Liuyi Song
Biographic Data
| ID | 3583762 |
|---|---|
| NAME | Liuyi Song |
| GIVEN NAMES | Liuyi |
| FAMILY NAME | Song |
| SIGNATURE | SONG L |
| AFFILIATIONS | Chinese University of Hong Kong |
| ORCID | 0000-0001-6018-6466 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 8 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Residential sentiments and spatial environments in urban villages: An X-minute community life circle analysis
Urban villages, a unique residential typology of China's rapid urbanization, and their residents' sentiments have become a prominent issue in the development and management of Shenzhen. While numerous studies have investigated the impact of the urban environment on residents' sentiments, most have focused on the general population and used administrative units, neglecting environmental impacts across spatial scales based on daily mobility. To add…
Exploring the influencing factors of noise complaints in New York City based on an interpretable machine learning model
Noise pollution significantly impacts public health and overall well-being, making research and control of noise issues critically important. Noise complaints serve as a self-reported source of official data, contributing to a more comprehensive and accurate understanding of noise problems and their effects on urban environments. Therefore, this study collected 5.57 million noise complaint records from New York City, as well as data on traffic no…
How mobility-based exposure measures may mitigate the underestimation of the association between green space exposures and health
Recent urban green space research highlighted that mobility-based measures of green space exposure may significantly mitigate a particular type of exposure measurement error (contextual errors) of residence-based measures. In this study, we examined an important manifestation of the contextual errors of residence-based measures: neighborhood effect averaging. We analytically illustrated that the contextual errors of residence-based measures may l…
Machine-based understanding of noise perception in urban environments using mobility-based sensing data
Greenspace exposure is conducive to the resilience of public sentiment during the Covid-19 pandemic
Predict the Suitable Places to Run in the Urban Area of Beijing by Using the Maximum Entropy Model
Many people in the world do not have enough physical activities to maintain good health, which has recently become a threat to public health. In addition to individual genetic and social factors, we considered the geographical environment of the city as a factor that affects these healthy physical activities. We used the location-based data in social media combined with the open geographic data to explore the impact mechanism of urban environment…
Exploring the influencing factors of noise complaints in New York City based on an interpretable machine learning model
Noise pollution significantly impacts public health and overall well-being, making research and control of noise issues critically important. Noise complaints serve as a self-reported source of official data, contributing to a more comprehensive and accurate understanding of noise problems and their effects on urban environments. Therefore, this study collected 5.57 million noise complaint records from New York City, as well as data on traffic no…
How mobility-based exposure measures may mitigate the underestimation of the association between green space exposures and health
Recent urban green space research highlighted that mobility-based measures of green space exposure may significantly mitigate a particular type of exposure measurement error (contextual errors) of residence-based measures. In this study, we examined an important manifestation of the contextual errors of residence-based measures: neighborhood effect averaging. We analytically illustrated that the contextual errors of residence-based measures may l…
Greenspace exposure is conducive to the resilience of public sentiment during the Covid-19 pandemic
Predict the Suitable Places to Run in the Urban Area of Beijing by Using the Maximum Entropy Model
Many people in the world do not have enough physical activities to maintain good health, which has recently become a threat to public health. In addition to individual genetic and social factors, we considered the geographical environment of the city as a factor that affects these healthy physical activities. We used the location-based data in social media combined with the open geographic data to explore the impact mechanism of urban environment…
Greenspace exposure is conducive to the resilience of public sentiment during the Covid-19 pandemic
Machine-based understanding of noise perception in urban environments using mobility-based sensing data
How mobility-based exposure measures may mitigate the underestimation of the association between green space exposures and health
Recent urban green space research highlighted that mobility-based measures of green space exposure may significantly mitigate a particular type of exposure measurement error (contextual errors) of residence-based measures. In this study, we examined an important manifestation of the contextual errors of residence-based measures: neighborhood effect averaging. We analytically illustrated that the contextual errors of residence-based measures may l…
Residential sentiments and spatial environments in urban villages: An X-minute community life circle analysis
Urban villages, a unique residential typology of China's rapid urbanization, and their residents' sentiments have become a prominent issue in the development and management of Shenzhen. While numerous studies have investigated the impact of the urban environment on residents' sentiments, most have focused on the general population and used administrative units, neglecting environmental impacts across spatial scales based on daily mobility. To add…
Exploring the influencing factors of noise complaints in New York City based on an interpretable machine learning model
Noise pollution significantly impacts public health and overall well-being, making research and control of noise issues critically important. Noise complaints serve as a self-reported source of official data, contributing to a more comprehensive and accurate understanding of noise problems and their effects on urban environments. Therefore, this study collected 5.57 million noise complaint records from New York City, as well as data on traffic no…
Computer Science (4 works) · Geography (3 works) · Noise Effects and Management (3 works) · Psychology (3 works) · Urban Transport and Accessibility (3 works) · Artificial Intelligence (2 works) · Environmental health (2 works) · Human Mobility and Location-Based Analysis (2 works) · Medicine (2 works) · Place Attachment and Urban Studies (2 works)