Spatial heterogeneity of the relationships between environmental characteristics and active commuting: towards a locally varying social ecological model
© Feuillet et al.; licensee BioMed Central. 2015
Received: 5 December 2014
Accepted: 6 February 2015
Published: 25 March 2015
According to the social ecological model of health-related behaviors, it is now well accepted that environmental factors influence habitual physical activity. Most previous studies on physical activity determinants have assumed spatial homogeneity across the study area, i.e. that the association between the environment and physical activity is the same whatever the location. The main novelty of our study was to explore geographical variation in the relationships between active commuting (walking and cycling to/from work) and residential environmental characteristics.
4,164 adults from the ongoing Nutrinet-Santé web-cohort, residing in and around Paris, France, were studied using a geographically weighted Poisson regression (GWPR) model. Objective environmental variables, including both the built and the socio-economic characteristics around the place of residence of individuals, were assessed by GIS-based measures. Perceived environmental factors (index including safety, aesthetics, and pollution) were reported by questionnaires.
Our results show that the influence of the overall neighborhood environment appeared to be more pronounced in the suburban southern part of the study area (Val-de-Marne) compared to Paris inner city, whereas more complex patterns were found elsewhere. Active commuting was positively associated with the built environment only in the southern and northeastern parts of the study area, whereas positive associations with the socio-economic environment were found only in some specific locations in the southern and northern parts of the study area. Similar local variations were observed for the perceived environmental variables.
These results suggest that: (i) when applied to active commuting, the social ecological conceptual framework should be locally nuanced, and (ii) local rather than global targeting of public health policies might be more efficient in promoting active commuting.
KeywordsWalking Cycling Active transportation Geographically weighted regression Spatial non-stationarity
Promoting active transportation (walking and cycling) has become a priority in public health policies. The practice of daily active transportation has been shown to provide significant economic and health benefits. It contributes substantially to improving household budgets (reducing car-related expenditure ), limiting gas emissions, and decreasing other negative externalities, e.g. congestion, noise and pollution . In addition, active transportation contributes to overall physical activity, which has been shown to have a protective effect against major chronic diseases (cardiovascular disease, type 2 diabetes, and certain cancers, according to the Physical Activity Guidelines for Americans ). However, active transportation represents only a small proportion of daily commuting. In France in 2008, 23% of commuting was by walking and 2.7% by cycling . In the US, the corresponding figures were 10.5% and 1% in 2009, according to the National Household Travel Survey .
One major concern in developing relevant policies is the need for a better understanding of walking and cycling, which are complex behaviors, and their multiple determinants. Based on the socio-ecological conceptual framework, interacting factors include those related to the individual-level sphere and those associated with the social and physical environment [6,7]. In turn, social and physical environmental factors comprise both perceived and objective dimensions . In this paper, a geographical approach was used to identify spatial variations in the associations between environmental characteristics and active transportation. Thus, the study could aid the development of more focused or locally adapted public policies and planning choices. For the purpose of this research, data on active commuting and explanatory factors were collected from a large sample of French adults and an innovative local-targeted regression technique was applied.
Associations between active commuting behaviors and social support (e.g. [9,10]), different physical environment dimensions, e.g. walkability and bikeability, land use, public transportation availability, safety, aesthetics, etc., in residential and/or work neighborhoods are documented in the literature (e.g. [11-24]). However, previous studies have largely been based on the implied and strong assumption that the relationship between individual/environmental factors and active commuting is spatially homogeneous, i.e. that pertinent factors operate in a similar manner everywhere. This is a necessary condition for the use of global regression models. Yet, non-stationarity, referring to the variation in relationships across space [25,26], is a very common phenomenon in any geographical dataset like place-level factors. For instance, a global positive association can be found between walking to work and overall walkability, but is this really the case everywhere throughout a city or a neighborhood?
In recent years, an innovative local-based regression technique has been gaining popularity for exploring spatial non-stationarity among data: the Geographically Weighted Regression model (GWR, ). GWR allows the parameter estimates to vary locally, unlike in global models where they remain constant. GWR fits local regression at each location by applying a weighting scheme (based on a kernel function) which gives more weight to neighboring locations [27,28]. The results emphasize the spatial patterning of relationships. The GWR technique has been successfully applied in a few health-related studies [29-37]. For instance, Chen and Truong  used GWR to highlight that township disadvantages increased obesity prevalence only in certain areas in Taiwan. Similarly, Chalkias et al.  showed that only certain zones in Athens (Greece) constituted an obesogenic environment.
