Measuring the Food Environment Using Geographical Information Systems a Methodological Review

Background

Food intake is considered a complex behaviour of multifactorial origin⁽ Reference Story, Kaphingst and Robinson-O'Brien ¹ ⁾. A socio-ecological approach to understanding such behaviour is recognised as being a useful framework for integrating the numerous influences present at both the individual and environmental levels⁽ Reference Story, Kaphingst and Robinson-O'Brien ^one ^– Reference Kawachi and Berkman ⁵ ⁾. There is growing involvement in the ecology context every bit related to nutrient behaviour; this includes both the social and physical surroundings. In this relatively recent field of research, Glanz et al.⁽ Reference Glanz, Sallis and Saelens ⁶ ^, Reference Glanz ⁷ ⁾ identified different aspects of the food environment. These include the 'community nutritional environs' defined as the number, type, location and accessibility of food outlets, and the 'consumer nutritional environment' defined by what consumers encounter in and around food outlets (prices, promotions, nutritional quality). In terms of community nutrition, a number of studies and reviews emphasise the influence of spatial accessibility of food upon the human relationship between food environment, individual nutrient choice and, ultimately, adventure of chronic diseases such as obesity⁽ Reference Booth, Pinkston and Poston ³ ^, Reference Glanz, Sallis and Saelens ⁶ ^, Reference Papas, Alberg and Ewing ⁸ ^– Reference White ¹² ⁾. An important research issue lies in identifying and describing the different methodological procedures that can exist used to specifically assess the spatial accessibility of food outlets.

Diverse methods, both objective and subjective, have been used to assess variables related to the presence and type of food outlet. Subjective methods include surveys of individual perception of food outlets bachelor to neighbourhood residents⁽ Reference Rose and Richards ¹³ ^, Reference Inglis, Ball and Crawford ¹⁴ ⁾. Among objective methods, geographic measures are most frequently used to appraise the food environs⁽ Reference McKinnon, Reedy and Morrissette ^fifteen ⁾. Some of these are provided by spatial analysis methods based on geographic information systems (GIS). GIS are computer-based methods and tools, which, via unlike information sources, enable spatial and thematic data to be organised, managed and combined, and results to be represented and analysed according to geographic location⁽ Reference Longley, Goodchild and Maguire ¹⁶ ⁾. Analyses tin then be carried out to localise and model potential spatial interactions between the dissimilar types of data at hand.

In public health, examples of the utilise of GIS methods include the assay of disparities in access to health-care⁽ Reference McLafferty ¹⁷ ⁾ and, more than recently, the association between built surroundings and physical activeness⁽ Reference Glanz, Sallis and Saelens ^{half dozen} ^, Reference Wendel-Vos, Droomers and Kremers ¹⁸ ⁾. Application of GIS to the nutrient surroundings is relatively new in public health nutrition. Use of a geographic model of analysis may assist to identify spatial inequalities in access to nutrient outlets, and in turn, influence policies and incite urban planners to modify the food surroundings accordingly. In this context, a major challenge lies in ensuring appropriate and effective use of GIS data and spatial assay methods to measure the food environment⁽ Reference Matthews, Moudon and Daniel ^xix ^, Reference McKinnon, Reedy and Handy ²⁰ ⁾. Despite the growing use of GIS, nosotros were unable to notice a literature review of GIS methods used to assess the nutrient environment. The aim of the present methodological commodity was to describe GIS methods already in utilize in this field and to discuss their relevance for increasing our understanding of food environment attributes.

Methods

We sought to identify all studies that used GIS to measure the proximity and/or density of food outlets and then as to characterise attributes of the nutrient environment. A search of the literature was conducted with the OVID interface in the following social and health science databases: Medline/Pubmed, PsycINFO, Francis and GeoBase. The search was conducted using different combinations of keywords (in the title or abstract) such as 'nutrient environs', 'nutrient outlets', 'admission', 'availability' and 'geographic information organisation'. The search was restricted to human populations, and studies on both adults and children were included. In improver, the reference sections of the manufactures included were reviewed. The search was restricted to English language articles published between January 1999 and June 2008.

