A Daily Activity Intensity Similarity Index (Daisi) using Time- Geography Framework: A Sequential Alignment-Based Measure of Activity Profile.


Until recently, time has often only been taken for granted as part of the activity participation experience of people but has not been explicitly modelled in activity pattern analysis.

Recent developments in time geography have increased interest in modelling time as an integral part of the human activity participation.

Important concepts in time geography framework that may be important in activity analysis include stations, space-time path, space-time prisms, activity constraints and the sequences of activities.

In this paper, a time geography framework is employed to develop the daily activity intensity similarity index (DAISI) to compare and examine daily activity participation rates of individuals and ultimately clustered groups of persons with similar activity intensity profiles.

DAISI fills in some of the gap in dearth of practical indices using the time geography framework to measure activity intensity profiles.


Background Of Study

Recent developments in time research have improved the analysis and modelling of human behaviour by explicitly incorporating the time dimension in human activity framework.

One of these frameworks is time geography, proposed and developed by Hagerstrand (1970). Time geography examines the inter-relationship between activities in space and time and their constraints (Miller, 2004; Yu, 2006).

A fundamental tenet of time geography is that all human activities have both spatial and temporal dimensions that cannot be meaningfully separated.

However, only recently has the time dimension been explicitly modelled in activity analysis (Shaw, 2006).

For quite some time, time geography provided an elegant conceptual basis for activity pattern analysis with little operational analytical capacity (Miller, 2004; Yu, 2006).

However, major developments in the field (e.g., Miller, 1991; Kwan, 2003; Yu and Shaw, 2004; Yu, 2006) have ignited a lot of interest in time geography and have opened new frontiers for the application of the time-geography framework.

Time geography possesses enormous potential for activity pattern analysis and modelling, which help to improve the understanding and prediction of spatial interactions.

However, very few studies have attempted to comprehensively model activity patternsin time- geography framework (Kwan, 2004; Scott, 2006).


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