Tackling urban rat populations using predictive data analytics

  1. Health/Civic Engagement: Tackling urban rat populations in Chicago
  1. Chicago kills rats with big data, predictive analysis (Fortune – April 29, 2015)
  • The City of Chicago has collected 12 years worth of data using resident complaints, ranging from calls about rodent sitting to graffiti, organizing the data into different clusters, which lead the engineers determine where the rats can potentially breed allowing a proactive response to rodent infestations
  • Using the data analytics ‘SmartData’ platform, the City of Chicago discovered that there is a relationship between calls regarding overflowing trash bins and food poisoning in restaurants and rodent infestations
  • The analysis and reporting is led by the Department of Innovation and Technology in partnership with the Event and Pattern Detection Laboratory at Carnegie Mellon University, and the data is shared with the city’s sanitation team
  • In addition to tracking formal resident complaints, the data engineers save every tweet and Facebook post geocoded in Chicago. Then the information is segregated in various clusters from crime to sanitation complaints. Similar to the complaint tracking through the 311 system, this information is saved and segregated into clusters and is shared with respective departments
  • The results of using this data forecasting has resulted in a 20% more effective rodent prediction and delivery of rodent abatement services
  1. The organizational stakeholders will include a number of governmental departments, such as the Department of Innovation and Technology, the Department of Sanitation and the 311 Department. Eventually, this data could also be used by the Communications Department for increased civic engagement with citizens. Residents are also key stakeholders in the successful implementation of this strategy, in reporting and responding to this information.
  1. To successfully deploy this technology in other urban settings, it should consider the following steps:
  • Step1: Identify and coordinate key stakeholders and data resources
  • Step 2: Coordinate a strategic plan and develop software (including the deployment of the data collection and analysis, as well as communication strategies between the different stakeholders)
  • Step 3: Begin testing and piloting the implementation of the platform and loopback to make improvements for a more robust roll-out
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