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.

A comprehensive and presentable public transport information system is deemed invaluable for local residents and tourists all over the world. This is particularly necessary in view of the complex city structure and transportation system in Hong Kong. There are more than ten public transportation modes available, all with different operation schedules, fare structures, and routing characteristics. To assist commuters in making better use of public transport, the system needs to be not only user friendly and informative but intelligent enough to provide optimal route choices in terms of users' travelling behavior or preference as well. The web-map public transport enquiry system, as described in this paper, gives a good example of providing bilingual (English and Chinese) information on all public transports run in Hong Kong in the form of interactive maps and texts, as well as real-time derivation of optimal travelling routes for users in terms of multiple criteria, i.e., preferred mode, least changes, shortest travelling time, or lowest fare.

Department of Land Surveying and Geo-Informatic

Many cities around the world face the challenge of an aging population. A full understanding of the mobility behavior characteristics of the elderly is one necessary and urgent consideration as regards the current aging trend if sustainable urban development is to be fully realized. This paper presents a systematic approach to analyzing the dynamic mobility characteristics of the elderly who travel by bus using smart card big data. The characteristics include temporal distribution, travel distance, travel duration, travel frequency, and also the spatial distribution of such travelers. The findings of these mobility characteristics can directly contribute to both public transport policy making, service, and management. In this study, the analytics of the elderly are also compared with that of the average adult group so as to identify both the similarities and differences between the two groups. Beijing, a megacity, with a very high life expectancy and in which the bus is the dominant mode of public transport for the elderly, was used as the study area. The significance of this research concerns a newly developed systematic approach that is able to analyze the dynamic mobility characteristics of the elderly using smart card data.

Path‐finding problems have attracted widespread research interest in GIS‐T. Previous research has largely been conducted on developing shortest path algorithms in GIS. The conventional approach is to adopt the arc‐node network model, which allows only users with expert knowledge to generate measurements such as optimal paths from a map. However, the generation and maintenance of a centreline network are difficult and tedious because these are not natural on‐ground features but imaginary lines on a map. To remedy the situation, a revolutionary approach has been suggested that path‐finding method is independent of extra arc‐node data creations by end‐users. By using the cell‐decomposition method borrowed from motion planning of a robot, general map users may obtain an optimal path by just identifying familiarized symbols/outlines from a digital topographic map without resorting to generating an extra set of centrelines.