Qunying Huang
Biographic Data
| ID | 3635870 |
|---|---|
| NAME | Qunying Huang |
| GIVEN NAMES | Qunying |
| FAMILY NAME | Huang |
| SIGNATURE | HUANG Q |
| AFFILIATIONS | University of Wisconsin–Madison |
| ORCID | 0000-0003-3499-7294 |
| VERIFIED | Yes |
| TOTAL WORKS | 14 |
| TOTAL CITATIONS | 10 |
| WORKS AS AUTHOR | 14 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Revealing time-sensitive latent public health and emotional distress topics in Reddit discourse during the 2025 Southern California wildfires
Wildfires have become increasingly frequent, irregular, and severe, contributing to diverse environmental health concerns across affected communities. Social media platforms offer an additional source of information for examining how the public discusses health risks and emotional distress during such events. This study analyzes Reddit discourse generated during the 2025 Southern California wildfires with a focus on content related to health and …
Digital Twins
Digital twin (DT) emerges as a frontier for integrating research, development, innovation, and problem‐solving through digital technologies. DT enables analyses and solutions to real‐world problems by leveraging advanced observation data from sources of in situ (monitoring stations), citizen sensing, and satellites. DT seeks to best replicate real‐world problems and conduct “what‐if” analyses for optimum solutions. Data fusion technologies are em…
Do You Know Your Neighborhood? Integrating Street View Images and Multi-task Learning for Fine-Grained Multi-Class Neighborhood Wealthiness Perception Prediction
Identifying Rich Clubs in Spatiotemporal Interaction Networks
Spatial networks are widely used in various fields to represent and analyze interactions or relationships between locations or spatially distributed entities. Existing studies have proposed methods for hub identification and community detection in spatial networks, but relatively few have focused on quantifying the strength or density of connections shared within a community of hubs across space and time. Borrowing from network science, there is …
Spatio-Temporal Transformer Recommender
Location-based social networks (LBSN) allow users to socialize with friends by sharing their daily life experiences online. In particular, a large amount of check-ins data generated by LBSNs capture the visit locations of users and open a new line of research of spatio-temporal big data, i.e., the next point-of-interest (POI) recommendation. At present, while some advanced methods have been proposed for POI recommendation, existing work only leve…
Revealing racial-ethnic segregation with individual experienced segregation indices based on social media data
Good Personality and Subjective Well-Being
These findings illustrate that the presence of meaning in life and perceived social support mediate the relationship between good personality and subjective well-being in the context of Chinese culture, which supports the model of normative well-being and can provide more targeted intervention guidance for research on promoting well-being in the Chinese context
Disaster Image Classification by Fusing Multimodal Social Media Data
Social media datasets have been widely used in disaster assessment and management. When a disaster occurs, many users post messages in a variety of formats, e.g., image and text, on social media platforms. Useful information could be mined from these multimodal data to enable situational awareness and to support decision making during disasters. However, the multimodal data collected from social media contain a lot of irrelevant and misleading co…
Activity knowledge discovery
Unfolding Spatial-Temporal Patterns of Taxi Trip based on an Improved Network Kernel Density Estimation
Taxi mobility data plays an important role in understanding urban mobility in the context of urban traffic. Specifically, the taxi is an important part of urban transportation, and taxi trips reflect human behaviors and mobility patterns, allowing us to identify the spatial variety of such patterns. Although taxi trips are generated in the form of network flows, previous works have rarely considered network flow patterns in the analysis of taxi m…
Introduction to Big Data Computing for Geospatial Applications
The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates the opportunities for using big data for …
Improving spatial decision making using interactive maps
Spatial decisions increasingly are made by both professional and citizen stakeholders using interactive maps, yet few empirically-derived guidelines exist for designing interactive maps that support complex reasoning and decision making across problem contexts. We address this gap through an online map study with 122 participants with varying expertise. The study required participants to assume two hypothetical scenarios in the North American haz…
Integrating big social data, computing and modeling for spatial social science
In the past decade, the social sciences are undergoing a dramatic shift toward analyzing ever-increasing amounts of large-scale diverse data with rich spatial, temporal, and thematic resolution (Sh
Modeling and Visualizing Regular Human Mobility Patterns with Uncertainty
Traditional space–time paths show the spatiotemporal trajectories of individuals in one to several days. Based on data for such short periods, these space–time paths might not be able to show regular activity patterns, which are pertinent to various types of planning and policy analysis. Travel data gathered for longer periods might capture regular activity patterns, but footprints captured by these data also include irregular activities, introdu…
Modeling and Visualizing Regular Human Mobility Patterns with Uncertainty
Traditional space–time paths show the spatiotemporal trajectories of individuals in one to several days. Based on data for such short periods, these space–time paths might not be able to show regular activity patterns, which are pertinent to various types of planning and policy analysis. Travel data gathered for longer periods might capture regular activity patterns, but footprints captured by these data also include irregular activities, introdu…
Identifying Rich Clubs in Spatiotemporal Interaction Networks
