VETUS - Visual Exploration of Time Use Data to Support Environmental Assessment of Lifestyles

Abstract

- The time-use (or activity) patterns individuals perform on a typical day - their individual lifestyles - fundamentally shape our society and the environment we live in. Not only are lifestyles evolving over time, driven by societal and technological change, they also significantly contribute to the achievement of Sustainable Development Goal 12 “responsible consumption and production”, namely through the resource use and emissions associated with goods and services consumed to perform activities. We created an interactive, browser-based tool to visualize and intuitively explore statistical time-use data. The visualization helps to gain an overview about the available data, identify and compare common time-use patterns and draw up hypotheses about the relationship between changes in lifestyles and their social and environmental consequences. We use the tool to compare time-use data from different regions, time periods as well as socio-economic and demographic backgrounds and estimate the associated energy consumption. From a time-use perspective, any technological change which triggers changes in time allocation can only be environmentally sustainable if the environmental impact of the total of the activities performed after the change is lower than before. Index Terms- Time use, time-use data, lifestyles, activities, energy intensity of activities, visualization, sustainability.

I. INTRODUCTION

Achieving “responsible consumption and production” patterns has been manifested as Sustainable Development Goal 12 by the United Nations [ 1 ]. Individual lifestyles, for this study defined as “dynamic pattern[s] of consumption activities” [2, p. 111] directly impact the environment through the resource use and emissions associated with goods and services consumed to perform the activities.

Lifestyles can be analyzed from various perspectives, e.g. from a functional perspective (products fulfilling stable needs), from a neo-classical budget constraint perspective (products fulfilling individual needs with a budget-constraint on consumption) or from a time-use perspective (individual needs and utility with a time constraint on consumption) [ 2 ]. Time use is a suitable perspective for the analysis of lifestyles, because time budget is naturally limited and constant (24 h per day) and the activities to which people assign their time can be related to environmental impacts [ 2 ], [ 3 ]. For example, someone can spend an evening reading a book at home or taking a trip with a private car (activities with significantly different environmental impacts). In that sense, goods and services are “best perceived not as ends in themselves [...], but as instrumental to the performance of an activity” [4, p. 825]. Building on these premises, time use of individuals has been the subject of interest in various disciplines yielding scientific theories such as the theory of time allocation [ 5 ], the time-use approach [ 2 ], social practice theory [ 6 ], [ 7 ], time geography [ 8 ], wealth in time [ 9 ], [ 10 ], or activity-based models of transport demand [ 11 ].

At the same time, individual lifestyles are subject to continuous change driven by societal and technological developments [ 12 ]. For example, as people are increasingly moving to urban environments, the commuting patterns – and thus the time spent in transport – can change. Also, the increasing use of information and communication technology (ICT) leads to a relaxation of some time and space constraints of activities [ 13 ]. For example, “virtual mobility” solutions, such as telecommuting or videoconferencing, can have direct impact on the time spent in transport [ 14 ]–[ 16 ]. They can even eradicate the need to live close to the employer and thus change land-use patterns (e.g. the attractiveness of living in urban or rural environments) and commuting patterns in the long run [ 17 ], [ 18 ]. To summarize, individual lifestyles (i) are a major determinant of environmental impact, (ii) are subject to continuous change, and, (iii) for these reasons, have been of interest in many academic disciplines.

Today, large collections of time-use data – diaries of the time individuals spend on activities – from various countries and time frames is available [ 19 ]–[ 21 ]. In this paper, we present a tool for visual exploration of time-use data (VETUS), developed to process the data provided by the Multinational Time Use Study (MTUS) of the Centre for Time Use Research at the University of Oxford [ 20 ]. The tool can be used to compare individual time-use patterns (the time individuals spend on various activities on a 24-hour day) from different regions, time frames as well as socio-economic and demographic backgrounds, and to draw up hypotheses on environmental impacts. As humans are good at visual perception [ 22 ], visualization of time-use data can help researchers to explore time-use data in an intuitive way [ 23 ].

