Yuhao Kang

University of Texas at Austin | UT · Department of Geography and The Environment

The growing global interest in Geographic Information System/Science (GIS) programs has led to an increased demand for higher education in this field. However, students often struggle to identify suitable programs and faculty due to the overwhelming options and the lack of personalized guidance. This paper presents GISphere‐KG, an AI‐powered platfo...

Study Objectives In-depth interviews are one of the most widely used approaches for qualitative studies in public health. The coding of transcripts is a critical step for information extraction and preliminary analysis. However, manual coding is often labor-intensive and time-consuming. The emergence of generative artificial intelligence (GenAI), s...

Do cities have a collective identity? The latest advancements in generative artificial intelligence (AI) models have enabled the creation of realistic representations learned from vast amounts of data. In this study, we test the potential of generative AI as the source of textual and visual information in capturing the place identity of cities asse...

An increasing number of studies suggest that biophilia encompasses benefits resulting from human-nature interactions. However, quantifying these effects remains challenging. Since natural features vary worldwide, this study explores whether people perceive biophilia universally or if it is influenced by local or geographical conditions. To this end...

Visual characteristics of the built environment affect how people perceive and experience cities. For a long time, many studies have examined visual perception in cities. Such efforts have accelerated in recent years due to advancements in technologies and the proliferation of relevant data (e.g., street view imagery, geo-tagged photos, videos, vir...

Researchers are constantly leveraging new forms of data with the goal of understanding how people perceive the built environment and build the collective place identity of cities. Latest advancements in generative artificial intelligence (AI) models have enabled the production of realistic representations learned from vast amounts of data. In this...

The rapid advancement of artificial intelligence (AI) such as the emergence of large language models including ChatGPT and DALLE 2 has brought both opportunities for improving productivity and raised ethical concerns. This paper investigates the ethics of using artificial intelligence (AI) in cartography, with a particular focus on the generation o...

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....

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...

Intercity patient mobility reflects the geographic mismatch between healthcare resources and the population, and has rarely been studied with big data at large spatial scales. In this paper, we investigated the patterns of intercity patient mobility and factors influencing this behavior based on >4 million hospitalization records of patients with c...

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,...

Bicycle-metro integration is an efficient method of solving the “last mile” issue around metro stations. Built environment is believed to have a significant effect on cycling behavior. However, transfer cycling around metro stations, as a specific type of cycling behavior, has often been overlooked in transport research. In addition, static context...

Auditing and mapping traffic infrastructure is a crucial task in urban management. For example, signalized intersections play an essential role in transportation management; however, effectively identifying these intersections remains unsolved. Traditionally, signalized intersection data are manually collected through field audits or checking stree...

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...

Effectively monitoring the dynamics of human mobility is of great importance in urban management, especially during the COVID-19 pandemic. Traditionally, the human mobility data is collected by roadside sensors, which have limited spatial coverage and are insufficient in large-scale studies. With the maturing of mobile sensing and Internet of Thing...

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...

With the rise of geospatial big data, new narratives of cities based on spatial networks and flows have replaced the traditional focus on locations. While plenty of research that have empirically analyzed network structures, there lacks a state-of-the-art synthesis of applicable insights and methods of spatial networks in the planning context. In t...

Urban street-level greenery is empirically documented to improve mental and physical health, increase productivity, increase urban environmental equality and reduce carbon footprints. In addition, these benefits raise residents’ welfare, which has been correlated with increases in residential house prices. We measure street-level greenness in New Y...

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...

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...

[This corrects the article DOI: 10.1371/journal.pone.0236347.].

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...

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...

Crime and perception of safety are two intertwined concepts affecting the quality of life and the economic development of a society. However, few studies have quantitatively examined the difference between the two due to the lack of granular data documenting public perceptions in a given geographic context. Here, by applying a pre-trained scene und...

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 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...

Various fields have widely used place emotion extracted from social networking sites (SNS) information in recent years. However, the emotional information may contain biases as users are a particular subset of the whole population. This research studies whether there are significant differences between place emotion extracted from SNS and the place...

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...

Measuring the semantic similarity between words is important for natural language processing tasks. The traditional models of semantic similarity perform well in most cases, but when dealing with words that involve geographical context, spatial semantics of implied spatial information are rarely preserved. Geographic information retrieval (GIR) met...

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...

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...

Human emotion is an intrinsic psychological state that is influenced by human thoughts and behaviours. Human emotion distribution has been regarded as an important part of emotional geography research. However, it is difficult to form a global scaled map reflecting human emotions at the same sampling density because various emotional sampling data...

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...

To contain the Coronavirus disease (COVID-19) pandemic, one of the non-pharmacological epidemic control measures in response to the COVID-19 outbreak is reducing the transmission rate of SARS-COV-2 in the population through (physical) social distancing. An interactive web-based mapping platform that provides timely quantitative information on how p...

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...

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...

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...

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...

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...

Location-based social networks (LBSNs) enable individuals to connect tighter through users' interdependency (e.g. friendship, common interests and shared knowledge) which derived from their physical locations and geo-tagged social media content. Nowadays, the development of mobile augmented reality (MAR) technology can enhance people's perception a...

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...

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...

Given the abundant quantities of big spatiotemporal geographic data that are available, interactions among spatial entities can now be extracted from various perspectives. This research investigates the spatial interactions within the metropolis of Beijing quantitatively. Two methods of quantifying the interactions are proposed. These interactions...

The emergence of big data enables us to evaluate the various human emotions at places from a statistic perspective by applying affective computing. In this study, a novel framework for extracting human emotions from large-scale georeferenced photos at different places is proposed. After the construction of places based on spatial clustering of user...

The advancement of the Artificial Intelligence (AI) technologies makes it possible to learn stylistic design criteria from existing maps or other visual art and transfer these styles to make new digital maps. In this paper, we propose a novel framework using AI for map style transfer applicable across multiple map scales. Specifically, we identify...

A sustainable city relies on renewable energy, which promotes the development of electric vehicles. To support electric vehicles, the concept of charging vehicles while driving has been put forward. Under such circumstances, constructing solar panels on urban roads is an innovative option with great benefits, and the accurate calculation of road ph...

This paper introduces a method of creating electronic map system based on virtual reality technology. This method explores the combination of VR and electronic map, which could be compatible with current huge amount of geographic information data. The system allows users to immerse in a map system with both macro and micro experience. In order to c...

When users browse beautiful scenery photos uploaded on a social media website, they may have a passion to know about where those photos are taken so that they could view the similar sceneries when they go to the same spot. Advancement in computer vision technology enables the extraction of visual features from those images and the widespread of loc...