To our knowledge, GWR has not yet been applied to explore the potential non-stationarity of environmental correlates of active commuting. The main objective of our study was thus to investigate whether several objective and perceived environmental determinants of active commuting behaviors varied across space, ceteris paribus (i.e. after adjusting for individual characteristics), in Paris and its immediate suburbs. The underlying idea was to question the relevance of using a general conceptual framework, such as the socio-ecological model, when analyzing spatial data including potential non-stationary processes. The study was thus positioned to raise questions about the potential gain of a more geographically nuanced theoretical model, taking into account area-specific attitudes and behaviors. To explore this issue, a multivariate geographically weighted Poisson regression (GWPR) model was used, based on data from French adults.
Study area and population
Information about active commuting behaviors and some of the explanatory factors was derived from the Nutrinet-Santé study, an ongoing web-based cohort launched in France in May 2009, which focuses on relationships between nutrition and health . Briefly, participants aged 18 y. or older completed a set of questionnaires assessing demographic and socio-economic characteristics, as well as physical activity and perceived residential environment (response rate of 48.5%). Residential addresses were obtained from all participants, geocoded to the parcel or street levels and implemented as a shapefile in a geographical information system (GIS). This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures were approved by the Institutional Review Board of the French Institute for Health and Medical Research (IRB Inserm n° 0000388FWA00005831) and the Commission Nationale Informatique et Libertés (CNIL n° 908450 and n° 909216). All participants gave their written electronic informed consent to take part in the study.
Outcome variable: active commuting (walking and cycling to and from work)
Participants reported their time spent on active commuting. For each subject, the variable used was the mean of the hours spent walking and biking to/from work per week during the past 4 weeks.
A set of environmental variables was assessed to characterize the residential neighborhood of each individual. Variables were categorized as objective (i.e. GIS-based) or perceived (i.e. reported via a questionnaire).
Objective variables obtained from GIS (built and social)
Land use and facilities
Level of bikeability
Public transportation availability
The socioeconomic level of the residential environment has been considered since it is expected to be positively associated with overall physical activity, including active commuting . Eight neighborhood-level socioeconomic variables, from the Census database (www.insee.fr), were included and disaggregated into the 200-m square grid (following the same procedure as used for land use and facilities) (see Figure 2): percentage of foreign residents, unemployed, part-time workers, university graduates, homes occupied by their owners, car owners, households with a parking space, and median income.
Statistical analyses of objective environmental variables
The 7 physical and 8 socio-environmental variables were substantially multicollinear. As a valid regression analysis requires the explanatory variables to be independent, a suitable solution to this statistical issue is to transform the set of correlated variables into synthetic uncorrelated variables, named principal components (PC). In PC analysis (PCA), the principal components retained are those that explain the maximum amount of variance of the original data. This method was used in our study to avoid dropping any explanatory variable and to keep as much information as possible. PC were retained as new explanatory variables when eigenvalues were greater than one. PC coordinates were assigned to individuals and then mapped to facilitate the interpretation. The PCA was carried out with varimax rotation to represent the linear proximity among variables. It was conducted only with objective variables since we were interested in investigating separately the spatial variation in the influence of objective and perceived variables on active commuting.
Results of the principal component analysis conducted with the fifteen objective environmental variables
Principal component 1 Densely built-up areas & facility availability
Principal component 2 Socio-economics: well-off neighborhoods
% households with a parking spot
% home owners
% car owners
% individual housing
% collective housing
% part-time workers
% foreign residents
% university graduates
% vegetation cover
Distance to public transportation
Bike path density
Overall KMO score = 0.78; Bartlett’s test p < 0.001
Perceived environment variables
Three perceived residential neighborhood variables were defined by specific questions in a self-administered questionnaire. These three questions were derived from the ALPHA questionnaire designed to measure the relationship between physical activity and the environment in a European context . Briefly, the three questions were related to (i) bike safety in road traffic (‘cycling is unsafe because of the traffic’), (ii) pollution (‘there is too much pollution in my neighborhood’), and (iii) aesthetics (‘my neighborhood is not clean and not well-maintained’). Responses included five modalities based on a Likert-type scale (strongly agree, somewhat agree, neither agree nor disagree, somewhat disagree, strongly disagree). To account for substantial multicollinearity among these three variables, an index was built using principal component analysis. This index accounted for 48% of the total variance. The factor loadings were 0.72 for bike safety, 0.69 for pollution and −0.68 for aesthetics. Therefore, a high value in this synthetic index indicates individuals who reported a low level of pollution, a feeling of safety for cycling and a low level of aesthetics. A positive relationship between this synthetic variable and active commuting is expected .
