Han Wang

• Master of Science
• PhD Student at The University of Hong Kong

Modeling spatial heterogeneity in the data generation process is essential for understanding and predicting geographical phenomena. Despite their prevalence in geospatial tasks, neural network models usually assume spatial stationarity, which could limit their performance in the presence of spatial process heterogeneity. By allowing model parameter...

Strengthening the ecological resilience of cities is essential for enhancing self-regulation and recovery mechanisms in response to disasters. This study examines the impact of urbanization on ecological resilience by utilizing panel data from 254 Chinese cities over the period 2017–2021. First, an urban ecological resilience assessment framework i...

The global shipping industry faces increasingly complex safety challenges due to the rapid growth of international maritime trade. This study develops a novel framework that combines spatial density analysis and machine learning (i.e., extreme gradient boosting model) to investigate the evolutionary patterns of global maritime accidents during 2001...

Anthropogenic NO\(_2\) concentrations cause climate change and human health issues. Previous studies have focused on the contribution of traffic factors to NO\(_2\) emissions but have ignored the spatially varying impact of public transport supply and demand on high-resolution NO\(_2\) concentrations. This study first applies a two-stage interpolat...

The surging accumulation of trajectory data has yielded invaluable insights into urban systems, but it has also presented challenges for data storage and management systems. In response, specialized storage systems based on non-relational databases have been developed to support large data quantities in distributed approaches. However, these system...

In recent decades, urbanization has led to an increase in building material stock. The high‐resolution quantification of building stock is essential to understand the spatial concentration of materials, urban mining potential, and sustainable urban development. Current approaches rely excessively on statistics or survey data, both of which are unav...

The geographically weighted regression (GWR) is an essential tool for estimating the spatial variation of relationships between dependent and independent variables in geographical contexts. However, GWR suffers from the problem that classical linear regressions, which compose the GWR model, are more prone to be underfitting, especially for signific...

Urban sustainability requires a coordinated development between urban built environment and human activities in cities. The irrational allocation of built environment stocks such as buildings and roads has led to urban problems like urban villages and ghost cities. However, the human use efficiency of urban built environment within cities and their...

Driving trajectory representation learning is of great significance for various location-based services such as driving pattern mining and route recommendation. However, previous representation generation approaches rarely address three challenges: (1) how to represent the intricate semantic intentions of mobility inexpensively, (2) complex and wea...

Driving trajectory representation learning is of great significance for various location-based services such as driving pattern mining and route recommendation. However, previous representation generation approaches rarely address three challenges: (1) how to represent the intricate semantic intentions of mobility inexpensively, (2) complex and wea...

The rising prosperity of Location-based Social Networks (LBSNs) witnessed an explosion in the availability of geo-tagged social media data, which enables tremendous location-aware online services, especially next point of interest (POI) recommendation. However, previous next POI recommendation studies usually adopt fix-length time windows for user...

Commuting flow prediction is a crucial issue for transport optimization and urban planning. However, the two existing types of solutions have inherent flaws. One is traditional models, such as the gravity model and radiation model. These models rely on fixed and simple mathematical formulas derived from physics, and ignore rich geographic semantics...

Road traffic is an important contributor to CO2 emissions. Previous studies lack enough spatiotemporal resolution in emission calculation at the road level and ignore the impact of the built environment on road traffic emissions. Therefore, this study develops a bottom-up methodology based on the traffic trajectory data to analyze the CO2 emission...

The site selection for hybrid offshore wind and wave power plants (HOWWPP) is a critical step to a successful HOWWPP project. In this study, a four-stage framework is presented for determining the most suitable marine areas for the siting of HOWWPP. First, wind and wave energy potentials are assessed as a foundation for the implementation of a HOWW...