Almost every area of a city has the capability to use big data, whether in the form of taxes, sensors on buildings and bridges, traffic pattern monitoring, location data, and data about criminal activity. Creating workable policies that make cities safer, more efficient, and more desirable places to live and work requires the collection and analysis of huge amounts of data from a variety of sources.
Much of the data that is pertinent to research on public policy improvements is collected by various city agencies and has historically taken months or years to analyze (such as annual census data, police records, and city tax records). Even within one specific agency, such as the police department, data may be collected by separate districts and not easily shared across the city and its surrounding communities.
As a result, city leaders have an abundance of information about how policies impacted people in their city in prior years, but it has been very challenging to share and leverage fast-changing data to make real-time decisions that can improve city life. What makes leveraging this data even more complicated is the fact that data is managed and stored in separate silos.
This causes problems because a direct relationship can exist between different aspects of city operations. Policy makers are beginning to realize that change can only happen if they can use the available data and data from best practices to transform the current state of their environment. The more complex a city, the more a need exists to leverage data to change things for the better.
This is changing as policy makers, scientists, and technology innovators team up to implement policies based on data in motion. For example, to design and implement a program to improve traffic congestion, you may need to collect data on population, employment figures, road conditions, and weather. Much of this data has been collected in the past, but is stored in various silos and represents a static view of historical information.
To make suggestions based on current streaming information, you need a new approach. Researchers at a technical university in Europe are collecting real-time traffic data from a variety of sources such as Global Positioning System (GPS) data from traveling vehicles, radar sensors on the roads, and weather data. They integrated and analyzed the streaming data to decrease traffic congestion and improve traffic flow.
By analyzing both structured and unstructured data as events are taking place, the systems can assess current travel conditions and make suggestions on alternative routes that will cut down on traffic. Ultimately, the goal is to have a major impact on traffic flow in the city. Data in motion is evaluated in connection with historical data so that the recommendations make sense in context with actual conditions.
Streaming data can have a significant impact on crime rates in cities. For example, a police department uses predictive analytics to identify crime patterns by time and location. If a sudden change is found in an identified pattern to a new location, the police can dispatch officers to the right location at the right time. After the fact, this data can now be used to further analyze criminal behavior patterns.