Individual data included age, gender and education (divided into two categories, < high school, ≥ high school), number of motor vehicles per household, number of bikes per household, and an indicator related to the possession of a transit pass (yes or no). Finally, two work-related variables were added: self-reported commuting time (divided into tertiles) and availability of a parking space at the workplace (yes or no).
Given the non-Gaussian, zero-inflated distribution of the outcome variables (walking and cycling to and from work), each value was rounded to the nearest half-unit (0.5). This procedure of discretization enabled the variable to be modeled with a Poisson regression. First, a global Poisson model (GPR) was carried out, i.e. parameter estimates were kept constant to explore global relationships between environmental variables and active commuting, while adjusting for individual variables. Secondly, a geographically weighted Poisson regression [28,30] was conducted to account for the possible spatial non-stationarity of these relationships. The statistical specifications of GPR are described in Appendix A.
Local model (GWPR)
The maximum number of neighbors in each regression model was determined by minimizing, in an iterative way, the corrected Akaike’s Information Criterion (AICc), which is a statistic based on the log-likelihood of the model, weighted by the actual number of parameters . In this study, the number of neighbors minimizing the AICc was 800 (after testing down from 4,000 to 50, every 50). This amounted to an adaptive bandwidth size varying from 3,150 m to 19,250 m and a mean distance of 5,650 m, knowing that the study area spreads over approximately 30,000 m.
AICc was also used to compare the performance of both global and local models. The model with the smallest AICc should be selected as an optimal model called MAICE (minimum AIC estimator, see . A difference in AICc values of more than 2 is considered substantial.
Global models were performed with SAS 9.3 software (SAS Institute Inc., Cary, NC, USA) and the academic piece of software GWR 4.0 was used to calibrate and run geographically weighted models. This software is a tool for modelling varying relationships among variables by calibrating GWR and Geographically Weighted Generalized Linear Models (GWGLM) with their semi-parametric variants (https://geodacenter.asu.edu/gwr_software, see Nakaya et al.  and Nakaya  for additional details). Maps with continuous values were based on an interpolation procedure (inverse distance weighting) and were processed with ArcGIS 10.1 (ESRI Inc., Redlands, CA, USA). Finally, a measure of statistical significance (pseudo t-value) of GWPR estimates was added visually as isolines for each local term map. A value greater than |1.96| indicates a p-value < 0.05. Mapped GWPR estimates are log-odds, with negative and positive values meaning negative and positive relationships, respectively.
Characteristics of the study population
Descriptive statistics of the variables used in the analysis
Variable (N = 4164)
Mean (or %)
Active commuting (outcome)
If yes (h/week)
% < high school
% ≥ high school
Parking at work
% 1st tertile
% 2nd tertile
% 3rd tertile
Number of motor vehicles owned
Number of bikes owned
GIS-based environmental variables
PC1 (densely built-up areas)
PC2 (well-to-do areas)
Perceived environmental variables
% neither agree nor disagree
Too much pollution
Neighborhood is not clean
Biking is unsafe
Characteristics of the environment
Global relationships between active commuting and environmental variables
Overall performances of both global and local (bi-square kernel) regressions used for modeling associations between active commuting behaviors and individual and environmental factors
Model (N = 4164)
Effective number of parameters (k)
AIC c (D + 2*k)
GWPR (800 neighbors)
Parameter estimations from the global Poisson regression model
Wald 95% CI
Gender (ref = male)
Education (ref = < high school)
Parking at work (ref = yes)
Transit pass (ref = yes)
Commuting time (ref = 1st tertile)
Number of vehicles owned
Number of bikes owned
PC1 (densely built-up areas)
PC2 (well-to-do areas)
Neighborhood perception (too much pollution and not clean) (0 = strongly agree; 5 = strongly disagree)
Regarding the environmental variables, after controlling for individual ones, the results show that active commuting is positively associated with the first principal component (OR = 1.05, 95% CI 1.03-1.07, implying that an increase of one unit of the densely-built level of the neighborhood is associated with an increase in active commuting), but no global relationship was detected with the second principal component (socio-economic level of the neighborhood). Finally, the perception of the neighborhood environment is significantly associated with active commuting (OR = 1.04, 95% CI 1.02-1.06).
