Yihong Yuan
Biographic Data
| ID | 3635641 |
|---|---|
| NAME | Yihong Yuan |
| GIVEN NAMES | Yihong |
| FAMILY NAME | Yuan |
| SIGNATURE | YUAN Y |
| AFFILIATIONS | Texas State University |
| ORCID | 0000-0001-6266-9744 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 14 |
| AUTHOR COUNT | 8 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 2 |
Galax: A Framework for Geospatial Analysis Leveraging AutoML and eXplainable AI
Comparing Machine Learning and Time Series Approaches in Predictive Modeling of Urban Fire Incidents: A Case Study of Austin, Texas
This study examines urban fire incidents in Austin, Texas using machine learning (Random Forest) and time series (Autoregressive integrated moving average, ARIMA) methods for predictive modeling. Based on a dataset from the City of Austin Fire Department, it addresses the effectiveness of these models in predicting fire occurrences and the influence of fire types and urban district characteristics on predictions. The findings indicate that ARIMA …
Factor decomposition analysis of urban transport CO2 emissions in Chinese mega cities: Case study of Beijing, Shanghai, Guangzhou and Shenzhen
Flow trace: A novel representation of intra-urban movement dynamics
Modeling activity spaces using big geo-data: Progress and challenges
The growing availability of big geo-data, such as mobile phone data and location-based social media (LBSM), provides new opportunities and challenges for modeling human activity spaces in the big data era. These datasets often cover a large sample size and can be used to model activity spaces more efficiently than traditional travel surveys. However, these data also have inherent limitations, such as the lack of reliable demographic information o…
Modeling User Activity Space from Location-Based Social Media: A Case Study of Weibo
Activity space studies are beneficial for discovering meaningful activity patterns and providing a deeper understanding of human behaviors. There is insufficient research, however, on how reliable location-based social media (LBSM) is as a new data source for discovering user activity spaces. To this end, this research calculates four external and three internal activity space indicators based on Weibo data from three Chinese cities. We compared …
The Missing Parts from Social Media-Enabled Smart Cities: Who, Where, When, and What
Social networking sites (SNS), such as Facebook and Twitter, have attracted users worldwide by providing a means to communicate and share opinions and experiences of daily lives. When empowered by pervasive location acquisition technologies, location-based social media (LBSM) has become a potential resource for smart city applications to characterize social perceptions of place and model human activities. There is a lack of systematic examination…
Exploring Georeferenced Mobile Phone Datasets - A Survey and Reference Framework
Nowadays, mobile phones and other information and communication technology (ICT) devices collect large numbers of measurements about their users. This review paper provides an overview of georeferenced mobile phone datasets by exploring and summarizing the metadata of multiple datasets based on a literature review. It also presents an abstract model to depict the connections of these datasets, serving as the basis for potential spatio-temporal da…
The Missing Parts from Social Media-Enabled Smart Cities: Who, Where, When, and What
Social networking sites (SNS), such as Facebook and Twitter, have attracted users worldwide by providing a means to communicate and share opinions and experiences of daily lives. When empowered by pervasive location acquisition technologies, location-based social media (LBSM) has become a potential resource for smart city applications to characterize social perceptions of place and model human activities. There is a lack of systematic examination…
Modeling activity spaces using big geo-data: Progress and challenges
The growing availability of big geo-data, such as mobile phone data and location-based social media (LBSM), provides new opportunities and challenges for modeling human activity spaces in the big data era. These datasets often cover a large sample size and can be used to model activity spaces more efficiently than traditional travel surveys. However, these data also have inherent limitations, such as the lack of reliable demographic information o…
Exploring Georeferenced Mobile Phone Datasets - A Survey and Reference Framework
Nowadays, mobile phones and other information and communication technology (ICT) devices collect large numbers of measurements about their users. This review paper provides an overview of georeferenced mobile phone datasets by exploring and summarizing the metadata of multiple datasets based on a literature review. It also presents an abstract model to depict the connections of these datasets, serving as the basis for potential spatio-temporal da…
Exploring Georeferenced Mobile Phone Datasets - A Survey and Reference Framework
Nowadays, mobile phones and other information and communication technology (ICT) devices collect large numbers of measurements about their users. This review paper provides an overview of georeferenced mobile phone datasets by exploring and summarizing the metadata of multiple datasets based on a literature review. It also presents an abstract model to depict the connections of these datasets, serving as the basis for potential spatio-temporal da…
The Missing Parts from Social Media-Enabled Smart Cities: Who, Where, When, and What
Social networking sites (SNS), such as Facebook and Twitter, have attracted users worldwide by providing a means to communicate and share opinions and experiences of daily lives. When empowered by pervasive location acquisition technologies, location-based social media (LBSM) has become a potential resource for smart city applications to characterize social perceptions of place and model human activities. There is a lack of systematic examination…
Modeling User Activity Space from Location-Based Social Media: A Case Study of Weibo
Activity space studies are beneficial for discovering meaningful activity patterns and providing a deeper understanding of human behaviors. There is insufficient research, however, on how reliable location-based social media (LBSM) is as a new data source for discovering user activity spaces. To this end, this research calculates four external and three internal activity space indicators based on Weibo data from three Chinese cities. We compared …
Flow trace: A novel representation of intra-urban movement dynamics
Modeling activity spaces using big geo-data: Progress and challenges
The growing availability of big geo-data, such as mobile phone data and location-based social media (LBSM), provides new opportunities and challenges for modeling human activity spaces in the big data era. These datasets often cover a large sample size and can be used to model activity spaces more efficiently than traditional travel surveys. However, these data also have inherent limitations, such as the lack of reliable demographic information o…
Comparing Machine Learning and Time Series Approaches in Predictive Modeling of Urban Fire Incidents: A Case Study of Austin, Texas
This study examines urban fire incidents in Austin, Texas using machine learning (Random Forest) and time series (Autoregressive integrated moving average, ARIMA) methods for predictive modeling. Based on a dataset from the City of Austin Fire Department, it addresses the effectiveness of these models in predicting fire occurrences and the influence of fire types and urban district characteristics on predictions. The findings indicate that ARIMA …
Factor decomposition analysis of urban transport CO2 emissions in Chinese mega cities: Case study of Beijing, Shanghai, Guangzhou and Shenzhen
Galax: A Framework for Geospatial Analysis Leveraging AutoML and eXplainable AI
Computer Science (6 works) · Human Mobility and Location-Based Analysis (5 works) · Urban Transport and Accessibility (5 works) · Data mining (4 works) · Data science (4 works) · Geography (4 works) · World Wide Web (4 works) · Social media (3 works) · Telecommunications (3 works) · Transportation Planning and Optimization (3 works)