Planning smart cities with big data

Planning smart cities with big data

These days city planners have more access to data than ever before. Did you know that up until 2003 civilisation created only 5 exabytes (that’s a quintillion if you were wondering) of data? By the end of 2012 the same amount of information was being created every two days.

And we’re expecting to see further data collection growth for many years to come. Every second, data is created by smartphones, credit cards, tablets, computers, fitness trackers, traffic lights and sensors on buildings, trains, planes, buses and automobiles – to name but a few.

Urban planners can harness this data to provide better public services and ultimately improve urban issues ranging from transport, natural disasters, disease outbreak to infrastructure, traffic, waste disposal, education, employment, poverty and energy use.

However, the sheer wealth, speed and unstructured nature of big data means that traditional database management and analysis are unable to extract the information held within these large data sets. So while the data is there, its true value remains just out of reach.

To effectively plan cities using big data, planners must adopt innovative approaches to data analysis. Computer memory, smart algorithms, intuitive software and mathematical statistics can all work to unlock big data’s potential.

In this article, we’ll explore how urban planners in select cities are using out-of-the-box approaches to big data to tackle transport, crime and waste management.

1. Fitness trackers can improve urban infrastructure

In Portland, Oregon, 6% of trips are taken by bicycle. City planners are continually looking to improve transport routes and make streets safer for cyclists. Yet in 2013 the city faced a challenge.They wanted to upgrade their cycling infrastructure, but didn’t know enough about cyclists’ movement patterns to understand what projects would have the biggest impact. This was due to ineffective methods of data collection, such as analysing traffic cameras or counting traffic with clipboard in hand. To resolve this, transportation planners turned to the fitness app, Strava.

When a user records their route in Strava, thousands of GPS data points are sent to Strava’s head office in San Francisco. These GPS points can then be viewed over a heat map revealing the chosen route. Oregon’s Department of Transportation (ODOT) realised that they could access this data to see how cyclists were traversing the city. In 2013, the department paid $20,000 to license a data set of 17,700 riders and 400,000 individual bicycle trips.

After analysing heat-maps generated from this data, planners discovered recurring patterns in cyclists’ routes. For instance, they found that:

  • Cyclists approaching a particular intersection from the south would slow before crossing, while those coming from the north would either stop to walk their bikes over or slow considerably.
  • Riders were consistently using the east side of an intersection over the left, and
  • Riders were using a church’s parking lot as part of one route.

From these findings planners re-organised intersections, and, instead of going ahead with a proposed bike path that bypassed the aforementioned church – officials collaborated with the church to make the parking lot safer.

Through this, planners ensured that they were implementing cyclist lanes that catered to citizen’s needs, thereby encouraging their use and improving safety.

This year, Portland was named the top U.S. city for biking to work. Other cities have taken note of Portland’s success. Today, Strava has partnered with 15 cities on similar programs.

2. Predictive software can reduce crime 

Predictive policing software, PredPol, reduces and prevents crime by identifying the time and locations where crime is most likely to occur.

While this may sound Minority Report-esque, it’s anything but science fiction. In the early 2000s Jeff Brantingham and his cofounder, George Mohler noticed that criminal activity follows a similar pattern to seismic activity. They developed a model to forecast crimes, based on the same algorithm used to predict earthquakes. PredPol partnered with the LAPD who fed this model with anonymised data of over 13 million crimes from the past 80 years and discovered that the PredPol predictions matched the data from the past. The model worked.

These days the software is being used in police departments in over 60 cities in the U.S.. At the start of a shift, police officers are provided with a map that highlights identified hotspots. In this way police departments can determine where enforcement is needed the most, ensuring the effective distribution of officers - with very successful results.

The City of Santa Cruz adopted the system in an effort to identify automotive theft hot spots. Within their first year of using the software (2011-2012), the city saw burglaries drop by 11% and robberies by 27%, while auto theft recoveries rose by 22% and arrests increased by 56%.

3. Sensors can implement smart waste management

Songdo in South Korea has implemented an innovative solution to improve waste management and reduce waste on the city's streets.

All household waste is sucked direct from individual kitchens, through a network of underground tubes, to waste processing facilities; where it is automatically sorted and recycled, buried, or burned for fuel.

However to dispose of their waste, citizens need to use a chip-card with garbage containers. This data is collected, analysed and monitored in real-time by Songdo central monitoring hub. This enables governments to measure how much waste is being disposed of where, and when, helping them optimise disposal routes.

Through this method, Songdo has eliminated the need for trash cans and garbage trucks. In fact this waste management system requires just seven employees for the entire city.

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