Urban Data Pioneers Initiative Overview

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The Urban Data Pioneers program in the City of Tulsa focuses on creating multi-disciplinary teams to deeply understand and solve city problems using data analysis. The initiative's mission, vision, values, history, goals, and options for upskilling are outlined to empower participants in improving data utilization throughout the city.


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  1. Urban Data Pioneers Team Formation Meet-Up September 6, 2018 Image result for oregon trail

  2. Agenda Purpose: Create multi-disciplinary learning teams of city employees and non-employees to deeply understand problems that can be informed by data analysis. Outcomes Learn from each other Balanced teams created Plan for next steps and meeting times Clear picture of the problem you re exploring Process Presentation Form teams Work in teams and identify next meeting date

  3. UDP Mission, Vision, Values Mission: Improve the use of data throughout the City of Tulsa Vision: Urban Data Pioneers are the trailblazers within the City of Tulsa who lead the way in identifying best ways to utilize data, educating others, and delivering results based upon data. Values: A Learning Culture Respect Integrity Transparency

  4. History of Urban Data Pioneers April 2017 1st cohort presentations 5 projects August 2017 2nd Cohort Presentations 3 Projects Feb. 2017 Kickoff first cohort March 2017 Data Fields of Dreams June 2017 Kickoff 2nd Cohort October 2017 Kickoff 3rd Cohort May 2018 Engaged Cities Award Jan. 2018 May 2018 3rd cohort presentations 5 projects Feedback Session and Survey Results July 2018 Call for Projects Sept. 2018 Kickoff 4th Cohort August 2018 Project Scoping November 2018 4th Cohort Presentations

  5. Goals 1. Increase data analytics skills 2. Deeply understand relevant problems at City Hall through analytics 3. Practice storytelling about data 4. Work across silos and with residents

  6. Option 1: Urban Data Pioneers Level-UP Sign up at DataCamp.com 1 Enroll in and complete Intro to SQL for Data Science and A. Intro to Python for Data Science -OR- B. Introduction to R 2 When complete, send your certificate to Catherine (Erica) Moore emoore@cityoftulsa.org 3 Must complete by October 1

  7. Two Tracks http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1523009719/main-qimg- 9dcf536c501455f073dfbc4e09798a51_vpijr0.png

  8. Option 2: Urban Data Pioneers Level-UP Lite Sign up at DataCamp.com 1 Enroll in and complete Spreadsheet Basics Data Analysis with Spreadsheets 2 When complete, send your certificate to Catherine (Erica) Moore emoore@cityoftulsa.org 3

  9. Project #1 Analyzing Tulsa s 311 Customer Care Data Overview Analyze 311 Customer Service call center data to understand patterns of interaction with the City s Interactive Voice Response (IVR) system Key Question What pathways are most likely to be successful (i.e. bulky waste, missed trash pickup, etc.)? Dataset(s) Individual IVR records for calls regarding the city s water and refuse utilities Project Sponsor Michael Radoff mradoff@cityoftulsa.org Dianna Phillips diannaphillips@cityoftulsa.org

  10. Project #2 Nuisance Frequent Fliers Overview The City can abate (clean up) any high grass/weeds or junk/debris complaints if the property and owner have had the same violation within two years. We want to create optimized routes for inspectors to optimize neighborhood health. Key Question Which properties and/or owners are the most frequent violators of city code? Can we identify property that is continually receiving nuisance complaints so that WIN can schedule routine inspections and abatements? Dataset(s) Nuisance Complaints (high grass/weeds, junk/debris) Summary Abatements Project Sponsor Dwain Midget dmidget@cityoftulsa.org

  11. Project #3 Zoning and Sales Tax: How can Land Use Policy Impact City Services? The City relies on sales tax revenue for operations. Zoning districts that allow taxable sales may impact the city s ability to provide services. Overview Key Question Can we estimate the sales tax productivity potential of each of the different zoning districts in the City of Tulsa? What attributes of each district have the greatest effect on tax productivity? Zoning, Aggregated Sales Tax Dataset(s) Project Sponsor Stephen Lassiter stephenslassiter@gmail.com Nathan Pickard npickard@cityoftulsa.org

  12. Project #4 Eviction Prevention Overview Evictions can have a dramatic effect on families. Tulsa ranks 11th in eviction rates nationwide. Key Question How can multiple datasets be leveraged to build an eviction risk score for properties where eviction is likely to occur in the future? Dataset(s) Historic eviction data, Property ownership data, Utility turnovers Project Sponsor April Merrill, Legal Aid Services of Oklahoma April.Merrill@laok.org Ryan Gentzler, OK Policy, rgentzler@okpolicy.org

  13. Project #5 Reducing Injury Rates at City Hall Overview The City collects data on injuries that occur at city hall, but lacks a repeatable method for measuring rates by department and division. Key Question What is the City of Tulsa s workplace injury rate by department and division? What other factors (age, experience, supervisor, etc.) correlate with injury rates? City of Tulsa Injury Reports, Employment by Department Dataset(s) Project Sponsor Brannon Dodd bdodd@cityoftulsa.org

  14. Project #6 Improving 911 Answer Times Overview The Tulsa 911 Public Safety Center could optimize overtime offering by analyzing patterns in call volume over the past two years. Key Question How can 911 use call volume data to optimize resource allocation and prioritize work for impact ? / If we could identify a pattern for calls, we could align the shifts and staffing to the known pattern and optimize the offering of overtime. 911 Calls (ECaTS) and Dispatch (CADS) data, Weather data Dataset(s) Project Sponsor Terry O Malley tomalley@cityoftulsa.org Belinda McGhie BMcGhie@cityoftulsa.org

  15. Form Teams Each team needs: 1. Team Lead (project manager) 2. Data Analyst (Excel, Tableau, GIS, Python, R, SQL) 3. Subject Matter Expert 4. Data Storyteller 5. Learners!

  16. Suggested Milestones Week 2: Sept. 20 - Datasets collected Oct. 1 (optional) DataCamp Prerequisites complete Week 5: Oct. 11 Preliminary Data Analysis Week 8: Nov. 1- Refined Data Analysis/Visualization Week 10: Nov. 15 Draft Slide Deck Week 12: November 29 Presentations

  17. Meet-Up Spaces City Hall Small meeting rooms available Ben Harris can book for you Public Wi-Fi Only between 7:00am-6:00pm M-F

  18. Meet-Up Spaces Tulsa Central Library PC and Mac Laptops available for checkout Small meeting rooms available Requires library card

  19. Slack https://urbandatapioneers.slack.com Recommend installing Slack App (Windows, Mac, iOS, Android)

  20. Virtual Meetings Uber Conference Screen Sharing (Need Google Chrome) Call-in or VOIP Call Recording Limited to 10 people for free version Fun hold music!

  21. Awards Best Overall Team: The team that exhibited strong teamwork and collaboration, exhibited by data analysis, presentation, and learning. Most Unique Insight: The most unusual or unexpected observation.

  22. Help! UDP Projects Ben Harris James Wagner jwagner@cityoftulsa.org 918.576.5578 bharris@cityoftulsa.org 918.576.5139 UDP Level UP Enrollment Advisement Catherine Moore emoore@cityoftulsa.org Kevin Gustavson kgustavson@cityoftulsa.org

  23. 1 Choose a Project 2 Form a Team Roster & Charter Post charter on Slack! 3 4 Meet & Repeat

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