Zhewei Liu
Biographic Data
| ID | 4181803 |
|---|---|
| NAME | Zhewei Liu |
| GIVEN NAMES | Zhewei |
| FAMILY NAME | Liu |
| SIGNATURE | LIU Z |
| AFFILIATIONS | Hong Kong Polytechnic University |
| ORCID | 0000-0002-4023-9142 |
| VERIFIED | Yes |
| TOTAL WORKS | 15 |
| TOTAL CITATIONS | 26 |
| AUTHOR COUNT | 15 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Mitigating urban heat exposure inequity with a seasonally adaptive machine-learning approach
Urban heat exposure poses significant risks to human well-being and contributes to emerging social inequity. While previous research has addressed urban heat exposure, mitigating the inequity associated with such hazards remains a significant challenge and an understudied area. To address this issue, this study proposes a machine-learning approach to assess the effectiveness of various urban development strategies in mitigating urban heat exposur…
Can transit-oriented development (TOD) cool the city? A multi-scale geospatial assessment of daytime surface heat exposure in Tokyo
● Links transit oriented development patterns to land surface temperature in Shinjuku ● Uses street view imagery, remote sensing and population data in one framework ● Shows well designed transit corridors can cool land surface by about 0.9°C ● Reveals that excessive density without greenery and shading increases urban heat ● Applies explainable machine learning to guide climate sensitive station area design Extreme heat is an escalating urban he…
Developing a Replicable ESG-Based Framework for Assessing Community Perception Using Street View Imagery and POI Data
Urban livability and sustainability are increasingly studied at the neighborhood scale, where built, social, and governance conditions shape residents’ everyday experiences. Yet existing assessment frameworks often fail to integrate subjective perceptions with multi-dimensional environmental indicators in replicable and scalable ways. To address this gap, this study develops an Environmental, Social, and Governance (ESG)-informed framework for ev…
Artificial intelligence for flood risk management: A comprehensive state-of-the-art review and future directions
Ml4ej: Decoding the role of urban features in shaping environmental injustice using interpretable machine learning
Understanding the key factors shaping environmental hazard exposures and their associated environmental injustice issues is vital for formulating equitable policy measures. Traditional perspectives on environmental injustice have primarily focused on the socioeconomic dimensions, often overlooking the influence of heterogeneous urban characteristics. This limited view may obstruct a comprehensive understanding of the complex nature of environment…
Generating equitable urban human flows with a fairness-aware deep learning model
Rethinking landscape ecological risk assessment and its applicability: Counterintuitive findings from coastal areas
Landscape ecological risk assessment (LERA) serves as a crucial tool for guiding effective environmental management. However, the conventional approach of LERA suffers from two notable drawbacks: the utilization of low‐resolution land‐use data (e.g., 30 × 30 m) and the application of arbitrary evaluation units (e.g., uniformly‐sized grids), both of which introduce uncertainty and inaccuracies into the assessment outcomes. Moreover, the extent to …
Interpretable machine learning for predicting urban flash flood hotspots using intertwined land and built-environment features
Resilience and recovery: Evaluating Covid pandemic effects on ride-hailing mobility and driver income dynamics
From isolation to linkage: Holistic insights into ecological risk induced by land use change
Online public opinion during the first epidemic wave of Covid-19 in China based on Weibo data
As COVID-19 spread around the world, epidemic prevention and control policies have been adopted by many countries. This process has prompted online social platforms to become important channels to enable people to socialize and exchange information. The massive use of social media data mining techniques, to analyze the development online of public opinion during the epidemic, is of great significance in relation to the management of public opinio…
Extracting Human Activity Areas from Large-Scale Spatial Data with Varying Densities
Human activity area extraction, a popular research topic, refers to mining meaningful location clusters from raw activity data. However, varying densities of large-scale spatial data create a challenge for existing extraction methods. This research proposes a novel area extraction framework (ELV) aimed at tackling the challenge by using clustering with an adaptive distance parameter and a re-segmentation strategy with noise recovery. Firstly, a d…
Categorisation of cultural tourism attractions by tourist preference using location-based social network data: The case of Central, Hong Kong
Staying at Home Is a Privilege: Evidence from Fine-Grained Mobile Phone Location Data in the United States during the Covid-19 Pandemic
The coronavirus disease 2019 (COVID-19) has exposed and, to some degree, exacerbated social inequity in the United States. This study reveals the correlation between demographic and socioeconomic variables and home-dwelling time records derived from large-scale mobile phone location tracking data at the U.S. census block group (CBG) level in the twelve most populated Metropolitan Statistical Areas (MSAs) and further investigates the contribution …
Detecting home countries of social media users with machine-learned ranking approach: A case study in Hong Kong
Staying at Home Is a Privilege: Evidence from Fine-Grained Mobile Phone Location Data in the United States during the Covid-19 Pandemic
The coronavirus disease 2019 (COVID-19) has exposed and, to some degree, exacerbated social inequity in the United States. This study reveals the correlation between demographic and socioeconomic variables and home-dwelling time records derived from large-scale mobile phone location tracking data at the U.S. census block group (CBG) level in the twelve most populated Metropolitan Statistical Areas (MSAs) and further investigates the contribution …