The main inclusion benchmark in the review was the apply of GIS-based techniques of spatial analysis to measure the nutrient environment. We excluded studies that relied merely on survey participants to characterise the food surroundings⁽ Reference Rose and Richards ^thirteen ⁾ and articles that used GIS only equally a geocoding tool (process assigning geographic coordinates to a point, e.g. street addresses to food outlet) to map various information⁽ Reference Liese, Weis and Pluto ²¹ ⁾. Data extracted in improver to GIS methods were: study location, scale (e.one thousand. demography tract), food environs outcome (eastward.yard. supermarkets, fast food), covariates and main findings.

Results

Report characteristics

An initial search of online databases and of the reference sections of the articles included identified 1070 papers. After preliminary screening based on the title or abstract, thirty-eight full-text papers were retrieved for further assessment. In the final step, twenty-nine articles with GIS-based measurements of the nutrient surround were included in the review. Table 1 summarises information extracted for each paper. Seventeen reviewed studies were conducted in the The states, four in Australia, three in Canada, three in New Zealand and two in England. The selected studies vicious into two categories: (i) studies that explore the relationships between characteristics of the food surroundings and measurements of individual food behaviours; and (two) studies that compare accessibility of nutrient outlets in different types of neighbourhoods.

Table 1 Summary descriptive table of the studies included in the review

GIS, geographic information systems; LGA, local government area; SES, socio-economic status; SEIFA, Socio-Economic Index For Areas; TV, tv; MESA, Multi-Ethnic Written report of Atherosclerosis; AHEI, Alternate Healthy Eating Alphabetize; NZDep, New Zealand impecuniousness index; IRSD; Alphabetize of Relative Socio-economical Disadvantage.

Relationship between food environment and private food behaviour

Among the twenty-nine articles reviewed, eleven (38 %) analysed associations betwixt food surround and individual food behaviours⁽ Reference Bodor, Rose and Farley ²² ^– Reference Moore, Diez Roux and Nettleton ²⁶ ⁾, weight status⁽ Reference Burdette and Whitaker ²⁷ ^– Reference Liu, Wilson and Qi ³¹ ⁾ or perceived availability of healthy food⁽ Reference Moore, Diez Roux and Brines ³² ⁾. In those studies, the addresses of respondents were geocoded and used as references for GIS analyses. Four studies were performed on children or teenagers⁽ Reference Timperio, Ball and Roberts ²⁴ ^, Reference Burdette and Whitaker ²⁷ ^, Reference Jago, Baranowski and Baranowski ²⁸ ^, Reference Liu, Wilson and Qi ³¹ ⁾, and the others on developed populations (1 specifically concerned meaning women⁽ Reference Laraia, Siega-Riz and Kaufman ³³ ⁾). The outcomes of selected studies were consumption of fruits and vegetables⁽ Reference Bodor, Rose and Farley ²² ^– Reference Timperio, Ball and Roberts ²⁴ ^, Reference Jago, Baranowski and Baranowski ²⁸ ⁾, perception of availability of healthy food⁽ Reference Moore, Diez Roux and Brines ³² ⁾, dietary patterns⁽ Reference Moore, Diez Roux and Nettleton ²⁶ ^, Reference Laraia, Siega-Riz and Kaufman ³³ ⁾ and prevalence of overweight or obesity⁽ Reference Burdette and Whitaker ²⁷ ^, Reference Jeffery, Baxter and McGuire ²⁹ ^– Reference Liu, Wilson and Qi ³¹ ⁾. In most data sets (seven out of xi), individual characteristics were collected from the year 2000⁽ Reference Bodor, Rose and Farley ²² ^– Reference Timperio, Ball and Roberts ²⁴ ^, Reference Moore, Diez Roux and Nettleton ²⁶ ^, Reference Jago, Baranowski and Baranowski ²⁸ ^, Reference Liu, Wilson and Qi ³¹ ^, Reference Moore, Diez Roux and Brines ³² ⁾. In three studies, the date on which food outlet lists were drawn upwardly was non mentioned⁽ Reference Jago, Baranowski and Baranowski ²⁸ ^– Reference Wang, Kim and Gonzalez ³⁰ ⁾. In the other studies, the date given for the food outlet list corresponded to the date of collection of individual information (±2 years).

