Basile Chaix
email: chaix@u707.jussieu.fr

Yan Kestens
email: yan.kestens@umontreal.ca

Dustin T Duncan

Ruben Brondeel

Julie Méline
email: julie.meline@inserm.fr

El Tarik

Bruno Aarbaoui

Juan Pannier

Merlo

Keywords:

GPS tracking in neighborhood and health studies: a step forward for environmental exposure assessment, a step backward for causal inference?

Recent studies have relied on GPS tracking to assess exposure to environmental characteristics over daily life schedules [START_REF] Almanza | A study of community design, greenness, and physical activity in children using satellite, GPS and accelerometer data[END_REF][START_REF] Elgethun | Time-location analysis for exposure assessment studies of children using a novel global positioning system instrument[END_REF][START_REF] Rodriguez | Out and about: association of the built environment with physical activity behaviors of adolescent females[END_REF]Wheeler et al., 2010;Zenk et al., 2011). Combining GPS with Geographic Information Systems offers the opportunity to take a step forward in the measurement of environmental exposures [START_REF] Duncan | Applying GPS to enhance understanding of transport-related physical activity[END_REF][START_REF] Krenn | Use of global positioning systems to study physical activity and the environment: a systematic review[END_REF]. However, there are concerns associated with the interpretation of the resulting associations with health outcomes, to the extent that these studies may represent a step backward in terms of assessment of causal environmental effects.

GPS tracking for improved assessment of environmental exposures

With the growing recognition that most people only spend a limited amount of time each day in their residential environment, there is a large consensus that one of the most serious limitations of neighborhood and health literature to date is its systematic focus on residential neighborhoods [START_REF] Chaix | Geographic Life Environments and Coronary Heart Disease: A Literature Review, Theoretical Contributions, Methodological Updates, and a Research Agenda[END_REF]Chaix et al., 2012c;[START_REF] Cummins | Commentary: investigating neighbourhood effects on health--avoiding the 'local trap[END_REF][START_REF] Matthews | Spatial polygamy and the heterogeneity of place: studying people and place via egocentric methods[END_REF][START_REF] Rainham | Conceptualizing the healthscape: contributions of time geography, location technologies and spatial ecology to place and health research[END_REF]. Strategies to incorporate daily mobility in neighborhood and health studies include standard mobility surveys [START_REF] Kestens | Association between activity space exposure to food establishments and individual risk of overweight[END_REF] or surveys of regular destinations based on electronic mapping tools (Chaix et al., 2012c). Additionally, GPS tracking appears as a way to move environmental exposure assessment from an exclusively residential to a more comprehensive multiplace perspective that accounts for the multiple daily activity places (Zenk et al., 2011).

Selective daily mobility as a major source of bias in GPS studies

A commentary of selected literature

Our aim was to evaluate the methods and the implicit and explicit rationale and objectives in the literature for correlating environmental information around GPS locations with health behaviors and outcomes. Rather than a systematic review that offers a high level of generalization [START_REF] Krenn | Use of global positioning systems to study physical activity and the environment: a systematic review[END_REF], the analytical strategy selected to achieve our aim was to perform a commentary of published articles, which allows for a detailed examination of studies and of the formulations used to report their objectives, analytical design, and interpretation of findings. The present commentary focuses, not on one article as usual, but on four articles for a more informative analysis, all four articles published in Health & Place [START_REF] Almanza | A study of community design, greenness, and physical activity in children using satellite, GPS and accelerometer data[END_REF][START_REF] Lachowycz | What can global positioning systems tell us about the contribution of different types of urban greenspace to children's physical activity[END_REF][START_REF] Rodriguez | Out and about: association of the built environment with physical activity behaviors of adolescent females[END_REF]Zenk et al., 2011).

However, the issues discussed in the present article also apply to a number of other GPS studies published in the field of Public health or Nutrition [START_REF] Duncan | GIS or GPS? A comparison of two methods for assessing route taken during active transport[END_REF][START_REF] Oliver | Combining GPS, GIS, and accelerometry: methodological issues in the assessment of location and intensity of travel behaviors[END_REF][START_REF] Quigg | Using accelerometers and GPS units to identify the proportion of daily physical activity located in parks with playgrounds in New Zealand children[END_REF]Wheeler et al., 2010). The four studies were selected for the differences in their objectives (descriptive or inferential) and related interpretation of findings and for the differences in their analytical strategies (GPS point-level analysis or individual-level analysis).

The first reviewed study [START_REF] Lachowycz | What can global positioning systems tell us about the contribution of different types of urban greenspace to children's physical activity[END_REF] analyzed GPS data collected every 10 seconds and accelerometer data collected for 10 second epochs (periods of data collection) during four school days and at least one weekend day for 614 children aged 11-12 years (Bristol, UK, PEACH cohort, 2007[START_REF] Chaix | Geographic Life Environments and Coronary Heart Disease: A Literature Review, Theoretical Contributions, Methodological Updates, and a Research Agenda[END_REF]. The authors performed a "momentary" investigation, i.e., analyzed the data at the epoch level (one statistical observation per 10 second epoch) with a random effect at the individual level. More precisely, we refer to this approach as the "contemporaneous momentary design" because information on the location and related context and on the outcome (accelerometry) was collected at the same moment.

The objectives of the study were descriptive, i.e., to "record the environments where different intensities of physical activity take place" and to "investigate the actual use of greenspaces".

The authors sought to describe behavioral contexts rather than to perform inferences on the effects of contexts on behavior (the "analysis did not consider how use of green space may be affected by how accessible it is to the child"). In accordance with these descriptive objectives and with their "contemporaneous momentary" analytical strategy, the authors did not report the results as associations that attempt to reflect the causal effects of environments on behavior. Instead, as their main findings, the authors descriptively indicated that the majority of moderate-to-vigorous physical activity took place indoors while a substantial proportion of outdoor physical activity was performed in green spaces.

Commenting on the literature, the authors criticized previous studies on the grounds that they measured exposures in residential environments and were "often unable to consider the actual locations where physical activity takes place". As discussed below, however, assessing where physical activity occurs does not permit causal inference of environmental effects on physical activity. Rather, for such an inferential aim, the challenge is to assess whether physical activity opportunities are accessible from the different geographic contexts visited in daily trajectories.

The second study reviewed here [START_REF] Rodriguez | Out and about: association of the built environment with physical activity behaviors of adolescent females[END_REF] analyzed data on 293 adolescent females (15-18 years old) collected for six consecutive days by GPS every 60 seconds and by accelerometers for 60 second epochs (Minneapolis and San Diego, USA). GPS points located within 50 m of the residence or school were discarded, to exclude activities at home or school.

The study relied on a contemporaneous momentary design: the analyses were conducted at the epoch level, considering point-by-point information on the intensity of physical activity and on the built environment in 50 m buffers around each GPS point.

Whereas the previous article [START_REF] Lachowycz | What can global positioning systems tell us about the contribution of different types of urban greenspace to children's physical activity[END_REF] mostly had descriptive aims, the article by Rodriguez switches between two perspectives: identifying causal environmental effects and describing behavioral contexts. The authors suggested that GPS tracking allows researchers to more accurately identify the environmental opportunities and barriers that influence physical activity. However, when interpreting their findings, they focused more descriptively on behavioral contexts, indicating that "understanding the places were physical activity and sedentary behaviors occur appears to be a promising strategy to clarify relationships". While we agree with this statement, we emphasize below that the sole description of behavioral contexts is not necessarily a step forward towards the appraisal of causal environmental effects on behavior.

The authors reported that, after adjustment, the odds of high physical activity intensity were higher in GPS locations with parks, schools, and high population density, and lower in GPS locations with more roads and food outlets. The descriptive nature of these findings is illustrated, for example, by the argument that the lower odds of intense physical activity near food outlets "may be capturing sedentary behavior, when participants visit malls with outdoor areas, or restaurants with outdoor seating". These findings simply suggest that people are by essence less physically active in specific places (e.g., restaurants, movie theaters, etc.) than in others (e.g., parks).