Categorisation of cultural tourism attractions by tourist preference using location-based social network data: The case of Central, Hong Kong
From isolation to linkage: Holistic insights into ecological risk induced by land use change
Detecting home countries of social media users with machine-learned ranking approach: A case study in Hong Kong
Online public opinion during the first epidemic wave of Covid-19 in China based on Weibo data
As COVID-19 spread around the world, epidemic prevention and control policies have been adopted by many countries. This process has prompted online social platforms to become important channels to enable people to socialize and exchange information. The massive use of social media data mining techniques, to analyze the development online of public opinion during the epidemic, is of great significance in relation to the management of public opinio…
Extracting Human Activity Areas from Large-Scale Spatial Data with Varying Densities
Human activity area extraction, a popular research topic, refers to mining meaningful location clusters from raw activity data. However, varying densities of large-scale spatial data create a challenge for existing extraction methods. This research proposes a novel area extraction framework (ELV) aimed at tackling the challenge by using clustering with an adaptive distance parameter and a re-segmentation strategy with noise recovery. Firstly, a d…
Categorisation of cultural tourism attractions by tourist preference using location-based social network data: The case of Central, Hong Kong
Staying at Home Is a Privilege: Evidence from Fine-Grained Mobile Phone Location Data in the United States during the Covid-19 Pandemic
The coronavirus disease 2019 (COVID-19) has exposed and, to some degree, exacerbated social inequity in the United States. This study reveals the correlation between demographic and socioeconomic variables and home-dwelling time records derived from large-scale mobile phone location tracking data at the U.S. census block group (CBG) level in the twelve most populated Metropolitan Statistical Areas (MSAs) and further investigates the contribution …
Rethinking landscape ecological risk assessment and its applicability: Counterintuitive findings from coastal areas
Landscape ecological risk assessment (LERA) serves as a crucial tool for guiding effective environmental management. However, the conventional approach of LERA suffers from two notable drawbacks: the utilization of low‐resolution land‐use data (e.g., 30 × 30 m) and the application of arbitrary evaluation units (e.g., uniformly‐sized grids), both of which introduce uncertainty and inaccuracies into the assessment outcomes. Moreover, the extent to …
Interpretable machine learning for predicting urban flash flood hotspots using intertwined land and built-environment features
Resilience and recovery: Evaluating Covid pandemic effects on ride-hailing mobility and driver income dynamics
From isolation to linkage: Holistic insights into ecological risk induced by land use change
Developing a Replicable ESG-Based Framework for Assessing Community Perception Using Street View Imagery and POI Data
Urban livability and sustainability are increasingly studied at the neighborhood scale, where built, social, and governance conditions shape residents’ everyday experiences. Yet existing assessment frameworks often fail to integrate subjective perceptions with multi-dimensional environmental indicators in replicable and scalable ways. To address this gap, this study develops an Environmental, Social, and Governance (ESG)-informed framework for ev…
Artificial intelligence for flood risk management: A comprehensive state-of-the-art review and future directions
Ml4ej: Decoding the role of urban features in shaping environmental injustice using interpretable machine learning
Understanding the key factors shaping environmental hazard exposures and their associated environmental injustice issues is vital for formulating equitable policy measures. Traditional perspectives on environmental injustice have primarily focused on the socioeconomic dimensions, often overlooking the influence of heterogeneous urban characteristics. This limited view may obstruct a comprehensive understanding of the complex nature of environment…
Generating equitable urban human flows with a fairness-aware deep learning model
Mitigating urban heat exposure inequity with a seasonally adaptive machine-learning approach
Urban heat exposure poses significant risks to human well-being and contributes to emerging social inequity. While previous research has addressed urban heat exposure, mitigating the inequity associated with such hazards remains a significant challenge and an understudied area. To address this issue, this study proposes a machine-learning approach to assess the effectiveness of various urban development strategies in mitigating urban heat exposur…
Can transit-oriented development (TOD) cool the city? A multi-scale geospatial assessment of daytime surface heat exposure in Tokyo
● Links transit oriented development patterns to land surface temperature in Shinjuku ● Uses street view imagery, remote sensing and population data in one framework ● Shows well designed transit corridors can cool land surface by about 0.9°C ● Reveals that excessive density without greenery and shading increases urban heat ● Applies explainable machine learning to guide climate sensitive station area design Extreme heat is an escalating urban he…
Geography (11 works) · Computer Science (9 works) · Human Mobility and Location-Based Analysis (6 works) · Business (5 works) · Environmental Science (4 works) · Ecology (3 works) · Environmental resource management (3 works) · Land Use and Ecosystem Services (3 works) · Political science (3 works) · Social media (3 works)