Song Gao

Song Gao
University of Wisconsin–Madison | UW

Ph.D.

About

128
Publications
71,523
Reads
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4,629
Citations
Additional affiliations
September 2009 - June 2012
Peking University
Position
  • Research Assistant

Publications

Publications (128)
Article
Full-text available
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...
Preprint
Full-text available
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,...
Article
Full-text available
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...
Preprint
Full-text available
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,...
Article
Full-text available
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...
Article
Full-text available
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...
Preprint
Full-text available
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...
Article
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...
Article
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...
Article
Full-text available
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...
Chapter
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...
Chapter
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...
Article
The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation 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 rel...
Article
Full-text available
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...
Article
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...
Article
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...
Article
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...
Chapter
Full-text available
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...
Chapter
Full-text available
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...
Article
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...
Article
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
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...
Preprint
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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-...
Preprint
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Preprint
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
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...
Preprint
Full-text available
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...
Article
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...
Preprint
Full-text available
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...
Preprint
Full-text available
Most models of the COVID-19 pandemic in the United States do not consider geographic variation, and their relevance to public policies is not straightforward. We developed a mathematical model that characterizes infections by state and incorporates inflows and outflows of interstate travelers. Modeling reveals that curbing interstate travel when th...
Article
Full-text available
Human movement and interaction across space and through time is full of economic and social opportunities. Access to information through location-based technologies offers potential for people to make better decisions about social activity participation needs and travel behavior preferences. Identifying an optimal trajectory (route) connecting desi...
Article
Full-text available
What is the current state-of-the-art in integrating results from artificial intelligence research into geographic information science and the earth sciences more broadly? Does GeoAI research contribute to the broader field of AI, or does it merely apply existing results? What are the historical roots of GeoAI? Are there core topics and maybe even m...
Preprint
The Huff model has been widely used in location-based business analysis for delineating a trading area containing potential customers to a store. 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-ba...
Article
Full-text available
With the booming economy in China, many researches have pointed out that the improvement of regional transportation infrastructure among other factors had an important effect on economic growth. Utilizing a large-scale dataset which includes 3.5 billion entry and exit records of vehicles along highways generated from toll collection systems, we att...
Article
Nowadays artificial intelligence (AI) is bringing tremendous opportunities and challenges to geospatial research. Big data enable computers to observe and learn the world from many different perspectives, while high performance machines support the development, training, and deployment of AI models within reasonable amount of time. Recent years hav...
Preprint
Full-text available
With the booming economy in China, many researches have pointed out that the improvement of regional transportation infrastructure among other factors had an important effect on economic growth. Utilizing a large-scale dataset which includes 3.5 billion entry and exit records of vehicles along highways generated from toll collection systems, we att...
Article
The significance of urban metro stations extends beyond their roles as transport nodes in a city. Their surroundings are usually well-developed and attract a lot of human activities, which make the metro station areas important cognitive places characterized by vague boundaries and rich semantics. Current studies mainly define metro station areas b...
Article
Full-text available
With the ubiquitous use of location-based services, large-scale individual-level location data has been widely collected through location-awareness devices. Geoprivacy concerns arise on the issues of user identity de-anonymization and location exposure. In this work, we investigate the effectiveness of geomasking techniques for protecting the geopr...
Article
Full-text available
Geospatial artificial intelligence (GeoAI) is an interdisciplinary field that has received tremendous attention from both academia and industry in recent years. This article reviews the series of GeoAI workshops held at the Association for Computing Machinery (ACM) International Conference on Advances in Geographic Information Systems (SIGSPATIAL)...
Article
Full-text available
Hidden biases of racial and socioeconomic preferences shape residential neighborhoods throughout the USA. Thereby, these preferences shape neighborhoods composed predominantly of a particular race or income class. However, the assessment of spatial extent and the degree of isolation outside the residential neighborhoods at large scale is challengin...
Article
Full-text available
The spatiotemporal variability in air pollutant concentrations raises challenges in linking air pollution exposure to individual health outcomes. Thus, understanding the spatiotemporal patterns of human mobility plays an important role in air pollution epidemiology and health studies. With the advantages of massive users, wide spatial coverage and...
Conference Paper
Searching for a parking spot in metropolitan areas is a great challenge comparable to the Hunger Games, especially in highly populated areas such as downtown districts and job centers. On-street parking is often a cost-effective choice compared to parking facilities such as garages and parking lots. However, limited space and complex parking regula...
Article
The field of urban analytics and city science has seen significant growth and development in the past 20 years. The rise of data science, both in industry and academia, has put new pressures on urban research, but has also allowed for new analytical possibilities. Because of the rapid growth and change in the field, terminology in urban analytics c...
Article
Full-text available
Searching for a parking spot in metropolitan areas is a great challenge, especially in highly populated areas such as downtown districts and job centres. On-street parking is often a cost-effective choice compared to parking facilities such as garages and parking lots. However, limited space and complex parking regulation rules make the search proc...
Article
Full-text available
The problem of discovering regions that support particular functionalities in an urban setting has been approached in literature using two general methodologies: top-down, encoding expert knowledge on urban planning and design and discovering regions that conform to that knowledge; and bottom-up, using data to train machine learning models, which c...