The covariates about oft used in the analyses included individual demographic and socio-economical characteristics, and individual behaviour such as smoking and physical activity or sedentary behaviour (Tabular array ane).

Spatial admission to food outlets according to the type of neighbourhood

The aim of about articles retrieved (eighteen out of twenty-nine; 62 %) was to assess and compare neighbourhoods co-ordinate to spatial access to nutrient outlets. All these articles considered the neighbourhood as the area of report. However, the scale of the neighbourhoods varied: it involved census tracts and postal sectors in Northward American studies, wards and postal codes in the United Kingdom and census meshblocks in Commonwealth of australia and New Zealand (Table 1). Most studies were based on demography tracts, since they had been conducted in the U.s.a. (seventeen out of twenty-nine studies), while four were performed in Australia⁽ Reference Timperio, Ball and Roberts ²⁴ ^, Reference Burns and Inglis ³⁴ ^– Reference Winkler, Turrell and Patterson ³⁶ ⁾, two in the United Kingdom⁽ Reference Clarke, Eyre and Guy ³⁷ ^, Reference Donkin, Dowler and Stevenson ³⁸ ⁾, three in Canadian cities⁽ Reference Apparicio, Cloutier and Shearmur ³⁹ ^– Reference Larsen and Gilliland ⁴¹ ⁾ and three in New Zealand⁽ Reference Pearce, Hiscock and Blakely ²³ ^, Reference Pearce, Blakely and Witten ⁴² ^, Reference Pearce, Witten and Bartie ⁴³ ⁾.

Two studies were related to fast nutrient outlets only⁽ Reference Austin, Melly and Sanchez ⁴⁴ ^, Reference Block, Scribner and DeSalvo ⁴⁵ ⁾, one to fast nutrient and convenience stores⁽ Reference Zenk and Powell ⁴⁶ ⁾ and one to fast nutrient, total-service restaurants, convenience and grocery stores⁽ Reference Frank, Glanz and McCarron ⁴⁷ ⁾. The remaining studies focused on a common type of nutrient store: the supermarket. In all of these studies, residential contexts were characterised past socio-economical indicators (including unemployment rates and unmarried-parent rates⁽ Reference Apparicio, Cloutier and Shearmur ³⁹ ^, Reference Larsen and Gilliland ⁴¹ ⁾, income⁽ Reference Liu, Wilson and Qi ³¹ ⁾, race/ethnicity⁽ Reference Zenk and Powell ⁴⁶ ^, Reference Baker, Schootman and Barnidge ⁴⁸ ⁾, households without cars⁽ Reference Clarke, Eyre and Guy ³⁷ ^, Reference Block and Kouba ⁴⁹ ⁾) and past other data such as degree of commercialisation⁽ Reference Austin, Melly and Sanchez ⁴⁴ ⁾, urban/rural condition⁽ Reference Pearce, Blakely and Witten ⁴² ^, Reference Zenk and Powell ⁴⁶ ⁾, safety⁽ Reference Burdette and Whitaker ²⁷ ⁾ and neighbourhood walkability⁽ Reference Frank, Glanz and McCarron ⁴⁷ ⁾ (environmental attributes that encourage walking⁽ Reference Owen, Humpel and Leslie ⁵⁰ ⁾). In nine out of eighteen studies, an index of deprivation (constructed from census information) was used to draw the social–residential context⁽ Reference Pearce, Hiscock and Blakely ²³ ^, Reference Burns and Inglis ³⁴ ^, Reference O'Dwyer and Coveney ³⁵ ^, Reference Clarke, Eyre and Guy ³⁷ ^– Reference Apparicio, Cloutier and Shearmur ³⁹ ^, Reference Larsen and Gilliland ⁴¹ ^, Reference Pearce, Blakely and Witten ⁴² ^, Reference Sharkey and Horel ⁵¹ ⁾.

