Prof. Gao joins the Associate Editors team of IJGIS

Recently, Prof. Song Gao was invited to join the Associate Editors team of International Journal of Geographical Information Science (IJGIS), which is a flagship international journal for publishing geographic information systems/science related research. Dr. Gao’s service term starts from January 1st, 2023.

Aims and Scope

The aim of International Journal of Geographical Information Science is to provide a forum for the exchange of original ideas, approaches, methods and experiences in the field of GIScience.

International Journal of Geographical Information Science covers the following topics:

  • Innovations and novel applications of GIScience in natural resources, social systems and the built environment
  • Relevant developments in computer science, cartography, surveying, geography, and engineering
  • Fundamental and computational issues of geographic information
  • The design, implementation and use of geographical information for monitoring, prediction and decision making

Prof. Song Gao was named the Highly Cited Researcher

Prof. Song Gao is on this year’s list of Global Highly Cited Researchers List of 2022 and the only scholar from UW-Madison listed in the category of Social Sciences. Kudos to his colleagues, students, and mentors!

On November 15 2022, Clarivate revealed its 2022 list of Highly Cited Researchers™ – individuals at universities, research institutes and commercial organizations who have demonstrated a disproportionate level of significant and broad influence in their field or fields of research. The methodology draws on data from the Web of Science™ citation index, together with analysis performed by bibliometric experts and data scientists at the Institute for Scientific Information (ISI)™ at Clarivate. ISI analysts have awarded Highly Cited Researcher 2022 designations to 6,938 researchers from across the globe who demonstrated significant influence in their chosen field or fields over the last decade. ISI analyzed all papers published and cited between 2011 and 2021, determining which authors ranked in the top 1% of cited papers in each field. The list is truly global, spanning 69 countries or regions and spread across a diverse range of research fields in the sciences and social sciences.

Prof. Gao is also on the list of top 2% highly cited scientists based on Stanford University’s analysis of Scopus data provided by Elsevier.

UW-Madison Research News: https://research.wisc.edu/uncategorized/2022/11/22/uw-madison-faculty-make-strong-showing-on-global-highly-cited-researchers-list/

GeoDS lab members in the 2022 ACM SIGSPATIAL and AutoCarto Conferences

During the week of November 1-4, 2022, all the GeoDS lab members were traveling to two academic conferences: ACM SIGSPATIAL 2022 and AutoCarto 2022.

Prof. Song Gao, Wen Ye (undergraduate student), Yunlei Liang (PhD student), Yuhan Ji (PhD student), Jiawei Zhu (visiting PhD student), and Jinmeng Rao (PhD Candidate), presented at the 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022) in Seattle, Washington, USA.

We published two short research papers in the main conference, three workshop full papers, and won a “Best Paper Award”.

  1. Region2Vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions. Yunlei Liang, Jiawei Zhu, Wen Ye, Song Gao. (2022) In SIGSPATIAL’22, DOI:10.1145/3557915.3560974
  2. Exploring multilevel regularity in human mobility patterns using a feature engineering approach: A case study in Chicago. Yuhan Ji, Song Gao, Jacob Kruse, Tam Huynh, James Triveri, Chris Scheele, Collin Bennett, and Yichen Wen. (2022) In SIGSPATIAL’22, DOI:10.1145/3557915.3560974
  3. (Best Paper Award) Understanding the spatiotemporal heterogeneities in the associations between COVID-19 infections and both human mobility and close contacts in the United States. Wen Ye, Song Gao. (2022) In SpatialEpi ’22,  pp 1–9, DOI:10.1145/3557995.3566117
  4. Measuring network resilience via geospatial knowledge graph: a case study of the us multi-commodity flow network. Jinmeng Rao, Song Gao, Michelle Miller, Alfonso Morales. (2022) In GeoKG’22, pp 17-25, DOI:10.1145/3557990.3567569
  5. Towards the intelligent era of spatial analysis and modeling. Di Zhu, Song Gao, Guofeng Cao. (2022) In GeoAI’22, pp 10-13, DOI:10.1145/3557918.3565863

As the Proceedings Chairs, Professor Song Gao co-organized the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI’22). There are two keynotes from both industry and academia and 12 oral presentations in the GeoAI workshop. The proceedings of the GeoAI’22 workshop is available at the ACM Digital Library: https://dl.acm.org/doi/proceedings/10.1145/3557918

In addition, Yuhao Kang (PhD Candidate) and Jake Kruse (PhD Student) presented two short papers in the AutoCarto 2022,  the 24th International Research Symposium on Cartography and GIScience.

