Laserfiche WebLink
Date: <br />EXHIBIT A <br />Center for Data Science & Public Policy <br />THE UNIVERSITY OF <br />CHICAGO <br />Overview <br />Water shutoffs are expensive, both for the residents and government of South Bend, Indiana and have <br />wide -reaching negative impacts. For residents, it causes them inconvenience, sometimes leads to <br />removal of minors by Child Protective Services from homes without water service, and hurts credit <br />ratings of individuals (often minors listed on the utility account by their parents). For the city <br />government, shutoffs generate a great deal of work, ranging from responding to phone inquiries from <br />residents to the process of physically shutting the water off and on (including travel time and <br />technician labor). South Bend wants to explore what they can do to identify households at risk of not <br />paying their water bills and understand what type of interventions, such as referring them to support <br />programs, can help prevent water shutoffs and reduce financial and personals costs for both the <br />government and the residents. <br />Project Description <br />This project will do exploratory analysis around water shutoffs to determine: <br />1. If the scale of the problem is sufficient to generate city policy or operational changes <br />2. Suggest possible interventions and <br />3. Scope a more operational project <br />Recognizing that the most vulnerable residents are affected by a complex set of circumstances related <br />to municipal services, this project will explore indicators of water shutoffs and future negative <br />outcomes to the individuals having their water shut off. <br />Aspects of this project might include: <br />• Exploring the relationship between 311 calls, water shutoffs, and housing violations in the City <br />of South Bend, with the intent of identifying possible interventions that minimize disruption of <br />water and sewer services. <br />• Analysis of narratives from 311 calls to see if calls to 311 precede or predict water shutoff. <br />• Analysis of utility payments including information about entities that make payments on behalf <br />of an account holder in the interest of assisting distressed customers, late payments, and shut <br />offs caused by delinquency. <br />• Analysis of consumption as determined by water meters, especially as it relates to vacancy in <br />the service locations. <br />• Analysis of the cost to the city to shut water off compared to <br />• Analysis of the housing violations associated with locations where utility services have been <br />interrupted. <br />Data Sources <br />• City Utility Data <br />6 <br />Center for Data Science and Public Policy 2016 <br />