Spatial variations in the relationships
Parameter estimations from the semiparametric geographically weighted Poisson regression model (after adjusting for individual variables)
PC1 (densely built-up areas)
PC2 (well-to-do areas)
Neighborhood perception (too much pollution and not clean) (0 = strongly agree; 5 = strongly disagree)
Perceived environmental variables also show some non-stationarity (Figure 5C). In northern and southern central parts of the area, associations are significantly positive, whereas they are non-significant elsewhere, or even negative in some parts of the Hauts-de-Seine and Val-de-Marne départements.
Using a GWPR, we have clearly demonstrated some spatial heterogeneity within the relationships across our study area. This represents the main novel result of our study. To the best of our knowledge, there is currently no other study on spatially varying relationships between the environment and walking or cycling for commuting purposes. We have shown that the associations of environmental factors with active commuting are area-specific. In other words, the relative influence of the specific environmental characteristics of the neighborhood (built, social and perceived) differs by location. For instance, the southern part of the study area (département of Val-de-Marne) is generally characterized by significant positive associations between environmental characteristics and active commuting. In contrast, in the major part of Paris or in the northern part of the Hauts-de-Seine département, environmental characteristics and perception of the neighborhood are not associated with walking and cycling for commuting purposes. Our results also show the complexity of the relationship between the environment and active commuting. At the same location, certain environmental variables may be associated with the outcome, while others may not.
Regarding the global relationships, we have highlighted that both perceived and objective environmental factors are significantly associated with active commuting, as expected. The importance of both the individual and the environmental level for active transportation is in line with existing evidence (e.g. [14,17,24,49,53-57]) based on the social ecological framework [8,58]. Although some environmental characteristics have significant relationships with active commuting, they only weakly contribute to explaining its total variance (as shown by the respective Nagelkerke’s pseudo R2 of the nested models (R2 = 0.30 without the environment level, and 0.32 with it)). This corroborates findings by Giles-Corti and Donovan  in Australia, and Ogilvie et al. in the UK . For instance, Ogilvie et al.  highlighted that in urban neighborhoods of Glasgow, 18.7% of the total variance in active travel was explained by personal correlates, and 20.1% when environmental-level variables were added. These authors concluded that including environmental characteristics did not substantially modify the influence of the personal characteristics on the associations studied.
Interpretation of the observed spatial non-stationarity
The non-stationarity could be due to random sampling variations and hence not related to any underlying spatial process.
The relationships might be intrinsically different across space, in other words “there are spatial variations in people’s attitudes or preferences or there are different administrative, political or other contextual issues that produce different responses to the same stimuli over space”.
The non-stationarity could also indicate that the model suffers from major misspecification or omission of key variables (or representation by an incorrect functional form).
The demarcation between the second and third potential causes of parametric instability may seem blurred in some cases. Can relationships really be intrinsically different across space? As discussed by Blainey , space may represent “merely a proxy for societal factors which are not captured by the model”. The crucial issue here is that such space-related societal factors are sometimes very difficult to define, and hence hard to quantify in a model.
However, in some instances, varying relationships seem to be driven both by the level of and the variance in the explanatory variable involved. For example, regarding the variable related to densely-built areas, the absence of a relationship in the major part of Paris could be explained by the fact that facility and building density is already extremely high in the city, so that one additional unit (of density or facility availability) would have a limited marginal effect on active commuting behavior. In contrast, in the less densely-built areas surrounding Paris (south and east), an increase of one unit regarding this variable would have a more noticeable impact. In that case, the instability of the parameters associated with the facility availability would only be due to a threshold effect of the variable itself and not directly linked to another contextual effect. The same rationale was used by Lu et al.  to explain the heterogeneity of relationships between the number of buildings and non-motorized traffic in Burlington, Vermont, USA. The number of buildings was associated with non-motorized traffic in the suburbs (with low building density), but not in the city center (with high building density).
Local associations with the perceived environment also show interesting spatial patterns. Unlike the objective environmental variables, the perceived ones do not follow any obvious spatial distribution (meaning that they are not correlated with objective measures). This means that spatial non-stationarity associated with these variables could be due to the omission of variables in each local context drawn through the GWPR estimates (Figure 5C). For instance, a local confounding factor may have been omitted from the model, leading to a spurious association. In some locations, reporting a low level of pollution and a strong feeling of safety for cycling is associated with a decrease in active commuting, which is counter-intuitive. We can hypothesize that in such places, these perceived neighborhood characteristics may be correlated with exposure to a limited traffic volume, while such little traffic may indicate a less walkable/bikeable environment in terms of infrastructure density .
