Song Gao

University of Wisconsin–Madison | UW

Understanding and measuring the resilience of food supply networks is a global imperative to tackle increasing food insecurity. However, the complexity of these networks, with their multidimensional interactions and decisions, presents significant challenges. This paper proposes FLEE-GNN, a novel Federated Learning System for Edge-enhanced Graph Ne...

Emerging advances in artificial intelligence, hardware accelerators, and big data processing architectures continue to reach the geospatial information sciences, with a transformative impact on many societal challenges. Recent breakthroughs in deep learning have brought forward an automated capability to learn representative features from massive a...

The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily fo...

Accurate traffic speed forecasting is a prerequisite for anticipating future traffic status and increasing the resilience of intelligent transportation systems. However, most studies ignore the involvement of context information ubiquitously distributed over the urban environment to boost speed prediction. The diversity and complexity of context in...

Large-scale trajectory data offer a finer lens into the regularity in individual mobility choices. Previous studies have exerted efforts to measure the regularity in people's location visiting patterns. However, the complexity of travel behavior at different spatial and temporal scales has not been adequately considered. To capture regularity in a...

Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes in language and vision tasks, we have yet seen an attempt to develop foundation models for geo...

With the increasing demands for geospatial analytics in industry and academia, the need for Geographic Information Systems/Science (GIS) education is on the rise. A growing number of departments in geography have launched or expanded their GIS graduate programs. However, the factors influencing students choosing GIS programs have not been examined...

Geospatial Artificial Intelligence (GeoAI) is an interdisciplinary field enjoying tremendous adoption. However, the efficient design and implementation of GeoAI systems face many open challenges. This is mainly due to the lack of non-standardized approaches to artificial intelligence tool development, inadequate platforms, and a lack of multidiscip...

In this commentary, we describe the current state of the art of points of interest (POIs) as digital, spatial datasets, both in terms of their quality and affordings, and how they are used across research domains. We argue that good spatial coverage and high-quality POI features — especially POI category and temporality information — are key for cr...

Research on commercial activities and their spatial structure is an important topic in urban geography and economic geography. As an online-to-offline business activity, takeout ordering is an important approach for urban residents to solve their catering needs. It can well reflect the working and living conditions of the population and is a source...

It has been well-established that human mobility has an inseparable relationship with COVID-19 infections. As social-distancing and stay-at-home orders lifted and data availability increased, our knowledge on how human behaviors including mobility and close interpersonal contacts associate with the pandemic progression also needs to stay updated. I...

The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily fo...

Quantifying the resilience in the food system is important for food security issues. In this work, we present a geospatial knowledge graph (GeoKG)-based method for measuring the resilience of a multi-commodity flow network. Specifically, we develop a CFSGeoKG ontology to describe geospatial semantics of a multicommodity flow network comprehensively...

Fine-grained crowd distribution forecasting benefits smart transportation operations and management, such as public transport dispatch, traffic demand prediction, and transport emergency response. Considering the co-evolutionary patterns of crowd distribution, the interactions among places are essential for modelling crowd distribution variations....

It has been well-established that human mobility has an inseparable relationship with COVID-19 infections. As social-distancing and stay-at-home orders lifted and data availability increased, our knowledge on how human behaviors including mobility and close interpersonal contacts associate with the pandemic progression also needs to stay updated. I...

Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of COVID-19 and other infectious diseases. This understanding will be paramount to prepare for fu...

Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of COVID-19 and other infectious diseases. This understanding will be paramount to prepare for fu...

Geospatial Artificial Intelligence (GeoAI) is an interdisciplinary field enjoying tremendous adoption. However, the efficient design and implementation of GeoAI systems face many open challenges. This is mainly due to the lack of non-standardized approaches to artificial intelligence tool development, inadequate platforms, and a lack of multidiscip...

Community Detection algorithms are used to detect densely connected components in complex networks and reveal underlying relationships among components. As a special type of networks, spatial networks are usually generated by the connections among geographic regions. Identifying the spatial network communities can help reveal the spatial interactio...

Quantifying the resilience in the food system is important for food security issues. In this work, we present a geospatial knowledge graph (GeoKG)-based method for measuring the resilience of a multi-commodity flow network. Specifically, we develop a CFS-GeoKG ontology to describe geospatial semantics of a multi-commodity flow network comprehensive...

Visual storytelling describes the communication of stories through illustrations, graphics, imagery, and video instead of, or in addition to, oral, written, and audio formats. Compared to their popularity and wide reach, empirical research on map-based visual stories remains limited. We work towards infilling this gap through an empirical study on...

In response to the coronavirus disease 2019 (COVID-19) pandemic, various countries have sought to control COVID-19 transmission by introducing non-pharmaceutical interventions. Restricting population mobility, by introducing social distancing, is one of the most widely used non-pharmaceutical interventions. Although similar population mobility rest...

