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Using Real-Time Data and Community Feedback for Better Policing in Grand Rapids

Grand Rapids, MI

Elucd provides actionable sentiment data to help the Grand Rapids Police Department better understand three key questions: 1. How are they doing when it comes to community sentiment? 2. Where should leaders focus efforts to improve? 3. How can leaders anticipate and respond to major events?

Topics Covered

Open Data
Transparency
Economic Inequality
Law Enforcement and Emergency Response

Cost

Initial: 75 Thousand USD

Funding

Grants

Public Private Partnership

Federal grants

State and local grants

Philanthropic grants

Project Status

Operational since 2022

Gov Champion

Police Department

Problem Addressed

Before his recent retirement, Grand Rapids Police Chief David M. Rahinsky had spent more than 32 years in law enforcement and had worked in four states: Pennsylvania, Florida, Tennessee, and Michigan.

As a police chief, his goals were clear: to reduce crime while at the same time improve community relations. To this end, he often employed innovative approaches to achieve his goals.

Like all other American cities, Grand Rapids had experienced its share of challenges. Following an unfortunate incident involving several teenagers that generated national headlines as well as highly charged public meetings, leaders began looking for new ways to gauge public sentiment and strengthen trust in the police force. Among the options considered were commissioning a professional study, inserting a mailer to be included in local water bills, even going door-to-door. While these options could provide a one-time snapshot of how local residents felt about their city government and the police force in particular, leaders wanted something that would help over the long-term.

Police Chief David M. Rahinsky, with support from the Mayor and City Council, looked for an innovative approach to improve community relations. Specifically, he was looking for a partner or tool that could help him track ongoing community sentiment data at a neighborhood scale.

Based on that data, he'd be able to better allocate limited resources, increase communication within his leadership team about the best tactics to employ to improve community trust, demonstrate to the residents of Grand Rapids his commitment to increasing their perception of trust, safety, satisfaction, and provide a new metric to discuss the effectiveness of the police force that went beyond discussions about crime and clearance rates.

Solutions Used

After learning about Elucd through the Mayor, Chief Rahinsky persuaded the City Council to hire Elucd to provide ongoing data about community sentiment.

Over the course of three months, Chief Rahinsky and his Service Area Captains worked with the Elucd team to identify and add new features to improve the usefulness and actionability of the data collected to enable the Grand Rapids Police Department (GRPD) to improve relationships with local residents. At the end of the process, Elucd's data model for Grand Rapids offered a granular level of data - still preserving resident anonymity - to aid GRPD's decision-making process. In addition, it created new opportunities to strengthen their policing and community-building efforts by:

1.Reporting down to the neighborhood level.

The Grand Rapids Police Department had divided the city into five sectors (north, south, east, west, and central), each with its own Service Area Captain. It soon became clear that these sectors were too large for the purposes of gathering actionable insights into community sentiment. Elucd modified their model to gather data at the neighborhood (beat) level. This additional layer provided the GRPD with a more nuanced way of identifying patterns and understanding how different residents were perceiving how safe they felt and how much trust and satisfaction they felt with the police department.

2. Providing new demographic breakdowns of community sentiment.

In addition to assessing sentiment across different neighborhoods, the GRPD wanted to know if changes in sentiment were being driven by specific groups of people. To answer this question, Elucd began to gather demographic data in addition to their other polling questions. This change enabled the GRPD to further drill down and analyze the results to see where they should be allocating their resources.

3. Institutionalizing a new way to learn about specific concerns affecting the community.

Like most police chiefs, Chief Rahinsky repeatedly received feedback from the same subset of residents. To help reach a wider pool of people, Elucd added an open-ended field where residents could easily share their concerns anonymously. In many cases, residents provided specific, actionable feedback that drew attention to safety-related problems in their community. This essentially gave the GRPD a roadmap to specific issues that their residents wanted solved before those issues became a larger problem.

Outcomes

1

Improved data-driven management through objective, neighborhood-level metrics about key aspects of resident sentiment.

2

Better, more innovative ways to track and understand specific concerns affecting the community.

3

Benchmarking capabilities about how your city compares to peer cities across the country in key areas of sentiment.

Something Unique

Chief Rahinsky wasn't overly concerned that Elucd would create yet another metric against which his department would be measured. Instead he focused on the fact that it could help him identify neighborhoods that had weak trust and safety scores so he would know where to deploy his community policing specialists.

Who Should Consider

Cities of all sizes that want better, more objective data about public opinions and sentiment to enhance data-driven management efforts.

Last Updated

Sep 5th, 2019
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