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:
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?
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.
Abstract: The nowadays ubiquitous location-aware mobile devices have contributed to the rapid growth of individual-level location data. Such data are usually collected by location-based service platforms as training data to improve their predictive models’ performance, but the collection of such data may raise public concerns about privacy issues. In this study, we introduce a privacy-preserving location recommendation framework based on a decentralized collaborative machine learning approach: federated learning. Compared with traditional centralized learning frameworks, we keep users’ data on their own devices and train the model locally so that their data remain private. The local model parameters are aggregated and updated through secure multiple-party computation to achieve collaborative learning among users while preserving privacy. Our framework also integrates information about transportation infrastructure, place safety, and flow-based spatial interaction to further improve recommendation accuracy. We further design two attack cases to examine the privacy protection effectiveness and robustness of the framework. The results show that our framework achieves a better balance on the privacy–utility trade-off compared with traditional centralized learning methods. The results and ensuing discussion offer new insights into privacy-preserving geospatial artificial intelligence and promote geoprivacy in location-based services.
ACKNOWLEDGMENT: We acknowledge the funding support provided by the American Family Insurance Data Science Institute Funding Initiative at the University of Wisconsin-Madison. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funder.
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.
The dwell time distribution of visitors in Target.
Frequency of Visits from home Census Block Groups to Whole Foods Markets.
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.
Our deep learning model was able to produce playability scores whose distribution closely matched that of the training data.
Using images labeled by our deep learning model, we produced a map of playability scores for Boston, Seattle, and San Francisco.
Downtown areas in the three cities studied had high lively scores but low playability scores.
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.
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.
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.
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.
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.
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.