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Large quantities of spatiotemporal (ST) data can be easily collected from various domains such as transportation, social media analysis, crime analysis, and human mobility analysis. The development of ST data analysis methods can uncover potentially interesting and useful information. Due to the complexity of ST data and the diversity of objectives, a number of ST analysis methods exist, including but not limited to clustering, prediction, and change detection. As one of the most important methods, clustering has been widely used in many applications. It is a process of grouping data with similar spatial attributes, temporal attributes, or both, from which many significant events and regular phenomena can be discovered. In this paper, some representative ST clustering methods are reviewed, most of which are extended from spatial clustering. These methods are broadly divided into hypothesis testing-based methods and partitional clustering methods that have been applied differently in previous research. Research trends and the challenges of ST clustering are also discussed.

Distance decay has long been used in geography to describe the effect of distance on cultural or spatial interactions between places. It is an important precept of spatial analysis, especially for spatial interaction models and notions of cultural diffusion. With the advent of transportation and communication technologies, the effect of mere physical distance has been diminishing and a new perspective needs to be developed for the key variables affecting the decay of spatial interactions. Besides, the increasing applications of positioning and geoinformation technologies have made the analysis on vast data of individual activity patterns feasible to formulate more reliable models.

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The importance of non-acoustical factors including the type of visual environment on human noise perception becomes increasingly recognized. In order to reveal the relationships between long-term noise annoyance and different types of neighborhood views, 2033 questionnaire responses were collected for studying the effect of perceptions of different combinations of views of sea, urban river, greenery, and/or noise barrier on the annoyance responses from residents living in high-rise apartments in Hong Kong. The collected responses were employed to formulate a multivariate model to predict the probability of invoking a high annoyance response from residents. Results showed that views of sea, urban river, or greenery could lower the probability, while views of noise barrier could increase the probability. Views of greenery had a stronger noise moderation capability than views of sea or urban river. The presence of an interaction effect between views of water and views of noise barrier exerted a negative influence on the noise annoyance moderation capability. The probability due to exposure to an environment containing views of noise barriers and urban rivers would be even higher than that due to exposure to an environment containing views of noise barriers alone.