Song Gao

Song Gao
University of Wisconsin–Madison | UW

Ph.D.

About

203
Publications
138,006
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
8,903
Citations
Additional affiliations
September 2012 - June 2017
University of California, Santa Barbara
Position
  • PhD Student
September 2009 - June 2012
Peking University
Position
  • Research Assistant

Publications

Publications (203)
Preprint
Full-text available
Spatial networks are widely used in various fields to represent and analyze interactions or relationships between locations or spatially distributed entities.There is a network science concept known as the 'rich club' phenomenon, which describes the tendency of 'rich' nodes to form densely interconnected sub-networks. Although there are established...
Article
Full-text available
Urban sensing has become increasingly important as cities evolve into the centers of human activities. Large language models (LLMs) offer new opportunities for urban sensing based on commonsense and worldview that emerged through their language-centric framework. This paper illustrates the transformative impact of LLMs, particularly in the potentia...
Article
Full-text available
Driver profiling can provide a human-centered approach to portraying individual travel behavior and revealing their motivation, objectives, and needs, thereby contributing to driving safety analysis, location-based service, and intelligent transportation. However, existing trajectory-based methods are limited to measuring low-level features, such a...
Preprint
Full-text available
Spatial networks are useful for modeling geographic phenomena where spatial interaction plays an important role. To analyze the spatial networks and their internal structures, graph-based methods such as community detection have been widely used. Community detection aims to extract strongly connected components from the network and reveal the hidde...
Article
Full-text available
Understanding the intricacies of fine-grained population distribution, including both predictability and uncertainty, is crucial for urban planning, social equity, and environmental sustainability. The spatial processes associated with the distribution of populations are complex, and enhancing their predictability involves revealing nonlinear inter...
Article
Full-text available
This paper reviews trends in GeoAI research and discusses cutting-edge advances in GeoAI and its roles in accelerating environmental and social sciences. It addresses ongoing attempts to improve the predictability of GeoAI models and recent research aimed at increasing model explainability and reproducibility to ensure trustworthy geospatial findin...
Article
Full-text available
Mining latent information from human trajectories for understanding our cities has been persistent endeavors in urban studies and spatial information science. Many previous studies relied on manually crafted features and followed a supervised learning pipeline for a particular task, e.g. land use classification. However, such methods often overlook...
Preprint
Full-text available
This research focuses on assessing the ability of AI foundation models in representing the trajectories of movements. We utilize one of the large language models (LLMs) (i.e., GPT-J) to encode the string format of trajectories and then evaluate the effectiveness of the LLM-based representation for trajectory data analysis. The experiments demonstra...
Article
Urban resilience, crucial for achieving sustainable development goals, entails the adaptation and recovery of urban systems from external shocks, such as the recent global pandemic. To investigate the response of urban systems to COVID-19, this study employs context-adjusted nighttime light data to model global urban resilience by combining an evol...
Preprint
Full-text available
Human mobility is intricately influenced by urban contexts spatially and temporally, constituting essential domain knowledge in understanding traffic systems. While existing traffic forecasting models primarily rely on raw traffic data and advanced deep learning techniques, incorporating contextual information remains underexplored due to the lack...
Article
Full-text available
Building appearances profoundly shape the urban visual landscape, influencing city images and the quality of urban life. Traditional methods for evaluating the perceptual and aesthetic qualities of building facades are often limited in scope. Despite recent studies that have sought to understand human perception of urban streetscapes, our grasp of...
Article
How people travel to receive health services is essential for understanding healthcare shortages. The rational service areas (RSAs) are defined to represent local healthcare markets and used as the basic units to evaluate whether people have access to health resources. Therefore, finding an appropriate way to develop RSAs is important for understan...
Poster
Full-text available
Aims and Scope: Over the last decade, GIScience has witnessed significant transformations with the advent of deep learning and artificial intelligence (AI). The rapid development in AI prompted a moonshot by Janowicz et al. (2020) 'Can we develop an artificial GIS analyst that passes a domain-specific Turing Test by 2030?'. The recent advancements...
Article
Full-text available
Convex and concave hulls originating from computational geometry are widely applied in practice. For instance, to determine the boundaries of a geographical area within a group of cities, convex hulls can represent the approximate boundaries of the areas. Concave hulls can accurately describe the shape of the area. The traditional methods for solvi...
Article
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...
Article
Full-text available
The Annual Meeting of the American Association of Geographers (AAG) in 2023 marked a five-year milestone since the first Geospatial Artificial Intelligence (GeoAI) Symposium was held at AAG in 2018. In the past five years, progress has been made while open questions remain. In this context, we organized an AAG panel and invited five panellists to d...
Article
Human mobility is intricately influenced by urban contexts spatially and temporally, constituting essential domain knowledge in understanding traffic systems. While existing traffic forecasting models primarily rely on raw traffic data and advanced deep learning techniques, incorporating contextual information remains underexplored due to insuffici...
Article
Full-text available
Human mobility data play a crucial role in many fields such as infectious diseases, transportation, and public safety. Although the development of Information and Communication Technologies (ICT) has made it easy to collect individual-level positioning records, raw individual trajectory data are still limited in availability and usability due to pr...
Conference Paper
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...
Article
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...
Article
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...
Article
Full-text available
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...
Preprint
Full-text available
This research focuses on assessing the ability of large language models (LLMs) in representing geometries and their spatial relations. We utilize LLMs including GPT-2 and BERT to encode the well-known text (WKT) format of geometries and then feed their embeddings into classifiers and regressors to evaluate the effectiveness of the LLMs-generated em...
Article
Full-text available
Abstract The global threat of antimicrobial resistance (AMR) varies regionally. This study explores whether geospatial analysis and data visualization methods detect both clinically and statistically significant variations in antibiotic susceptibility rates at a neighborhood level. This observational multicenter geospatial study collected 10 years...
Article
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...
Article
Full-text available
With the ongoing development of information and communications technology, geospatial technologies have become increasingly important in monitoring, managing, and predicting tourism activities. These tools can also uncover tourism’s social, economic, cultural, environmental, and political impacts. In this viewpoint article, we discuss applications...
Article
The safety perception of the built environment, rather than the sheer number of crimes and incivility behavior, is a fundamental driver of public policies intended to improve urban safety. Traditional surveys often capture neighborhood residents’ perceived safety, but may not fully reflect the perceptions of people who are unfamiliar with the area....
Preprint
Full-text available
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...
Article
Objective: - To identify and assess whether three major risk factors that due to differential access to flexible resources might help explain disparities in the spread of COVID-19 across communities with different socioeconomic status, including socioeconomic inequalities in social distancing, the potential risk of interpersonal interactions, and...
Article
Full-text available
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...
Article
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...
Article
Full-text available
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...
Article
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...
Conference Paper
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...
Preprint
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...
Article
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...
Article
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....
Conference Paper
Full-text available
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...
Article
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...
Article
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...
Preprint
Full-text available
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...
Preprint
Full-text available
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...
Preprint
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Chapter
Full-text available
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...
Article
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
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
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...
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
Background The global threat of antimicrobial resistance (AMR) varies regionally. Regional differences may be related to socio-economic factors such as the Area Deprivation Index (ADI) score. Our hypothesis is that AMR spatial distribution is not random. Methods Patient level antibiotic susceptibility data was collected from three regionally disti...
Article
Full-text available
Background ‘One Health’ recognizes the interconnectivity of humans with their production and companion animals, and the environment. Emergence and transmission of antimicrobial resistance (AMR) within and between these compartments is a recognized global threat that requires further understanding to design interventions protecting both human and an...