  1. A Review and Synthesis of Recent GeoAI Research for Cartography: Methods, Applications, and Ethics. Yuhao Kang, Song Gao, Robert Roth (2022)
  2. Interactive Web Mapping for Multi-Criteria Assessment of Redistricting Plans. Jacob Kruse, Song Gao, Yuhan Ji and Kenneth Mayer (2022)

Also, Congrats to Yuhao who won the International Cartographic Association (ICA) Scholarship Award!

Wen Ye received the 2022 University HILLDALE FELLOWSHIP

Please join us congratulating our junior student Wen (Wendy) Ye, who is currently an undergraduate triple-majoring in computer science, data science, and statistics as well as a research assistant in the GeoDS Lab under Prof. Song Gao’s mentorship, just got the UW-Madison “Hilldale Undergraduate/Faculty Research Fellowship” and will be awarded  in the 2022 Chancellor’s Undergraduate Awards Ceremony! 

The awarded research project is: Understanding spatial inequality to health care access in Wisconsin through deep learning-based network analysis.

In 2019, our GeoDS Lab’s alumnus Timothy Prestby (who is currently a PhD student at PSU Geography) also got this university fellowship under Prof. Gao’s mentorship.

Other Previous Hilldale Fellows at the University of Wisconsin-Madison:

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

Prof. Gao joins the Editorial Board of Transactions in GIS

Recently, Prof. Song Gao is invited to join the Editorial Board of Transactions in GIS, which is a key international journal for publishing geographic information systems/science related research.

Aims and Scope

Transactions in GIS is an international, peer-reviewed journal that publishes original research articles, review articles, and short technical notes on the latest advances and best practices in the spatial sciences. The spatial sciences include all of the different ways in which geography may be used to organize, represent, store, analyze, model and visualize information. The submission of manuscripts that focus on one or more of the following topics among others – is strongly encouraged:

  • GIS, GPS, Remote Sensing and related geospatial technologies;
  • geospatial data acquisition and sensing; maps and spatial reasoning;
  • spatial data infrastructures; standardization and interoperability;
  • spatial data structures and databases; geocomputation;
  • spatiotemporal analysis, integration and modeling;
  • spatial data quality and uncertainty;
  • GIS education and certification; GIS and society;
  • location privacy;
  • and desktop, mobile and Web-based spatially-enabled applications and services.

Keywords

Geographic Knowledge Discovery and Data Mining; Geographic Information Retrieval; Geosensor Networks; Geosimulation; Geospatial Data Integration; Geospatial Semantic Web; Geovisualization; Geographic Information Science; Geographic Information Systems; GIS Architectures and Middleware; GIS and Society; GIS Standardization and Interoperability; GIS&T Education; Global Positioning Systems; Local, Enterprise, Mobile and Web Applications; Location-Based Services; Location Privacy, Data Sharing and Security Maps and Map Services; Ontologies and taxonomies; Public Participation; GIS Remote Sensing; Spatial Analysis; Spatial Cognition and Reasoning; Spatial Data Infrastructures; Spatial Data Quality and Uncertainty; Spatial Databases, Data Structures and Algorithms; Spatial Decision Support Systems; Spatial Dynamics; Spatial Modeling; Spatial Networks; Spatial Thinking; Spatiotemporal Analysis and Modeling; The Spatial Sciences

GeoDS members in 2022 AAG Annual Meeting

Here are a set of sessions in which the GeoDS members will make presentations during the American Association of Geographers (AAG) 2022 Annual Meeting. The time is in the US East Time Zone.

Why geoprivacy matters:An international perspective

Day: 2/26/2022
Start Time: 5:20 PM
End Time: 6:40 PM

Panelist : Song Gao https://aag-annualmeeting.secure-platform.com/a/solicitations/19/sessiongallery/3154

Teaching and Research with ArcGIS: Best Practices, Challenges and Opportunities

Day: 2/27/2022
Start Time: 11:20 AM
End Time: 12:40 PM

PanelistSong Gao https://aag-annualmeeting.secure-platform.com/a/solicitations/19/sessiongallery/5101

Is Artificial Intelligence good for Geography?

