Smart Columbus​ Powered by Integrated Data Exchange (IDE) Department of Technology Translational Data Analytics Institute Shoreh Elhami, Director, GIS/IDE Program Manager Christopher Stewart, Faculty-In-Residence Smart Columbus Confluence of Civic, Research & Startup Cultures • Winner $40M DOT Smart City Challenge • $367M investment local businesses & partners • $93M for research (largely from The Ohio State University) • $10 M Vulcan Inc. “Smart Columbus is just the start. There is a lot to this program no one has ever done before.” Mayor Andrew Ginther, 2016 Slide 2 Confluence of 3 Disruptive Data Trends Anyone can access, understand, use and share Internet Open Data of Open Things IoT: Devices embedded in the physical world– Data e.g., bodies, busses, and businesses Smart Cities Smart cities actively collect data and adapt policies and services to make citizenry successful Smart Columbus Slide 3 Data Flows From and Through Cities Central Ohio Transit Authority Ridership (Millions of Rides Per Year) 20 19 18 17 16 15 14 13 12 11 10 2006 2008 2010 2012 2014 • Public -Private Partnerships: COTA is funded by 0.5% sales tax through 2019 • Starting 2017, On-board Wi-Fi will produce 4.7 TB per month • How does the city ingest, analyze and use all of this data? Slide 4 Smart City Applications Abound • CoGo opens trip data – https://www.cogobikeshare.com/system-data – Discover new bike paths – Over -provisioned and under-provisioned paths – Activity levels of citizens Slide 5 Who rises to the challenge? Who makes Smart Columbus? Challenges: Align For-Profit and Not-For-Profit Incentives, Find people with the right skills, Politics Any problem in computer science can be solved with another level of indirection. David Wheeler, 1993 Slide 6 Who rises to the challenge? Who makes Smart Columbus? • Solution: The city needs a system to (1) manage data resources from multiple sources and (2) provide a common interface for application developers & researchers using data APPLICATION DATA • Integrated Data Exchange (IDE) – Collect data from multiple sources (IoT), govern access, ensure privacy and Public Public Building Development Utilities Culture & enable analysis (data API) Safety Service & Zoning Events – Moving data is a new city service Finance Health Government Neighbor Mapping Recreation hoods & Parks Slide 7 Integrated Data Exchange Benefits/Challenges IDE makes application developers and entrepreneurs the bottleneck, not data access IDE makes researchers and IoT builders the bottleneck, not integration • Challenges • Provide security • Provide fair access to the public • Protect the public from dangerous analysis • Protect the city from legal problems • Scale to support city-scale data ingest • Scale to support city-scale IoT Slide 8 Smart Columbus Partnerships Powered by Integrated Data Exchange Smart Open Translational Columbus Data Data Analytics Sandbox & Research Columbus Integrated Data Exchange Slide 9 Giving developers access to Smart City datasets • Smart Columbus Sandbox brings together entrepreneurs, developers to use existing open data sets Slide 10 Parking Startup: Easton Mall Pitch Request Reply Semantics apiKey "result": "success", "trafficCounts": [ { sensor_group "reading_time": "2017-01-01 11:00:00", sensor_zone "occupancy": "2.36", "volume": "65", "median_speed": "0\r" start_time },{ end_time "reading_time": "2017-01-01 12:00:00", "occupancy": "3.38", "volume": "93", "median_speed": "0\r" Slide 11 Early Success: A Hack-A-Thon link Slide 13 Smart Columbus Partnerships Smart Open Translational Columbus Data Data Analytics Sandbox & Research Columbus Integrated Data Exchange Slide 14 Translational Data Analytics Institute (TDAI) is the foundational element of the University’s $500 million investment into Discovery Themes. University-wide Research across Educational 30+ disciplines Investments TDA 105 faculty & faculty-in-residents Communities of Practice Strategic business partnerships Slide 14 TDAI Community of Practice – Smart Cities Harmony Bench Andre Carrel – Public Transportation What modes, networks, (1) Collect GPS and sensor data from IoT embedded in and types of transit vehicles (e.g., phones & wi-fi access points) transportation link (2) Model and manage demand, using OSU COTS performers within a city testbed for future deployment in COTA Prof. Anish Arora Battery powered radar mesh networks for pedestrian and bicycle identification & wireless, sensor networks to Smart Campus Testbed - Prof. Rabi Mishalani expand Internet access Route planning and fleet management using video & passively & cost effectively other sensing and tracking technologies. Slide 16 Smart Columbus Partnerships TDAI Smart Open Data Translational Columbus Data Commons Data Analytics Sandbox & Research Comm. Practice Columbus