Information Resources Exchange Group
The ACP Building - 8th Floor
190 N. Independence Mall West
Philadelphia, PA 19106
For directions Call (215) 592-1800
Please Use Sixth Street Entrance
1. Call to Order and Introductions
2. Machine learning better public policy: 3 case studies - Ken Steif, PhD, Director, Master of Urban Spatial Analytics, Univ. of Pennsylvania (email@example.com)
In this talk, Dr. Ken Steif will present 3 case studies that use machine learning to generate actionable public policy intelligence. Examples include the use of built environment data to predict vehicle crashes; predicting lead inundation to prioritize health inspections and predicting opiate overdoses to allocate opioid clinics. Ken will conclude with a framework other cities can use to generate similar Smart Cities use cases.
3. Smart Assets in Philly - Richard Quodomine, Lead GIS Analyst , City of Philadelphia (firstname.lastname@example.org)
The City’s Public Property Department has been working for several years on building an asset database, but it’s more than just knowing what you have, it’s knowing what you can do with it that makes it smart. The city has two major projects that help it look at past investments, and then guide its future. One is IWAMS, the city’s asset management system, and the other is a GIS-based decision support tool to guide public safety investments. Taking together, the city looks to achieve savings and make better investments into the future.
4. What kind of place is a smart city? - Brett Fusco, Manager Long-Range Planning , DVRPC (email@example.com)
Smart city technologies are moving forward, and their benefits are well touted. But what are the concerns and challenges we should be aware of? How can we better address them?
5. Applying Real-Time Data and Analytics with Waze and ArcGIS - Daniel Wickens Solution Engineer - Esri ( firstname.lastname@example.org)
The ArcGIS platform provides capabilities to ingest, analyze, and store high-volume, high-velocity observation data, which can be transformed into actionable information. This session highlights a scenario in which real-time alert data from Waze is ingested and analyzed to support decision making.
6. Collecting Your Own Big Data - Clinton J. Andrews, Associate Dean for Planning & New Initiatives, Professor of Urban Planning & Policy Development, Director, Center for Green Building, Edward J. Bloustein School of Planning & Public Policy, Rutgers, The State University of New Jersey (email@example.com)
Ubiquitous sensors are a key element of smarter cities. They may be owned by disparate parties and used for a variety of purposes, public and private. High-quality consumer-grade sensors and a citizen-science ethos are inspiring many people to collect their own big data on everything from pedestrian traffic flows to ambient air quality. Handling, reducing, and interpreting these data are often quite a challenge. When people share high-quality data from their sensor networks, generally valuable insights may emerge. This talk shares technical, analytical, and organizational lessons learned from a recent project, funded by HUD and NSF, that deployed sensors in public housing.
7. DVRPC Work Program Update
8. Member Roundtable
- Recent Projects - Attendees are encouraged to share recent accomplishments and to discuss any ongoing projects that you or your agency is working on that is related to information gathering and sharing.
- Recent Event Reports - Attendees are encouraged to provide a brief summary of any recent meeting or conference they attended.
- Upcoming Events - Attendees are encouraged to announce any upcoming meeting or conference.