Author: Song Gao

Prof. Gao received a NSF RAPID grant in response to COVID-19

Song Gao

Recently, a multidisciplinary research team led by Prof. Song Gao (Geography) who serves as the Principal Investigator (PI) and collaborates with three other Co-PIs at UW-Madison: Prof. Kaiping Chen (Life Sciences Communication), Prof. Qin Li (Mathematics), and Prof. Jonathan Patz (Population Health Sciences), was awarded an NSF RAPID grant in response to the COVID-19 pandemic. The project title is: “Geospatial Modeling of COVID-19 Spread and Risk Communication by Integrating Human Mobility and Social Media Big Data”.

This project will investigate the gap between the science of epidemic modeling and risk communication to the general public in response to the COVID-19 pandemic. With the rapid development of information, communication, and technologies, new data acquisition and assessment methods are needed to evaluate the risk of epidemic transmission and geographic spreading from the community perspective, to help effectively monitor social distancing policies, and to understand social disparities and environmental contexts in risk communication. This project will make theoretical, methodological, and practical contributions that advance the understanding of the COVID-19 spread across both time and space. The communication aspects of this research will serve to educate communities about the science, timing, and geography of virus transmission in order to enhance actions for addressing such global health challenges. This project explores the capabilities and potential of integrating social media big data and geospatial artificial intelligence (GeoAI) technologies to enable and transform spatial epidemiology research and risk communication. Results will be disseminated broadly to multiple stakeholder groups. Further, this project will support both researchers and students from underrepresented groups, broadening participation in STEM fields. Lastly, the Web platform developed in this project will serve as an education tool for students in geography, communication, mathematics, and public health, as well as for effectively engaging with communities about the science of COVID-19.

Past health research mainly focuses on quantitative modeling of human transmission using various epidemic models. How to effectively communicate the science of an epidemic outbreak to the general public remains a challenge. When an epidemic outbreak occurs without specific controls in place, it can be particularly challenging to improve community risk awareness and action. The research team, composed of experts from geography, mathematics, public health and life sciences communication will (1) develop innovative mathematical predictive models that integrate spatio-temporal-social network information and community-centered approaches; (2) integrate census statistics, human mobility and social media big data, as well as policy controls to conduct data-synthesis-driven and epidemiology-guided risk analysis; And (3) utilize panel surveys and text mining techniques on social media data for better understanding public awareness of COVID-19 and for investigating various instant message and visual image strategies to effectively communicate about risks to the public. The results of this project will lead to a better understanding of the geography and spread of COVID-19. Additionally, it is expected that the methods developed in this project can be applied to mitigate the outbreak risks of future epidemics.

The research team will also collaborate with The Wisconsin State Cartographer’s Office (SCO), The Wisconsin Department of Health Services (DHS), The American Family Insurance Data Science Institute (DSI), and The Global Health Institute (GHI).

Read our recent work: Mobile location big data can help predict the potential infected areas as coronavirus spreads


Call for papers: GIScience 2020 International Conference

Song Gao

11th International Conference on Geographic Information Science (GIScience 2020)

http://www.giscience.org

Poznań, Poland, 15-18 September, 2020

The 11th International Conference on Geographic Information Science will be held in Poznań, Poland, 15-18 September 2020. Hosted by Adam Mickiewicz University, GIScience 2020 continues the long tradition of the series as a flagship conference for researchers in geographic information science and related disciplines that are interested in spatial and temporal information.

The biennial conference series typically attracts over 300 international participants from academia, industry, and government to advance the state-of-the-art in geographic information science. The first conference day (September 15, 2020) will be dedicated to workshops and tutorials, while the main conference will be taking place on September 16-18, 2020. The conference offers two separate paper tracks, one for full papers and the other for short papers, both of which will undergo full peer-review. Authors of accepted papers will be given the opportunity to present their work at the conference in an oral presentation or as a poster.

The GIScience conference series is deeply interdisciplinary with contributions frequently involving domains such as geography, earth science, cognitive science, information science, computer science, linguistics, mathematics, philosophy, life sciences, and social science. It attracts contributions from experts in geo-visualization, geographic information retrieval, geostatistics, geo-semantics, geosimulation, spatial optimization, transportation, computational geometry, and data structures. Topics of interest are not restricted to the geo-spatial realm but involve spatial and temporal information more broadly.