The third reviewed study [START_REF] Almanza | A study of community design, greenness, and physical activity in children using satellite, GPS and accelerometer data[END_REF] relied on GPS and accelerometer data (30 second intervals/epochs) collected for seven days for 208 children aged 8-14 years from The Preserve smart growth community in California (USA) and six conventional communities situated nearby. Interestingly, the study was designed to rule out selective residential migration biases by comparing families who moved to the smart growth community with families who initially considered moving there but did not. Analytically, the authors compared the contemporaneous momentary analytical design used in the Lachowycz and Rodriguez studies (epoch-level analyses) with a more conventional individual-level analysis.

Contemporaneous momentary analyses revealed a positive relationship between greenness at the GPS point and the likelihood of moderate-to-vigorous physical activity. In individuallevel analyses, greenness exposure in the residential neighborhood was defined in two ways: (i) average greenness in the 500 m buffer around the residence; (ii) cumulated time of exposure to greenness at all the GPS points recorded in the residential neighborhood. The association between greenness and physical activity identified in the momentary analysis was retrieved only with the second version of the individual-level greenness exposure variable.

Because of their "spatially-explicit" design (considerable number of locations examined for each participant), contemporaneous momentary analyses were described by the authors as increasing the power to detect associations compared to individual-level analyses. Whether true or not, whereas such simple epoch-level analyses which assess the spatial milieu around individuals at each observation are useful to describe behavioral contexts, they may be inadequate to assess environmental effects on behavior. For example, such simple contemporaneous momentary analyses are unable to demonstrate that an improved spatial accessibility to greenness causally increases physical activity; they simply highlight that green spaces are a more common place for physical activity than many other places such as railway stations or shopping areas.

The individual-level greenness exposure variable defined in 500 m radius buffers around the residence was qualified as "coarser" than the individual-level variable based on the aggregation of greenness exposure at GPS activity locations. Again, in our view, the latter variable is not only more accurate, its meaning is also qualitatively different: whereas the former variable reflects potential access to green spaces from the residence, the latter captures the actual patterns of use of green spaces in local daily trajectories. As discussed below, such difference has major implications for the interpretation of the associations estimated between the environmental variables and the behavioral or health outcomes.

The standard contemporaneous momentary design was described above as providing descriptive information on behavioral contexts. However, the momentary analysis in Almanza et al. was able to provide richer information because it also examined whether the greennessphysical activity association was of different magnitude for residents living or not in the smart growth community (interaction of effects). Such an interaction in this enhanced momentary analysis allowed the authors to assess whether the overall community design (smart growth or not) influenced the extent to which spending time in greenspaces was associated with increased physical activity levels. The estimated interaction revealed that the greennessphysical activity association was slightly stronger in the smart growth community, even if the confidence intervals were overlapping.

Finally, the fourth reviewed study (Zenk et al., 2011) (Detroit, 2008[START_REF] Chaix | Geographic Life Environments and Coronary Heart Disease: A Literature Review, Theoretical Contributions, Methodological Updates, and a Research Agenda[END_REF] analyzed GPS data for 120 adults and older adults (30 second intervals) and accelerometer data for 97 of these participants (one minute epochs). Zenk and colleagues primarily relied on GPS to improve the assessment of environmental exposures, i.e., in the perspective of a contextual expology as denominated in a recent publication of ours (Chaix et al., 2012c). The notion of expology is sometimes used in environmental epidemiology and toxicology to refer to the characterization of individual risks of exposure; in our view, "contextual expology" is a subdiscipline of neighborhood and health research interested in the multiple places and times of exposure to contexts. The objective of Zenk et al. was to overcome the mischaracterization of environmental exposures of studies focused on residential neighborhoods. The authors' aim was not to assess the actual contexts of behavior but the (potential) spatial accessibility to services such as fast-food outlets, supermarkets, or parks that may influence health behavior or health. In their analyses conducted at the individual level, exposures were assessed in "daily path areas" derived by buffering all GPS points at 0.5 miles. Interestingly, a comparison of measures of exposures concluded that exposures in the residential neighborhood correlated weakly with exposures in the GPS-based activity space.

Fast-food outlet density measured in the residential neighborhood was associated with none of the three dietary intake outcomes (intake of saturated fat, fruits and vegetables, and whole grains). However, fast-food outlet density in the daily path area (around each GPS point) was positively related to saturated fat intake and negatively associated with whole grain intake. The comment of the authors that "the daily path area may better capture fast-food outlets that were actually utilized" suggests that such an exposure measure reflects the actual behavioral contexts rather than potential access as needed for causal inference on environmental effects.

The study by Zenk is the only reviewed article to discuss this issue of bias, indicating that "activity space fast-food outlet density may be associated with dietary behavior because individuals who want to consume fast-food seek out environments with higher fast-food outlet concentration in order to obtain it" (p. 1158). Arguing that exposure measures that reflect actual behavior generate bias, the authors interestingly suggested that future research should investigate whether the actual use of resources mediates relationships between the potential access to resources around daily activity locations (accessibility) and weight-related behaviors.

Selective daily mobility bias in GPS studies

The description of behavioral contexts (e.g., of the places actually used to exercise) can help plan the provision of health-enhancing services [START_REF] Duncan | GIS or GPS? A comparison of two methods for assessing route taken during active transport[END_REF][START_REF] Lachowycz | What can global positioning systems tell us about the contribution of different types of urban greenspace to children's physical activity[END_REF][START_REF] Quigg | Using accelerometers and GPS units to identify the proportion of daily physical activity located in parks with playgrounds in New Zealand children[END_REF]. Moreover, this descriptive information is useful to generate hypotheses on environmental resources that support behavior; these causal hypotheses then need to be formally tested through appropriate designs. Our focus here is on such causal inferences on environmental effects ("does the presence of X in the environment influence the behavior of people who live, work, or spend time nearby?").

In the reviewed studies, exposures to the built and food environments were determined around valid GPS points, including places where individuals specifically go to practice the behavior investigated (e.g., to buy or eat specific foods or to practice physical activity). With such measures, participants with a particular taste for energy-dense food that leads them to eat several days a week in fast-food restaurants would be classified as often "exposed" to a significant density of fast-food restaurants. Similarly, people with advanced knowledge on the benefits of physical activity, positive attitudes toward exercise, and a sufficient self-efficacy to convert intentions into regular physical activity more frequently visit sport or recreational facilities and would therefore appear as more "exposed" to physical activity opportunities.

Such intrapersonal factors that encourage specific study participants to regularly use the corresponding environmental opportunities contribute to generate a relationship between environmental factors and health behavior that could be spuriously interpreted as a causal effect of the environment (either in contemporaneous momentary or individual-level analyses). Even GPS studies with refined research questions examining, e.g., which types of greenspaces visited or which characteristics of parks visited are associated with higher physical activity levels do not allow the identification of causal environmental effects because people select the type of parks they go to according to their intended use of these facilities.

In a recent article (Chaix et al., 2012c), in analogy to the well-known "selective residential migration bias" in studies of residential neighborhoods and health [START_REF] Frank | Stepping towards causation: do built environments or neighborhood and travel preferences explain physical activity, driving, and obesity[END_REF]van Lenthe et al., 2007), we described "selective daily mobility" as a source of confounding of a comparable nature for environment-health studies that account for daily mobility. Such confounding bias is attributable to the causal effect that unmeasured factors (e.g., intrapersonal variables) have on both the places visited during daily life and the health behaviors of interest. The bias stems from the fact that measures of accessibility to given environmental resources are also determined from the locations that were specifically visited to use the corresponding resources, i.e., to practice the behavior of interest. As explained elsewhere, selective daily mobility bias is particularly expected when investigating environmental resources supporting behavior, but is also possible for passive exposure to environmental hazards that influence health (such as air and noise pollution) (Chaix et al., 2012c).