Day: 2/27/2022
Start Time: 2:00 PM
End Time: 3:20 PM

PanelistSong Gao https://aag-annualmeeting.secure-platform.com/a/solicitations/19/sessiongallery/3749

Geospatial Health Symposium #10: Access and Utilization of Health Care Services 2

Day: 2/27/2022
Start Time: 3:40 PM
End Time: 5:00 PM

Yunlei LiangSpatially-Constrained Community Detection for Health Professional Shortage Area Delineation with Human Mobility Data 

AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: CyberGIS-enabled spatial epidemiology

Day: 2/27/2022
Start Time: 3:40 PM
End Time: 5:00 PM

Panelist: Song Gao https://aag-annualmeeting.secure-platform.com/a/solicitations/19/sessiongallery/3391

Role of GIS in planning smart and resilient cities III

Day: 3/1/2022
Start Time: 11:20 AM
End Time: 12:40 PM

Yuhan JiA Data-driven Method for Identifying Potential Zones for Airport Shuttle Bus Services 

AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: UCGIS GeoAI & CyberGIS Research Initiative- GeoAI and CyberGIS for Advancing Spatial Decision Making

Day: 3/1/2022
Start Time: 11:20 AM
End Time: 12:40 PM

Song Gao: University of WisconsinReflections on the Development of Spatially Explicit Methods for GeoAI 

AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: Urban Visual Intelligence

Day: 3/1/2022
Start Time: 2:00 PM
End Time: 3:20 PM

Jacob KrusePlaces for play: Understanding human perception of playability in cities using street view images and deep learning 

Yuhao Kang: University of Wisconsin-MadisonHuman settlement value assessment from a place perspective: Considering human dynamics and perceptions in house price modeling 

AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: GeoAI for Social Sensing

Day: 3/1/2022
Start Time: 3:40 PM
End Time: 5:00 PM

Jinmeng Rao: University of Wisconsin – MadisonCATS: Conditional Adversarial Trajectory Simulation for Privacy-Preserved Data Publication 

A review of location encoding for GeoAI published on IJGIS

Gengchen Mai, Krzysztof Janowicz, Yingjie Hu, Song Gao, Bo Yan, Rui Zhu, Ling Cai & Ni Lao (2022): A review of location encoding for GeoAI: methods and applications. International Journal of Geographical Information Science, DOI: 10.1080/13658816.2021.2004602

Abstract: A common need for artificial intelligence models in the broader geoscience is to encode various types of spatial data, such as points, polylines, polygons, graphs, or rasters, in a hidden embedding space so that they can be readily incorporated into deep learning models. One fundamental step is to encode a single point location into an embedding space, such that this embedding is learning-friendly for downstream machine learning models. We call this process location encoding. However, there lacks a systematic review on location encoding, its potential applications, and key challenges that need to be addressed. This paper aims to fill this gap. We first provide a formal definition of location encoding, and discuss the necessity of it for GeoAI research. Next, we provide a comprehensive survey about the current landscape of location encoding research. We classify location encoding models into different categories based on their inputs and encoding methods, and compare them based on whether they are parametric, multi-scale, distance preserving, and direction aware. We demonstrate that existing location encoders can be unified under one formulation framework. We also discuss the application of location encoding. Finally, we point out several challenges that need to be solved in the future.

Spatial Data Science Symposium 2021

The Center for Spatial Studies (spatial@ucsb) at the University of California, Santa Barbara hosts the 2nd Spatial Data Science Symposium virtually this year with a focus on “Spatial and Temporal Thinking in Data-Driven Methods.”

The symposium aims to bring together researchers from both academia and industry to discuss experiences, insights, methodologies, and applications, taking spatial and temporal knowledge into account while addressing their domain-specific problems.

Professor Song Gao joins as one of the speakers for the following panel discussion sessions:

Spatial Data Scientist Career Panel Discussion
Panel Discussion: From Analysis to Action: Engaging through Spatial Data Science Storytelling

AAG Webinar on Ethical Issues of Using Geospatial Data in Health Research

Webinar: Ethical Issues of Using Geospatial Data in Health Research or Policies During the COVID-19 Pandemic and Beyond
Date and Time: Thursday, December 2, 2021 9:00 am – 11:00 am U.S. Eastern Time

Registration: https://aag-geoethics-series.secure-platform.com/a/solicitations/10/sessiongallery/200

This conversation is co-organized by AAG and the Institute of Space and Earth Information Science (ISEIS), at The Chinese University of Hong Kong (CUHK). During this webinar you will first hear presentations from speakers who are longtime scholars in the field of health geography. Presentations from academic speakers will set the stage for a discussion with panelists who are non-academic stakeholders on this topic in and outside the U.S.