Small single patterns of non-stationarity often remain difficult to interpret in detail, partly because the possibly omitted local variables are often not easily quantifiable, being the results of complex local interactions. In particular, the strength of social interactions within a place may lead to a homogenization of the relationships . However, exploring these interactions, opening “the black boxes of places” , needs further and deeper quantitative and qualitative investigations. It is still advisable to keep an overview of the overall spatial patterning of non-stationarity, since this enables some boundaries between different local contexts to be drawn, wherein, for complex reasons, relationships converge.
Implications of the findings
The non-stationarity of spatial datasets has been demonstrated in other studies dealing with health-related outcomes, such as obesity , cardiovascular mortality , and health care system organization . Such observations lead to questions about the relevance of using global models, which tend to smooth the effects of one or another variable across the entire area, whereas these effects are in fact area-specific. Considering local models over global ones has potential implications for public health policies. As emphasized by Yang and Matthews , GWR-based analyses in health-related research could be used as a tool for place-specific targeting and/or tailoring of public health interventions. For example, potential interventions by local authorities regarding bike safety in traffic, e.g. installing separate bike paths away from roads, might have a stronger impact on cycling behaviors in the southern and northern parts of our study area than in the eastern part (Figure 5C). In addition, increasing bike-sharing stations and facility density (i.e. contributing to increasing PC1) would be more useful in the Val-de-Marne département than in the Hauts-de-Seine département, or in Paris (Figure 5A), in terms of active commuting. Such place-level prioritizing could be not only more efficient than whole-area interventions for promoting active transportation, but could also ensure substantial cost-efficiency in planning policies.
Theoretical considerations: toward a locally varying social ecological model
According to Sallis et al. , the first principle of ecological models is that multiple levels of factors, including individual and environmental ones, influence health behaviors. Our results on active commuting behaviors, namely the spatial variability of the relative influence of individual and environmental factors, suggest the importance of considering the local context. In other words, the social ecological framework needs to be locally adapted, according to the spatial patterning of the relationships. In certain areas, policy-makers might achieve better results by acting on one particular level rather than on multiple ones. This also suggests expanding the fourth principle proposed by Sallis et al. : ecological models are most efficient when they are not only behavior-specific, but also area-specific.
Beyond the physical activity domain, the idea of spatially varying relationships also fits the theoretical framework developed by Lytle  for eating behaviors. This author hypothesized that the relative influence of individual, environmental and social factors on the proportion of variance explained in eating behaviors varied as a function of the level of restriction of the environment, specifically: “the more restricted an environment is with regard to availability and accessibility of healthy, inexpensive options, the more influence the physical environment may have with regard to food choices that are made” . Our GWPR analyses provide empirical evidence for the application of Lytle’s assumption to active commuting behaviors.
Strengths and limitations
One major advantage of GWR modeling, which is based on individual locations, is its ability to reveal spatial variations beyond the actual administrative units, and therefore to highlight spatial patterning. Regression parameters can then be seen as new continuous variables, which can lead to a data-based spatial clustering of the relationships for each explanatory variable. As previously shown, GWR also identifies significant associations at the local level that do not appear when fitting the usual global models. Finally, the mapped results are easily readable and can be considered turnkey products for policy-makers. Despite these advantages, GWR has some limitations. First, there are border effects inherent in the concept of spatial kernels. Local regressions for individuals located near the boundary of the study area do not follow exactly the same weighting scheme as those for individuals located in the center, since the former do not have neighbors all around them. This leads to a larger adaptive spatial kernel for these individuals. Second, GWR can lead to local multicollinearity among the explanatory variables, even if the variables are not collinear at a global scale. These facts can affect the validity of the model and the results and therefore require careful consideration.
From a statistical point of view, we performed GWPR models by rounding the outcome variable values to the nearest half-unit (i.e. 30 minutes) in order to get integer values. We also run models using other discretization procedures (rounding to the nearest unit and double unit) to check for statistical stability and findings were essentially unaltered, with unchanged spatial patterning of relationships (data not shown). We also run a geographically weighted logistic regression model by separating active commuters from non-active ones, followed by a second Gaussian GWR model only applied to active commuters, and results again were very similar in terms of direction, intensity and spatial structure of the relationships (data not shown).