We expect selective daily mobility to be a much more powerful source of bias than selective residential migration. Indeed, even if food, physical activity, and transportation preferences influence neighborhood choice when moving to a new residence, other criteria likely play a significant and perhaps larger role in the choice of the residential neighborhood and dwelling (Kestens et al., 2010b). Obviously, nutritional preferences have a stronger and more overwhelming influence on the daily choice of the physical activity facilities and food outlets visited or not. Accordingly, sorting participants by types of facilities effectively visited in daily life provides much more information on their nutritional preferences than sorting them according to the characteristics of their residential neighborhoods. Hence, a stronger confounding bias is expected from selective daily mobility than from selective residential migration.

Therefore it would be problematic to conclude that an increase in the strength of environment-behavior associations, when switching from a classical residential neighborhood study to a GPS mobility study (both analyzed at the individual level), is attributable to a better assessment of the causal effect of the environment. Even if GPS tracking is a promising strategy to improve environmental exposure assessment, our concern is that the appraisal of causal environmental effects moves one step backward rather than one step forward with such studies, if carelessly implemented.

Strategies to address confounding from selective daily mobility in GPS studies

Correction of measures of exposures and improvement of analytical designs

A first approach, for analyses conducted at the individual level, is to correct measures of multiplace environmental exposures. Theoretically, it is relevant to determine spatial accessibility to resources from so-called anchor points (Chaix et al., 2012c;[START_REF] Flamm | The concept of personal network of usual places as a tool for analysing human activity spaces: a quantitative exploration[END_REF]Kestens et al., 2010a), i.e., places with important material and symbolic meaning for the individuals, around which they organize their daily activities, and/or where they are relatively constrained to go. At the very least, reference locations from which to compute accessibilities should exclude the places specifically visited to perform activities related to the outcome under study. Accordingly, instead of using GPS data that include all the places visited, it seems important to filter GPS data based on the nature of the activities practiced at the different places, in order to include as much daily activity places as possible for measuring environmental exposures but without biasing the estimated associations. For example, assuming that a participant visits a park for exercise on the way back from his/her workplace to his/her residence, the park would have to be excluded from the set of locations considered to determine greenspace accessibility, to assess whether parks are accessible from the residence and workplace. A more technical issue is whether the trip from the workplace to the residence with its specific itinerary should be included as a set of locations around which to compute greenspace accessibility. A simple answer is that the locations along the itinerary should be taken into account in the assessment of accessibility if the itinerary corresponds to the one that the individual would have followed, had he/she not made a stop at the park. A practical solution for accessibility assessment if the itinerary implies a detour to the park is to replace it by the shortest path between the workplace and the residence.

Another approach that applies to data disaggregated into the multiple activity places visited by each participant may be, as suggested elsewhere (Thierry et al.), to estimate associations between the spatial accessibility to resources from each activity place and the practice of the behavior of interest either within the time frame that follows or within the time frame just before. In comparison with the contemporaneous momentary design discussed above, we refer to this approach as to the lagged momentary design because a time lag is introduced between the measurement of exposure and the measurement of the outcome. In that momentary design, adequate anchor points or reference locations from which to determine spatial accessibility to resources are both the activity place just before the practice of the behavior of interest and the activity place that follows it (Thierry et al.). For example, if a person makes a stop at a fastfood restaurant (as the outcome of interest) just before arriving to his/her workplace, what matters is the spatial accessibility to fast-food outlets from the workplace. However, in specific cases, either or both the activity place just before and the one just after the behavioral outcome may be inadequate anchors or reference locations from which to determine accessibility, for example if the participant makes a stop at a bookstore just nearby his/her sport facility after exercising there in a study of physical activity. In the latter example, the high degree of spatial accessibility to sport facilities from the bookstore spuriously reflects that the bookstore itself was reached from the visited sport facility. As a general rule, valid reference locations around which to compute accessibility include all the activity places that would have been visited by the participant independently of whether the behavior of interest was practiced or not around them.

Overall, assessing the nature of activities practiced at the different places is a necessary complement to GPS tracking to mitigate selective daily mobility biases by selecting/excluding activity places from which to determine accessibilities/exposures.

Data requirement: integrating the Public health / Nutrition and the Transportation approaches to GPS studies

As summarized in Table 1, current studies in the fields of Public health and Nutrition, including the 4 articles reviewed here, do not attempt to decompose GPS tracks into distinct activity places and trips between them. These studies, with few exceptions still at an experimental stage [START_REF] Oliver | Combining GPS, GIS, and accelerometry: methodological issues in the assessment of location and intensity of travel behaviors[END_REF][START_REF] Southward | Contribution of the school journey to daily physical activity in children aged 11-12 years[END_REF], also do not systematically assess the nature of activities practiced at the different places and the transportation modes for each trip. Differently, in the Transportation field (see Table 1), studies combine GPS tracking with precise mobility surveys that collect information on activities and transportation modes, though often only over one day [START_REF] Auld | An automated GPS-based prompted recall survey with learning algorithms[END_REF][START_REF] Bohte | Deriving and validating trip purposes and travel modes for multiday GPS-based travel surveys: a large-scale application in the Netherlands[END_REF][START_REF] Flamm | The concept of personal network of usual places as a tool for analysing human activity spaces: a quantitative exploration[END_REF][START_REF] Stopher | Conducting a GPS prompted recall survey over the internet[END_REF]. However, Transportation researchers usually do not incorporate accelerometers as Public health / Nutrition researchers do, and therefore lack information on physical activity and energy expenditure, which is also important to detect transportation modes. Clearly, a key challenge for future research is to incorporate the strengths of the two strategies in an integrated approach that combines GPS, accelerometers, and mobility surveys.

To gather data on activities and transportation modes, first one has to identify the activity places in the continuous stream of GPS data and to segment trips between them into a succession of trip stages (a trip stage is a part of a trip based on a single transportation mode); then one has to assess the nature of activities practiced at the different places and confirm the transportation modes between them. The present commentary does not describe in detail the hardware and software infrastructure employed in the RECORD GPS Study to address these two challenges, but provides sufficient information to allow one to understand the difference between the various strategies.

Regarding the first issue, automated algorithms make it possible to detect activity places (e.g., from the stationarity of the GPS trajectory at a given place) and to segment trips between these activity locations according to the transportation modes that were used [START_REF] Marchal | Postprocessing procedures for person-based Global Positioning System data collected in the French National Travel Survey 2007-2008[END_REF][START_REF] Thierry | Mobile technologies and people-place interaction analysis: Identifying significant activity places for health research[END_REF][START_REF] Tsui | Enhanced system for link and mode identification for personal travel surveys based on global positioning systems[END_REF]. Related challenges pertain to the parameterization of the algorithms (i.e., decisions on the minimum time at a fixed place for activity place detection, on cutoff values for speed and acceleration related to each transportation mode, etc.).

Regarding the second issue, whereas automated algorithms are useful to impute transportation modes [START_REF] Bohte | Deriving and validating trip purposes and travel modes for multiday GPS-based travel surveys: a large-scale application in the Netherlands[END_REF], they are limited in their capacity to impute the exact nature of activities, e.g., based on data from Geographic Information Systems on points of interest and facilities. In the RECORD Study (Chaix et al., 2012a;[START_REF] Chaix | Individual/neighborhood social factors and blood pressure in the RECORD Cohort Study: which risk factors explain the associations[END_REF][START_REF] Chaix | Neighborhood effects on health: correcting bias from neighborhood effects on participation[END_REF]Chaix et al., 2012b), an interactive web mapping application (VERITAS) is used to geocode the regular activity places of participants (Chaix et al., 2012c) prior to the GPS assessment. These data can improve the performance of activity recognition algorithms. Still, such algorithms are unable to identify the exact activities practiced in many places [START_REF] Bohte | Deriving and validating trip purposes and travel modes for multiday GPS-based travel surveys: a large-scale application in the Netherlands[END_REF]. Investigators are therefore constrained to survey participants during or just after the GPS collection to validate, correct, or complement information on activities and transportation modes.

