Towards better understanding the current obesity epidemic, there has been a concerted effort to examining the association between food environments that an individual is exposed to and their body mass index 
. Studies have been looking at relations between food environments and food purchasing, diet, or more distal health outcomes like BMI, cardio-vascular outcomes or mortality 
. But findings are mixed. For instance, association between fast food access and diet or BMI have been positive, negative or null 
The majority of studies on food environments and health have relied on measures of foodstore accessibility 
. Some studies have considered more specific elements, such as food availability and costs 
, portion sizes 
, visual food cues, or availability of specific food types 
. Geographic analyses of food environments generally use spatial proximity or density estimates to measure accessibility or exposure to foodstores 
. Measures are established for point data such as postal codes or addresses, or for areal units, most often administratively defined and sometimes purposely designed, for example using ego-centered circular 
or road-network buffers 
. Proximity generally accounts for travel times or distance between the reference units and the closest foodstores 
. Alternative accessibility measures based on gravity theory or space-time geography principles have more rarely been used 
. Density measures are usually computed within a chosen areal unit by dividing the count of observation by the area or the population, or using kernel density estimation methods 
In spatial epidemiology, the relationship between environmental exposures and individuals – and their corresponding health behaviors or disease outcome – is traditionally grounded to one reference location - most often, place of residence. Some have looked at exposure in non-residential locations such as schools 
. However, even then, the relation between access and health outcomes is assessed for one reference location only. A study integrating exposure to both residential and five non-residential regular activity places showed that ignoring non-residential exposures underestimated the association between residential exposure and self-reported health 
. Another multi-location exposure study assessed the relation between BMI and accessibility to restaurants including fast food outlets around both home and the workplace 
. No association was found for women, and for men, a significant inverse relation between BMI and restaurant proximity was found around workplaces only, and not around home.
Limiting measures of exposure to the local residential area may constitute a ‘local’ 
or ‘residential’ 
trap, and thus ignores actual ‘spatial polygamy’ 
, or the fact that we live and spatially relate to more than one ‘anchor point’, through a network of usual places 
. Already in the 1950's, researchers in sociology and geography documented how daily activities included destinations outside of the residential neighbourhood 
. The resulting multiple exposures may collectively influence health behaviors and health outcomes.
Current focus on residential areas is mainly due to the absence of data on people's activity destinations, at least in health surveys. Recent calls have been made to develop and test novel methodologies to collect such information, for example through web-based interactive mapping questionnaires that allow for precise collection of regular destinations or routes 
, or using wearable sensors such as Global Positioning Systems (GPS) devices. In a recent pilot study using such devices 
, collected tracks were used to derive activity space exposure to fast food outlets and supermarkets. Path area measures of exposure to fast food outlet were positively associated with dietary fat intake and negatively with whole grain intake. Whereas resorting to precise GPS data allows to show the potential importance of accounting for multiple exposures, use of GPS devices also presents some limitations, and has not yet been applied to large samples.
Alternatively, data on daily mobility are often collected in travel surveys, mainly developed for transportation planning purposes, or in other sources interested in specific aspects of mobility such as commuting. Such mobility information was recently used to estimate non-residential exposure to air pollutants in Vancouver and California 
. Not accounting for non-residential exposure to NO2
underestimated the relative risk from 20 to 30% in Vancouver and 7% in California. Quite logically, this bias furthermore increased with distance and time spent away from home.
Mobility data are most often used to model travel behavior and to support land use and road network planning. But such data also showed that people with similar characteristics had similar exposure patterns to foodstores when their mobility was accounted for 
. In continuity with these findings, we hypothesize that it is possible to use such mobility data to predict the types
of places 
people experience and, consequently, to better assess exposures to environmental determinants of health. This feasability study tests a novel method combining various datasets to assess activity space patterns of exposure to foodstores, and their relation to overweight. Travel surveys, foodstore listings and health surveys are combined using a GIS and modelling techniques. Models of multiple exposures to foodstores are developed and related associations with individual risk and local differences in overweight are tested in a multilevel framework 
. The results of this feasability study have important implications relevant to multilevel policy and public health interventions which must target multiple settings to more effectively respond to the epidemic of overweight/obesity.