City of Milwaukee Case Study: Using MDC To Identify Homeless Population

Mapping the Homeless population

City of Milwaukee is the largest city in the State of Wisconsin, the 31st most populous city in the United States and is located on the southwestern shore of Lake Michigan. On any given day there are more than 1,500 homeless men, women, and families in the Milwaukee area. Milwaukee Continuum of Care (CoC) and the State of Wisconsin decided to measure progress toward ending homelessness and identifying unmet needs of the homeless through the bi-annual Point In Time Count*. This survey would capture the number of individuals who are homeless and living in Emergency Shelters, Safe Havens, and Transitional Housing as well as persons on the street of another place not meant for human habitation.

Challenges

The idea behind using GIS Cloud was to have an enhanced counting mechanism for identifying the homeless population in Milwaukee City/County.

  • track locations overtime
  • communicate where other outreach teams have already visited
  • using the notes options for tracking the number or people who took the survey, the number who refused, or stated they were not homeless
  • use the data gathered for further data tracking

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Solution

During the event, local Milwaukee shelter agencies, numerous advocates and volunteers conducted interviews on homeless individuals to obtain different types of feedback using Mobile Data Collection app. Prior to going to the field, a custom form was created using Mobile Data Collection Portal so that all the data collectors would have the same form, simplifying and unifying the process.
Using the MDC app, real time information was obtained on how or why a person becomes homeless, the current length of homelessness, recidivism, and chronic homelessness. Data was able to be reviewed directly from the field and also cross-referenced with data from previous collections.
Existing collected data was uploaded to Map Editor, along with other types of data like streets, police station locations, parks, food banks, meal sites etc. Adding these different types of data made the map more intuitive for the later on analysis when identifying and analyzing different homelessness patterns.

Results

Combining the PIT method and GIS Cloud apps, this undertaking accomplished more in one day than what might normally take several months. This is an example of how to extend your workflow to Cloud, making it faster, efficient and accurate by using what you have, creating and collecting new as well as collaborating between you and your data collection team. GIS Cloud enables you to have:

  • real time data collection based on a custom form specified for City of Milwaukee’s project
  • possibility of having multiple users collecting data simultaneously
  • upload and use previously obtained data in different vector and raster formats
  • review and edit existing data from the field and office
  • make custom maps to use in field data collection as well as in office analysis
  • export all the data in different formats and continue using the data in different Desktop solutions

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*“You Count,” Milwaukee’s CoC’s Point In Time Count, is a nationally recognized survey model, and this bi-annual event occurs locally every January and July. It is a statistically reliable, unduplicated count of people experiencing homelessness during a designated one-night period