GIS measurements of the food environment

In the manufactures reviewed, two master notions were used to assess the food surround: density and proximity. (i) Density is usually the number of food outlets (food stores, restaurants) in an administratively defined area (demography or postal units) or an area defined by the authors (specific zone). (two) Proximity is defined between 2 locations such as respondent address (home, school) and the closest nutrient outlet. Information technology could exist measured by a straight-line altitude (Euclidean distance) or past travel fourth dimension (time needed to travel to a nutrient outlet). Table 2 lists the various methods described in the literature concerning the food surroundings used for assessing density and proximity, along with the number of respective studies for each method. Amidst the xx-9 studies examined, twelve combined both spatial approaches (Table ii).

Table 2 Summary measures of food availability used in twenty-nine published articles

Density

Buffer

The most common GIS approach (xviii studies out of twenty-9) was the buffer. This consists of defining a zone effectually a given location within a specified altitude (or shape). The location can be a point (abode, schoolhouse, work or food outlet address), a line (road) or a polygon (neighbourhood).

About studies defined buffers in order to quantify the availability or accessibility of food outlets. Seven of these studies used a buffer zone around the respondent's dwelling house⁽ Reference Bodor, Rose and Farley ²² ^, Reference Timperio, Ball and Roberts ²⁴ ^, Reference Laraia, Messer and Kaufman ²⁵ ^, Reference Jago, Baranowski and Baranowski ²⁸ ^– Reference Liu, Wilson and Qi ³¹ ⁾, three around the schoolhouse⁽ Reference Austin, Melly and Sanchez ⁴⁴ ^, Reference Zenk and Powell ⁴⁶ ^, Reference Frank, Glanz and McCarron ⁴⁷ ⁾, 4 around the food store⁽ Reference Clarke, Eyre and Guy ³⁷ ^, Reference Donkin, Dowler and Stevenson ³⁸ ^, Reference Larsen and Gilliland ⁴¹ ^, Reference Cake and Kouba ⁴⁹ ⁾ and four around the centroid (geometric middle) of each neighbourhood⁽ Reference O'Dwyer and Coveney ³⁵ ^, Reference Winkler, Turrell and Patterson ³⁶ ^, Reference Smoyer-Tomic, Spence and Raine ^forty ^, Reference Block, Scribner and DeSalvo ⁴⁵ ⁾. For one of these studies, analyses were performed using buffers around both the dwelling house and the piece of work address⁽ Reference Jeffery, Baxter and McGuire ²⁹ ⁾, while only 1 study combined a buffer around a point (supermarket) or around a line (bus route)⁽ Reference Larsen and Gilliland ⁴¹ ⁾. It should be noted that in that location are ii means to ascertain the shape of a buffer for the GIS user. It can be constructed either past a zone surrounding a location (round buffer when the given location is a bespeak) or past a zone along the street network (network buffer; e.g. see figures in Frank et al.⁽ Reference Frank, Andresen and Schmid ⁵² ⁾).

Circular buffer

In the studies we reviewed, the values used for the radius of a circular buffer were between 100 and 2500 grand. Depending on the study, these distances were selected on the basis of estimations of neighbourhood walkability or distances that individuals might exist ready to cover to reach nutrient outlets⁽ Reference Timperio, Ball and Roberts ²⁴ ^, Reference Jago, Baranowski and Baranowski ²⁸ ^, Reference Austin, Melly and Sanchez ⁴⁴ ^, Reference Cake, Scribner and DeSalvo ⁴⁵ ⁾. In a study by Bodor et al.⁽ Reference Bodor, Rose and Farley ²² ⁾, different distances were called co-ordinate to the type of food store: 100 m for minor food stores (e.g. the gauge size of a city block) and 1000 m for large supermarkets. Two authors⁽ Reference O'Dwyer and Coveney ³⁵ ^, Reference Winkler, Turrell and Patterson ³⁶ ⁾ used a much wider radius of 2500 m around the geometric centre of the neighbourhood to ascertain the area in which residents were likely to shop.

Network buffer

A network buffer can be defined as existence based on the accessibility of food outlets via the mode of transportation used and the type of destination. Larsen and Gilliland⁽ Reference Larsen and Gilliland ⁴¹ ⁾ used 2 network buffers in the boondocks of London (Ontario, Canada). The first buffer was based on a distance of 1000 g by pes effectually each supermarket. The second buffer was created around each bus route to estimate a 500 m network service line surface area with public transport access to supermarkets.

