Administrative Lessons Learned Philadelphia Neighborhood Information System

1. Administrative Lessons Learned PhiladelphiaNeighborhood Information System University Of Pennsylvania Cartographic Modeling Lab http://cml.upenn.edu/nis Presenter: Dr. Dennis Culhane, CML Faculty Co-Director

2. Agenda • Neighborhood Information System • City of Philadelphia and University Of Pennsylvania Partnership Model

3. 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: • parcelBase • neighborhoodBase • crimeBase • muralBase • PhillySiteFinder • schoolBase*

4. 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

5. 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) The Philadelphia- University of Pennsylvania Partnership Model

6. City staffs a Data Policy Group of the Key Agencies’ Data Management Staff Establishing A Team Penn provides Project Manager, Database Administrator, Applications Design Team, and Applications Developer +

7. 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) NIS Data Providers

8. What are they? Data routinely gathered for operational or business Purposes by public or private agencies Examples: Medicaid claims, vital statistics, housing code violations, school attendance and achievement, police incident reports Administrative Records

9. Needs assessments for program targeting Monitoring progress on select indicators Program siting decisions Grantee proposals Grantee reporting Funder reporting Policy and Program Uses for Administrative Records

10. 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 Data Security/Access Issues

11. 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 Research Advantages

12. 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 Accessibility

13. 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) Confidentiality

14. For project overviews: http://cml.upenn.edu To try our aggregate application on neighborhoods: http://cml.upenn.edu/nbase Additional Info