Complexity in daily life – a 3D-visualization showing activity patterns in their contexts
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- anonymous, 2003. "Focus on Authors," Marketing Science, INFORMS, vol. 22(3), pages 435-436.
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Cited by:
- Mattioli, Giulio & Anable, Jillian & Vrotsou, Katerina, 2016. "Car dependent practices: Findings from a sequence pattern mining study of UK time use data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 89(C), pages 56-72.
- Sandels, C. & Widén, J. & Nordström, L., 2014. "Forecasting household consumer electricity load profiles with a combined physical and behavioral approach," Applied Energy, Elsevier, vol. 131(C), pages 267-278.
- Ellegård, Kajsa & Palm, Jenny, 2011. "Visualizing energy consumption activities as a tool for making everyday life more sustainable," Applied Energy, Elsevier, vol. 88(5), pages 1920-1926, May.
- Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
- Widén, Joakim & Wäckelgård, Ewa, 2010. "A high-resolution stochastic model of domestic activity patterns and electricity demand," Applied Energy, Elsevier, vol. 87(6), pages 1880-1892, June.
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More about this item
Keywords
household division of labour; time-geography; 3D method; visualization; diaries; everyday life; activity patterns. Complexity in daily life – a 3D-visualization showing activity patterns in their contexts;All these keywords.
JEL classification:
- C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
- D13 - Microeconomics - - Household Behavior - - - Household Production and Intrahouse Allocation
- P46 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Consumer Economics; Health; Education and Training; Welfare, Income, Wealth, and Poverty
- R29 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Other
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