Track 3

Sybil Derrible is an Associate Professor in Civil and Materials Engineering and Computer Science and the Director of the Complex and Sustainable Urban Networks (CSUN) Laboratory (csun.uic.edu) at the University of Illinois at Chicago. His research is at the nexus of urban metabolism, infrastructure planning and design, complexity science, and data science to redefine how cities are planned, designed, and operated for smart, sustainable, and resilient urban systems. Professor Derrible received a US National Science Foundation CAREER Award and he serves as an Associate Editor for the ASCE Journal of Infrastructure Systems. He is also the Chair of the Sustainable Urban Systems (SUS) Section of the International Society for Industrial Ecology (ISIE) and the Vie-Chair of the Critical Transportation Infrastructure Protection committee of the Transportation Research Board (TRB). He obtained a M.Eng. from Imperial College London and a PhD from the University of Toronto.

Track 3- Presentation Title:  Smart Data Management and Operations of Urban Infrastructure using Geographic Information Systems

Abstract: Cities all over the world are converting maps of their infrastructure systems from legacy formats (such as paper maps and CAD drawings) to Geographic Information Systems (GIS). While GIS offers myriads of opportunities to better manage infrastructure, the conversion process to GIS can be extremely challenging from a technical point of view. Moreover, the original data in legacy format often contain errors and pieces of infrastructure are often missing. What is more, even once the conversion process is complete, the maintenance of the data and the fusion of the dataset with other datasets can be challenging. In this talk, a smart data management protocol is presented to successfully convert infrastructure maps from CAD to GIS that includes a data cleaning procedure in CAD and machine learning algorithmic solutions to validate or suggest edits of the infrastructure once converted to GIS. In addition, the protocol includes elements of version control to keep track of how urban infrastructure evolves over time and a procedure to combine GIS infrastructure maps with other datasets (such as socio-demographic data) that can be used for optimal scheduling of asset maintenance and repair. Overall, although much work remains to be done, smart data management is a key component of smart cities and aided by recent technological advances (such as machine learning), smart data management has the potential to completely transform how urban infrastructure systems are planned and operated.