The Third International Workshop on Smart Cities and Urban Analytics (UrbanGIS) 2017
NOVEMBER 7, 2017
Paper Submission: September 19, 2017
Notification of Acceptance: October 10, 2017
Camera Ready Paper Due: October 17, 2017
Paper Submission Site: https://easychair.org/conferences/?conf=urbangis2017
Welcome to the Third International Workshop on Smart Cities and Urban Analytics 2017, in conjunction with the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2017)
About half of humanity lives in urban environments today and that number will grow to 80% by the middle of this century; North America is already 80% in cities, and will rise to 90% by 2050. Cities are thus the loci of resource consumption, of economic activity, and of innovation; they are the cause of our looming sustainability problems but also where those problems must be solved. Smart cities are leveraging advanced analytics solutions, usually with spatiotemporal data, to support urban management and more informed decision making. Big urban data, if properly acquired, integrated, and analyzed, can take us beyond today’s imperfect and often anecdotal understanding of cities to enable better operations, informed planning, and improved policy.
Despite many efforts in tackling challenges of smart cities through big data and spatio(-temporal) analysis, there is no standard spatio(-temporal) data infrastructure able to support the wide range of requirements in different problem areas. This workshop will provide a forum for researchers from various domains to present their results and to work together toward developing such an infrastructure. This includes, but not limited to, techniques, policies, and standards required to acquire, process, and use spatio(-temporal) data,particularly in the urban context.
We are soliciting papers (including significant work-in-progress) that describe academic research efforts as well as applications and prototypes that leverage spatial or spatiotemporal data analysis to address urban challenges. Areas of research include but are not limited to:
- Application and experimental experiences in smart cities
- Data indexing techniques for massive spatio-temporal dataset
- Human mobility modeling and analytics
- Large-scale visualization of urban data
- Machine learning for predictive models
- Parallel and distributed computing of big urban data
- Safety, security, and privacy for smart cities
- Smart buildings, grids, transportation, and utilities
- Social computing, sensing and IoT for smart cities
- Streaming/realtime processing of spatio-temporal data
- Urban informatics
Submissions should be at most 8 pages for full papers and at most 4 pages for short papers/work-in-progress, formatted according to ACM formatting guidelines. Papers will be evaluated by the program committee members for the significance and relevance of their research contributions, as well as their presentation. Short papers are expected to be work in progress or of smaller scale but the same evaluation criteria will be applied as for full papers.
- Bill Howe, University of Washington
- Huy T. Vo, CCNY/NYU-CUSP
Paper Chair
- Harish Doraiswamy, NYU CDS
Steering Committee
- Juliana Freire, New York University
- Claudio T. Silva, New York University
- Alex Chohlas-Wood, New York Police Department
- Theo Damoulas, University of Warwick
- Rishee Jain, Stanford University
- James T. Klosowski, AT&T Labs – Research
- Marcos Lage, Federal Fluminense University
- Beibei Li, Carnegie Mellon University
- Carlos Scheidegger, University of Arizona
- Mai Vu, Tufts University
- Lucien Wilson, Kohn Pedersen Fox Associates PC
- Zheng Yang, Stanford University
- Jianting Zhang, City College of New York
- Kai Zhao, New York University
- Yu Zheng, Microsoft Research
Keynote Speaker
Bill Howe: Bill Howe is Associate Professor in the Information School, with Adjunct appointments in both Computer Science & Engineering and Electrical Engineering. His research is centered in data management systems, with emphasis on analytics, curation, and visualization in the sciences. Howe was the Founding Associate Director of the eScience Institute, and played a leadership role in the Data Science Environment program at UW through a $32.8 million grant awarded jointly to UW, NYU, and UC Berkeley. With support from the MacArthur Foundation and Microsoft, Howe directs the Urbanalytics group at UW and UW’s participation in the Cascadia Urban Analytics Cooperative with the University of British Columbia, where he focuses on data-intensive urban science. He founded the UW Data Science Masters Degree and serves as its inaugural Program Director and Faculty Chair. He has received two Jim Gray Seed Grant awards from Microsoft Research for work on managing environmental data, has had two papers selected for VLDB Journal’s “Best of Conference” issues (2004 and 2010), and co-authored what are currently the most-cited papers from both VLDB 2010 and SIGMOD 2012. Howe serves on the program and organizing committees for a number of conferences in the area of databases and scientific data management, developed a first MOOC on data science that attracted over 200,000 students across two offerings, and founded UW’s Data Science for Social Good program. He has a Ph.D. in Computer Science from Portland State University and a Bachelor’s degree in Industrial & Systems Engineering from Georgia Tech.
Keynote Talk
Techniques and Technologies for Responsible Urban Data Science
The techniques and technologies of data science are poised to have a transformative impact on the way cities deliver services. But complications in managing sensitive data and ensuring fairness, accountability, and transparency in algorithmic decision-making are limiting uptake in the public sector. We interpret these problems as technical issues, and are working to design new data management systems that can help agencies use predictive analytics responsibly.
In this talk, I’ll describe some of our projects in the Urbanalytics group at the University of Washington across homelessness, transportation, and urban sensing, and use these projects to motivate new systems requirements to protect privacy, control algorithmic bias, and ensure transparency.