We analyzed existing work in the field of time-use research, environmental impact assessment of everyday activities and data visualization, developed the tool considering visualization trade-offs and appropriate visualization idioms, and used it for environmental assessments of lifestyles extracted from time-use data.

II. TIME-USE DATA, ACTIVITIES AND ENVIRONMENTAL

IMPACTS

The time-use approach is a perspective to analyze lifestyles from a consumption perspective focusing on temporal constraints (as opposed to financial budget constraints). A time-use pattern is an observable set of activities and the time spent on these activities, in our case by an individual in 24-hours. Timeuse data provided by the MTUS describes the time (in minutes) individuals spend on distinct activities on a specific day and combines over a million diary days from 23 countries from the 1960s to the 2010s [ 20 ].

Jalas describes sustainable lifestyles as “the requirement of no increase in the materials-intensity of everyday life” [2, p. 113]. By applying decomposition analysis on household expenditure, energy consumption, time-use and input-output data, he estimates the energy intensities of activities for Finnish households considering direct energy use (e.g. the fuel consumption of a car) and indirect energy use (“energy use of producing the goods and services that are needed in the activity” (p. 114) – Tab. I).

Due to the high energy intensity of transportation, outsideof-home activities, even if not very energy-intensive as such, can cause relatively high energy consumption if transportation is included. Sleeping has an energy intensity of zero since domestic heating is not allocated to any activitiy. Work has an energy intensity of zero since no final consumption is allocated to it.

Many researchers followed this approach, e.g. Aal et al. [ 25 ] estimated the energy intensity of leisure activities in Norway in 2001, Minx and Baiocchi [ 4 ] estimated activity material intensities in West Germany in 1990, Yu et al. [ 26 ] activity CO2 intensities in China in 2008 and Druckmann et al. [ 3 ] activity greenhouse gas intensities in Great Britain in 2005.

The time-use approach can also be used to explain indirect environmental effects of technological change. For example, telecommuting allows employees to work from home, save commuting time and the related energy consumption. However, net energy savings depend on how the time saved is spent. Depending on the energy intensity of the substitute activities, the environmental benefits can be partially compensated or even overcompensated for – a phenomenon called time rebound effect [ 2 ]. The time-use approach is especially useful to investigate such rebound effects because of the hard 24-hour constraint, which provides a natural system boundary to behavior. Exemplary research questions that can be investigated with the time-use approach are: Does a given ICT use case increase or decrease the environmental impact? Does a given ICT use case increase or decrease the time individuals spend in transport? Does a given ICT use case increase the pace of life (“the speed and compression of actions and experiences” [27, p. 8/9])? Do people who live in urban environments spend less or more time traveling than people who live in rural environments?

III. VISUALIZATION

A. Data Visualization

Visualization “transforms the symbolic into the geometric, [..] offers a method for seeing the unseen” and “enriches the process of scientific discovery and fosters profound and unexpected insights” [23, p. 3]. Specifically, as the volume of available data is increasing at a tremendous pace, it becomes more challenging to derive meaningful insights from the data without adequate visualization [ 28 ]. Visualization helps especially researchers who want to explore data to find interesting hypotheses. Visualization methods are suitable where human pattern recognition capabilities are to be supported, rather than replaced, in our case for the exploratory analysis of time-use patterns to support environmental assessment of lifestyles [ 29 ].

B. Used data

For developing the application, we focus on the ‘adult’ aggregate dataset using the 69-activity typology. In this dataset, each record represents a 24-hour observation day, providing the time spent on 69 activities plus socio-economic and demographic variables of the diary person. We compared the variables with socio-economic and demographic indicators commonly used to describe populations (e.g. by federal statistical offices) and selected 96 variables (all 69 activity plus 27 demographic and socio-economic variables, Tab. II) to be used as a core set for visualization We did not include energy intensities of activities directly into the visualization because such data is only available for few time frames and regions and has to be considered in a later step of the process.