One option to collect information to further characterize GPS tracks is to rely on web mapping applications denominated prompted recall survey tools because they are based on the electronic presentation of GPS tracks to facilitate or prompt the recall of the places visited [START_REF] Auld | An automated GPS-based prompted recall survey with learning algorithms[END_REF][START_REF] Bohte | Deriving and validating trip purposes and travel modes for multiday GPS-based travel surveys: a large-scale application in the Netherlands[END_REF][START_REF] Stopher | Conducting a GPS prompted recall survey over the internet[END_REF]. Important features of such survey software are that: (i) they have to be compatible with algorithms that identify activity places and impute information on the nature of activities and transportation modes to minimize the burden for the participants; (ii) they have to allow the participants to confirm or modify the imputed information and to provide answers to additional questions on activity places and trips; and (iii) they should offer both a simplified application usable by the participants themselves and an expert mode with extended functionalities for survey technicians.

Collecting survey data on activities and trips would be useful to Public health researchers, not only to correct the estimated environmental effects on health behavior from selective daily mobility biases. It would also allow them to reconstruct missing portions of trajectories. In the absence of GPS data (loss of signal, empty battery, etc.), surrogate locational information from the survey could be matched to accelerometry data. With this approach, it might be possible to include in the analyses indoor activity places that are often excluded from momentary analyses and to not exclude participants with insufficient GPS data. As a consequence, coupling GPS tracking with a precise mobility survey may help reduce selection biases.

Moreover, information on transportation modes for each trip from a mobility survey combined with GPS and accelerometer data would allow researchers to investigate the relationships between transportation behavior, physical activity, and health as an emerging field of research. For example, in the RECORD Study, GPS and mobility survey data are used to elaborate for each participant an accurate timetable over seven days as the succession of the activity places visited and of the transportation modes between them. Our aim is to analyze the accelerometer data according to this timetable, for example to assess differences in energy expenditure between trips with different transportation modes or chains of modes.

Conclusion

Improving measures of exposure to environmental conditions by accounting for daily mobility patterns is critical. In no way, however, should such improvement be obtained at the expense of the quality of causal inference. An integrated approach combining GPS tracking, accelerometers, and an electronic web-based mobility survey was described as a potentially relevant strategy to neutralize selective daily mobility processes that otherwise bias the estimated effects of multiplace environmental exposures on health and health behavior.

Table 1

Two approaches for incorporating GPS tracking in studies, and their implications for investigating

  environmental determinants of active living and health The classical Public health or Nutrition approach to GPS studies The Transportation approach to GPS studies How does it proceed? -Does not systematically identify activity places in the GPS stream of data -Relies on automated algorithms to identify activity places -Does not survey the nature of activities practiced at the different places (often only the location of the residence and workplace/school is known) -Surveys the nature of activities (with start and end times) practiced over the follow-up period (or a part of it) -Usually does not assess the transportation modes used for each trip -Surveys the transportation modes used for each trip over the follow-up period (or a part of it) -Determines the number of steps walked, the intensity of physical activity, and energy expenditure based on accelerometry -Usually does not rely on accelerometry What are the consequences for the study of environmental determinants of active living and health? -Determines environmental exposures based on all GPS locations, irrespective of activities -Would enable filtering activity places and related trips based on the nature of activities -Estimates associations that are vulnerable to selective daily mobility biases -Offers the opportunity to mitigate selective daily mobility biases -Investigates the correlates of the number of steps walked, activity intensity, and energy expenditure (accelerometry) but lacks precise information on the time spent in the different transportation modes -Investigates the correlates of time spent in the different transportation modes but would need accelerometry to establish a connection with physical activity and energy expenditure

In addition to the funding sources of the overall RECORD Study, the RECORD GPS Study is supported by: CERTU (Center for studies on road networks, transport, urban planning, and public construction) and DGITM (General Directorate of Infrastructures, Transport, and the Sea) of the French Ministry of Ecology; INPES (the National Institute for Prevention and Health Education) and IReSP (the Institute for Public Health Research); ARS of Ile-de-France (the Ile-de-France Region Health Agency); STIF (the Ile-de-France Region Transportation Authority); and RATP (the Paris Public Transport Operator). We are grateful to these institutions that provide support to the RECORD GPS Study and to the members of these institutions for their follow-up of the study (Laurent Jardinier, Tristan

van Lenthe, F.J., Martikainen, P., Mackenbach, J.P., 2007. Neighbourhood inequalities in health and health-related behaviour: results of selective migration? Health Place, 13, 123-137. Wheeler, B.W., Cooper, A.R., Page, A.S., Jago, R., 2010. Greenspace and children's physical activity: a GPS/GIS analysis of the PEACH project. Prev Med, 51, 148-152. Zenk, S.N., Schulz, A.J., Matthews, S.A., Odoms-Young, A., Wilbur, J., Wegrzyn, L., Gibbs, K., Braunschweig, C., Stokes, C., 2011. Activity space environment and dietary and physical activity behaviors: A pilot study. Health Place, 17, 1150Place, 17, -1161. .


Bob Spence
email: r.spence@imperial.ac.uk

Teaching Information Visualization Via Creative Design

The importance of Information Visualization is reflected in the many courses on that subject taught worldwide. The number of those courses, however, is not of prime importance: what is of real concern is how they are presented, and to whom.

Students

It is my view that education in information visualization is required primarily by two groups of people: those who will become interaction designers and those who will, at some time in their careers, either commission systems with a significant element of visualization or be responsible for evaluating such systems. I happen to teach both types of student: post-Master professional designers at the Technical University of Eindhoven in the Netherlands and first year undergraduates in Information Systems Engineering at Imperial College London. The material that follows represents a personal view, one that will hopefully trigger debate.

Theory or Practice

It is my strong belief that the best way to become educated in Information Visualization (and human-computer interaction in general) is to do it. For that reason my condition for presenting those courses is that there be no examination paper, favouring as it does those with photographic memories: rather I wish to encourage creativity and design ability. So I ask my students to undertake design exercises (mostly individually and once as a group) and one design critique.

Teaching Environment

The environment of my teaching of Information Visualization is of great importance to me. I strive for -and largely achieve -a 'studio' environment, first to approximate that of a real design house and, second, to avoid the traditional 'me and them' attitude: I certainly learn from the course as well as teach it. For example, in keeping with a studio environment I ensure that students paste their early designs on a wall, provide a one minute explanation and then for two minutes respond to questions from anyone who cares to ask them. In that way they receive instant and valuable feedback. Although perhaps unexpected, sketching on large sheets of paper with thick pens is encouraged to capture design ideas in the early exploration and design stages (Greenberg et al [START_REF] Greenberg | Sketching User experiences[END_REF]): the use of laptops at this stage is frowned upon and virtually forbidden.

Content

My emphasis on design is not at odds with a concern with theory, though it is theory to support design rather than for its own sake that is my concern. However, difficult decisions must be made about the content of a course on Information Visualization: what theory is relevant and the appropriate mix of theory and design. One decision I found easy to make -was the need for clear definitions.

Definitions

A student would surely be surprised, and disappointed in their teacher, if that teacher cannot provide precise definitions for important concepts. My first concern is to provide definitions where possible, even if they may be contentious. For example, the definition of information visualization I have always used is not shared by most leaders in the field. I refer to two respected dictionaries for the following definition: visualization (v): the formation of a mental model of something, with the implication that visualization is a cognitive activity undertaken by a human being and has nothing fundamentally to do with computers. I am pleased that recent publications are recognising the importance of this definition, instead of taking the attitude that the letters v-i-s-u-a-l in the word visualization implies that data can only be encoded graphically. My attitude may seem pedantic, but if we forget what the user is trying to achieve we tread a dangerous path. A visualization is not what we see on a display -it is in the mind.

