New Research Chapter about Store Visit Patterns during COVID-19

Yunlei Liang, Kyle W. McNair, Song Gao, Aslıgül Göçmen. (2021). Exploring Store Visit Changes During the COVID-19 Pandemic Using Mobile Phone Location Data. In Shih-Lung Shaw and Daniel Sui (Eds): Mapping COVID-19 in Space and Time: Understanding the Spatial and Temporal Dynamics of a Global Pandemic (Chapter 13). pp. 253-275, Springer.

Abstract:

When the World Health Organization (WHO) announced the pandemic of COVID-19, people around the globe scattered to stores for groceries, supplies, and other miscellaneous items in preparation for quarantine. The dynamics of retail visits changed dramatically due to the pandemic outbreak. The study intends to analyze how the store visit patterns have changed due to the lockdown policies during the COVID-19 pandemic. Using mobile phone location data, we build a time-aware Huff model to estimate and compare the visiting probability of different brands of stores over different time periods. We are able to identify certain retail and grocery stores that have more or fewer visits due to the pandemic outbreak, and we detect whether there are any trends in visiting certain retail establishments (e.g., department stores, grocery stores, fast-food restaurants, and cafes) and how the visiting patterns have adjusted with lockdowns. We also make comparisons among brands across three highly populated U.S. cities to identify potential regional variability. It has been found that people in large metropolitan areas with a well-developed transit system tend to show less sensitivity to long-distance visits. In addition, Target, which is a department store, is found to be more negatively affected by longer-distance trips than other grocery stores after the lockdown. The findings can be further applied to support policymaking related to public health, urban planning, transportation, and business in post-pandemic cities.

Highlighted results:

  • The dwell time distribution of visitors in Target.
  • Frequency of Visits from home Census Block Groups to Whole Foods Markets.

New research article about Playability in Urban Environments published in CEUS

Jacob Kruse, Yuhao Kang, Yu-Ning Liu, Fan Zhang, and Song Gao. “Places for play: Understanding human perception of playability in cities using street view images and deep learning.” Computers, Environment and Urban Systems 90 (2021): 101693.

Abstract: Play benefits childhood development and well-being, and is a key factor in sustainable city design. Though previous studies have examined the effects of various urban features on how much children play and where they play, such studies rely on quantitative measurements of play such as the precise location of play and the duration of play time, while people’s subjective feelings regarding the playability of their environment are overlooked. In this study, we capture people’s perception of place playability by employing Amazon Mechanical Turk (MTurk) to classify street view images. A deep learning model trained on the labelled data is then used to evaluate neighborhood playability for three U.S. cities: Boston, Seattle, and San Francisco. Finally, multivariate and geographically weighted regression models are used to explore how various urban features are associated with playability. We find that higher traffic speeds and crime rates are negatively associated with playability, while higher scores for perception of beauty are positively associated with playability. Interestingly, a place that is perceived as lively may not be playable. Our research provides helpful insights for urban planning focused on sustainable city growth and development, as well as for research focused on creating nourishing environments for child development.

Highlighted results:

  • Our deep learning model was able to produce playability scores whose distribution closely matched that of the training data.
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  • Using images labeled by our deep learning model, we produced a map of playability scores for Boston, Seattle, and San Francisco.
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  • Downtown areas in the three cities studied had high lively scores but low playability scores.
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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/

GeoDS Lab students’ industry internship experience

Besides schoolwork, students in the GeoDS lab also have the opportunity to work as interns in geospatial industries over the summer. They are able to apply their Cartography/GIS/Spatial Data Science knowledge & skills learned at school to solve some real-world problems and build a better understanding of what are key knowledge & skills that can make a difference! Two students Yunlei Liang and Jinmeng Rao are sharing their summer internship experience in summer 2020 in this post.

In addition, please join us to congratulate lab members and alumni: Yuhao Kang (Google X), Jake Kruse (Arity, Allstate), Jinmeng Rao (Google X), and Timothy Prestby (Apple Maps) will take their 2021 summer internships .

Yunlei Liang :

Last summer, I worked as a Data Science Intern at Arity, a mobility data and analytics company under Allstate. I was very lucky to work on two teams. In the first team, I worked on understanding the impact of COVID-19 on the user trajectories and analyzing how the model and statistics have changed because of the reduced travel. In the second team, I was responsible for evaluating Points of Interest (POIs) from different vendors. I matched their classification and locations, identified the coverage quality, assigned scores to each vendor and produced a recommendation report to the team.

