administrative lessons learned philadelphia neighborhood information system presenter: dr. dennis...
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Administrative Lessons Learned
PhiladelphiaNeighborhood Information System
http://cml.upenn.edu/nis
Presenter:Dr. Dennis Culhane, CML Faculty Co-Director
University Of PennsylvaniaCartographic Modeling Lab
• Neighborhood Information System
• City of Philadelphia and University Of
Pennsylvania Partnership Model
Agenda
The Philadelphia Neighborhood Information System is a family of interactive mapping applications that allow you to find information about your neighborhood.
The NIS consists of:
parcelBaseneighborhoodBase
crimeBasemuralBase
PhillySiteFinderschoolBase*
The Philadelphia- University of Pennsylvania Partnership Model
City agencies:
• Provide data and in-kind services of data processing staff
• Provide internal political support for interagency data requests
• Identify critical policy questions to address
The Philadelphia- University of Pennsylvania Partnership Model
University Responsibilities:
• Archives the data (data warehousing)
• Coordinates data exchange agreements
• Designs GIS applications for end-users
• Hosts and maintains websites for applications
• Conducts basic research and policy analysis (supports nonproject researchers as well)
Establishing A Team
City staffs a City staffs a Data Policy Data Policy
Group of the Group of the Key Agencies’ Key Agencies’
Data Data Management Management
StaffStaff
++
Penn provides Penn provides Project Manager, Project Manager,
Database Database Administrator, Administrator,
Applications Design Applications Design Team, and Team, and
Applications Applications DeveloperDeveloper
NIS Data Providers• City Planning Commission
City-wide parcel coverage• Licenses and Inspections
Housing code violations, demolitions, clean and seals, vacancy
• Philadelphia Gas Works Shutoffs, housing characteristics
• Revenue Department Property tax arrearages, lien sales
• Water Department Shutoffs, suspended service, delinquency, vacancy
• Board of Revision of Taxes Owner’s name, sales date/price, land and building
characteristics • Office of Housing and Community Development
Digital photographs of vacant lots and houses, vacancy survey
• Post Office Vacancy (suspended mail service)
Administrative Records
What are they?
Data routinely gathered for operational or businessPurposes by public or private agencies
Examples: Medicaid claims, vital statistics, housingcode violations, school attendance andachievement, police incident reports
Policy and Program Uses for Administrative Records
• Needs assessments for program targeting
• Monitoring progress on select indicators
• Program siting decisions
• Grantee proposals
• Grantee reporting
• Funder reporting
Data Security/Access IssuesData Security/Access Issues
• Scheduled, periodic updates of data essential• Consistent data quality audits needed• Data warehousing is to the mutual benefit of researchers
and city government
• The City’s Data Policy Groups are the arbiters of authorization for access
• Agreements between City and University protect city’s data, set requirements for security
• Property-specific information is currently accessible to City agencies and contracted CDCs
• SUMS application is available to City Agencies only• These arrangements are subject to change
Research Advantages
• Produces repeated measures data, ideal for time series analyses
• Produces data amenable to user-defined small area geographies (below tract, block group, or even block); ideal for studying the "natural" clustering of phenomena, and for creating more sensitive space-dependent models
• Supports spatial analytic statistical approaches: econometrics, social ecology, epidemiology, multi-level modeling
• Creates new variable opportunities: clustering-contiguity measures, distance, travel time, social boundaries/buffers, displacement effects, controls for spatial autocorrelation
• Improves research collaborations between University researchers and city agencies’ policy analysts
Accessibility
• Requires “Data Exchange Agreements” or “Memoranda of Understanding” (MOUs) or “Business Agent Agreements” (HIPAA)
• Usually requires political support of the agency, and an agency purpose
• Spirit of mutuality and shared benefit b/w agency staff and researchers
• Web applications can be used to distribute aggregate data; making them broadly accessible
Confidentiality
• Identified data require technical and human data security standards, and
• Identified data usually require specific study approvals
• Aggregated data (including raster) generally do not require data access approvals
• Some suppression rules may be necessary with vector aggregations (HIPAA/FERPA)
Additional Info
• For project overviews: http://cml.upenn.edu http://cml.upenn.edu
• To try our aggregate application on neighborhoods: http://cml.upenn.edu/nbasehttp://cml.upenn.edu/nbase
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