Examples

In view of the paucity of relevant theory, Information Visualization is largely taught by a critical review of illustrative examples and the discussion of concepts. For example, a student undertaking their first design of a representation (the marks out of ten in 8 subjects taken by 5 students) may be informed by Bertin's [START_REF] Bertin | Graphics and graphic information Processing[END_REF] work and the concepts of object and attribute visibility [START_REF] Teoh | Hifocon': object and dimensional coherence and correlation in multidimensional visualization[END_REF], but will find that there is no 'algorithm' guaranteed to lead to the 'best' design (there simply isn't one) and that a design's effectiveness depends upon the ultimate user(s) of the representation.

The human user

Information Visualization as a discipline acquired a very bad name in its early years by the preponderance of displays that took no account whatsoever of the human user: they were often primarily exercises in programming. I therefore lay considerable stress on the characteristics of the human user, initially by providing actual -and to my students surprising -examples of phenomena such as Change Blindness, Attention Blindness and Preattentive Processing, but these topics are introduced in context rather than being taught within a separate set of lectures. Similarly, I emphasise the importance of considering the human user by ensuring that the first stage of an interaction design (the final group project) is a careful consideration of who the user is and what their goal and modus operandi are. Some students are not aware of the need for this first step and immediately propose, in detail, their 'final' and often impoverished design.

Restrictions

Occasionally the result of a student project identifies an omission in the teaching of Information Visualization. For example, in response to an exercise asking for a proposal for a new kind of family tree -a task conventionally interpreted as requiring a visual display -one group of my students proposed a family tree made of wood: yes, wood. That tree was visually attractive, stood on a table and had embedded communication facilities such that a family member in one part of the world could easily maintain social contact with another member located many miles away, perhaps with a simple touch and the utterance Hi Gran! Examples such as these are a reminder that a (perhaps unconscious) emphasis on computers needs to be guarded against.

Interaction

At an introductory level I try to present information visualization in a structured manner, using the generally accepted though simple model of Figure 1 to identify the essentials of the subject. I have little problem with the topic of representation, by combining a selection of techniques with concepts such as those due to Bertin [START_REF] Bertin | Graphics and graphic information Processing[END_REF] and to Teoh and Ma [START_REF] Teoh | Hifocon': object and dimensional coherence and correlation in multidimensional visualization[END_REF]. Equally, many examples can illustrate the topic of presentation in both space (e.g., the distortion concept) and time (e.g., Rapid Serial Visual Presentation) and a combination of the two. It is with the topic of Interaction that a structure seems a little elusive, notwithstanding its immense importance to information visualization. The search for a science of interaction advocated by Thomas and Cook [START_REF] Thomas | Illuminating The Path[END_REF] appears to be exceedingly challenging, but interaction in the restricted context of information visualization should surely be achievable and taught at first year level. Consideration of the features of interaction in this context strongly suggests the relevance of Norman's Stages of Action [START_REF] Norman | The Design of Everyday Things[END_REF], a concept I introduce very early in my courses. After all, we have a clear goal (the enhancement of a mental model of data); we have 'changes in the world' (changes in D, R and P -see Figure 1); we have the ever present requirement to formulate an action plan before interaction takes place (and relevant concepts such as sensitivity and affordance); and we have a multitude of devices permitting execution -the 'action' of 'interaction'. And the discussion provided by Yi et al [START_REF] Yi | Toward a Deeper Understanding of the Role of Interaction in Information Visualization[END_REF] may well be related to another component of Norman's Cycle, the formulation of an intention. Consideration of the perception and interpretation following whatever change occurs in the world (often a visual display) can lead to improvements in the interface being designed. The relevance of Norman's Cycle would appear to extend from conventional navigation in discrete information spaces to the extremely rapid cycle of execution-perception-interpretation-execution involved in the rapid dynamic exploration of complex relationships (see, for example, Neufeld et al [START_REF] Neufeld | Exploring Causal Influences[END_REF]).

Presentation Representation Data

D R P

Interaction Fig. 1: Model of a system supporting information visualization

Visual Analytics

In the context of information visualization the principal value of the newly defined 'discipline' of Visual Analytics can be summarised in a simple statement: Information Visualization exists in context. Acknowledgement of this obvious fact must exist in any course, but not overwhelm it. Simple examples can often suffice to underline this message: the more complex example provided by Colgan et al. [START_REF] Colgan | The Cockpit Metaphor[END_REF] is only one of many that can be drawn from many publications extending over the last two or three decades

Conclusions

My personal conclusion is that the topic of information visualization is best taught via creative design in a realistic application context.


Margit Pohl

Florian Scholz

How to Investigate Interaction with Information Visualisation: an Overview of Methodologies

Keywords: thinking aloud, eyetracking, log files, interaction patterns, triangulation

Advanced information visualisation systems offer many different forms of interaction. Nevertheless, we do not know how useful these interactions are. Researchers have suggested to develop a science of interaction. In this paper we discuss which research methods might be appropriate to study interaction with information visualisation systems. We suggest that thinking aloud, log files and eye tracking are promising candidates. These methods enable researchers to study interaction in more detail than other methods. All these methods have strengths and weaknesses. A combination of two or three of these methods might help to overcome the weaknesses.

Introduction

Advanced IT systems enable users to interact with them in multiple ways. Users of interactive information visualisation systems, for example, can filter data, show data in more or less detail or represent the data in different visual forms (e.g. as scatterplots or as a graph etc). Such interactions have to be designed appropriately to be useful. User-centered design can help to develop interactive features with a high usability.

The investigation of various forms of interaction has become more important in HCI in recent years [START_REF] Preece | Interaction Design[END_REF]. Mirel [START_REF] Mirel | Interaction Design for Complex Problem Solving[END_REF], for example, argues that human problemsolving and open-ended inquiry consist of different high-level activities (e.g. wayfinding, sensemaking, ...). It is necessary to identify interaction patterns, that is "recurring sets of actions and strategies that have a successful record in resolving particular types of problems" [2, p. 35]. The strategies the users adopt to solve problems or find relevant information consist of a certain number of such interaction patterns. Such investigations are necessary for HCI in general, but they are especially relevant in information visualisation and visual analytics. In these areas, systems are developed which are supposed to support human reasoning processes and open-ended exploration specifically. Therefore, Pike et al [START_REF] Pike | The science of interaction[END_REF] argue that a science of interaction is necessary. They assume that interaction and cognition are closely coupled and that InfoVis should be designed as dialogic systems where both users and computers pose questions and answers. To design such dialogic systems it is necessary to investigate interaction processes in detail.

The following chapter discusses possible methods of analysis which are especially appropriate for this kind of investigation. In this context, researchers need methods able to represent the various activities which the users engage in while they work with information visualisations. Therefore, methods like interviews or questionnaires are not really appropriate because they cannot give a detailed overview of sequences of activities. In contrast to that, there are other methods like thinking aloud, eye tracking or log files which provide a fairly comprehensive overview of these activities. Categorisation of activities or utterances is necessary to be able to analyse the results of these methods. Based on these categorisations are mathematical approaches like transition matrices (see e.g. Ratwani et al [START_REF] Ratwani | Thinking graphically: Connecting vision and cognition during graph comprehension[END_REF]) and Markov models. In this chapter, we will first describe these methods (eye tracking, log files, thinking aloud). We will discuss their advantages and disadvantages for the analysis of sequences of interactions with information visualisations. We will briefly discuss whether it is beneficial to combine two or more of these methods. Finally, we will present a few examples of the application of these methods in information visualisation and visual analytics. The discussion of the application examples is not exhaustive and can only give a brief idea of how these methods are applied in this area.