Advances in geospatial technologies and the availability of geospatial big data have enabled researchers to analyze and visualize geospatial data in great detail. Geospatial methods are now widely used to uncover the complex patterns of diverse social phenomena, such as human mobility and the COVID-19 pandemic. However, using or mapping individual-level confidential geospatial data (e.g., the locations of people’s residences and activities) involves certain risk of disclosure and privacy violation. Such risk of geoprivacy violation has recently become a widespread concern as many COVID-19 control measures (e.g., digital contact tracing; self-quarantine methods; and disclosure of location visited by infected persons) used by governments or public health agencies collected individual-level geospatial data. These COVID-19 control measures pose a particularly serious geoprivacy threat because recent advances in geospatial artificial intelligence (GeoAI) and high-performance computing may significantly increase the accuracy of spatial reverse engineering (e.g., by linking high-resolution geospatial data with other data such as census or survey data to discover the identity of specific individuals). On the other hand, false inference, such as false positives from facial recognition for example, can result in big consequences.

This webinar will focus on ethical issues of using geospatial data analytics in health research and practices, especially in the context of the COVID-19 pandemic and beyond. The presentations will cover a wide range of topics, including uncertainties in analyzing relationships between disease spread and geographic environment, geoprivacy concerns for different COVID-19 control measures (e.g., digital contact tracing), addressing people’s concerns for geoprivacy in times of pandemics, IRB issues in health research during COVID-19, legal issues arose and policy implications of using individual-level confidential geospatial for controlling the spread of pandemics. Questions to be explored include: How can researchers protect people’s geoprivacy when using individual-level geospatial data to gain insights into the dynamics and patterns of infectious diseases? What disease control measures have higher risk of geoprivacy violation, which may significantly affect people’s acceptance of these measures and undermine their effectiveness in controlling the spread of COVID-19 or future pandemics? How can public health authorities balance the need for disease control and individual geoprivacy protection? What are the legal and technical issues in data sharing? How to minimize the unintended negative consequences such as the stigmatization of and discrimination against infected persons as a result of geoprivacy breaches or location disclosure?

Prof. Gao presents at the GIScience Research UK International Seminar Series

Beginning in 2021, GISRUK launched a series of international seminars celebrating innovation in Geographical Information Science, Chaired by Dr. Peter Mooney.

Dr. Song Gao was invited to give a seminar titled “GeoAI for Human Mobility Analytics and Location Privacy Protection” on 3rd November 2021.

Geographical Information Science Research UK (GISRUK) is the largest academic conference in Geographic Information Science in the UK. For the last 30 years, GISRUK has attracted international researchers and practitioners in GIS and related fields, including geography, data science, urban planning and computer science, to share and discuss the latest advances in spatial computing and analysis. The event in 2022 will be the 30th annual GISRUK conference. The conference will be held on the 5th – 8th April 2022 and hosted by the Geographic Data Science Lab and Department of Geography and Planning at the University of Liverpool. We look forward to welcoming you in person to the conference next year.

Prof. Gao joins the new NSF funded AI Institute: ICICLE

Today, the U.S. National Science Foundation (NSF) announced the establishment of 11 new NSF National Artificial Intelligence Research Institutes. Each institute will receive $20 million for a total $220 million investment by NSF. Building off of seven institutes funded in 2020, the new program is meant to broaden access to AI to solve complex societal problems.

Prof. Song Gao joins the Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE).

Led by The Ohio State University, ICICLE will build the next generation of Cyberinfrastructure to render Artificial Intelligence (AI) more accessible to everyone and drive its further democratization in the larger society.

ICICLE will build and prove its system around three use-inspired science application domains: smart foodsheds, digital agriculture, and animal ecology. Analogous to watersheds, foodsheds define the geographical and human elements that affect how, when and where food is grown and consumed. Digital agriculture seeks to use technology to improve the yield and efficiency of crops, while animal ecology focuses on the roles of animals in agriculture and the environment.

