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Data for Local Communities: Meta data Practices and Problems

This article discusses the practices and problems of meta data in community data intermediaries. It explores the challenges and suggests approaches for managing and providing access to data from state and local agencies. The text language is English.

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Data for Local Communities: Meta data Practices and Problems

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  1. Mandel School of Applied Social Sciences (MSASS)

  2. Data for Local Communities: Meta data practices and problems Claudia J. Coulton (Professor and Co-Director) Perspective of community data intermediary Local Practice Need for Meta Data at several points Problems when data originates from the state or local agency Releasing indicator data Releasing point or records data Conclusions : Challenges and Suggested Approaches

  3. Perspective of community data intermediary • Community data and indicators systems are information tools providing a wide range of data about specific locations • Synthesize data from wide-range of sources to provide multifaceted picture of a particular geography or issue • Inform public and private investment and action in communities, building blocks of successful regions • “One-stop shop” gives citizens, governments and organizations equal access to analytic tools • (GIS, graphics, digital data, etc.)

  4. Local Practice • Obtain administrative records data files from state and local agencies • Extract, format and geo-code records • Calculate selected “indicators” • e.g. rates, ratios, medians, pct. change, etc. • aggregated to neighborhoods, towns • Make “indicators” data available to “users” • Or make records data available to “users” • e.g. mapping, aggregating, downloading

  5. Need for Meta Data at several points • When data originates from the state or local agency 1 • When new individual record data set is crafted by the “intermediary”. 2 • When “aggregated” indicator data is released 3 • When individual record data is released 4

  6. Problems when data originates from the state or local agency • Extract from data gathered to manage a program • Typically comes with no meta data • Through conversations and trial and error, intermediary determines what data mean • Result is a new file for intermediary use with improvements • But this may not have good meta data either

  7. Example: Water shut off-hook up • Receive comma delimited text file from main frame system via ftp • Negotiate initial file extract and transfer • Have repeated conversations to determine what fields mean and can be used • Craft variables, definitions, labels • Establish revised extract and regular delivery schedule • Prepare meta data

  8. Releasing indicator data • Many sites provide little meta data • Users need information about • Original data source I • Cleaning and accuracy • Geographic identifiers • Indicator calculation • Suppression rules

  9. Examples: Indicator data 1 2 3

  10. Releasing point or records data • Users need to know • Original data source • Timing of information • Standards of geographic data • Definitions of variables • Base maps • Example

  11. Conclusions : Challenges and Suggested Approaches • What to do about source data • Challenges • usually extract from management system • errors, over-writing, changing input, etc • agency staff don’t always know • Many local jurisdictions • Approaches • National guidelines (e.g. Birth certificates) • Consortia • Agency valuing data

  12. Conclusions : Challenges and Suggested Approaches • What to do about intermediary data sets • Challenges • Users usually insiders • Some don’t update regularly • Frequent changes in source data discouraging • Approaches • National guidelines for meta data • Training • Multi-site applications

  13. Conclusions : Challenges and Suggested Approaches • What to do about delivery to users • Challenges • Many novice users • Numerous data sources in “one stop” warehouse • Approaches • Embed meta data at many points (e.g. Filtering, Variable selection, Geographic selection, Data downloading) • Customize meta data for user choice

  14. …..…seeks to address the problems of persistent and concentrated urban poverty and is dedicated to understanding how social and economic changes affect low-income communities and their residents. Based in Cleveland, the Center views the city as both a tool for building communities and producing change locally, and as a representative urban center from which nationally-relevant research and policy implications can be drawn. Mandel School of Applied Social Sciences Case Western Reserve University10900 Euclid Avenue Cleveland, Ohio 44106-7164Tel: (216) 368-6946, Fax: (216) 368-5158 E-mail: povcenter@case.eduWebsite: http://povertycenter.cwru.edu, http://neocando.cwru.edu

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