In addition, a limitation of our study is the fact that the outcome variable, walking and cycling to/from work, was self-reported. This may be a source of potential misclassification, knowing that physical activity usually tends to be over-reported [18,66,67]. Second, we only focused on active transportation for commuting, but some studies have shown that relationships can be inversed when dealing with walking for leisure or errands. For instance, in four Japanese cities, Inoue et al.  showed the expected associations between neighborhood aesthetics and walking in the neighborhood, walking for leisure and walking for daily errands, while no relationship was found with walking for commuting purposes. Finally, this study did not take into account the environmental characteristics of the workplace or the commuting routes, which may also be associated with active transportation behaviors [10,69].
After showing global, significant associations between individual/environmental factors and active commuting (walking and cycling to/from work) in a French web-cohort, this study implemented a geographically weighted Poisson regression to investigate possible non-stationarity among these associations. At a local scale, GWPR-based analyses enable nuances to be understood by clearly highlighting the spatial heterogeneity of the relationships. For instance, the influence of the overall neighborhood environment appears to be more pronounced in the southern part of the study area (département of Val-de-Marne) than in Paris, whereas more complex patterns were revealed elsewhere. We also showed that socio-economic level is significantly and positively associated with the outcome in the extreme northern and southern parts of the area. On the contrary, in some locations, the built environment appears to be non-significantly, or even correlated, in an unexpected way with active commuting (for example in Paris). Perception-based variables are also subject to non-stationarity. Specifically, a better perception of bike safety in traffic is mainly associated with an increase in walking and cycling, except in the northwestern part of the area (Seine-Saint-Denis) where the relationships were inversed. This non-stationarity in the relationships has two main implications. First, from a practical point of view, our results suggest that public policies should follow the spatial patterning of the relationships in order to strengthen their efficiency. GWPR modeling, and its easily readable associated maps, can be a useful tool to guide the design of tailored and area-targeted public policies promoting physical activity for health. Second, from a theoretical point of view, our data suggest that ecological models of health behavior should be not only population- and behavior-specific but also location-specific.
This work is part of the ACTI-Cités project (coordinator: J.M. Oppert) carried out with financial support from the French National Cancer Institute (Institut National du Cancer, INCa) through the Social sciences and humanities and public health programme (2011-1-PL-SHS-10).
The NutriNet-Santé cohort study is funded by the following public institutions: Ministère de la Santé, Institut de Veille Sanitaire (InVS), Institut National de la Prévention et de l’Education pour la Santé (INPES), Fondation pour la Recherche Médicale (FRM), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Recherche Agronomique (INRA), Conservatoire National des Arts et Métiers (CNAM) and Paris 13 University.
The authors also wish to thank the two anonymous referees for their constructive comments.
- Garrard J. Active Transport: Adults. An Overview of Recent Evidence. Melbourne: Victorian Helth Promotion Foundation; 2009.Google Scholar
- Commission of the European Communities. Green Paper. From the Commission to the Council, the European Parliament, the European Economic and Social Committee and the Committee of the Regions. Adapting to Climate Change in Europe – Options for EU Action. Brussels: Commission of the European Communities; 2007.Google Scholar
- USDHHS. Physical Activity Guidelines for Americans. Washington DC: USDHHS; 2008.Google Scholar
- Department for observation and statistics. National Survey on Transport and Mobility (in French). Paris: French ministry of sustainable development; 2010.Google Scholar