C. Visualization requirements and trade-offs

The visualization tool should enable the user to browse through available time-use data in an exploratory, tentative way and allow to derive initial interpretations of differences in timeuse patterns among regions or time-frames or among groups defined by socio-economic and demographic properties of individuals. Therefore, the tool needs to display the time spent on activities in an intelligible and comprehensible way and allow the researcher to set filters on geographic, temporal, socioeconomic and demographic variables. After having applied filters to the dataset, visualized the data and derived an interpretation, the user should be well prepared for applying statistics software, e.g. to test a hypothesis1.

To meet these requirements, we needed to address several trade-offs caused by three limitations of resources (humans, computers, displays) [ 30 ]:

1) Cognitive limitations of humans: The dataset contains in total 69 activity variables and 27 demographic and socioeconomic variables. This choice could be criticized for inducing a bias by limiting the flexibility for the researcher. On the other hand, including a high number of variables in the core set can harm the simplicity and usability of the tool.

2) Limitations in displays: To increase usability, we decided that the tool should be accessible through a standard web browser and show all required information on one single page, without the need to scroll. Therefore, space for visual elements 1 For detailed investigations of MTUS data users should also refer to the MTUS User Guide: https://www.timeuse.org/MTUS-User-Guide is limited by the size of the page, which is bound to (normal) display size.

3) Limitation in computing power: The number of included

variables, the size of the dataset and the used visual elements impact the performance of the tool with respect to response time in displaying data.

D. Selected visualization idioms

“A vis idiom is a distinct approach to creating and manipulating visual representations” [30, p. 10], i.e. “any specific sequence of data enrichment and enhancement transformations, visualization mappings, and rendering transformations that produce an abstract display of a scientific dataset” and are usually based on “intuitive analogies between familiar objects and […] physical abstractions” (e.g. bar, scatterplot or line charts) [31, p. 77].

We first created a prototype to test different visualization idioms and then developed the final version, which is described in the following.

1) Time spent on 69 activities by day of the week: Time-use patterns can significantly change from day to day, especially between working and non-working days. Therefore, we visualize the average time spent by individuals on 69 activities in minutes by day of the week. This yields a matrix of 69 activities by seven days. Displaying such a large amount of information is challenging and can best be done with heat maps (Fig. 1), an intuitive way to display matrix alignment of two key attributes. Each matrix cell holds an area mark denoting a quantitative value attribute encoded with color (time spent on activities). Additionally, when hovering over a field, the average time spent on the activity on the respective day will be displayed.

2) Time spent on activity categories: Visually comparing 69 distinct activities is cognitively challenging, which is why we show the average time spent on eight activity categories as described in column 3 of Tab. 1. For displaying this variable, we use a pie chart, to visualize how the single parts (activity categories) contribute to the whole (24 hours) [ 30 ].

3) Day of the week, age group, family status, working hours:

Days of the week and family status are categorical variables, whereas age and working hours are continuous variables which are often transformed into categorical variables by creating bins (e.g. age groups “18-30” or “30-40”). These variables are mainly used to filter the data set and compare timeuse patterns among individuals with different demographic and socio-economic backgrounds. Also, the number of observations for each category of a filter variable can be displayed to provide information on the distribution of the socioeconomic and demographic variables. We used bar charts (Fig. 2) to visualize the distributions of these variables because they are useful to compare quantitative values of different categories of a variable [ 30 ].

4) Occupation: The occupation of the diarist is also a categorical variable, however with significantly more categories than the variables described above (MTUS distinguishes 14 occupation categories such as “farming, forestry and fishing”). We used a pie chart (Fig. 3) because bar charts require much space as the number of categories increases. The limitation of display size then pose a harder constraint than the fact that the legibility of pie charts suffers with increasing numbers of categories.

5) Country where the survey was conducted: The most natural way to display the country where the survey was conducted is a choropleth map (Fig. 4). This is a geographic map of regions which displays a quantitative attribute (i.e. the number of observations from each country) encoded as color over the different regions [ 30 ]. In our case, the more color-intense a country, the more observations for that particular country are contained in the dataset.

6) Year the survey was conducted: For visualizing the year the diary was kept, we created a timeline using a vertical bar chart (Fig. 5). The vertical axis denotes the number of observations and the horizontal axis shows the years. Users can filter the dataset by selecting a time frame using a draggable selector frame.