Integrated Data Exchange Slide 17 Smart Columbus Partnerships Smart Open Translational Columbus Data Data Analytics Sandbox & Research Columbus Integrated Data Exchange Slide 18 Smart Columbus Partnerships “The big play and leverage point for us is that internet of things space. But the only way we get there is making sure that we have Smart Open Translational Columbus a secure, sound, Dataaccessible integrated data exchangeData Analytics that’s going Sandbox to stand the test of time.” & Research Mayor Andrew Ginther, 2017 Columbus Integrated Data Exchange Slide 19 Slide 19 Slide 21 Slide 21 Multi-Modal Trip Common Payment Mobility Assistance for Planning Application Smart Mobility Hubs System People with Cognitive Disabilities Pedestrian Collision Smart Street Lighting Avoidance Slide 23 Connected Electric Autonomous Vehicle • Electric Autonomous Transit Shuttle • Automated Routes • COTA to logistics • Transit center to corporate campus • Remote parking to retail center Slide 24 Delivery Zone Enhanced Permit Event Parking Availability Parking Management Slide 25 Truck Oversize Vehicle Interstate Truck Platooning Routing Parking Availability Slide 26 CONTROL SYST & APP 15 Projects (USDOT Grant) DATA MGT PLATFORM ENABLING SYSTEMS & APPLICATIONS PUBLIC APPLICATIONS Connected Vehicle Smart Street Transit Pedestrian Collision CCTN Environment Lighting Avoidance System IDE Integrated Data Exchange (IDE) DISTRICTS Mobility Assistance for Common Payment Multi-Modal Trip Smart Mobility People with Cognitive RESIDENTIAL System Planning Application Hubs Disabilities Connected Electric COMMERCIAL Automated Vehicle Delivery Zone Enhanced Permit Event Parking DOWNTOWN Availability Parking Management Truck Oversize Vehicle Interstate Truck LOGISTICS Platooning Routing Parking Availability Slide 27 Smart Columbus Electrification Plan’s (Vulcan Foundation) 5 Elements Integrated Data Exchange Vision The IDE is a web based dynamic governed platform at the heart of the Smart Columbus data environment that integrates data and data services from multiple sources and tenants, including the planned Smart Columbus technologies and traditional transportation data. The IDE embodies an open-data approach using best of breed technologies, including both open-source and commercial off the shelf components to enable better decision-making and problem solving for all users to support a replicable, extensible, sustainable platform for data ingestion and dissemination. The IDE drives performance metrics for program monitoring and evaluation. Slide 27 The IDE will serve the needs of internal and external users Researchers Journalists Developers Citizens Applications GIS/Mapping NGO’s Internal Search City Wide Collaboration & Sharing Data Files 3rd Party Vendors IoT Sensor Integrated Governments Fabric API Access Visualization Analytics Data Exchange Public Public Building& Development Utilities Culture & Safety Service Zoning Events External City Public Engagement & Value Creation Finance Health Government Neighbor Mapping Recreation hoods & Parks Slide 28 Integrated Data Exchange: Nascent to Exemplar? Starting Premise: We don’t know what we don’t know BLUF: Other governments are ahead of us, we need to learn from them Understanding Data Management: Are There Reference Architectures? BLUF: It isn’t just about a place to store data Seeking Exemplars: What Can We Learn From Other Governments? BLUF: Data quality and governance matter Open-Source: What Can We Learn From Proprietary Solutions? BLUF: Nobody has all the answers, but architectures are similar Slide 29 Challenge: Current Data Environment • No Catalog of Data EDI Sources • Point to Point Integration is Messy SharePoint • Data is Not Shared • No Universal Data Quality Processes • Data Tethered to Applications • Change is Risky, Expensive and Time Consuming Slide 30 © 2017 Informatica Cities As Data Owners • Information Technology in organizations has evolved primarily as a means to: • Automate business processes • Enable better decisions APPLICATION DATA • For most “users” of IT systems, the data is the “application” • Cities have massive amounts of data within their information technology systems Public Public Building Development Utilities Culture & Safety Service & Zoning Events • City of Columbus has approximately 500 software applications and Terabytes of data Finance Health Government Neighbor Mapping Recreation hoods & Parks Slide 31 Lessons Learned: Data Management Architecture RESEARCH BUSINESS BRAINS & INTEGRATED DATA ANALYTICS Master/Reference Data (meta) Enterprise DW Operational data stores DATA VIRTUALIZATION Query Engine Semantic Modeling DATA LAKE Security Structured & APIs DATA VISUALIZATION DATA Unstructured Data LAYER ENGAGEMENT ARCHIVAL APPLICATIONS Slide 32 Lessons