More information on: https://icicle.ai/

Two COVID-19 research papers published in PNAS

  1. Xiao Hou, Song Gao*, Qin Li*, Yuhao Kang, Nan Chen, Kaiping Chen, Jinmeng Rao, Jordan S. Ellenberg, Jonathan A. Patz (2021) Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race. Proceedings of the National Academy of Sciences. June 15, 2021, 118 (24) e2020524118; DOI: 10.1073/pnas.2020524118

Abstract:

The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Meta-population epidemic modeling studies in the susceptible–exposed–infectious–removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What’s more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions (e.g., varying peak infection timing). To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.

2. Xiaoyi Han, Yilan Xu, Linlin Fan, Yi Huang, Minhong Xu, Song Gao. (2021) Quantifying COVID-19 importation risk in a dynamic network of domestic cities and international countries. Proceedings of the National Academy of Sciences. August 3, 2021, 118 (31) e2100201118; DOI: 10.1073/pnas.2100201118

Abstract:

Since its outbreak in December 2019, the novel coronavirus 2019 (COVID-19) has spread to 191 countries and caused millions of deaths. Many countries have experienced multiple epidemic waves and faced containment pressures from both domestic and international transmission. In this study, we conduct a multiscale geographic analysis of the spread of COVID-19 in a policy-influenced dynamic network to quantify COVID-19 importation risk under different policy scenarios using evidence from China. Our spatial dynamic panel data (SDPD) model explicitly distinguishes the effects of travel flows from the effects of transmissibility within cities, across cities, and across national borders. We find that within-city transmission was the dominant transmission mechanism in China at the beginning of the outbreak and that all domestic transmission mechanisms were muted or significantly weakened before importation posed a threat. We identify effective containment policies by matching the change points of domestic and importation transmissibility parameters to the timing of various interventions. Our simulations suggest that importation risk is limited when domestic transmission is under control, but that cumulative cases would have been almost 13 times higher if domestic transmissibility had resurged to its precontainment level after importation and 32 times higher if domestic transmissibility had remained at its precontainment level since the outbreak. Our findings provide practical insights into infectious disease containment and call for collaborative and coordinated global suppression efforts.

Prof. Gao joins the Editorial Board of CaGIS and Scientific Reports

Recently, Prof. Gao was invited to serve on the Editorial Board for the following two journals:

Cartography and Geographic Information Science (CaGIS) is the official publication of the Cartography and Geographic Information Society. The Society supports research, education, and practices that improve the understanding, creation, analysis, and use of maps and geographic information. The CaGIS journal implements the objectives of the Society by publishing authoritative peer-reviewed articles that report on innovative research in cartography and geographic information science.

Scientific ReportsNature is an open access journal publishing original research from across all areas of the natural sciences.

Prof. Gao receives a new geospatial data science research grant

The American Family Insurance Data Science Institute (AFIDSI) is honored to announce the results of the new round of the American Family Funding Initiative, a research competition for data science projects. American Family Insurance has partnered with UW–Madison through the Institute to offer “mini grants” of $75k-to-150k per year for data science research. This is the second installation of a $10 million research agreement.

The goal of the American Family Funding Initiative is to stimulate and support highly innovative research. The successful projects, reviewed by faculty and staff from across UW-Madison campus, were evaluated based on their potential contributions to the field of data science, practical use and the novelty of their approaches.

AFIDSI brings people together to launch new research in data science and apply findings to solve problems. In collaboration with researchers across campus and beyond, AFIDSI focuses on the fundamentals of data science research and on translating that research into practice.

New projects funded in the second round of the American Family Funding Initiative include:

A Deep Learning Approach to User Location Privacy Protection
Principal Investigator: Song Gao, Assistant Professor of Geography.
Co-Principal Investigator: Jerry Zhu, Computer Sciences.

Location information is among the most sensitive data being collected by mobile apps, and users increasingly raise privacy concerns. The proposed research aims to develop a deep learning architecture that will protect users’ location privacy while keeping the capability for location-based business recommendations such as usage-based insurance (UBI).

Machine Learning Approaches for Metadata Standardization
Principal investigator: Colin Dewey, Professor of Biostatistics and Medical Informatics.
Co-Principal Investigator: Mark Craven, Biostatistics and Medical Informatics.

The need to manually standardize metadata describing records in large data sets, compiled from many sources, is a major bottleneck in both research and business. This project will develop machine learning approaches for automating metadata standardization and identifying records that would most benefit from expert human input.

Adaptive Operations Research and Data Modeling for Insurance Applications
Principal Investigator: Michael Ferris, Professor of Computer Sciences.