Eye Tracking

Eye tracking has been used quite extensively in human-computer interaction and usability research (see e.g. [START_REF] Jacob | Eye Tracking in Human-Computer Interaction and Usability Research: Ready to Deliver the Promises[END_REF]). Goldberg and Wichansky [START_REF] Goldberg | Eye Tracking in Usability Evaluation: A Practitioner's Guide[END_REF] summarize usability recommendations based on eye tracking research. These recommendations concern screen elements such as icons, menues, navigation etc. There is also some relevant research on cognitive load. [START_REF] Goldberg | Eye Tracking in Usability Evaluation: A Practitioner's Guide[END_REF] A fundamental assumption of eye tracking research is the so-called eye-mind hypothesis [6][7] which posits that the gaze direction is an indication of what the user is currently thinking about. There is empirical research indicating that this is not always the case. Duchowski [START_REF] Duchowski | Eye Tracking Methodology -Theory and Practice[END_REF] argues that peripheral vision also plays an important role in perception. Subjects often remember objects only seen in peripheral vision [START_REF] Holmqvist | Eye Tracking -A Comprehensive Guide to Methods and Measures[END_REF]. Another difficulty in this context is, that it is sometimes challenging to infer what users really are thinking from gaze directions and scanpaths. If a user looks at an object on the screen for an extended period of time, this might indicate that the object is interesting or, on the other hand, that its functionality is not clear.

Eye tracking has several advantages [START_REF] Webb | Eyetracking in HCI[END_REF]. Eye tracking provides a large amount of objective data about users' attention processes. Such processes sometimes are very fast and unconscious and, therefore, difficult to investigate. It is fairly unobtrusive (in contrast to, e.g., thinking aloud). Eye trackers also usually come with software which provides researchers with interesting visualisations (e.g. heat maps).

There are also disadvantages. Jacob & Karn [START_REF] Jacob | Eye Tracking in Human-Computer Interaction and Usability Research: Ready to Deliver the Promises[END_REF] In addition, it should be pointed out that there are usually considerable individual differences concerning the users' scanpaths [START_REF] Nielsen | Eyetracking Web Usability[END_REF]. This makes generalisation of results of eye tracking studies difficult. A possible solution for this problem is to use small and well defined tasks with clear solutions to get comparable results. Such tasks are usually not very realistic. Results for explorative, openended tasks usually differ considerably.

Eye tracking is especially appropriate for investigating interaction with information visualisation tools because it reflects attention to visual stimuli. It can provide detailed information about the users' scanning strategies [START_REF] Goldberg | Comparing information graphics: a critical look at eye tracking[END_REF]. Eye tracking can show sequences of the users' activities. The interaction processes of the users with the InfoVis (information visualisation) tool can be analysed in great detail.

There are some specific challenges concerning the usage of eye tracking in information visualisation. Defining areas of interest (AOIs) or regions of interest (ROI) is especially important for visualisations (we will use the terms interchangeably). AOIs should be related to the research question [START_REF] Holmqvist | Eye Tracking -A Comprehensive Guide to Methods and Measures[END_REF]. Unluckily, it is not possible to provide rules for the definition of AOIs. Another problem is the fact that exploratory tasks are especially important in information visualisation and visual analytics. As mentioned above, it is difficult to investigate such tasks with eye tracking methods. In addition, it is still challenging to analyse dynamic data with eye tracking methodologies [START_REF] Webb | Eyetracking in HCI[END_REF]. Such data are highly relevant for information visualisation and visual analytics. To conclude, eye tracking seems to have great potential for the analysis of interactions with information visualisations and visual analytics, but it is not yet entirely clear how to use this potential.

Log Files

Log files are a very well-known methodology of research in Human-Computer Interaction [START_REF] Preece | Human-Computer Interaction[END_REF]. They were originally used for the analysis of so-called WIMP interfaces (window, icon, menu, pointer), but nowadays their main application area is the assessment of the usability of websites [START_REF] Kuniavsky | Observing the User Experience -A Practitioner's Guide to User Research[END_REF].

Log files which can serve as data source for cognitive research are often developed by the researchers themselves. An example for this is described in section 6.3. [START_REF] Pohl | Analysing interactivity in information visualisation[END_REF] The addition of log files to software to analyse the users' behaviour is called instrumenting [START_REF] Lazar | Research Methods In Human-Computer Interaction[END_REF]. The goal of most usability studies using log files, in contrast to that, is to find usability errors and improve the interface. To reach this goal, log files produced automatically by servers or by off-the-shelf software is usually appropriate.

Ivory and Hearst [START_REF] Ivory | The state of the art in automating usability evaluation of user interfaces[END_REF] give an overview of various complex systems for the analysis of log files. They distinguish between automated capturing of usage data and the automated analysis of these data. Log file analysis in a narrow sense is the analysis of log file data based on metrics or a mathematical model. The system AMME developed by Rauterberg et al [START_REF] Rauterberg | A petri net based analysing and modelling tool kit for logfiles in human-computer interaction[END_REF] e.g. uses Markov models and Petri nets to investigate the users' problem-solving processes.

Information visualisation systems often offer interaction possibilities going beyond navigation. Users are supported in zooming and panning, filtering the data, choosing different ways of representing data on the screen or other interaction activities (for an overview of categorisations of interactions see e.g. Gotz & Zhou [START_REF] Gotz | Characterizing users' visual analytic activity for insight provenance[END_REF], Yi et al [START_REF] Yi | Toward a deeper understanding of the role of interaction in information visualization[END_REF]). Logging all these activities can enable researchers to get some insights about the users' cognitive processes. It is, for example, interesting to know that users sometimes concentrate on details of the visualisation and sometimes on overall aspects and prediction of the behaviour of the whole system [START_REF] Friel | Making sense of graphs: Critical factors influencing comprehension and instructional implications[END_REF]. In addition, log files also provide information about the sequence of activities. This enables researchers to investigate whether there are patterns in the users' behaviour. In this context, it is essential to decide which data to capture and and how to aggregate these data [START_REF] Lazar | Research Methods In Human-Computer Interaction[END_REF]. It is possible to collect data on a keystroke level, but in many cases such data is not very informative. Higherlevel data might be more interesting. To decide which data to collect, a clear hypothesis about the cognitive processes involved in the interaction with the information visualisation system is often necessary. These data often have to be aggregated (e.g., when the same goal can be reached by several methodsmenus or keyboard shortcuts) and categorised to be useful for an analysis of cognitive processes. Such an analysis process of log files is seldom described or discussed in the literature.

Using log files has several advantages. Compared to other methodologies in Human-Computer Interaction, as e.g. thinking aloud or observation of user actions, log file analysis is less time-consuming, although the amount of work involved in the analysis process should not be underestimated. Log file analysis reflects the actual behaviour of the users, not their attitudes toward a certain piece of software (as in questionnaires or interviews). In addition, log file analysis is not intrusive. Users usually do not notice that their actions are being logged. The method, therefore, does not change their behaviour (in contrast to thinking aloud which influences the users' interaction with the system). One serious disadvantage of log files is that it is often difficult to interpret the users' actions without any knowledge of the context in which this interaction happened. When a user repeats an interaction sequence again and again, this might be an indication of a usability problem or an indication of the user's attempt to gain a more thorough mental model of the information represented on the screen. Log files, are, therefore, often used in conjunction with other methods (thinking aloud, observation,...).

Log Files General advantages

Less time consuming than other methodologies Reflects actual behaviour of the users Not intrusive, does not influence the users' behaviour

General disadvantages

Difficult to interpret the data (lack of context)

Advantages for the analysis of interaction with InfoVis Provides detailed information about sequences of interaction Especially appropriate for the analysis of interactive and explorative InfoVis tools

Challenges for the analysis of interaction with InfoVis Identification of the appropriate level of granularity of interactions Identification of the appropriate system of categorisation for the activities Table 2. Advantages and Disadvantages of Log Files.

