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Abstract. Development in spatial data acquisition techniques, facilitate the process of analyzing movement characteristics and removed the lack of spatial data challenge. Annually, an enormous amount of spatial data are produced, and interpretation of this volume of data has become a major challenge. In this study, the movement data of 157 users in Geneva, Switzerland, were used and attempted to analyze their movement patterns. After the pre-processing stage, in order to investigate the dense areas, Kernel Density is calculated for each point for its neighborhood. The size of each cell of the output raster is approximately 100 meter. Afterward, in order to find the point of interests in the Geneva city, Weighted K-means is used for clustering of the raster. The kernel value of each cell is considered as the weight of the cell. Subsequently, the centroid of each final cluster has reflected the point of interest. As a final point, with the intention of assessing the results, the land use of the area is compared to each point of interest. Eventually, an interpretation is given.

Commission VI, WG VI/4 ABSTRACT:Movement data are becoming extensive and comprehensive with the advent of GPS (global positioning system) and pervasive use of smartphones, which has led to an increasing rate of studies about movement such as mobility pattern of oil spills, taxies, storms and animals. Studying the movement of people has long been the topic of much thought and debate among researchers within the field of transportation, social issues, and policy. One of the basic prerequisites for studying human movement behavior is modeling the movement, which show how people move so that the effect of different variables can be revealed. For this purpose, this research intends to deploy the concept of activity space (i.e., the part of the space in which a person is active) and its determinants to display the trajectory of individuals, and then modeling the effect of different variables on human mobility behavior. This study explores the effect of time (movement on weekends and weekdays) and demographic (age, gender, occupation state) factors on the characteristics of human mobility pattern and analyzes the extent to which the mobility pattern of different group of people is related to time by using Swiss human movement sample dataset, called MDC. These movement characteristics can be used later in a wide range of applications, such as predictions, urban planning, and traffic forecasting.