Kernel density interpretation

Kernel density is a spatial smoothing method employed to transform a sample of geographically referenced point information (eastward.g. accost of food outlet) into a smooth continuous surface⁽ Reference Bailey and Gatrell ⁵³ ^– Reference Portnov, Dubnov and Barchana ⁵⁶ ⁾. As described by Kloog et al.⁽ Reference Kloog, Haim and Portnov ⁵⁷ ⁾, the full general principles of this statistical technique are to judge the 'intensity of referenced points across a surface, by calculating the overall number of cases situated within a given search radius from a target bespeak'. A distance function is introduced in the adding so that 'points lying near the middle of the search area are weighted more heavily than those lying near the edge'⁽ Reference Kloog, Haim and Portnov ⁵⁷ ⁾. The various steps for generating kernel densities with GIS software take been described past Guagliardo⁽ Reference Guagliardo ⁵⁸ ⁾.

Only ii studies, both by Moore et al.⁽ Reference Moore, Diez Roux and Nettleton ²⁶ ^, Reference Moore, Diez Roux and Brines ³² ⁾, used kernel density estimation to assess the spatial distribution of food outlets (Table 2). In that example, the aim was to create a smooth map of food store density per square mile where the domicile location proximity was emphasised and more weight was put on closer outlets.

Spatial clustering

A spatial scan statistic is used to assess whether events are randomly distributed within the study surface area, and if not, to identify significant spatial clusters⁽ Reference Ozdenerol, Williams and Kang ⁵⁹ ^, Reference Alves de Souza, Da Silva-Nunes and Dos Santos Malafronte ⁶⁰ ⁾. This method consists of creating moving windows of various shapes (circles, squares) and sizes (radius, sides of square). These windows are moved systematically across the map, which enables assessment of the likelihood that events are more prevalent within than exterior that given window (see SatScan process⁽ Reference Kulldorff and Nagawalla ⁶¹ ⁾). With this method, Bakery et al.⁽ Reference Baker, Schootman and Barnidge ⁴⁸ ⁾ identified spatial neighbourhood variation in the charge per unit of supermarkets and fast food outlets in St Louis, MO, The states, and observed clusters of food supermarkets and fast food outlets (i.e. areas with higher or lower rates than expected).

Network analysis and proximity measures

Proximity defined as a altitude

Several types of distances are typically used to assess proximity with GIS: Euclidean distance (straight line altitude), Manhattan (city block distance) and network altitude. The Manhattan distance corresponds to the distance between two points measured along axes at right angles⁽ Reference Apparicio, Abdelmajid and Riva ⁶² ⁾. In other words, Manhattan distance represents an approximate distance close to a street map and is mainly used on squared city maps.

In our review, half-dozen studies measured the distance betwixt home/school and food outlets via the Euclidean altitude⁽ Reference Bodor, Rose and Farley ²² ^, Reference Jago, Baranowski and Baranowski ²⁸ ^, Reference Wang, Kim and Gonzalez ³⁰ ^, Reference Laraia, Siega-Riz and Kaufman ³³ ^, Reference Apparicio, Cloutier and Shearmur ³⁹ ^, Reference Austin, Melly and Sanchez ⁴⁴ ⁾ (Table 2). In Eastside Detroit areas with no supermarket, Zenk et al.⁽ Reference Zenk, Schulz and State of israel ⁶³ ⁾ used the Manhattan distance to evaluate the shortest distance between dwelling addresses and food outlets in a population of African-American women. Two studies used network distance by road⁽ Reference Timperio, Ball and Roberts ²⁴ ^, Reference Zenk, Schulz and Israel ⁶³ ⁾. In other studies, the network distance by street travel was used to evaluate the minimum distance residents must walk from their home/school to the closest food outlet⁽ Reference Burdette and Whitaker ²⁷ ^, Reference Liu, Wilson and Qi ³¹ ^, Reference Donkin, Dowler and Stevenson ³⁸ ^, Reference Smoyer-Tomic, Spence and Raine ⁴⁰ ^, Reference Larsen and Gilliland ⁴¹ ^, Reference Frank, Glanz and McCarron ⁴⁷ ⁾.

