The advances in location-acquisition and mobile computing techniques have generated
massive spatial trajectory data, which represent the mobility of a diversity of moving objects …

Emerging big datasets about human mobility provide new and powerful ways of studying
cities and addressing various urban issues. However, human mobility has usually been …

Data embedding is used in many machine learning applications to create low-dimensional
feature representations, which preserves the structure of data points in their original space …

Urbanization's rapid progress has modernized many people's lives but also engendered big
issues, such as traffic congestion, energy consumption, and pollution. Urban computing …

With the advancement of GPS and remote sensing technologies, large amounts of
geospatial data are being collected from various domains, driving the need for effective and …

Real estate appraisal, which is the process of estimating the price for real estate properties,
is crucial for both buyers and sellers as the basis for negotiation and transaction …

Learning urban community structures refers to the efforts of quantifying, summarizing, and
representing an urban community's (i) static structures, eg, Point-Of-Interests (POIs) …

House price prediction is of utmost importance in forecasting residential property prices,
particularly as the demand for high-quality housing continues to rise. Accurate predictions …

Ranking residential real estates based on investment values can provide decision making
support for home buyers and thus plays an important role in estate marketplace. In this …

The property value assessment in the real estate market still remains as a challenges due to
incomplete and insufficient information, as well as the lack of efficient algorithms. House …