The space-place dichotomy has long been discussed in human geography, digital humanity, and more recently in cartography and geographic information science. Place-based GIS are not yet well developed, although there is an increasing interest in semantic and ontological approaches. In this chapter, I present the technological building blocks towards...

Spatial clustering has been widely used for spatial data mining and knowledge discovery. An ideal multivariate spatial clustering should consider both spatial contiguity and aspatial attributes. Existing spatial clustering approaches may face challenges for discovering repeated geographic patterns with spatial contiguity maintained. In this paper,...

In this study, we aim to reveal hidden patterns and confounders associated with policy implementation and adherence by investigating the home-dwelling stages from a data-driven perspective via Bayesian Inference with weakly informative priors and by examining how home-dwelling stages in the U.S. varied geographically, using fine-grained, spatial-ex...

Spatial clustering has been widely used for spatial data mining and knowledge discovery. An ideal multivariate spatial clustering should consider both spatial contiguity and aspatial attributes. Existing spatial clustering approaches may face challenges for discovering repeated geographic patterns with spatial contiguity maintained. In this paper,...

Spatial‐query‐by‐sketch is an intuitive tool to explore human spatial knowledge about geographic environments and to support communication with scene database queries. However, traditional sketch‐based spatial search methods perform inadequately due to their inability to find hidden multiscale map features from mental sketches. In this research, we...

Spatial-query-by-sketch is an intuitive tool to explore human spatial knowledge about geographic environments and to support communication with scene database queries. However, traditional sketch-based spatial search methods perform insufficiently due to their inability to find hidden multi-scale map features from mental sketches. In this research,...

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 sp...

This article provides a state-of-the-art summary of location privacy issues and geoprivacy-preserving methods in public health interventions and health research involving disaggregate geographic data about individuals. Synthetic data generation (from real data using machine learning) is discussed in detail as a promising privacy-preserving approach...

In this paper, we design and implement a map dashboard that combines spatio-temporal visualization and interactive narrative to comprehensively illustrate the 2020 US presidential election. Specifically, our dashboard takes campaign rallies and major events as narrative clues and integrates multi-perspective factors (e.g., the spatial spread of COV...

A common need for artificial intelligence models in the broader geoscience is to represent and encode various types of spatial data, such as points (e.g., points of interest), polylines (e.g., trajectories), polygons (e.g., administrative regions), graphs (e.g., transportation networks), or rasters (e.g., remote sensing images), in a hidden embeddi...

A better formalization of place - where people live, perceive, and interact with others - is crucial for understanding socioeconomic environment and human settlement. The widely used hedonic pricing model for houses was proposed from the perspective of space, focusing mostly on static house structural information and objective built environment fac...

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, whil...

The COVID-19 pandemic has profoundly impacted the economy and human lives worldwide, particularly the vulnerable low-income population. We employ a large panel data of 5.6 million daily transactions from 2.6 million debit cards owned by the low-income population in the U.S. to quantify the joint impacts of the state lockdowns and stimulus payments...

Analyzing large volumes of big geo‐data through social sensing provides new research opportunities in urban studies. Such big geo‐data include mobile phone records, social media posts, vehicle trajectories, and street view images. They can be used to extract human behavior patterns and infer the geographical characteristics of cities. This chapter...

Significance In the COVID-19 pandemic, countries need to manage both the domestic spread and the spread of the virus from foreign countries, with their relative urgency varying over time. Based on a dynamic network of cities and countries connected by travel flows, we demonstrate that imported cases would have a limited effect on a country’s confir...

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...

Significance A human mobility flow-augmented stochastic susceptible–exposed–infectious–removed–style epidemic modeling framework is developed, which combines with data assimilation and machine learning to reconstruct the historical growth trajectories of COVID-19 infection in the two largest counties in Wisconsin. The associations between the sprea...

The Coronavirus disease 2019 (COVID-19) has exposed and, to some degree, exacerbated the social inequity in the U.S. This study reveals the correlation between demographic/socioeconomic variables and home-dwelling time records derived from large-scale mobile phone location tracking data at the U.S. Census Block Group (CBG) level in twelve most-popu...

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 st...

Extracting hidden information from human mobility patterns is one of the long-standing challenges of urban studies. In addition, exploring the relationship between urban functional structure and traffic spatial interaction pattern has long been of interest. Recently, vehicle GPS trajectory data emerged as a popular data source for revealing human m...

Dynamic human activity intensity information is of great importance in many location-based applications. However, two limitations remain in the prediction of human activity intensity. First, it is hard to learn the spatial interaction patterns across scales for predicting human activities. Second, social interaction can help model the activity inte...

This chapter summarizes different types of user-generated content (UGC) in urban informatics and then gives a systematic review of their data sources, methodologies, and applications. Case studies in three genres are interpreted to demonstrate the effectiveness of UGC. First, we use geotagged social media data, a type of single-sourced UGC, to extr...