Insurance claims applications must be operated efficiently under normal conditions and allow for rapid reconfiguration in crisis situations. The proposed work will develop optimization models, data and solution processes to schedule resources over time, servicing normal workloads, while creating resilience to abrupt changes from random disturbances.

GAN-mixup: A New Approach to Improve Generalization in Machine Learning
Principal Investigator: Kangwook Lee, Assistant Professor of Electrical and Computer Engineering.
Co-Principal Investigator: Dimitris Papailiopoulos, Electrical and Computer Engineering.

Recent machine learning successes rely on predictive models that adapt to previously unseen data. This research will provide a new approach to improve such generalization, with provable performance guarantees.

Integer Programming for Mixture Matrix Completion
Principal Investigator: Jeff Linderoth, Professor of Industrial and Systems Engineering.
Co-Principal Investigators: Jim Luedtke, Industrial and Systems Engineering; Daniel Pimentel-Alarcon, Biostatistics and Medical Informatics.

Matrix completion, or filling in the unknown entities in a matrix, is used in applications such as recommender systems and systems for analyzing visual images. This project will apply integer programming techniques to develop algorithms for solving a mixture matrix completion problem, paving the way towards applying this method to large-scale data science problems.

Developing a State-of-the-Science Regional Weather Forecasting System
Principal Investigator: Michael Morgan, Professor of Atmospheric and Oceanic Sciences.
Co-Principal Investigator: Brett Hoover, Space Science and Engineering Center.

This project will develop a weather prediction system for American Family Insurance, run entirely in cloud computing infrastructure, that will improve the accuracy of forecasting hazards such as hail and hurricanes. The probabilistic system will also estimate the uncertainty associated with the predictability of hazardous weather.

Model Recycling: Accelerating Machine Learning by Re-using Past Completions
Principal Investigator: Shivaram Venkataraman, Assistant Professor of Computer Sciences.
Co-Principal Investigator: Dimitris Papailiopoulos, Electrical and Computer Engineering.

Training machine learning models that are used in a wide range of domains, from drug discovery to recommendation engines, takes significant time and resources. This project will automate and accelerate this process of fine-tuning by reusing and sharing past computations from prior training jobs, using a technique called model recycling.

Additionally, two projects from the first round received continued funding:

Question Asking with Differing Knowledge and Goals
Principal investigator: Joe Austerweil, Assistant Professor of Psychology.

Despite tremendous progress in machine learning, automated answers to questions are still inferior to answers from humans. This project investigates whether incorporating psycholinguistic factors that influence how people respond to language can improve automated question-answering methods.

Lightweight Natural Language and Vision Algorithms for Data Analysis
Principal investigator: Vikas Singh, Professor of Biostatistics and Medical Informatics. Collaborators: Zhanpeng Zeng, Computer Sciences; Shailesh Acharya and Glenn Fung, American Family Insurance.

Natural language processing is a form of artificial intelligence that helps computers read and understand human language. The overarching goal of this project is to accelerate the time it takes to train and test efficient, accurate natural language processing models.

National Fellowships Engage Geospatial Research And Education On COVID-19

Projects address human mobility patterns, access to health care and food systems, racial and disability disparities during the pandemic.

The Geospatial Software Institute (GSI) Conceptualization Project has announced 16 fellowships to researchers at 13 institutions to tackle COVID-19 challenges using geospatial software and advanced capabilities in cyberinfrastructure and data science. Prof. Song Gao was selected as one of the geospatial fellows. A full list of the fellows, with biographies and project information, is at https://gsi.cigi.illinois.edu/geospatial-fellows-members/.

The GSI Conceptualization Project is supported by the National Science Foundation (NSF), and carried out in partnership with the American Association of Geographers (AAG), Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI), the National Opinion Research Center (NORC) at the University of Chicago, Open Geospatial Consortium (OGC), and University Consortium for Geographic Information Science (UCGIS). Technical and cyberinfrastructure support are provided by the CyberGIS Center for Advanced Digital and Spatial Studies (CyberGIS Center)  at the University of Illinois at Urbana-Champaign. 

“The COVID-19 crisis has shown how critical it is to have cutting-edge geospatial software and cyberinfrastructure to tackle the pandemic’s many challenges,” said Shaowen Wang, the principal investigator of the NSF project and founding director of the CyberGIS Center. “We are extremely grateful for NSF’s support to fund this talented group of researchers, whose work is so diverse yet complementary.”