- Pucher J, Buehler R, Merom D, Bauman A. Walking and cycling in the United States, 2001–2009: evidence from the National Household Travel Surveys. Am J Public Health. 2011;101 Suppl 1:S310–7.View ArticlePubMed CentralPubMedGoogle Scholar
- Sallis JF, Owen N, Fisher EB. Ecological models of health behavior. In: Glanz K, Rimer BK, Viswanath K, editors. Health Behavior and Health Education: Theory, Research, and Practice. 4th ed. San Francisco: Wiley: Jossey-Bass; 2008. p. 465–82.Google Scholar
- Richard L, Gauvin L, Raine K. Ecological models revisited: their uses and evolution in health promotion over two decades. Annu Rev Public Health. 2011;32:307–26.View ArticlePubMedGoogle Scholar
- Saelens BE, Sallis JF, Frank LD. Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning literatures. Ann Behav Med Publ Soc Behav Med. 2003;25:80–91.View ArticleGoogle Scholar
- De Bourdeaudhuij I, Teixeira PJ, Cardon G, Deforche B. Environmental and psychosocial correlates of physical activity in Portuguese and Belgian adults. Public Health Nutr. 2005;8:886–95.PubMedGoogle Scholar
- Titze S, Stronegger WJ, Janschitz S, Oja P. Environmental, social, and personal correlates of cycling for transportation in a student population. J Phys Act Health. 2007;4:66–79.PubMedGoogle Scholar
- Nelson A, Allen D. If you build them, commuters will use them: association between bicycle facilities and bicycle commuting. Transp Res Rec J Transp Res Board. 1997;1578:79–83.View ArticleGoogle Scholar
- Trost SG, Owen N, Bauman AE, Sallis JF, Brown W. Correlates of adults’ participation in physical activity: review and update. Med Sci Sports Exerc. 2002;34:1996–2001.View ArticlePubMedGoogle Scholar
- Dill J, Carr T. Bicycle commuting and facilities in major U.S. cities: if you build them, commuters will use them. Transp Res Rec. 2003;1828:116–23.View ArticleGoogle Scholar
- Troped PJ, Saunders RP, Pate RR, Reininger B, Addy CL. Correlates of recreational and transportation physical activity among adults in a New England community. Prev Med. 2003;37:304–10.View ArticlePubMedGoogle Scholar
- Humpel N, Owen N, Iverson D, Leslie E, Bauman A. Perceived environment attributes, residential location, and walking for particular purposes. Am J Prev Med. 2004;26:119–25.View ArticlePubMedGoogle Scholar
- De Geus B, De Bourdeaudhuij I, Jannes C, Meeusen R. Psychosocial and environmental factors associated with cycling for transport among a working population. Health Educ Res. 2008;23:697–708.View ArticlePubMedGoogle Scholar
- Titze S, Stronegger WJ, Janschitz S, Oja P. Association of built-environment, social-environment and personal factors with bicycling as a mode of transportation among Austrian city dwellers. Prev Med. 2008;47:252–9.View ArticlePubMedGoogle Scholar
- Charreire H, Weber C, Chaix B, Salze P, Casey R, Banos A, et al. Identifying built environmental patterns using cluster analysis and GIS: relationships with walking, cycling and body mass index in French adults. Int J Behav Nutr Phys Act. 2012;9:59.View ArticlePubMed CentralPubMedGoogle Scholar
- Dalton AM, Jones AP, Panter JR, Ogilvie D. Neighbourhood, route and workplace-related environmental characteristics predict adults’ mode of travel to work. PLoS One. 2013;8:e67575.View ArticlePubMed CentralPubMedGoogle Scholar
- Hino AAF, Reis RS, Sarmiento OL, Parra DC, Brownson RC. Built environment and physical activity for transportation in adults from Curitiba, Brazil. J Urban Health Bull N Y Acad Med. 2014;91:446–62.View ArticleGoogle Scholar
- Panter J, Desousa C, Ogilvie D. Incorporating walking or cycling into car journeys to and from work: the role of individual, workplace and environmental characteristics. Prev Med. 2013;56:211–7.View ArticlePubMed CentralPubMedGoogle Scholar
- Panter J, Griffin S, Dalton AM, Ogilvie D. Patterns and predictors of changes in active commuting over 12 months. Prev Med. 2013;57:776–84.View ArticlePubMed CentralPubMedGoogle Scholar
- Adams MA, Frank LD, Schipperijn J, Smith G, Chapman J, Christiansen LB, et al. International variation in neighborhood walkability, transit, and recreation environments using geographic information systems: the IPEN adult study. Int J Health Geogr. 2014;13:43.View ArticlePubMed CentralPubMedGoogle Scholar
- Bopp M, Child S, Campbell M. Factors associated with active commuting to work among women. Women Health. 2014;54:212–31.View ArticlePubMedGoogle Scholar