Proximity measured past travel time

The travel time between a given place (e.chiliad. school or abode address) and the accost of a nutrient outlet can exist calculated past GIS according to the ways of ship and the specificities of the network. Four out of twenty-ix studies used travel time every bit a proximity measurement (Table ane)⁽ Reference Pearce, Hiscock and Blakely ²³ ^, Reference Burns and Inglis ³⁴ ^, Reference Pearce, Blakely and Witten ⁴² ^, Reference Pearce, Witten and Bartie ⁴³ ⁾. Burns and Inglis⁽ Reference Burns and Inglis ³⁴ ⁾ adult a travel time model between home, fast nutrient outlet and supermarket according to a number of variables including means of send (car, bus, on foot), type of road (speed limit), topography (barriers as rivers or railway lines) and other characteristics of the public ship network (i.eastward. frequency of buses). Travel time for each type of transport was compared betwixt underprivileged and privileged neighbourhoods, with the latter having amend access to supermarkets.

Discussion

In this review, we investigated which GIS methods have been used to define the food surround and the types of spatial measurements they generate. We constitute xx-nine manufactures that reported GIS methods for measuring spatial accessibility of food outlets equally a key characteristic of the local food environs. Nosotros identified 2 main types of spatial measures to quantify the food environment: density and proximity. The density approach quantifies the availability of nutrient outlets using the buffer method, kernel density estimation or spatial clustering. The proximity arroyo assesses the distance to food outlets by measurements of altitude or travel times. Numerous studies combined both approaches.

How practice GIS methods contribute to research on the food environs?

It is clear from the present piece of work that the number of studies that include geographic measurements of density and/or proximity of food outlets as operational variables in the nutrient environment have increased chop-chop in recent years. Twenty-ii of the twenty-nine articles examined here were published between Jan 2006 and June 2008. It is likely that the continuous refinement of GIS software and the increased availability of precisely geocoded databases have contributed substantially, and will keep to contribute, to this increase⁽ Reference Matthews, Moudon and Daniel ¹⁹ ⁾.

In the studies included in this review, two approaches based on GIS methods were used to characterise the local food environment. One involved assessing the number of food outlets in an surface area (density) and the other assessed proximity to facilities. Interestingly, a large number of studies combined both approaches. Indeed, as argued by Apparicio et al.⁽ Reference Apparicio, Cloutier and Shearmur ³⁹ ⁾, a single measure of access cannot fully describe accessibility of food outlets. Focusing on the upshot of 'food desert' (areas characterised by relatively poor access to salubrious and affordable food⁽ Reference Beaulac, Kristjansson and Cummins ⁶⁴ ⁾), Apparicio et al.⁽ Reference Apparicio, Cloutier and Shearmur ³⁹ ⁾ proposed a methodology based on iii measurements of access using the shortest network distance: multifariousness, proximity and variety (average distance to the three closest different chain-name supermarkets).

An important reward of the GIS approach is that information technology enables assessment of spatial variations in prevalence independently of administrative boundaries⁽ Reference Chaix, Merlo and Chauvin ⁶⁵ ⁾. Many phenomena are continuously distributed over infinite and are independent of arbitrarily defined boundaries⁽ Reference Chaix, Merlo and Subramanian ⁶⁶ ^, Reference Matthews, Detwiler and Burton ⁶⁷ ⁾. Estimating the density of food outlets inside buffers, or by ways of kernel density estimation rather than administrative area, enables one to take into business relationship the fact that individuals often cross the boundaries of their residential area to go shopping. Yet, it should exist emphasised that the appropriate size of the area around the place of residence to be defined as the neighbourhood remains subject to debate⁽ Reference Spielman and Yoo ⁶⁸ ^– Reference Chaix, Merlo and Evans ^lxx ⁾. The option of this surface area size is based on assumptions concerning the geographic zone that includes food environs elements influencing nutrient behaviour. In the studies reviewed hither, the distance used to define the residential area varied depending on different criteria such equally the historic period of the respondent, blazon of food outlet and type of transportation. It is also of import to underline that few studies be which question individuals as to the distance they would exist prepared to encompass for food needs. Thus, because of the complexity of the relationship between surround and behaviour, defining the size of the neighbourhood in which this human relationship operates remains a challenging methodological issue⁽ Reference Spielman and Yoo ⁶⁸ ^, Reference Brownson, Hoehner and Day ⁷¹ ⁾.