7) Further demographic and socio-economic variables: Fi

nally, we wanted to improve the filter options for the user, while staying within the display limitation of one single page. For this purpose, we added additional select lists for variables with few filter options at the bottom of the page (Fig. 6). The number of observations by category for these variables is displayed as a number in the end of each category name.

At startup of the tool, the whole data set is loaded and the visualization idioms are created showing the average time spent on activities and the number of observations by category for the described variables. In order to compare time spent on different activities by regions, daytimes and other variables, users can filter the data set by clicking on variable categories in the visualization idioms (e.g. the bar representing a specific age group) and select/deselect it. When deselected, all observations of the respective category are filtered and the displayed values for each other variable are recalculated and updated in all visualization idioms.

Finally, we show the number of currently selected observations at the top center of the page, and a menu for options in the sidebar. The whole dashboard (Fig. 7) can be considered a visualization idiom itself, combining the idioms described above. All charts are interconnected and changes in one chart trigger changes in the other charts.

IV. IMPLEMENTATION

A. Software technologies

For building the tool, we needed three main components: an output panel which displays the visual representation, a visualization engine which transforms the data into the visual representation and a database storing the data.

We developed the tool as a web application to make it accessible to anyone with a standard web browser (output panel). As database system, we used MongoDB and as a visualization engine the JavaScript libraries D3.js and dc.js, which together can be used to create and render charts providing instant feedback on user input [ 32 ], [ 33 ], [ 34 ]. To layout the charts, we used the frontend framework Bootstrap, as it is particularly user-friendly and easy to implement [ 35 ]. A repository on GitHub was used for version control and documentation: https://github.com/Sonnenstrahl/datavis [ 36 ]. The dashboard can be accessed at: https://files.ifi.uzh.ch/datavis

B. Performance and testing

In a first step, we created the dashboard without the heat map. The performance was exceptionally good and had no input lag when displaying all observations for Europe. In a second step, we added the heat map, which significantly lowered performance, as it is multidimensional and requires two key attributes (day of the week and activities). Therefore, we created custom launch parameters which enable the user to launch the application without the heat map or grouped activities (this functionality is not available in the online version of the tool). To inform the user that the system is busy while loading data, a loading wheel was added.

The prototype and the final dashboard were tested by two researchers and used for environmental assessment of lifestyles in a pilot use case (see section V). The researchers reported that they successfully used the tool to compare time-use patterns. A list of further potential improvements can be found on GitHub. Additional tests would help to improve the tool, especially because of the many degrees of freedom in visualization design.

V. EXEMPLARY APPLICATION OF THE TOOL TO ASSESS

LIFESTYLES AND THEIR ENVIRONMENTAL IMPACTS

We used the visualization tool for an initial analysis of differences in 24-hour time-use patterns across regions, time frames, socio-economic and demographic backgrounds. For each time-use pattern we also estimated the total energy consumption associated with the activities performed on the day using average energy intensities of activity categories (see Tab. I; energy intensities are based on an analysis of finish households in 1998-2000 and need to be interpreted with care because of their age). Tab. III shows the result of the analysis and potential interpretations of differences in time-use patterns. The table illustrates one example how the visualization tool can be applied to investigate time-use data and environmental impacts. Due to methodological differences in surveys across countries, different numbers of observations for each time frame and country, and high numbers of missing values for some variables the results need to be interpreted with caution. They do not imply causality and only have value as a starting point for more detailed investigations. In the following we describe the main results by variable to demonstrate the approach and the tool.

A. Age, gender, number of children

Younger people spend more time on pvw and wtc than older people, who spend more time on lr and fd. In this analysis, spending few time on pvw reduces environmental impacts as no energy consumption is allocated to pvw (0 MJ/hr), however wtc seems to be related to pvw and is energy intensive (73 MJ/hr).

