People in motion help planners design better cities

Spatial data are much more than a map on your phone. They can help planners to create cleaner cities that work better, and to make life more comfortable and safer, says Laura Ruotsalainen, recently appointed associate professor of spatio-temporal data analysis.

The smartphone in your pocket is constantly collecting data on where and how you move. Your movements also leave information on the various cameras in our environment, travel card readers and payment terminals in shops.

Data accumulated in various databases could help city planners who are trying find ways of building safer cities that work better.

“However, spatial data from different sources are still spread out and exist in several formats, where their quality varies and the quantity is huge. The fragmented data must be combined into a form that we can use,” asserts Laura Ruotsalainen, an expert of navigation research.

She began her work as associate professor of spatiotemporal data analysis at the Helsinki Centre for Data Science, HiData in August. The perspective is that of sustainability science, particularly urban planning. Ruotsalainen’s duties include the development of algorithms to model measuring errors in location-based data and to aggregate the results.

“In an ideal world, our smartphones would be collecting correct data for navigation without the user even noticing or having to do anything. The device would be able to switch location methods seamlessly depending on which one is the most viable in each environment,” says Ruotsalainen.

For the average smartphone addict, this could mean that it would be possible to navigate from home to a meeting in another city, while directions for routes, different modes of transportation and the correct meeting room would automatically update on the phone. At the moment, this is only possible by using a number of different applications.

“This means that life will become safer and more comfortable without fanfare,” says Ruotsalainen.

Future fusion

But we do not live in an ideal world. For example, satellite navigation, popular as it is, is never perfect. It works poorly in environments with high buildings and is useless indoors.

The small, cheap sensors in our smartphones are currently unable to produce precise location data. Individual measuring results must also be cleaned before they can be used, which requires the use of error models created through statistical analysis. After that, the data must be combined through data fusion.

Machine learning is also necessary to work on the data. The use of machine learning is also a significant part of the field of Ruotsalainen’s associate professorship.

“Machine learning can help us recognise movement and model the ways in which people move. We can teach the system to notice that when these kinds of measurements start showing up from the smartphone, the person is walking or running. This will help us improve the location data and produce relevant information about human behaviour in a variety of different places and settings,” Ruotsalainen explains.

Map providers, transportation authorities and tech companies should also be encouraged to develop services that could form a seamless, universal location ecosystem. One of Ruotsalainen’s duties is to generate the technological foundation for such an ecosystem.

“We need a variety of technologies which could be combined through different forms of data fusion as the situation warrants. For example, a self-driving car should be able to change its navigation method on the fly, according to the environment and traffic situation,” states Ruotsalainen.

Security cameras generate urban data

According to Ruotsalainen, location-based services should be taking advantage of image data more frequently. Photos could help urban planners better understand how humans move through a city. One way of doing this would be to use material from security cameras, but this is questionable in terms of privacy.

Ruotsalainen emphasises that data protection is a primary concern.

“People must be able to decide how their data are used. Images from security cameras should only be used for purposes that have been explicitly agreed upon. It must be possible to erase faces, or to only extract data which can be used without identifying people.”

Ruotsalainen points out that this is currently difficult in practice, and not just in terms of face recognition. Corporations such as Apple and Google, which operate with huge amounts of location data, claim that they do not identify information to specific users. In reality, it would be easy to deduce a person’s home or place of work based on repeated location pings.

“We must keep ethics at the core of development. Our goal is an easier life, not surreptitious surveillance.”

Introducing people behind HiData 

This series will introduce new professors in the tenure track system of the University of Helsinki working at the Helsinki Centre for Data Science. 

Other parts of the series: 

Keijo Heljanko, professor of parallel and distributed data science: In­creas­ing masses of data may leave com­puters be­hind and cause an en­ergy crisis

Kai Puolamäki, associate professor of data science and atmospheric sciences: Data science in­ter­prets at­mo­spheric particles and helps find the clean­est urban routes – if we know what to ask com­puters

Nikolaj Tatti, associate professor of privacy-aware and secure data science: Data science may soon ex­pose fake news

Antti Honkela, associate professor of data science - machine learning and AI:  Every­one has their secrets – ma­chine learn­ing needs to re­spect pri­vacy

Dorota Głowacka, assistant professor of machine learning and data science: Fu­ture search en­gines will help users find in­for­ma­tion they don’t even know they are look­ing for

Laura Ruotsalainen
  • Began as associate professor of spatio-temporal data analysis at the Helsinki Centre for Data Science (HiData) in August 2018.
  • Led the Sensors and indoor navigation research group at the Finnish Geospatial Research Institute of the National Land Survey of Finland before coming to the University of Helsinki.

  • Master of Science 2003, University of Helsinki, major in computer science.

  • Doctor of Science (Technology) 2013, Tampere University of Technology. In her doctoral dissertation, she studied the use of machine vision in pedestrian navigation.