Nowadays, artificial intelligence (AI) is bringing tremendous new opportunities and challenges to geospatial research. Its fast development is powered by theoretical advancement, big data, computer hardware (e.g., the graphics processing unit, or GPU), and high-performance computing platforms that support the development, training, and deployment o...

Local governments and scholars in China have recently proposed and developed community-based comprehensive service facilities for older adults in response to population aging and greater service needs of older adults in urban areas. This study proposes a method to identify the ideal distribution of such facilities by combining the nested ecological...

Digital footprints collected from social media platforms are often clustered using methods such as the density-based spatial clustering of applications with noise (DBSCAN) and its variants to identify daily travel activities (e.g., dwelling, working, entertainment, and eating). However, these clustering methods mostly only consider the spatial dist...

Most models of the COVID-19 pandemic in the United States do not consider geographic variation and spatial interaction. In this research, we developed a travel-network-based susceptible-exposed-infectious-removed (SEIR) mathematical compartmental model system that characterizes infections by state and incorporates inflows and outflows of interstate...

Near realtime flood mapping in densely-populated urban areas is critical for emergency response. The strong heterogeneity of urban areas poses a big challenge for accurate near realtime flood mapping. However, previous studies on automatic methods for urban flood mapping perform infeasible in near realtime or fail to generalize well to other floods...

The technological progress in the field of artificial intelligence (AI) has brought new opportunities and challenges to the intelligent development and innovative research in geospatial related fields. Geospatial artificial intelligence (GeoAI) refers to the interdisciplinary research direction that combines geography, earth science and artificial...

Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset acr...

The availability and use of geographic information technologies and data for describing the patterns and processes operating on or near the Earth’s surface have grown substantially during the past fifty years. The number of geographic information systems software packages and algorithms has also grown quickly during this period, fueled by rapid adv...

The novel coronavirus disease (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 and shaping policy making to mitigate the spread...

Background: The novel human coronavirus disease 2019 (COVID-19) pandemic has claimed more than 600,000 lives worldwide, causing tremendous public health, social, and economic damages. While the risk factors of COVID-19 are still under investigation, environmental factors, such as urban air pollution, may play an important role in increasing populat...

Importance: A stay-at-home social distancing mandate is a key nonpharmacological measure to reduce the transmission rate of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), but a high rate of adherence is needed. Objective: To examine the association between the rate of human mobility changes and the rate of confirmed cases of SARS-...

Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for monitoring and measuring the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the pandemic. In this data descriptor, we introduce a multiscale dynamic human mobility flow dataset across the Unit...

Urban physical environments are the physical settings and built environments in neighbourhoods and cities which provide places for human activities. Evidence suggests that there are substantial associations between urban physical environments and various health outcomes, e.g. people’s physical activities might be influenced by surrounding physical...

Understanding house price appreciation benefits place-based decision makings and real estate market analyses. Although large amounts of interests have been paid in the house price modeling, limited work has focused on evaluating the price appreciation rate. In this study, we propose a data-fusion framework to examine how well house price appreciati...

The prevalence of location-based services contributes to the explosive growth of individual-level trajectory data and raises public concerns about privacy issues. In this research, we propose a novel LSTM-TrajGAN approach, which is an end-to-end deep learning model to generate privacy-preserving synthetic trajectory data for data sharing and public...

To contain the COVID-19 epidemic, one of the non-pharmacological epidemic control measures is reducing the transmission rate of SARS-COV-2 in the population through social distancing. An interactive web-based mapping platform that provides timely quantitative information on how people in different counties and states reacted to the social distancin...

To contain the COVID-19 epidemic, one of the non-pharmacological epidemic control measures is reducing the transmission rate of SARS-COV-2 in the population through social distancing. An interactive web-based mapping platform that provides timely quantitative information on how people in different counties and states reacted to the social distancin...

The Huff model has been widely used in location‐based business analysis to delineate a trade area containing a store’s potential customers. Calibrating the Huff model and its extensions requires empirical location visit data. Many studies rely on labor‐intensive surveys. With the increasing availability of mobile devices, users in location‐based pl...

Background: The novel human coronavirus disease 2019 (COVID-19) pandemic has claimed more than 240,000 lives worldwide, causing tremendous public health, social, and economic damages. While the risk factors of COVID-19 are still under investigation, environmental factors, such as urban air pollution, may play an important role in increasing populat...

There is a Chinese proverb, “if your wine tastes really good, you do not need to worry about the location of your bar (酒香不怕巷子深)”, which implies that the popular places for local residents are sometimes hidden behind an unassuming door or on unexpected streets. Discovering these unassuming places (e.g. restaurants) of a city will benefit the underst...

The emergence of SARS-CoV-2 and the coronavirus infectious disease (COVID-19) has become a pandemic. Social (physical) distancing is a key non-pharmacologic control measure to reduce the transmission rate of SARS-COV-2, but high-level adherence is needed. Using daily travel distance and stay-at-home time derived from large-scale anonymous mobile ph...