Since 2018, GIScience proceedings are published in LIPIcs, the Leibniz International Proceedings in Informatics series (https://www.dagstuhl.de/en/publications/lipics). LIPIcs volumes are peer-reviewed and published according to the principle of open access, i.e., they are available online and free of charge. Each article is published under a Creative Commons CC BY license (http://creativecommons.org/licenses/by/3.0/), where the authors retain their copyright. Also, each article is assigned a DOI and a URN. The digital archiving of each volume is done in cooperation with the Deutsche Nationalbibliothek/German National Library. A number of other high-standing international conferences have already made the move to LIPIcs.

CONFERENCE TOPICS

Contributions are invited from a wide range of disciplines related to geographic information science, such as geography, earth science, cognitive science, information science, computer science, linguistics, mathematics, philosophy, life sciences, and social science. Topics of interest include (but are not limited to):

  • Agent-based modeling
  • Computational geometry
  • Events and processes
  • GeoAI
  • Geo-APIs
  • Geo-knowledge graphs
  • Geo-semantics
  • Geographic information observatories
  • Geographic information retrieval
  • Geosimulation and spatio-temporal modelling
  • Geovisualization and visual analytics
  • High-performance computing algorithms for spatial-temporal data
  • Human-Computer Interaction (with mobile devices)
  • Image classification methods
  • Internet of Things
  • Location privacy
  • Location-Based Services
  • Navigation
  • Replicability and reproducibility in GIScience
  • Scene recognition
  • Sensitivity analysis for spatial-temporal models
  • Spatial and spatio-temporal statistics
  • Spatial and temporal language
  • Spatial aspects of social computing
  • Spatial data infrastructures
  • Spatial data structures and algorithms
  • Spatially-explicit decision support
  • Spatially-explicit machine learning
  • Standardization and interoperability
  • Time series analysis
  • Trajectory and movement analysis
  • Uncertainty quantification and error propagation
  • Virtual reality


INFORMATION FOR AUTHORS

Full Paper Track

Full research papers will be thoroughly reviewed by at least three members of the international program committee. For this edition of the GIScience series, we will include an optional rebuttal phase during which authors can respond to the (initial) reviews. The rebuttal phase provides an opportunity to address misunderstandings, answer questions, or provide further details on issues that remained unclear to the reviewers. The reviewers will be able to react to these rebuttals by adjusting their review scores, if appropriate. Review criteria include novelty, significance of results as compared to previous work, the quality of the presented evaluations (if applicable), the clarity of the research statement, as well as the quality of writing and supporting illustrations. High-quality submissions will be accepted for presentation at the conference and published in LIPIcs, the Leibniz International Proceedings in Informatics series. Manuscripts must describe original work that has neither been published before, nor is currently under review elsewhere. Papers must be written in English and should not exceed fifteen pages (including title, figures, and references) in the required layout (see below). 

Short Paper Track

Short papers can report on the latest breaking results, present visions for the future of the field, or describe early work and experiments, as well as novel application areas. Short papers will also be reviewed by at least three reviewers. Review criteria include novelty, expected impact of early results, evaluation or evaluation plans for the future, plausibility of presented visions, as well as the quality of writing and supporting illustrations. Accepted papers in this track will be selected for either oral or poster presentations. Short papers must be written in English and should not exceed six pages (including title, figures, and references) in the requested LIPIcs layout. In addition, each submission must include Short Paper as a subtitle. 

The submission Web page for both tracks of GIScience 2020 is: https://easychair.org/conferences/?conf=giscience2020.


IMPORTANT DATES 

  • FULL PAPER TRACK
    • Full paper submissions: March 16, 2020
    • Full paper rebuttal phase: April 24-30, 2020
    • Full paper notification: May 15, 2020
    • Camera-ready papers: June 1, 2020
    • Full paper author registration deadline: June 1, 2020
       
  • SHORT PAPER TRACK
    • Short paper submission: May 25, 2020
    • Short paper notification: July 3, 2020
    • Camera-ready papers: July 15, 2020
    • Short paper author registration deadline: July 15, 2020
FORMATTING INSTRUCTIONS (all tracks)

The layout of any PDF submission to GIScience, whether full paper or short paper, should follow the 2019 template provided by LIPIcs (http://drops.dagstuhl.de/styles/lipics-v2019/lipics-v2019-authors.tgz). LIPIcs also provides a LaTeX class and template for papers. Authors unfamiliar with LaTeX, but keen to try, are highly encouraged to use Overleaf (http://www.overleaf.com), an online LaTeX editor that is easy to use and does not require any local installation. Overleaf comes with the LIPIcs class and template pre-loaded. Authors who want to use other word processors or text editors should stay close to the sample article’s layout for their paper submitted for review. Should their papers be accepted for publication, they will have to be converted to LaTeX using the LIPIcs LaTeX class and template. Authors are responsible for the conversion of their papers to LaTeX. There are also commercial conversion services such as http://wordtolatex.com/upload providing a one-step solution in case you do not want to do the conversion yourself. 