GIS methods enable the modelling of proximity to food outlets using metric distance and travel time to food outlets. In general, modelling of travel time using the GIS leads to more realistic measurements (taking into account speed limit, topography and network complexity) than the usual mathematical distances, particularly at the local level in sub-metropolitan areas⁽ Reference Apparicio, Abdelmajid and Riva ⁶² ⁾ or in rural areas⁽ Reference Lovett, Haynes and Sunnenberg ⁷² ⁾. Even so, the employ of this travel time model, which requires spatial data, is more circuitous than calculating the mathematical distances between two points.

In the articles that nosotros reviewed, which used travel fourth dimension to nutrient outlets, the car was the type of transportation evaluated in four papers, with public transport evaluated in just i⁽ Reference Burns and Inglis ³⁴ ⁾. None dealt with travel time past human foot or 'mixed' travel. This is an important bespeak because families with low income may non own a car or even have access to public transportation. In futurity studies, a methodological challenge therefore lies in measuring travel time from the respondent's address to food outlets according to the dissimilar types of transport available (motorcar, public transport, or on foot). In improver, modelling travel time according to public transport or on human foot requires more sophisticated GIS modelling than individual car transport⁽ Reference Martin, Wrigley and Barnett ⁷³ ⁾.

On the other manus, Larsen et al.⁽ Reference Larsen and Gilliland ⁴¹ ⁾ showed that, with the GIS, the geographic distribution of supermarkets has inverse over time, thus influencing the relationship betwixt people and places in a spatial access arroyo. Through GIS employ, it is possible to capture the temporal changes in localisation of food outlets and land use, which volition ameliorate our understanding of the relationship between food environs and food behaviour over time⁽ Reference Matthews, Moudon and Daniel ¹⁹ ^, Reference Burgoine, Lake and Stamp ⁷⁴ ⁾.

Ane of the major challenges when using GIS for studying the food environs concerns the quality of the information bachelor. The validity of GIS-based measures of ecology features of the food environment has recently been discussed⁽ Reference Matthews, Moudon and Daniel ^nineteen ⁾. Since street addresses of facilities were often obtained from commercial databases or had been nerveless for other purposes, data accurateness and comprehensiveness must be viewed with caution⁽ Reference Brownson, Hoehner and Mean solar day ⁷¹ ^, Reference Boone, Gordon-Larsen and Stewart ⁷⁵ ⁾. In add-on, there may exist a mismatch between the geocoded location of a facility and its true location, eastward.g. via the GPS (global positioning system) technique⁽ Reference Boone, Gordon-Larsen and Stewart ⁷⁵ ^, Reference Porter, Kirtland and Neet ⁷⁶ ⁾.

A major challenge: which concepts should be used to characterise admission?

The articles reviewed hither focused on spatial access every bit estimated by GIS methods. Nevertheless, information technology should be noted that few authors specifically employ the term 'spatial' or 'geographic' when dealing with the broad concept of admission⁽ Reference Apparicio, Cloutier and Shearmur ³⁹ ^, Reference Sharkey and Horel ⁵¹ ⁾. Access that includes cloth and social dimensions is a complex notion, and geographic proximity does not systematically imply accessibility. Gould⁽ Reference Gould ⁷⁷ ⁾ describes accessibility equally 'a notion hard to grasp… one of these common terms everybody uses until the problem arises of defining and measuring the concept'. Penchansky and Thomas⁽ Reference Penchansky and William Thomas ⁷⁸ ⁾ defined five dimensions for access, including availability, accessibility, affordability, acceptability and accommodation. But the first two dimensions, corresponding to spatial measures, reflect the geographic distribution (e.thousand. of facilities around the home address) and can be estimated past GIS methods. This may be viewed as a possible weakness of these methods. However, by definition, the other dimensions reflecting the cultural, social and economical factors are not taken into account.