Through this 12-week internship, I learned a lot of technical skills, which also helps me realize what are important knowledge I should improve back to school. The cross-team experience made me learn how to work in a team. It was very different than what I did in school. In a company, I am expected to communicate with different people: my mentor, my teammates, and people from other teams. Understanding what others are doing is extremely important as collaboration is fairly common, and people always help each other by discussing solutions to various problems. Being active and always reaching out to others are my main takeaways from this internship. I also learned a lot of such experience from my previous internship in the Data Science team at Wework Inc.

Jinmeng Rao:

Last summer, I worked as a Geospatial Vision Intern at Sturfee Inc., a spatial intelligence company focusing on Visual Positioning Service (VPS), to design and implement computer vision algorithms and toolkits on geospatial data (e.g., street/satellite view images, GPS traces) to improve city-scale AR experience.

During my 3-month internship at Sturfee, our team developed a cross-view Perspective-n-Point (PnP) aligner tool for estimating and refining camera pose based on satellite images and street view images. My main tasks were to design an efficient algorithm to synthesize aerial view images from street view images and to integrate the algorithm into the tool. After the integration, the camera pose estimation accuracy is significantly improved, and the PnP aligner tool becomes much easier to use. I also worked on designing a grid-based keypoint matching algorithm to automatically find matching points between two different views and search for the best camera pose accordingly.

My internship experience at Sturfee is great and fruitful. As an intern, I had a chance to learn state-of-the-art industrial solutions, and I got a general picture of what the industry cares about more. The biggest takeaway for me is that I learned how to apply our skills to solve some real-world problems in the industry. I believe my experience at Sturfee will help me do better in research or work in the future.

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/.

New IJGIS Editorial on Movement Data Science

There has not been a time in the history of GIScience when movement analytics and mobility insights have played such an important role in policymaking as in today’s global responses to the COVID-19 crisis. This special section further builds on previous efforts by the editorial team and others from the GIScience community and beyond to advance the body of knowledge in Computational Movement Analysis (CMA). CMA generally refers to series methods and analytical approaches to process, structure, visualize and analyze tracking data and movement patterns to facilitate knowledge discovery and modeling of movement. Specifically, this special section was proposed as part of a pre-conference workshop on Analysis of Movement Data (AMD 2018) at the GIScience 2018 meeting, 28 August 2018, Melbourne, Australia. The focus of this special section is on three aspects of CMA: (1) representation and modeling of movement; (2) urban mobility analytics; and (3) movement analytics using social media data. With the papers presented in the special section, we highlight recent advancements in CMA with the development of methods and techniques for big movement data analytics and utilization of trajectories constructed using user-generated crowdsourced contents such as geo-tagged social media posts. Traditional CMA methods were often developed and evaluated using a smaller set of movement data involving smaller numbers of individuals and contextual variables.

As the momentum to generate more geo-enriched movement data at large volumes, high frequencies and for longer durations continues, this is a timely and significant achievement towards movement data science. As the papers of this special section illustrate, movement data science leverages the advancements in big data analytics, cyberinfrastructure, parallel computing and data fusion to enhance the analysis of large, multi-faceted and multi-sourced movement data. Below are the editorial and the six original papers presented in this special section on the International Journal of Geographical Information Science (IJGIS). 

Dodge, S., Gao, S., Tomko, M., & Weibel, R. (2020). Progress in computational movement analysis – towards movement data science. International Journal of Geographical Information Science, 1-6.

Buchin, M., Kilgus, B., & Kölzsch, A. (2019). Group diagrams for representing trajectories. International Journal of Geographical Information Science, 1-33.

Graser, A., Widhalm, P., & Dragaschnig, M. (2020). The M³ massive movement model: a distributed incrementally updatable solution for big movement data exploration. International Journal of Geographical Information Science, 1-24.

Qiang, Y., & Xu, J. (2019). Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data. International Journal of Geographical Information Science, 1-17.

Li, W., Wang, S., Zhang, X., Jia, Q., & Tian, Y. (2020). Understanding intra-urban human mobility through an exploratory spatiotemporal analysis of bike-sharing trajectories. International Journal of Geographical Information Science, 1-24.

Ma, D., Osaragi, T., Oki, T., & Jiang, B. (2020). Exploring the heterogeneity of human urban movements using geo-tagged tweets. International Journal of Geographical Information Science, 1-22.

Xin, Y., & MacEachren, A. M. (2020). Characterizing traveling fans: a workflow for event-oriented travel pattern analysis using Twitter data. International Journal of Geographical Information Science, 1-20.