Thinking Aloud

Thinking aloud is a methodology which was developed by Ericsson and Simon [START_REF] Ericsson | Protocol Analysis -Verbal Reports as Data -Revised edition[END_REF]. The original goal was the investigation of cognitive processes, especially in the context of problem solving activities. It is difficult to analyse such processes exclusively on the basis of observation of visible behaviour because only the results of such activities can be perceived, not the process itself. Ericsson and Simon looked for a methodology to get more information about the processes which happen during problem solving and the strategies problem solvers adopt to reach their goals. This model is based on several assumptions. The theoretical context of thinking aloud is the information processing model of cognition (see e.g. [START_REF] Anderson | Kognitive Psychologie[END_REF]), a theoretical approach in psychology which uses computer based models as metaphors for the explanation of human cognition. Related to this is the assumption that thinking is a serial process which takes place in the working memory and the assumption that thinking aloud provides a complete overview of the cognitive processes. These assumptions are controversial (for a description of these discussions see e.g. [START_REF] Gilhooly | Protocol analysis: theoretical background[END_REF]).

Boren and Ramey [START_REF] Boren | Thinking aloud: reconciling theory and practice[END_REF] give a comprehensive overview of problems which might arise when thinking aloud is applied in usability research. They point out that cognitive psychology is quite different to usability research, and some of the problems encountered when thinking aloud is used in usability research is due to that fact. In usability research, researchers often encounter system crashes or bugs in using the system which is being investigated. The consequence of such problems is that thinking aloud is interrupted. It is not clear whether an investigation where many such crashes and bugs happen really gives an accurate impression of the users' thought processes. In addition, difficulties in using novel and unstable prototypes often necessitates that researchers talk to subjects to explain relevant issues of using the system. Such behaviour is not acceptable in the context of the original methodology. Boren and Ramey [START_REF] Boren | Thinking aloud: reconciling theory and practice[END_REF] suggest another theoretical framework -speech communication -to allow researchers to conduct more consistent and well defined investigations in usability. The theoretical framework they propose accommodates the current practice in usability research much better than the original theoretical positions formulated by Ericsson and Simon [START_REF] Ericsson | Protocol Analysis -Verbal Reports as Data -Revised edition[END_REF]. In addition, we would like to point out that there is another problem with thinking aloud which was already mentioned by Ericsson and Simon [START_REF] Ericsson | Protocol Analysis -Verbal Reports as Data -Revised edition[END_REF]. They point out that verbalisation is difficult when problems are presented in a physical form (e.g. the problem of the towers of Hanoi has to be solved by manipulating the disks physically). Subjects concentrate on the manipulation of the objects and are less able to verbalise their thought processes. Using a computer program to solve problems might be a similar situation. Users interact with an artifact and concentrate on mouse movements and navigation, and less on their thought processes.

Nevertheless, thinking aloud has significant advantages compared to methods like eye tracking and log file analysis. It gives insights into the thought processes, goals and motivation of the users of information visualisations. It also provides context to activities of the users and helps researchers to understand what strategies users adopt. Thinking aloud also provides a good impression of sequences of actions, although the granularity of these actions is usually more coarse than the one of log files and eye tracking.

Thinking aloud also has some disadvantages. As mentioned above, the application of thinking aloud in usability research, and more generally to investigate interactions with computer programs poses some problems. In addition, thinking aloud is disruptive and unnatural. Ericsson and Simon [START_REF] Ericsson | Protocol Analysis -Verbal Reports as Data -Revised edition[END_REF] argue that thinking aloud only makes the problem solving process longer. Otherwise, the procedure of problem solving is unchanged. There is some reason to assume that this is sometimes not the case [START_REF] Preece | Human-Computer Interaction[END_REF]. We would also argue that thinking aloud is not a natural behaviour, although people adapt to it fairly quickly. Despite all these difficulties, we think that results gained from the application of thinking aloud can yield valuable insights about the nature of the interaction processes of users of information visualisations.

Thinking Aloud

General advantages Direct investigation of cognitive processes during problem-solving behaviour Information about thought processes, goals and motivation of the users

General disadvantages

Difficult to use in the context of HCI [START_REF] Gilhooly | Protocol analysis: theoretical background[END_REF] Disruptive and unnatural

Advantages for the analysis of interaction with InfoVis

Provides detailed information about sequences of interaction Provides more direct information about strategies and reasoning processes of users of InfoVis tools Provides context for the interpretation of data

Challenges for the analysis of interaction with InfoVis Adaptation of the model of Boren and Ramey [START_REF] Gilhooly | Protocol analysis: theoretical background[END_REF] for the analysis of interaction with InfoVis tools Table 3. Advantages and Disadvantages of Thinking Aloud.

Mixing Methodologies

The methodologies described above all have strengths and weaknesses. It has often been suggested that a combination of these methodologies might yield more valid results. Lazar et al [START_REF] Lazar | Research Methods In Human-Computer Interaction[END_REF], e.g., argue that log files are difficult to interpret and seldom provide contextual information about users and their cognitive processes. They suggest to combine log file analysis with video recordings (analysis of videos of users' interaction processes with software) or direct observation to get more information about the problem at hand. Gilhooly and Green [START_REF] Gilhooly | Protocol analysis: practical implications? In: Handbook of Qualitative Research Methods for Psychology and the Social Sciences[END_REF], on the other hand, suggest the combination of thinking aloud and log files. Log files enable the researchers to get more detailed and fine grained information about the users' activities. Sometimes, such data can clarify what users meant with their utterances. Thinking aloud can also be combined with eye tracking [START_REF] Webb | Eyetracking in HCI[END_REF] to be able to interpret the results of eye tracking studies. Subjects are often motivated to talk while they work with the system. A problem occuring in this context is that their eye movements are affected by thinking aloud, and eye movement recordings do not provide a realistic representation of what people are attending to any more. Webb and Renshaw suggest that retrospective methods should be applied in this case (e.g. discussing gaze plots with the users after the experiment). Holmqvist et al [START_REF] Holmqvist | Eye Tracking -A Comprehensive Guide to Methods and Measures[END_REF] argue that the accuracy of eye tracking data especially suffers in the case of a remote eye tracking system because such systems cannot compensate the effects of fast head movements which usually accompany verbal behaviour. In the case of head-mounted systems, the problem is less serious.

Such combinations of methodologies enable researchers to get more detailed and accurate data than when only one single methodology is used. Such an approach is called triangulation (see e.g. [START_REF] Lazar | Research Methods In Human-Computer Interaction[END_REF]). This also increases the reliability and validity of the results. In their book on mixing methods, Creswell and Plano Clark [START_REF] Creswell | Designing and Conducting Mixed Methods Research[END_REF] give an overview of how different research methodologies can be combined. Usually, quantitative and qualitative approaches are mixed.

One example for mixing methods is the study conducted by Jakobsen and Hornbaek [START_REF] Jakobsen | Fisheyes in the field: using method triangulation to study the adoption and use of a source code visualization[END_REF]. They combined grounded theory, thinking aloud, activity logging, probes and interviews. They argue that the methods in combination "provide stronger evidence of particpants' adoption and use" of a specific software. Activity logging, e.g., does not provide any information about the subjects' intents and the context of their work. Thinking aloud, therefore, complements the data from activity logging.

Applications in Information Visualization

To illustrate how these methods could be used we will now give some examples of how they were already applied in the field of information visualisation and Visual Analytics.