GeoDS Lab at the Emerging Technology Leadership Summit 2019

Song Gao

Hyper Innovation is working with the Wisconsin Institutes for Discovery to establish the Emerging Tech Hub@UW-Madison to highlight applications for emerging technologies (e.g., VR/AR), create inspiration for innovation, and provide collaboration opportunities for startups, universities, and corporations.

GeoDS Lab among other campus teams were invited to show the Augmented Reality Beacons demo at the Innovation and Emerging Technology Leadership Summit – November 14, 2019.

VR Playground
AR SandBox

GeoDS Lab at ACM SIGSPATIAL’19

Song Gao

During November 5 – 8, 2019, GeoDS lab members presented at the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2019) held in Chicago. We had two presentations and got one “Best Poster Award” in the Workshop on Ride-hailing Algorithms, Applications, and Systems (RAAS 2019)

  1. Analyzing the Gap Between Ride-hailing Location and Pick-up Location with Geographical Contexts (Best Poster Award). Yunlei Liang, Song Gao, Mingxiao Li, Yuhao Kang, and Jinmeng Rao (2019). In Proceedings of 1st ACM SIGSPATIAL International Workshop on Ride-hailing Algorithms, Applications, and Systems (RAAS’19) DOI: 10.1145/3357140.3365493 [PDF]
  2. A Data-Driven Approach to Understanding and Predicting the Spatiotemporal Availability of Street Parking (Short Paper). Mingxiao Li, Song Gao, Yunlei Liang, Joseph Marks, Yuhao Kang, and Moyin Li (2019). In Proceedings of 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems(SIGSPATIAL’19) DOI: 10.1145/3347146.3359366 [PDF]

As the General Chairs, Professor Song Gao co-organized the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI 2019). There are two keynotes from both industry and academia and 17 oral presentations in the GeoAI workshop. The proceedings of the GeoAI’19 workshop is available at the ACM Digital Library (Table of Contents): https://dl.acm.org/citation.cfm?id=3356471

GeoAI’19
SIGSPATIAL’19

A roundtable discussion: defining urban data science

Song Gao

Reference: Wei Kang, Taylor Oshan, Levi J Wolf, Geoff Boeing, Vanessa Frias-Martinez, Song Gao, Ate Poorthuis, Wenfei Xu. (2019) A roundtable discussion: defining urban data science. Environment and Planning B: Urban Analytics and City Science. 46(9), 1756-1768.  DOI: 10.1177/2399808319882826 [PDF]

Abstract:

The field of urban analytics and city science has seen significant growth and development in the past 20 years. The rise of data science, both in industry and academia, has put new pressures on urban research, but has also allowed for new analytical possibilities. Because of the rapid growth and change in the field, terminology in urban analytics can be vague and unclear. This paper, an abridged synthesis of a panel discussion among scholars in Urban Data Science held at the 2019 American Association of Geographers Conference in Washington, D.C., outlines one discussion seeking a better sense of the conceptual, terminological, social, and ethical challenges faced by researchers in this emergent field. The panel outlines the difficulties of defining what is or is not urban data science, finding that good urban data science must have an expansive role in a successful discipline of “city science.” It suggests that “data science” has value as a “signaling” term in industrial or popular science applications, but which may not necessarily be well-understood within purely academic circles. The panel also discusses the normative value of doing urban data science, linking successful practice back to urban life. Overall, this panel report contributes to the wider discussion around urban analytics and city science and about the role of data science in this domain.

Dr. Clio Andris Visited UW-Madison Geography

Song Gao

Dr. Clio Andris (Assistant Professor of City & Regional Planning and Interactive Computing) from Georgia Tech was invited to the renowned Yi-Fu Lecture at UW-Madison Geography. Our GeoDS Lab was honored to host Dr. Andris’ visit and had a great conversation on collaborative projects and joint research.

Dr. Andris is giving her talk on spatial social network analysis.
Grads Brown-Bag Talk
UW-Madison’s own made ice cream

Prof. Song Gao received a new NSF Research Grant

Song Gao

Recently, Dr. Song Gao (Co-PI) received a NSF grant together with Dr. Qunying Huang (PI), Dr. Daniel Wright (Co-PI), Dr. Nick Fang (Co-PI), and Dr. Yi Qiang (Co-PI).

