Deciphering activity patterns using time-geography framework: A case study of Oklahoma State University, Stillwater Campus

Abstract

Human societies are organized around activities. Every individual participates in certain activities at all times, which are organized in both time and space. Therefore to understand how human societies are organized, it is important to understand how human activities are organized. Traditionally, methods of activity analysis have employed transportation planning, structural equation, simulation and other computational models. Most of these models use trips and trip making as the bases for activity analysis. Current practice however recognizes activities as the focus of activity analysis since trips are derived from the demand of people to participate in activities. This and other shortcomings of the traditional models have resulted in the search for new perspectives and tools to analyze activity patterns. Hagerstrand's time-geography presents an elegant framework to study and understand activity patterns through several important and clearly defined concepts such as stations, space-time paths, space-time prisms, and activity constraints. One of the most important attribute of this framework is its capacity to capture and represent the sequence of human activities in simple but effective ways. The space-time path is a three-dimensional (3D) trajectory that represents the locations of human activities in a two-dimensional (2D) plane and captures the time and sequence of activity participation through the third dimension - time. Activity constraints also provide an understanding of the necessary conditions needed for human activity to take place. Unfortunately, only a few studies have developed methods of activity analysis using this framework. This study adopts the time-geography framework and concepts to develop two new methods to decipher activity patterns. The daily activity schedule fragmentation index (DASFI) examines the propensity of individuals to organize their activities in chains or fragments. The daily activity intensity similarity index (DAISI) measures the degree of similarity between the activity profiles of people. Both indices can be used in cluster analysis to derive clusters which group individuals with similar characteristics in their activity patterns. A case study with the population at Oklahoma State University - Stillwater Campus proves useful in understanding how people organize their activities and could help in planning geographical space to meet the activity needs of people.