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Moving from Central Clearinghouse to Virtual Systems Dilemmas Faced by Milwaukee Michael Barndt and Todd Clausen, Nonprofit Center of Milwaukee, Data Center.

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Presentation on theme: "Moving from Central Clearinghouse to Virtual Systems Dilemmas Faced by Milwaukee Michael Barndt and Todd Clausen, Nonprofit Center of Milwaukee, Data Center."— Presentation transcript:

1 Moving from Central Clearinghouse to Virtual Systems Dilemmas Faced by Milwaukee Michael Barndt and Todd Clausen, Nonprofit Center of Milwaukee, Data Center Nancy Olson, City of Milwaukee, COMPASS Program June 17, 2005 All opinions in this PowerPoint expressed by Michael Barndt

2 An Embarrassment of Riches  Milwaukee has the best, integrated, detailed housing series in the country – Since 1976  Other time series – Public health – 1995, Child Census – 1991, Crime  The public has access to data, analysis and support through a number of city, university and nonprofit organizations.  Data organizations are building a collaboration through the Milwaukee Data Consortium  Many national models stressing the value of data use by community have included Milwaukee components – NNIP, COMPASS, Reentry Mapping Network, Making Connections, Center for Economic Development, HUD-COPC  BUT – Integrated technologies are more difficult to implement in a diverse, decentralized environment.

3 Climate of public access Some form of online access to detailed properrty information since late 1980’s

4 Consolidated file structure  Master Property File  Consolidates data from multiple city departments  Assessment  Property description  Property ownership  Service districts  Tax delinquency  But, independent data sets continue to be used for administrative purposes

5 On-line Transaction detail

6 Frequent use by residents monitoring neighborhood conditions and responsiveness

7 City of Milwaukee - COMPASS An ARC-IMS application backed by a City Government “Warehouse” extracted from administrative systems

8 Data Extraction A variety of tools permit downloads by spatial or value attributes

9 “Live” Indicators Several pages of indicators from multiple sources by selected census tracts or other “neighborhood” designations

10 Housing Coalition – City Collaboration Neighborhood Associations survey areas – Work with city to triage enforcement policy Survey data is automatically merged with city records

11 City of Milwaukee Health Department Public Health Time Series Surveillance Systems Indicators/ Outcomes Children tracked across tests by age cohort. Population rates include migration estimates for 2 year period

12 U W Milwaukee – Employment Training Institute  Employment and Economic Well-Being of Families  Unique access – Released only at zip code  State tax files – EITC/ income by household type  Child care subsidies  Food stamps  TANF - W-2  Public medical insurance  ES-202 Business lists  Driver’s license revocations  Plus data available from multiple sources and national zip-code level data

13 One of many time series Annual Report for Central City Zip Codes

14 Public Health Approach to Understanding Violent Deaths VICTIM Vital Records Medical Examiner/Coroner AGENT/VEHICLE WI Crime Laboratory Bureau of Alcohol, Tobacco & Firearms (ATF) ENVIRONMENT Law Enforcement Uniform Crime Reports Criminal Information Bureau WI Court System Medical College of Wisconsin Firearm Injury Center

15 Connecting the Dots….. Connecting the Dots….. Coroner/ Medical Examiner Supplementary Homicide Reports Police Case Reports Violent Injury Reporting System of Wisconsin Alcohol, Tobacco & Firearms (ATF) ( firearms only) WI Dept of Justice & Supreme Court (criminal history) Vital Records Crime Laboratory (firearms only)

16 Nonprofit Center of Milwaukee Data Center  National Neighborhood Indicators Partnership  Making Connections Outcomes/ Results  Community Development Block Grant customized services  Member services for program and planning needs of the nonprofit community

17 Washington Park Macros create visual graphics for any neighborhood

18 Customized maps

19 Template draws from two data sets – for any neighborhood

20 Trend lines Normalized to City Values

21 Micro-level population estimates Census Child Census Births Housing stock

22 Detailed risk assessment Map is detailed, but not confidential

23 Desktop applications with local annotation by housing developers

24 Desktop to web interface within Microsoft Access

25 Other Potential Data Sources  Community Assets – 211 Information & Referral system  Government performance – 311 Service request/ tracking  Educational achievement – Student tracking system  Housing production/ investment systems  Integrated medical information systems  Employment – LEHD  Consolidated customer systems – youth development agencies  Social, Welfare and Health Services – State Systems

26 Challenges  Security requirements  Analytical capacity  Effects of data limitations  Statistical issues  Policy and political barriers  Effective utilization

27 Security Requirements  Security systems need to be recognized as “safe”  Query tools should allow those with lower security to extract safe aggregate data from case files  Reporting systems should allow for post processing for confidentiality, confidence and data limitation annotation  Flexible geography should be available with “hacker” protection

28 Analytical Capacity  Case extraction – search for patterns – best match  Case management – track over time – especially mobile people/ students  Blend people and place tools  Blend point data and “ecological” data  Generate trend lines  Incorporate spatial statistics and spatial mapping  Pay special attention to denominators for population data – annualized estimates

29 Effects of data limitations  Data is likely to have time variations that lead to difficult to resolve contradictions  Transaction data can be difficult to summarize  Mobility affects people/ place connections  Gaps in data linkages can be substantial  A “data cube” that is swiss cheese  Spatial – Temporal dilemmas – changing parcel shapes/ school district shapes

30 Statistical Issues  Limit data access to small numbers based upon “confidence” standards rather than “confidentiality” standards  Express data results with confidence intervals  Use spatial statistics to test for significant patterns – clusters of data are not detected by traditional statistics

31 Policy and Political Barriers  The legal issues – Guardians retain the legal responsibility and consequences for data security  Limited rules – HIPAA “de-identified data” limits geography to zip code  Privatization – raises costs and limits access  MOA Transition – Toward acceptance for generalized use of data by unspecified parties  Merging business models –public web systems/ measured data access/ formal reports/ custom services  The usual defensiveness - CYA

32 Effective Utilization  Public and non-technical users require support  Web sites – “Throw the data over the wall”  The equivalent of operator free phone systems  Content providers fear misuse, misinterpretation, misunderstanding of data

33 For Milwaukee Web Sites:  See Milwaukee Data Consortium web site  Follow to “Links” 


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