creating opportunity through connection

1. Towards A National Infrastructure for Community Statistics: Local data Sharing Issues and Resources creating opportunity through connection Presented for IASSIST 2007: Building Global Knowledge Communities with Open Data Rebecca Blash, RBlash@Brookings.edu Claudia Coulton, claudia.coulton@case.edu Kathryn Pettit, KPettit@ui.urban.org Laura Smith, LSmith@Brookings.edu Dan Gillman, Gillman.Daniel@bls.gov BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

2. What is the National Infrastructure for Community Statistics (NICS)? • NICS is focused on data sharing and fostering linkages among “information silos” • NICS is focused on the removal of barriers and facilitating communication and connections between web-based data intermediaries, making them more robust, effective, and efficient • NICS will not be one place, but rather a “system of systems” connecting local, state, and federal data information providers BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

3. Who is the audience for NICS? Communities: Local areas in which people organize to create positive change and foster an environment for communication regarding real problems and needs of constituents • NICS is intended to support organizations and individuals attempting to positively transform their communities through better access to data • NICS is focused on the dissemination of data and information, real-time for practical application • NICS promotes making data “actionable” through the creation of connections between community statistical systems and private and public decision makers BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

4. How will NICS achieve this? • provide resources such as guides and how-to manuals in support of more efficient and effective data dissemination • encourage and promote data sharing at all levels: local-to-local, state-to-local, and federal-to-local • provide an environment for web based data intermediaries to create linkages through advanced metadata management BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

5. Guide to Administrative Data Records Claudia CoultonProfessor and Co-Director Center on Urban Poverty and Community Development BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

6. Neighborhood indicators require administrative records • Intermediaries acquire, analyze, disseminate and apply information about neighborhood conditions • Rely on administrative data –few other sources for small areas • Numerous challenges presented by administrative records data

7. Windshield Surveys MIS Survey Link diverse data sources Water Community Planning (Zoning) City Juvenile Court Crime Job/Child/ Family Services B and H Regional Intermediary Coroner Local Agencies County Recorder Auditor National Intermediaries Sheriff Foreclosures Census HMDA State Data Zip code patterns

8. Track multiple needs and concerns Example: Child maltreatment rates by location and groups

9. Integrate data from many sources and formats Example: Overlay delinquent taxes and violent crimes

10. What do we know about administrative data? • Catalog of Administrative Data Sources for Neighborhood Indicators Partners: Case Western Reserve University Urban Institute, National Neighborhood Indicators Partnership Brookings Institution, Urban Markets Initiative Fannie Mae Foundation

11. Findings: Types of data used • Communities using administrative records to craft measures of: • Economic conditions • Educational participation and achievement • Health status and access to care • Social service use and availability • Safety and security • Community assets and participation • Property, housing and land use • Physical and built environment

12. Municipal government Police Housing department Utilities County government Tax assessor Courts Social service agencies State government Health department Motor vehicles Regulatory agencies Federal government Internal Revenue Service Federal Reserve Bank Census Bureau Private companies Credit bureaus Business directories National intermediaries DataPlace National centers Local NGO’s CDCs Arts organizations Findings: More than 50 data sources identified

14. Example: Distressed housing markets

15. Findings: Key issues • Designation of neighborhood • Data analysis • Data Access • Data quality • Metadata

16. Effective Data Sharing:Lessons from NNIP Kathy PettitThe Urban Institute National Neighborhoods Indicators Partnership kpettit@ui.urban.org BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

17. Data Sharing Guidebook • Today’s Presentation • Introduction to NNIP • Strategies for negotiating data access • Elements of formal data agreements • Also included in guidebook • Overview of the legal framework • Basics of handling confidential data responsibly BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

18. National Neighborhood Indicators Partnership (NNIP) • Building and operating information systems with integrated and recurrently updated data on neighborhood conditions • Facilitating and promoting the direct practical use of data by community and city leaders in community building and local policy making • Giving emphasis to using information to build the capacities of institutions and residents in distressed neighborhoods BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

19. National Neighborhood Indicators Partners AtlantaBaltimore Boston Camden Chattanooga Chicago Cleveland Columbus Dallas Denver Des Moines Grand Rapids Hartford Indianapolis Louisville Los Angeles Memphis Miami Milwaukee Minneapolis Nashville New Orleans New York City Oakland Philadelphia Providence Sacramento Seattle Washington, DC