Title: A GeoAI Data-Fusion Framework for Real-Time Assessment of Flood Damage and Transportation Resilience by Integrating Complex Sensor Datasets

Abstract: Traditional modeling approaches for flood damage assessment are often labor-intensive and time-consuming due to requirements for domain expertise, training data, and field surveys. Additionally, the lack of data and standard methodologies makes it more challenging to assess transportation network resilience in real-time during flood disasters. To address these challenges, this project aims to integrate novel data streams from both physical sensor networks (e.g., remotely-sensed data using unmanned aerial vehicles [UAVs]), and citizen sensor networks (e.g., crowdsourced traffic data, social media and community responsive teams connected through a developed mobile app). The goal is to develop a framework for real-time assessment of damage and the resilience of urban transportation infrastructures after coastal floods via the state-of-the-art computer vision, deep learning and data fusion technologies. The project will also advance Data Science through multi-disciplinary and multi-institutional collaborations. The project is expected to improve the sustainability, resilience, livability, and general well-being of coastal communities by having a direct impact on the effectiveness, capability, and potential of using both physical and social sensor data. This will in turn enable and transform damage assessments, and identify critical and vulnerable components in transportation networks in a more effective and efficient manner. The interdisciplinary research team, along with students and collaborators from different coastal regions, will facilitate the sharing of knowledge and technologies from different socio-environmental contexts and testing the transferability of the research outcomes.

The project will harmonize physical and citizen sensors within a geospatial artificial intelligence (GeoAI) data-fusion framework with a focus on three research thrusts: (1) unsupervised flood extent detection by integrating UAV images collected throughout this project with existing geospatial data (e.g., road networks and building footprints); (2) flood depth estimation using deep learning and computer vision techniques combined with crowdsourced photos and UAV imagery; and (3) assessment of the impact on and resilience of transportation networks based on near real-time flood and damage information. The innovative methodology will be demonstrated and deployed through collaborative efforts in response to future flood events as well as several historical storms. The project will produce open-source algorithms for future educational use, raw and processed datasets and associated processing software, a mobile app to engage community responsive science teams, and three research publications.

Source: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940091

Prof. Song Gao received an AI for Earth Grant from Microsoft

Song Gao

[Madison, WI/USA] – [August 8, 2019] – Professor Song Gao as the Principal Investigator (PI) has been awarded an AI for Earth research grant from Microsoft to help further the efforts in the area of Geospatial Artificial Intelligence (GeoAI).

This new grant will provide Dr. Song Gao and his research assistants Yuhao Kang and Jake Kruse at the GeoDS@UW-Madison lab, and Dr. Fan Zhang (Postdoc Researcher at the MIT Senseable city Lab and Peking University) with the Azure cloud computing resources and AI data labelling services to accelerate their work on understanding the playability of cities and metropolitan areas from the human-environment interaction perspective using multi-source geospatial big data (e.g., images, texts, and videos).

The Microsoft AI for Earth is a $50 million, 5-year program that brings the full advantage of Microsoft technology to those working to solve global environmental challenges in the key focus areas of climate, agriculture, water and biodiversity. Through grants that provide access to cloud and AI tools, opportunities for education and training on AI, and investments in innovative, scalable solutions, AI for Earth works to advance sustainability across the globe. 

Learn more about the Microsoft AI for Earth program: https://www.microsoft.com/en-us/aiforearth 

Timothy Prestby Received the HILLDALE FELLOW Award

Song Gao

Please join us congratulating our junior student Timothy Prestby, who is currently an undergraduate research assistant in the GeoDS Lab under Prof. Song Gao’s mentorship, just got the university “Hilldale Undergraduate/Faculty Research Fellowships” and was awarded  in the 2019 Chancellor’s Undergraduate Awards Ceremony! 

The awarded research project title is: “Understanding Neighborhood Isolation through Big-Data Human Mobility Analytics”. 

Previous Hilldale Fellows at the University of Wisconsin-Madison:

https://awards.advising.wisc.edu/campus-wide-award-recipients/test-hilldale-fellows/

Professor Gao was appointed as the Associated Editor of Annals of GIS

Song Gao

Professor Song Gao was invited and appointed as the Associated Editor for the CPGIS flagship journal: Annals of GIS  published by Taylor & Francis.

Annals of GIS is an international peer-reviewed journal that encourages the interdisciplinary exchange of original ideas on theory, methods, development and applications in the fields of geo-information science. Research papers are invited to cover the latest development in the following areas:

  • remote sensing and data acquisition
  • geographic information systems
  • geo-visualization and virtual geographic environments (VGE)
  • spatial analysis and modeling
  • uncertainty modeling

and their applications in natural resource, ecosystem, urban management, and other humanities and social science areas.