Using Analytics to Create Smarter Cities

Regional and municipal governments strive to deliver higher levels of service, but face challenges with little funding to support their efforts. One solution that delivers services more efficiently combines open data and smart city programs.

Software as a service helps cities and municipalities acquire swifter data-driven decision-making capabilities. In a recent IBM report, we get a better look at how city leaders and department heads are applying descriptive, predictive, prescriptive, and geospatial analytics to municipal data to make better-informed decisions.

Facing the Flood of Data
From public safety and transportation, to water and energy utilities, effective stewardship of natural and financial resources is critical to providing public services. But reducing waste and increasing efficiencies in such processes can be a huge challenge to local governments because of the increasing flood of data to manage.

Local governments gather large volumes of data on everything from public works, public safety and public facilities, to elections and taxes. Every day, new data is created from a city’s infrastructure through sensors and video cameras, mobile apps, and even people interacting with government.

Additionally, governments face increasing citizen demand for more convenient access to services and heightened expectations for seamless, easy-to-use experiences. At the same time, local government expenses continue to rise due to increasing urbanization, aging infrastructure, and new program requirements.

Although these demands along with the flood of data pose significant challenges, there is ample opportunity to enable cities to deliver swifter and better services.

Analytics Applied to City Problems
Fortunately, there are many ways to manage citizen demands and the flood of data, particularly through software as a service and analytics. Many forward-looking organizations seek such methods for to achieve their visions of becoming smart cities. A smart city uses digital or information technologies to enhance the quality and performance of urban services while reducing costs and resource consumption.

Business intelligence and analytics also have strong footholds in modern government. In public safety, for instance, data mining and analytics are helping to shift crime-fighting work from reactive to predictive and preventative modes in cities like Vancouver, Memphis, and London. The use of analytics in other domains, such as education, water, and public transportation, illustrate how cities are integrating analytic capabilities into many domains to meet citizen demands, harness the flood of data, and increase cost efficiency.

The following types of analytics are particularly useful for local governments tackling such challenges:

  • Geospatial Analytics: Geospatial analytics allow city managers and other stakeholders to visualize events and conditions as they happen in real time and over time. This type of analytics can produce density and heat maps, time-distance relationships, and group optimization that can assist city and departmental officials allocate resources to better understand their populations and meet their needs.
  • Descriptive Analytics: Descriptive analytics provide city leaders and departmental managers situational awareness and an understanding of historic trends. For example, users can establish baseline departmental budgetary performance and produce achievement indicators of each department. This includes the frequency and nature of inquiries or complaints registered as well as response time to address them. This type of analytics also enables administrators to determine eligibility for benefit programs and match the right services to citizen needs while preventing fraud and waste of funds.
  • Predictive Analytics: Predictive analytics develop insight into possible future conditions or events within a city or region. This type is particularly useful for public safety efforts because it measures the probable impact of programs and policies, and potential citizen reactions to new initiatives. Using predictive analytics, government can measure the efficacy of services delivered, test scenarios during emergency conditions (i.e. extreme weather conditions), and develop contingency plans before any of these scenarios are streamlined.
  • Prescriptive Analytics: Prescriptive analytics are based on an understanding of how systems behaved in the past and how they are likely to behave in the future, in a variety of circumstances. They not only enable insight into the likely results of decisions, but also suggest the optimal path for improved outcomes. Cities can use this type of analytics to support informed decision-making around developing new service delivery programs, providing personalized services to specific segments of a city’s population, or optimizing the use of limited city resources and assets.

Philadelphia, for example, uses descriptive analytics to identify tax delinquent properties that are available for auction. A property in Philadelphia is considered delinquent if taxes owed are not paid within nine months of the city’s deadline. The city publishes an interactive map showing such properties where interested buyers can easily identify opportunities to invest, which then can turn into revenues for the city.

Making Data-Driven Decisions
Citizens and stakeholders are demanding higher levels of service from government and municipally owned organizations. City leaders and department heads are applying geospatial, descriptive, predictive, and prescriptive analytics to municipal data to make better-informed decisions. With such capabilities available as a service, cities can easily and cost-effectively evolve into smart cities.


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