Moving forward, we see a clear need for more reproducible research in CMA, following a growing mega-trend in data-driven sciences. Data quality and privacy challenges as well as uncertainty in data, analytics, and modeling have been largely overlooked in the CMA literature so far. For a more responsible movement data science, careful considerations should be given to the quality, uncertainty and representativeness of ‘large’ mobility data that are being used for generating important mobility insights for policymaking. Lastly, with the recent exciting developments in data access, as a community, we should think about leveraging this advantage to make movement data science more relevant to real-world problems for the mitigation of societal and environmental challenges such as disease outbreaks, population mobility, natural hazards and human-wildlife conflicts.

Link: GIScience 2020 Workshop on Advancing Movement Data Science (AMD’20) turns to 2021

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!

Digital Contact Tracing and Surveillance: Geospatial opportunities, limitations, and research directions

Reference: Trisalyn Nelson, Peter Kedron,  Michael F. Goodchild,  Stewart Fotheringham,  Amy Frazier,  Wenwen Li, Song Gao, Yingjie Hu,  Ming-Hsiang Tsou, May Yuan, Bo Zhao (2020). Digital Contact Tracing and Surveillance: Geospatial opportunities, limitations, and research directions. ASU Spatial Analysis Research Center (SPARC) White Paper. pp 1-13.

Executive Summary

As efforts to mitigate and suppress COVID-19 continue, many decision makers are asking if digital contact tracing—a method for determining contact between an infected individual and others using tracking systems commonly based on mobile devices—can help us safely transition from population-wide social distancing to targeted case-based interventions such as individualized self-quarantine. In response, the Spatial Analysis Research Center (SPARC) at Arizona State University organized a panel of national experts to discuss the use of geospatial technologies in digital contact tracing and identify the practical challenges researchers can address to make digital contact tracing as effective as possible.

The major themes of the discussion included (i) the capabilities and limitations of geospatial technology, (ii) privacy, and (iii) future research directions. Key takeaways from each of these areas include:

Capabilities and limitations of geospatial technology: There are many geospatial technologies (e.g., GPS, Bluetooth, Cellular, WiFi) embedded in mobile devices that can be leveraged for digital contact tracing. However, GPS technology in smartphones lacks accuracy to map interactions in the detailed way one might expect. For instance, the horizontal accuracy of GPS is 15m, and the vertical accuracy is insufficient to pick up which floor of a building a person is on. Indoor accuracy is particularly poor, which is problematic given people spend 87% of their time indoors. However, information about the absolute location of an individual may not be as important to digitally tracing epidemiologically meaningful contacts as identifying the types of interactions most likely to result in the spread of the virus. The importance of tracing interactions creates an opportunity to use Bluetooth-based exchange of encrypted keys to record person-to-person contacts that can then be analyzed within the space-time prism framework. This approach will not require storing of all individuals’ movement data, which will reduce computation complexity. Geotargeted and geotagged social media are useful for tracking transmission between cities or within cities, detecting large gatherings, and helping individuals recall location and contact history during contact tracing interviews. Social media can also provide useful context, such as check-in locations and textual content, to reduce false positives in interactions identified through other forms of digital contact tracing.

Privacy: Digital contact tracing raises numerous privacy concerns. By creating some record of the location history or contacts of an individual, digital contact tracing creates an opportunity to identify an individual without their consent. At present, the privacy implications of digital contact tracing are unclear because these systems have yet to be fully developed or deployed in the US. An evaluation of pros and cons in the existing digital contact tracing plans operating in other countries can inform policy makers on privacy mediation during and after contact tracing. While companies and officials working on this issue have made statements that preserving privacy is an important goal, the details of how privacy will be preserved and the safeguards that will be put in place are not yet available. If any privacy protections are lifted to enable contact tracing, a plan should be put in place to restore protections once the pandemic subsides.

Future Research: To support digital contact tracing and surveillance, several research areas must be advanced. Key technical areas include increasing the accuracy of indoor positioning, developing approaches for reducing false positive of potential exposure (not to be confused with false negatives which are more common in COVID-19 diagnostic test) ensuring a focus on high accuracy in relative positioning, addressing computational complexities, developing group or bubble based approaches to surveillance, and developing a system for the creation and distribution of high resolution risk data and to enable self-determination of the need of quarantine and testing based on possible exposure. Research into how digital contact tracing systems link with existing contact tracing infrastructure and with other digital contact tracing systems also needs to be conducted. The implications of digital contact tracing for society and privacy will emerge along with these systems. Researchers need to study these issues as they emerge to ensure that we have the ability to hold an informed public debate about the effectiveness and costs of digital contact tracing.