Eye tracking

Goldberg and Helfman [START_REF] Goldberg | Comparing information graphics: a critical look at eye tracking[END_REF], compared three different graph types using eye tracking. They defined Regions-of-Interest (Areas-of-Interest) for the sub-elements of the graphs concerned. They measured the time until their participants' fixations first met the ROIs in which the information necessary for the given task was encoded. They also looked at sequences of fixations, observing, for example, that "The second viewed bar graph was generally scanned left to right (except the first AOI), but the first viewed bar graph was not regularly scanned in a particular direction". [11, p. 77] A similar approach is used by Siirtola et al [START_REF] Siirtola | Visual perception of parallel coordinate visualizations[END_REF] where ROIs are defined for elements of a parallel coordinate visualisation, divided in ROIs deemed necessary and relevant to a given task and those which are not. From the eye tracking data they derive the number of fixations that happened before a fixation in certain ROIs, as well as the total number of fixations and the total time spent in it, comparing these for ROI-and task-type, and relating their results to the interactions offered by the visualisation. [START_REF] Siirtola | Visual perception of parallel coordinate visualizations[END_REF] Another interesting way to use eye tracking is described in Convertino et al [START_REF] Convertino | Exploring context switching and cognition in dual-view coordinated visualizations[END_REF] who investigate multiple-view visualisations in different configurations. They use the data to derive a measure of how often their participants moved their focus between views, and relate these values to other measurements taken. [START_REF] Convertino | Exploring context switching and cognition in dual-view coordinated visualizations[END_REF] Huang et al [START_REF] Huang | Beyond time and error: a cognitive approach to the evaluation of graph drawings[END_REF] on the other hand, consciously avoided using eye tracking data for quantitative analysis, after observing that "currently available measures" are "difficult to relate [...] to specific graph elements such as nodes, links or paths" and decided to employ "Eye movement videos", that is, a video of of the screen content overlaid with a marker for the gaze direction during the experiment [30, p. 3:3]. By analysing these they developed new theories concerning human behaviour reading these graphs and then tested the theory by designing and implementing experiments employing classical time-and-error measurements. [START_REF] Huang | Beyond time and error: a cognitive approach to the evaluation of graph drawings[END_REF]

Thinking Aloud

As with eye tracking there are different kind data and results one can get with thinking aloud. In evaluating information visualisation systems the concept of insight often is used, though definitions (for a discussion see, for example, Chang et al [START_REF] Chang | Defining insight for visual analytics[END_REF]) as well as categorization approaches vary, depending among other things on the goals and questions of the study concerned.

Bautista and Carenini [START_REF] Bautista | An empirical evaluation of interactive visualizations for preferential choice[END_REF], for example, use this approach as part of a qualitative and quantitative triangulation (see section 5) in an effort to demonstrate usefulness and improve an information visualisation tool for preferential choice.

HCI often uses time to complete tasks and number of errors as variables of analysis. For explorative tasks in InfoVis other variables are also interesting, e.g. the insights gained during interaction processes. Saraiya et al [START_REF] Saraiya | Comparing benchmark task and insight evaluation methods on timeseries graph visualizations[END_REF] conducted a study comparing this kind of evaluation to the more classical method of measuring time and error in the information visualisation domain. They compared three information visualisation techniques of the same dataset and the two methods between subjects. Concerning the insights they use the number of insights (total and in categories, developed in cooperation with domain specialists) and the time spent in the open ended exploration scenario in an analysis of variance. They also note that even though they did not prompt their participants for it they additionally got feedback concerning the usability and visualisations in the insight condition, which might be another interesting factor to consider in the context of methodology choice. [START_REF] Saraiya | Comparing benchmark task and insight evaluation methods on timeseries graph visualizations[END_REF] Thinking aloud data is usually collected in sessions spanning hours at most, but it can also be a part of a longer term field study, as with Jakobsen and Hornbaek [START_REF] Jakobsen | Fisheyes in the field: using method triangulation to study the adoption and use of a source code visualization[END_REF], who studied real-life workplace adoption of a source code visualisation tool spanning multiple weeks employing thinking aloud and logging methods, among others. Comparing and contrasting the thinking aloud results with the other methods, they mention that thinking aloud "showed use of the interface across a range of tasks, including some surprising ad-hoc uses" [27, p. 1586].

Log Files

Log files can clarify many open questions about the interaction processes of users with information visualisations. Bautista and Carenini [START_REF] Bautista | An empirical evaluation of interactive visualizations for preferential choice[END_REF], for example, mention that their log files revealed to them the reason why a certain task showed no significant difference in time-to-complete, even though this task required a functionality that was hidden in one condition and quite prominent in the other. According to them the log files showed that their participants traded the time gained by quicker access to the function for time spent using it. [START_REF] Bautista | An empirical evaluation of interactive visualizations for preferential choice[END_REF] A study conducted at our institute [START_REF] Pohl | Exploring information visualization: describing different interaction patterns[END_REF] is an example for a more in-depth report of a statistical analysis of log files created during a study of an information visualisation tool (Gravi++).

Building on this, a later study [START_REF] Pohl | Analysing interactivity in information visualisation[END_REF] explored the issue of emerging patterns of interactions. Beside the log file data the authors also analysed log files generated by another information visualisation application (VisuExplore) for comparison. While the specific interactions afforded by different tools usually also differ, the authors were able to use a variant of the general categorisation scheme proposed by Yi et al [START_REF] Yi | Toward a deeper understanding of the role of interaction in information visualization[END_REF] to group the logged events into higher-level activities, to see if the same general patterns could be found in both tools. To that end they qualitatively identified recurring patterns and also computed transition probabilities between categories. [START_REF] Pohl | Analysing interactivity in information visualisation[END_REF] Based on the theory of Distributed Cognition, the authors interpret these patterns as indicators of cognitive activities.

In the study by Convertino et al [START_REF] Convertino | Exploring context switching and cognition in dual-view coordinated visualizations[END_REF] already mentioned as an example for eye tracking investigations the authors also statistically analysed their log file data, using the number of different interactions as dependent measure and finding some significant effects for condition and task type, and also relating them to completion time measurements. [START_REF] Convertino | Exploring context switching and cognition in dual-view coordinated visualizations[END_REF] One advantage of using log files is that the data can be potentially collected over a longer period of time and that the method is relatively non-intrusive, therefore viable for medium or long term field studies, as Jakobsen and Hornbaek [START_REF] Jakobsen | Fisheyes in the field: using method triangulation to study the adoption and use of a source code visualization[END_REF] demonstrated in the study already mentioned above concerning the inthe-field adoption of a fisheye source code visualisation. Visualising and quantitatively analysing the logs, they could observe the real life adoption of the visualisation component over time and in the context of the surrounding workflow. [START_REF] Jakobsen | Fisheyes in the field: using method triangulation to study the adoption and use of a source code visualization[END_REF]

Conclusion

In this paper, we describe three methods for the analysis of interactions with information visualisations. All these methods have advantages and disadvantages. A possibility to overcome the disadvantages would be to combine some of these methods. A careful study of the users' interaction processes with information visualisations could provide more information about cognitive processes accompanying interaction with information visualisation.

We think that in the future it is necessary to discuss methodological problems regarding the investigation of interactions with information visualisations in more detail. Important research has been done to clarify research methods for the evaluation of information visualisations in general [START_REF] Carpendale | Evaluating information visualization[END_REF]. The investigation of interaction sequences is, however, a very specific problem requiring more detailed research.

Table 1 .

 1 summarize disadvantages of eye tracking as follows: technical problems, labour-intensive data extraction, and difficulties in data interpretation. Advantages and Disadvantages of eye tracking.

	Eye Tracking
	General advantages
	Large amount of data about users' attention processes
	Fairly unobtrusive
	Eye trackers already provide simple statistics and visualisations
	General disadvantages
	Technical problems
	Labour-intensive data extraction
	Difficulties in data interpretation
	Advantages for the analysis of interaction with InfoVis
	Reflects attention to visual stimuli
	Yields very detailed information about users' scanning strategies
	Challenges for the analysis of interaction with InfoVis
	Generalisations of results from scanpaths difficult because of individual
	differences
	Definition of AOIs
	Investigation of exploratory tasks
	Difficulty of the analysis of dynamic data

Acknowledgments

This work is conducted in the context of the CVAST -Centre of Visual Analytics Science and Technology project. It is funded by the Austrian Federal Ministry of Economy, Family and Youth in the exceptional Laura Bassi Centres of Excellence initiative.