techniques more universally will we be able to scale the collection, validation, summarisation and the privacy scrubbing of spatial data in a sustainable way. Increasingly, automated sensor-based data collection and data grids will also be harvested to feed rules-based data cleansers and data portals with information that is e-Government, industry and consumer ready – accurate, up-to-date and safely anonymous for the public.

These trends represent a major challenge for the custodians of spatial data and for those developing smart cities. Underpinning a successful smart city will be a single, trusted source of spatial data, onto which other information can be effectively layered. This will mean that councils can not only share their data with other interested groups, such as transport companies, utilities, health organisations, but also give developers and entrepreneurs information from which to create new and innovative services.

To find out more about 1Spatial, visit


[1] A New Global Partnership: Eradicate Poverty and Transform Economies through Sustainable Development (2013).

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