Spatial networks are widely used in various fields to represent and analyze interactions or relationships between locations or spatially distributed entities. Existing studies have proposed methods for hub identification and community detection in spatial networks, but relatively few have focused on quantifying the strength or density of connections shared within a community of hubs across space and time. Borrowing from network science, there is …
Integrating big social data, computing and modeling for spatial social science
In the past decade, the social sciences are undergoing a dramatic shift toward analyzing ever-increasing amounts of large-scale diverse data with rich spatial, temporal, and thematic resolution (Sh
Modeling and Visualizing Regular Human Mobility Patterns with Uncertainty
Traditional space–time paths show the spatiotemporal trajectories of individuals in one to several days. Based on data for such short periods, these space–time paths might not be able to show regular activity patterns, which are pertinent to various types of planning and policy analysis. Travel data gathered for longer periods might capture regular activity patterns, but footprints captured by these data also include irregular activities, introdu…
Integrating big social data, computing and modeling for spatial social science
In the past decade, the social sciences are undergoing a dramatic shift toward analyzing ever-increasing amounts of large-scale diverse data with rich spatial, temporal, and thematic resolution (Sh
Improving spatial decision making using interactive maps
Spatial decisions increasingly are made by both professional and citizen stakeholders using interactive maps, yet few empirically-derived guidelines exist for designing interactive maps that support complex reasoning and decision making across problem contexts. We address this gap through an online map study with 122 participants with varying expertise. The study required participants to assume two hypothetical scenarios in the North American haz…
Unfolding Spatial-Temporal Patterns of Taxi Trip based on an Improved Network Kernel Density Estimation
Taxi mobility data plays an important role in understanding urban mobility in the context of urban traffic. Specifically, the taxi is an important part of urban transportation, and taxi trips reflect human behaviors and mobility patterns, allowing us to identify the spatial variety of such patterns. Although taxi trips are generated in the form of network flows, previous works have rarely considered network flow patterns in the analysis of taxi m…
Introduction to Big Data Computing for Geospatial Applications
The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates the opportunities for using big data for …
Disaster Image Classification by Fusing Multimodal Social Media Data
Social media datasets have been widely used in disaster assessment and management. When a disaster occurs, many users post messages in a variety of formats, e.g., image and text, on social media platforms. Useful information could be mined from these multimodal data to enable situational awareness and to support decision making during disasters. However, the multimodal data collected from social media contain a lot of irrelevant and misleading co…
Activity knowledge discovery
Good Personality and Subjective Well-Being
These findings illustrate that the presence of meaning in life and perceived social support mediate the relationship between good personality and subjective well-being in the context of Chinese culture, which supports the model of normative well-being and can provide more targeted intervention guidance for research on promoting well-being in the Chinese context
Spatio-Temporal Transformer Recommender
Location-based social networks (LBSN) allow users to socialize with friends by sharing their daily life experiences online. In particular, a large amount of check-ins data generated by LBSNs capture the visit locations of users and open a new line of research of spatio-temporal big data, i.e., the next point-of-interest (POI) recommendation. At present, while some advanced methods have been proposed for POI recommendation, existing work only leve…
Revealing racial-ethnic segregation with individual experienced segregation indices based on social media data
Digital Twins
Digital twin (DT) emerges as a frontier for integrating research, development, innovation, and problem‐solving through digital technologies. DT enables analyses and solutions to real‐world problems by leveraging advanced observation data from sources of in situ (monitoring stations), citizen sensing, and satellites. DT seeks to best replicate real‐world problems and conduct “what‐if” analyses for optimum solutions. Data fusion technologies are em…
Do You Know Your Neighborhood? Integrating Street View Images and Multi-task Learning for Fine-Grained Multi-Class Neighborhood Wealthiness Perception Prediction
Identifying Rich Clubs in Spatiotemporal Interaction Networks
Spatial networks are widely used in various fields to represent and analyze interactions or relationships between locations or spatially distributed entities. Existing studies have proposed methods for hub identification and community detection in spatial networks, but relatively few have focused on quantifying the strength or density of connections shared within a community of hubs across space and time. Borrowing from network science, there is …
Revealing time-sensitive latent public health and emotional distress topics in Reddit discourse during the 2025 Southern California wildfires
Wildfires have become increasingly frequent, irregular, and severe, contributing to diverse environmental health concerns across affected communities. Social media platforms offer an additional source of information for examining how the public discusses health risks and emotional distress during such events. This study analyzes Reddit discourse generated during the 2025 Southern California wildfires with a focus on content related to health and …
Computer Science (10 works) · Human Mobility and Location-Based Analysis (7 works) · Data mining (6 works) · Artificial Intelligence (5 works) · Data science (5 works) · Geographic Information Systems Studies (5 works) · Geography (5 works) · Urban Transport and Accessibility (4 works) · Engineering (3 works) · Information retrieval (3 works)