Women seem to cause high energy consumption by spending more time on phf (30 MJ/hr) and less time on pvw than men. However, this energy consumption should be allocated to all members of a household, as the activity phf commonly serves all of them, not just the person who performs the activity. Gerushny et al. [ 37 ] showed that time women spent on phf continuously decreases since the 1960s, and increases for men.

Unsurprisingly, people without children seem to spend more time on lr and less on phf.

B. Education, motorized vehicle computer/Internet access

People with higher education, a motorized vehicle, or a computer and/or Internet access tend to spend more time on pvw and travel (pt + wtc)2, which increases their energy consumption. One possible explanation is that individuals with these characteristics have a higher-than-average income which is related with time spent on pvw and wtc.

C. Working hours and employment status

Compared to the average, people who spend more time on pvw and wtc (see variables employment status and working hours in Tab. III) mainly sacrifice time spent on phf, followed by lr. Sacrifice of time spent on sr, fd and pt for pvw and wtc is lower. 2 We have to consider that diary years span from 1974-2010. Having a computer and Internet access was not always common in this time frame.

D. Urban/rural living environment

It seems that living in an urban or rural environment has no strong impact on time-use patterns. People in urban environments spend slightly more time on travel. For assessing the environmental consequences, differences in the modal split in rural and urban environments need to be considered.

E. Country and year

In Southern European countries people spend more time on sr than in Northern European countries. Compared to the 1970s until 1990s it seems that in the 2000s people travel slightly more (see also V.F).

Comparing results across countries and time periods has to be done with caution because the data from different years or countries usually stems from different studies which might differ in survey methodology. E.g., time spent on wtc in Italy and, on pt and wtc in Austria, seems to be implausibly low.

F. Energy consumption

Highest (private) energy consumption is found for women, people with children in the same household and part-time employees. These effects occur as we are not considering energy consumption at the workplace and thus people who work less (0 MJ/hr), spend the time on more energy intensive activities (e.g. phf, lr). It is an interesting question how to include energy consumed during the time spent on pvw in such analyses.

Traveling should be treated with special attention, because it is highly energy-intensive. Time spent on traveling in the 2000s seems to be higher than in the 1970s, a phenomenon which increases energy consumption (however, this also depends on development of passenger miles, modal split and transport energy intensity).

This result potentially contradicts results of other studies which find that time spent on travel did not change in the past 25 to 30 years (based on Hungarian time-use survey [ 38 ]). This aspect needs to be further investigated. Also, full-time employees/people with high-working hours and people who have a computer and/or Internet access travel more than others.

Finally, it is unclear if spending more time on an activity really increases the energy consumption for that activity. For example, in Southern European countries people spend more time on fd, but does this imply they eat more? In fact, if people just eat slower and therefore spend less time on other energy intensive activities, total energy consumption might decrease.

VI. DISCUSSION

The application of the visualization tool shows that it can be used to compare lifestyles and associated environmental impacts. The chosen visualization idioms display the data in a meaningful way that is easy to interpret by an end user; context-dependent guidance is directly provided. However, the set of visualization idioms provided is not exhaustive and receiving feedback from more users could yield valuable information for further refinement and extensions.

Directly enhancing the tool with environmental data (in this case energy intensities or emission factors of activities) would allow users to immediately compare the environmental impacts of various lifestyles with the tool. However, this is also subject to availability of such data, which so far is only available for specific countries and time frames. A full list of potential improvements can be found on GitHub.

VII. CONCLUSION

We created a tool to visually explore time-use data and derive initial hypotheses regarding changes in lifestyles which can have relevant environmental impacts.

In our pilot application of the tool, we found initial evidence that increased use of ICT does not necessarily reduce energy consumption of individual lifestyle. From a time-use perspective, any technological change which triggers changes in time allocation can only be environmentally sustainable if total environmental impacts of activities performed after the change is lower than of the activities performed before.

There is much potential to improve the tool, i.e. directly including environmental data in the tool or improving the performance. We encourage researchers interested in time-use data to use this visualization and even add further functionality.

ACKNOWLEDGMENT

We thank the Centre for Time Use Research of the University of Oxford for collecting and standardizing time-use data from various countries and providing the data free of charge.

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