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Activity pattern analysis by means of sequence-alignment methods


  • W C Wilson


The author describes a method of comparing sequences of characters,called sequence alignment or string matching, and illustrates its use in the analysis of daily activity patterns derived from time-use diaries. It allows definition of measures of similarity or distance between complete sequences, called global alignment, or the evaluation of the best fit of short sequences within longsequences, called local alignment. Alignments may be done pairwise to develop similarity or distance matrices that describe the relatedness of individuals in the set of sequences being examined. Pairwise alignment methods may be extended to many individuals by using multiple alignment analysis. A number of elementary hand-worked examples are provided. The basic concepts are discussed in terms of the problems of time-use research and the method is illustrated by examining diary data from a survey conducted in Reading, England. The CLUSTAL software used for the alignments was written for molecular biological research. The method offers a powerful technique for analyzing the full richness of diary data without discarding the details of episode ordering, duration, or transition. It is also possible to extend the analysis to include the context of activities, such as the presence of other persons or the location, but such extensions would require software designed for social science rather than biochemical problems. The method also offers a challenge to researchers to begin to develop theories about the determinants of daily behavior as a whole, rather than about participation in single activities or about time-budget totals.

Suggested Citation

  • W C Wilson, 1998. "Activity pattern analysis by means of sequence-alignment methods," Environment and Planning A, Pion Ltd, London, vol. 30(6), pages 1017-1038, June.
  • Handle: RePEc:pio:envira:v:30:y:1998:i:6:p:1017-1038

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    Cited by:

    1. Charles Raux & Tai-Yu Ma & Eric Cornelis, 2016. "Variability in daily activity-travel patterns: the case of a one-week travel diary," Post-Print halshs-01389479, HAL.
    2. Jianchuan Xianyu & Soora Rasouli & Harry Timmermans, 2017. "Analysis of variability in multi-day GPS imputed activity-travel diaries using multi-dimensional sequence alignment and panel effects regression models," Transportation, Springer, vol. 44(3), pages 533-553, May.
    3. A. Prinzie & D. Van Den Poel, 2005. "Incorporating sequential information into traditional classification models by using an element/position- sensitive SAM," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/292, Ghent University, Faculty of Economics and Business Administration.
    4. Joh, Chang-Hyeon & Arentze, Theo & Hofman, Frank & Timmermans, Harry, 2002. "Activity pattern similarity: a multidimensional sequence alignment method," Transportation Research Part B: Methodological, Elsevier, vol. 36(5), pages 385-403, June.
    5. Thomas King, 2013. "A framework for analysing social sequences," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(1), pages 167-191, January.
    6. Yusak Susilo & Kay Axhausen, 2014. "Repetitions in individual daily activity–travel–location patterns: a study using the Herfindahl–Hirschman Index," Transportation, Springer, vol. 41(5), pages 995-1011, September.
    7. Tim Schwanen & Martin Dijst, 2003. "Time windows in workers' activity patterns: Empirical evidence from the Netherlands," Transportation, Springer, vol. 30(3), pages 261-283, August.
    8. Laurent Lesnard & Thibaut Saint Pol, 2009. "Patterns of Workweek Schedules in France," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 93(1), pages 171-176, August.
    9. Marlies Vanhulsel & Carolien Beckx & Davy Janssens & Koen Vanhoof & Geert Wets, 2011. "Measuring dissimilarity of geographically dispersed space–time paths," Transportation, Springer, vol. 38(1), pages 65-79, January.
    10. Robert Schlich & Kay Axhausen, 2003. "Habitual travel behaviour: Evidence from a six-week travel diary," Transportation, Springer, vol. 30(1), pages 13-36, February.
    11. Charles Raux & Tai-Yu Ma & Eric Cornelis, 2011. "Variability versus stability in daily travel and activity behaviour. The case of a one week travel diary," Working Papers halshs-00612610, HAL.
    12. George Sammour & Tom Bellemans & Koen Vanhoof & Davy Janssens & Bruno Kochan & Geert Wets, 2012. "The usefulness of the Sequence Alignment Methods in validating rule-based activity-based forecasting models," Transportation, Springer, vol. 39(4), pages 773-789, July.
    13. Clarke Wilson, 2008. "Activity patterns in space and time: calculating representative Hagerstrand trajectories," Transportation, Springer, vol. 35(4), pages 485-499, July.
    14. Ignace Glorieux & Ilse Laurijssen & Joeri Minnen & Theun Tienoven, 2010. "In Search of the Harried Leisure Class in Contemporary Society: Time-Use Surveys and Patterns of Leisure Time Consumption," Journal of Consumer Policy, Springer, vol. 33(2), pages 163-181, June.
    15. Joh, Chang-Hyeon & Arentze, Theo A. & Timmermans, Harry J. P., 1999. "Multidimensional Sequence Alignment Methods for Activity Pattern Analysis: A comparison of dynamic programming and genetic algorithms," ERSA conference papers ersa99pa279, European Regional Science Association.
    16. Janssens, Davy & Wets, Geert & Brijs, Tom & Vanhoof, Koen & Arentze, Theo & Timmermans, Harry, 2006. "Integrating Bayesian networks and decision trees in a sequential rule-based transportation model," European Journal of Operational Research, Elsevier, vol. 175(1), pages 16-34, November.

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