I’ll then describe Fides, a new system we are designing as part of a broad collaboration in responsible data management. I’ll describe two initial projects: DataSynthesizer, a system for generating privacy-preserving synthetic datasets to facilitate collaboration and evaluate bias in models, and FairQuery, a query rewriting system to eliminate some forms of sampling bias.
I’ll wrap up by describing some of the organizational structures we’ve established to support applied research collaborations between students, faculty, and city agencies.
Technical talks are 15 minutes each including time for Q&A.
NOVEMBER 7, 2017
08:00-09:00 : Breakfast and Registration
09:00-09:10 : Opening Remarks
09:10-10:00 : Keynote
Techniques and Technologies for Responsible Urban Data Science [Slides]
Bill Howe (University of Washington)
10:00-10:30 : Session on Urban Spatial Data Modeling and Integration
Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land Use [Slides]
Xueqing Deng (University of California, Merced), Shawn Newsam (University of California, Merced)
Rapid development of semantic 3D city models for urban energy analysis based on free and open data sources and software [Slides]
Jochen Wendel (European Institute for Energy Research), Alexander Simons (European Institute for Energy Research), Alexandru Nichersu (European Institute for Energy Research), Syed Monjur Murshed (European Institute for Energy Research)
10:30-11:00 : Coffee Break
11:00-12:00 : Session on Urban Spatial Data Modeling and Integration (cont)
Towards a hybrid framework for the visualization and analysis of 3D spatial data [Slides]
Alejandro Graciano (University of Jaén), Antonio Rueda (University of Jaén), Lidia Ortega (University of Jaén), Francisco Feito (University of Jaén)
Continuously Generalizing Buildings to Built-up Areas by Aggregating and Growing [Slides]
Dongliang Peng (Universität Würzburg), Guillaume Touya (COGIT, IGN)
Extraction of Road Maintenance Criteria using Machine Learning and Spatial Information [Slides]
Hiroya Maeda (The University of Tokyo), Yoshihide Sekimoto (The University of Tokyo), Toshikazu Seto (The University of Tokyo), Takehiro Kashiyama (The University of Tokyo), Hiroshi Omata (The University of Tokyo)
Ontology-based Instance Matching for Geospatial Urban Data Integration [Slides]
Vivek Shivaprabhu (University of Illinois at Chicago), Booma Sowkarthiga Balasubramani (University of Illinois at Chicago), Isabel Cruz (University of Illinois at Chicago)
12:00-14:00 : Lunch (not provided)
14:00-15:30 : Session on Urban Data Science
The Relationship between Timing of Development and Bus Rapid Transit [Slides]
Klara Berger (KTH), Joel Franklin (KTH), Tatiana Gadda (UTFPR), Nadia Kozievitch (UTFPR)
Real-Time Bayesian Micro-Analysis for Metro Traffic Prediction [Slides]
Eric Lin (Thomas Jefferson High School for Science and Technology), Jinhyung Park (The Hill School), Andreas Zuefle (George Mason University)
Trajectory Annotation by Discovering Driving Patterns [Slides]
Sobhan Moosavi (Ohio State University), Behrooz Omidvar-Tehrani (University of Grenoble Alpes), Rajiv Ramnath (Ohio State University)
Using Yelp to Find Romance in the City: A Case of Restaurants in Four Cities [Slides]
Sohrab Rahimi (Pennsylvania State University), Clio Andris (Penn State University), Xi Liu (Pennsylvania State University)
Predictive Analytics Using Text Classification for Restaurant Inspections [Slides]
Zhu Wang (University of Illinois at Chicago), Booma S. Balasubramani (University of Illinois at Chicago), Isabel F. Cruz (University of Illinois at Chicago)
Linked Data and Visualization: Two Sides of the Transparency Coin [Slides]
Auriol Degbelo (Institute for Geoinformatics – University of Muenster)
15:30-16:00 : Coffee Break
16:00-17:00 : Session on Urban Data Query
Automatic Spatio-temporal Indexing to Integrate and Analyze the Data of an Organization [Slides]
Craig Knoblock (University of Southern California), Aparna R. Joshi (National Institute of Technology Karnataka), Abhishek Megotia (University of Southern California), Minh Pham (University of Southern California), Chelsea Ursaner (City of Los Angeles)
QUIET ZONE: Reducing The Communication Cost of Continuous Spatial Queries [Slides]
Arif Hidayat (Monash University), Muhammad Aamir Cheema (Monash University)
Flow HDBSCAN: A Hierarchical and Density-Based Spatial Flow Clustering Method [Slides]
Ran Tao (University of Southern California), Jean-Claude Thill (UNC Charlotte), Craig Depken (UNC Charlotte), Mona Kashiha (UNC Charlotte)
Trajectory Query Based on Trajectory Segments with Activities [Slides]
Kaiwei Kong (Hangzhou Dianzi University), Jian Xu (Hangzhou Dianzi University), Ming Xu (Hangzhou Dianzi University), Liming Tu (Hangzhou Dianzi University), Yang Wu (Hangzhou Dianzi University), Zhi Chen (Hangzhou Dianzi University)