20. Strategies for negotiating data access

21. What You Need to Get Started • Knowledge of regulations that can restrict or facilitate access to data • Time and patience to identify and cultivate the right people • Careful procedures for handling data • Informal trust, formal MOUs • Staff to evaluate, process, analyze data BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

22. Why Data Providers Say No… • Preparing the file will burden my already overworked staff. • We’re afraid of being burned by bad publicity. • I’m worried about mishandling or improper release of the data. • The source data is a mess. • We’re making money from selling the data. BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

23. Why Data Providers Should Say Yes… • “Preparing the file will burden • my already overworked staff.” • Quid pro quo • Access to information from other agencies • Geocoding/maps/supplemental analysis • We have qualified staff to deal with the data • And future requests can be referred to us. BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

24. Why Data Providers Should Say Yes… • “We’re afraid of being burned by bad publicity.” • Examples where agencies and communities have benefited (or at least not been harmed) • Defining credit or disclaimers • Peer pressure: other cities have these systems, which gives them a competitive advantage BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

25. Common Stumbling Blocks to Sharing Data • Staff turnover on both sides • Data really is too terrible to be useful • Potential tensions between data provider relationships & issue advocacy BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

26. Elements of Formal Data Agreements

27. Major Sections of MOU • Purpose of agreement & organizations involved • Data transmission and description • Treatment of data and analysis • Procedural and contractual issues BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

28. ELEMENTS OF FORMAL AGREEMENTS Data Transmission & Description • Data transmission • Format, approved delivery methods • Data description • fields, time period, geographic levels, identifiers • Agency disclaimers of quality and liability BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

29. ELEMENTS OF FORMAL AGREEMENTS Treatment of Data and Analysis • Data security requirements and confidentiality protections • Conditions for release of data to third parties • Conditions for release of data analysis • Source requirements BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

30. ELEMENTS OF FORMAL AGREEMENTS Procedural & contractual issues • Update schedule and process • Amendment process • Termination causes • Authorized signatures BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

31. Towards a Culture of Data Sharing • Over time, trusted community-based institutions can develop an expectation of data-sharing, and can level the playing field around access to information. • A peer network and tools developed from experience in the field facilitates the spread of these ideas and practices to other communities. BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

32. The Role of Statistical Metadata in Local Data Sharing Laura Smith The Brookings Institution lsmith@brookings.edu Dan Gillman Bureau of Labor Statistics Gillman.Daniel@bls.gov BROOKINGS INSTITUTION METROPOLITAN POLICY PROGRAM

33. Understanding Metadata Requirements • Uniform ways to describe and manage diverse information are needed • Essential for interoperability • Recognize need to produce metadata at outset of data production • Impetus to convince agencies to focus on metadata production

34. Needs Identified • A complicating factor in using administrative data is ensuring that the correct records have been extracted for the measure that is desired • Time period in question • Duplicate records

35. Needs Identified • Data creators must anticipate disparate uses of dataset • Multiple interpretations for single data set require different metadata elements • Example: data variables that deal with both frequency and rate (“number of cases” could mean number of people or number of instances, which might be higher)

36. “Here and Now” of Metadata • Where do we want to go? • How can the current metadata system adapt to meet future goals? • How can we expand what we have now to provide more utility in creating/managing metadata?

37. Metadata Complexity • Complexity • The amount of intervention required for capturing, linking, and maintaining metadata • Based on • Sharing metadata • Sharing meaning • Complexity increases when • Number of descriptions • Kinds of descriptions • Links between descriptions • Identify common meanings

38. Complexity Levels • Levels • Data set • Survey or Program • Organization • Multi-Organization • Achieving complexity levels • Capture descriptions • Find and share descriptions • Standardization • For one survey or program at a time • For many • Find and share meanings • Harmonization

39. Problem • Research question – • What is the level of computer literacy in various communities? • Data sources • Federal data • States • Counties • Localities

40. Problem with Concepts • Same term – Not necessarily the same concept • What you really need to know: • Is the definition the same or similar? • Examples – “Computer”, “Communities”, “Literacy”, “Level” • Computer: desktop, laptop, handheld, cell phone, video game • Communities: neighborhood, city, county, state, other • Literacy: How does one define this? • Level: How does one measure this?

41. Evolution of the Web • World Wide Web • Link documents • Semantic Web • Link meanings • Requirements • Concept system • Sets of concepts + relations • Computational model • A specification for the rules and operations allowed on some set of objects.

42. Consequences • Never replace data librarians • Computer assisted concept comparison • Requires • Serious concept analysis • Identify characteristics, especially delimiting • Develop computational models • Not “just around the corner”