The 'platonic' study of access to nutrient outlets would appear to exist 1 that associates all dimensions related to accessibility: proximity, diversity, availability, affordability (cost) and perception, with the term 'diversity' referring to the types of food outlets and 'availability' referring to the nutrient supply at the food outlets. Only four of the manufactures⁽ Reference Bodor, Rose and Farley ²² ^, Reference Frank, Glanz and McCarron ⁴⁷ ^– Reference Block and Kouba ⁴⁹ ⁾ combined assessment of spatial access to food outlets with an evaluation of the actual food content of the outlet. Amid those manufactures, but two took into account cost and quality⁽ Reference Frank, Glanz and McCarron ⁴⁷ ^, Reference Block and Kouba ⁴⁹ ⁾ in addition to the availability of foods, specially good for you foods. Access to food outlets may also be limited by the subject's perception of the surroundings in his/her neigbourhood⁽ Reference Moore, Diez Roux and Brines ³² ^, Reference Kamphuis, van Lenthe and Giskes ⁷⁹ ⁾. Moore et al.⁽ Reference Moore, Diez Roux and Brines ³² ⁾ suggested that the availability of healthy foods every bit reported by residents (perception) and their availability equally measured past GIS awarding (density) provide complementary data for characterising the local nutrient environment. In other words, methodology for conducting an 'platonic' research study would have to combine GIS potential and survey approaches to describe both spatial and social accessibility of healthy foods.

Conclusions

Accessibility to services and facilities and, in particular, to healthy food, is an of import social equity issue⁽ Reference Apparicio, Cloutier and Shearmur ³⁹ ⁾. Geographic assay models may provide local government and policy makers with new views and possibilities for making decisions as to the location of services in social club to offer a fair option to the entire population. For case, Banos et al.⁽ Reference Banos and Huguenin-Richard ⁵⁴ ^, Reference Banos and Banos ^fourscore ⁾ have designed a GIS awarding that identifies hot spots by spatial regression⁽ Reference Anselin, Syabri and Kho ⁸¹ ⁾. These results enabled the targeting of parts of the road network that needed modifications⁽ Reference Banos and Banos ⁸⁰ ⁾. Gatrell and Naumann⁽ Reference Gatrell and Naumann ⁸² ⁾ adapted this tool to the field of health-intendance and suggested potential sites for building new hospitals, with various scenarios being examined according to traffic density.

Information technology should also be noted that spatial accessibility of salubrious food is just one of the multiple determinants of a good for you lifestyle, equally emphasised by socio-ecological models of behaviours⁽ Reference Sallis and Glanz ² ^– Reference Townshend and Lake ⁴ ⁾. Further development of spatial analysis methods should assistance to better ascertain its importance in various settings⁽ Reference Cummins ⁸³ ⁾. On the ground of the articles reviewed here, nosotros suggest two avenues for future methodological research when analysing accessibility of facilities relevant to food behaviour. First, in that location is a need to exam and compare more sophisticated spatial GIS modelling such equally travel time or potential model principles and gravity models⁽ Reference Guagliardo ⁵⁸ ^, Reference Weber and Hirsch ⁸⁴ ⁾. The latter combine diversity (type of facilities) and accessibility by using distribution of facilities throughout the area, together with a distance role to calculate the attractiveness of a food outlet (catchment area). Second, future research should benefit from a combination of GIS methods and survey approaches to describe both spatial and social food outlet accessibility, and to better understand how the food environment influences food behaviour and health.

Acknowledgements

This piece of work is office of the ELIANE (Environmental LInks to physical Activity, Nutrition and hEalth) study. ELIANE is a project supported by the French National Inquiry Bureau (Agence Nationale de la Recherche, ANR-07-PNRA-004). The authors declare that they have no competing interests. H.C. designed the study, performed the literature search and data extraction, and drafted the manuscript. J.-M.O. supervised the study design and information collection, and contributed to the finalisation of the paper. R.C., P.S., C.S., B.C., A.B., D.B. and C.W. assisted with the literature search and the writing of the manuscript. J.-M.O. is the coordinator of the ELIANE study; C.S., B.C. and C.West. are the principal investigators in the ELIANE study. All the authors read and canonical the concluding version of the manuscript.

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