Date of Award


Document Type

Open Access Dissertation



First Advisor

Diansheng Guo


Geo-social networks are formed by flows of physical entities (e.g., humans, vehicles, sensors, animals), and communication (e.g., information, ideas, innovation) that connect places to places and individuals to individuals. Several major problems remain to be addressed for understanding the complex patterns in geo-social networks. This dissertation makes the following contributions to the theory and methodologies that aim at understanding complex geo-social data by integrating methods of computation, visualization and usability evaluation. Chapter 2 introduces a novel network-based smoothing approach that addresses the size-difference and small area problem in calculating and mapping locational (graph) measures in spatial interaction networks. The new approach is a generic framework that can be used to smooth various graph measures which help examine multi-space and multi-scale characteristics of geo-social data. Chapter 3 introduces a space-time visualization approach to discover spatial, temporal and relational patterns in a dynamic geo-social network embedded in space and time. By developing and visualizing a measure of connectedness across space and time, the new approach facilitates the discovery of hot spots (hubs, where connectedness is strong) and the changing patterns of such spots across space and time. Chapter 4 introduces a series of user evaluations to obtain knowledge on how map readers perceive information presented with flow maps, and how design factors such as flow line style (curved or straight) and layout characteristics may affect flow map perception and users’ performance in addressing different tasks for pattern exploration. The findings of this study have significant implications for iterative design, interaction strategies and further user experiments on flow mapping.


© 2014, Caglar Koylu

Included in

Geography Commons