Michael Goodchild, chair of the NSF project advisory committee and professor emeritus in geography at UC-Santa Barbara, agreed. “Geospatial data and tools have enormous potential for helping us address the challenges of COVID-19, and these 16 Fellows have exactly the right qualifications and experience. I’m very excited to see what they are able to achieve.”

The Fellows come from varied professional, cultural, and institutional backgrounds, representing many disciplinary areas, including public health, food access, emergency management, housing and neighborhood change, and community-based mapping. The fellowship projects represent frontiers of emerging geospatial data science, including for example geospatial AI and deep learning, geovisualization, and advanced approaches to gathering and analyzing geospatial data.

Pioneered by multi-million dollar research funded by NSF, cyberGIS (i.e., cyber geographic information science and systems based on advanced computing and cyberinfrastructure) has emerged as a new generation of GIS, comprising a seamless integration of advanced cyberinfrastructure, GIS, and spatial analysis and modeling capabilities while leading to widespread research advances and broad societal impacts. Built on the progress made by cyberGIS-related communities, the GSI conceptualization project is charged with developing a strategic plan for a long-term hub of excellence in geospatial software infrastructure, one that can better address emergent issues of food security, ecology, emergency management, environmental research and stewardship, national security, public health, and more.

The Geospatial Fellows program will enable diverse researchers and educators to harness geospatial software and data at scale, in reproducible and transparent ways; and will contribute to the nation’s workforce capability and capacity to utilize geospatial big data and software for knowledge discovery. With a particular focus on COVID-19, the combined research findings of the Fellows will offer insight on how to make geospatial research computationally reproducible and transparent, while also developing novel methods, including analysis, simulation, and modeling, to study the spread and impacts of the virus. The Fellows’ research will substantially add to public understanding of the societal impacts of COVID-19 on different communities, assessing the social and spatial disparities of COVID-19 among vulnerable populations.

“I look forward to seeing the results of these projects, particularly as FAIR and open datasets, software, and models that others can then build on,” says Daniel S. Katz, Assistant Director for Scientific Software and Applications at the National Center for Supercomputing Applications (NCSA), the University of Illinois.

For more information about the GSI conceptualization project, see their website: https://gsi.cigi.illinois.edu/.

For a list of Geospatial Fellows and their projects, visit https://gsi.cigi.illinois.edu/geospatial-fellows-members/.

Prof. Gao received the 2020 Distinguished Honors Faculty Award

Each year, the University of Wisconsin-Madison College of Letters & Science Honors Program solicits student nominations of faculty members (or instructional academic staff) who have had a special impact as teachers of Honors courses, as supervisors of Honors theses, or as teachers and mentors of Honors students. The Faculty Honors Committee reviews these nominations and votes to confer Distinguished Honors Faculty status on the strongest nominees for these awards each spring. Below, we recognize each of these incredible educators and thank them for their contributions to the lives of all students, but particularly those in the Honors program.

This year, Prof. Song Gao received the 2020 Distinguished Honors Faculty Award along with five other faculty members on campus.

Also, congrats to Timothy Prestby for finishing his L&S undergraduate honor thesis “Understanding Neighborhood Isolation Through Big Data Human Mobility Analytics”. Best wishes to his graduate school life at PSU Geography!

GeoDS Lab members won multiple awards in the AAG 2020 Annual Meeting

Due to the COVID-19 pandemic, The American Association of Geographers (AAG)  2020 Annual Conference was held online virtually. GeoDS Lab members participated the meeting and fortunately won several awards as follows.

Congratulations to Yuhao Kang who won the 1st place in the 2020 AAG GIS Specialty Group Annual Best Student Paper Competition and the 2020 AAG Cartography Specialty Group Master’s Thesis Research Grant.
http://aag-giss.org/2020-aag-geographic-information-science-and-systems-specialty-group-annual-student-paper-competition-winners/

https://aagcartography.wordpress.com/awards-competitions/masters-thesis-research-grant/

In addition, GeoDS Lab’s recent COVID-19 mapping work was awarded the winner of static mapping group for the “AAG Health and Medical Geography Health Data Visualization Contest”.

Also, GeoDS Lab’s recent COVID-19 work was featured by the AAG Newsletter:

http://news.aag.org/2020/03/geographers-act-on-covid19/

County-to-County- Spring Travel Flow Tracking

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

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

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.