- Brunsdon C, Fotheringham AS, Charlton ME. Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr Anal. 1996;28:281–98.View ArticleGoogle Scholar
- Brunsdon C, Fotheringham S, Charlton M. Geographically weighted regression. J R Stat Soc Ser Stat. 1998;47:431–43.View ArticleGoogle Scholar
- Fotheringham AS, Brunsdon C, Charlton M. Quantitative Geography: Perspectives on Spatial Data Analysis. London: SAGE; 2000.Google Scholar
- Fotheringham AS, Brunsdon C, Charlton M. Geographically weighted regression: the analysis of spatially varying relationships. Chichester, England, Hoboken, NJ, USA: Wiley-Blackwell; 2002.Google Scholar
- Congdon P. Modelling spatially varying impacts of socioeconomic predictors on mortality outcomes. J Geogr Syst. 2003;5:161–84.View ArticleGoogle Scholar
- Nakaya T, Fotheringham AS, Brunsdon C, Charlton M. Geographically weighted Poisson regression for disease association mapping. Stat Med. 2005;24:2695–717.View ArticlePubMedGoogle Scholar
- Maroko AR, Maantay JA, Sohler NL, Grady KL, Arno PS. The complexities of measuring access to parks and physical activity sites in New York City: a quantitative and qualitative approach. Int J Health Geogr. 2009;8:34.View ArticlePubMed CentralPubMedGoogle Scholar
- Chen VY-J, Wu P-C, Yang T-C, Su H-J. Examining non-stationary effects of social determinants on cardiovascular mortality after cold surges in Taiwan. Sci Total Environ. 2010;408:2042–9.View ArticlePubMedGoogle Scholar
- Comber AJ, Brunsdon C, Radburn R. A spatial analysis of variations in health access: linking geography, socio-economic status and access perceptions. Int J Health Geogr. 2011;10:44.View ArticlePubMed CentralPubMedGoogle Scholar
- Chen D-R, Truong K. Using multilevel modeling and geographically weighted regression to identify spatial variations in the relationship between place-level disadvantages and obesity in Taiwan. Appl Geogr. 2012;32:737–45.View ArticleGoogle Scholar
- Yang T-C, Matthews SA. Understanding the non-stationary associations between distrust of the health care system, health conditions, and self-rated health in the elderly: a geographically weighted regression approach. Health Place. 2012;18:576–85.View ArticlePubMed CentralPubMedGoogle Scholar
- Broberg A, Salminen S, Kyttä M. Physical environmental characteristics promoting independent and active transport to children’s meaningful places. Appl Geogr. 2013;38:43–52.View ArticleGoogle Scholar
- Chalkias C, Papadopoulos AG, Kalogeropoulos K, Tambalis K, Psarra G, Sidossis L. Geographical heterogeneity of the relationship between childhood obesity and socio-environmental status: empirical evidence from Athens, Greece. Appl Geogr. 2013;37:34–43.View ArticleGoogle Scholar
- Hercberg S, Castetbon K, Czernichow S, Malon A, Mejean C, Kesse E, et al. The Nutrinet-Santé study: a web-based prospective study on the relationship between nutrition and health and determinants of dietary patterns and nutritional status. BMC Public Health. 2010;10:242.View ArticlePubMed CentralPubMedGoogle Scholar
- Saelens BE, Handy SL. Built environment correlates of walking: a review. Med Sci Sports Exerc. 2008;40(7 Suppl):S550–66.View ArticlePubMed CentralPubMedGoogle Scholar
- Bergeron P, Reyburn S. L’impact de L’environnement Bâti Sur L’activité Physique, L’alimentation et Le Poids. Québec: Direction du développement des individus et des communautés. Institut national de Santé Publique du Québec; 2010.Google Scholar
- Knuiman MW, Christian HE, Divitini ML, Foster SA, Bull FC, Badland HM, et al. A longitudinal analysis of the influence of the neighborhood built environment on walking for transportation. The RESIDE study. Am J Epidemiol. 2014;180:453–61.View ArticlePubMedGoogle Scholar
- Larsen K, Gilliland J. Mapping the evolution of “food deserts” in a Canadian city: supermarket accessibility in London, Ontario, 1961–2005. Int J Health Geogr. 2008;7:16.View ArticlePubMed CentralPubMedGoogle Scholar
- Larsen K, Gilliland J, Hess P, Tucker P, Irwin J, He M. The influence of the physical environment and sociodemographic characteristics on children’s mode of travel to and from school. Am J Public Health. 2009;99:520–6.View ArticlePubMed CentralPubMedGoogle Scholar
- Gatrell AC, Bailey TC, Diggle PJ, Rowlingson BS. Spatial point pattern analysis and its application in geographical epidemiology. Trans Inst Br Geogr. 1996;21:256.View ArticleGoogle Scholar
- Fan Y, Khattak AJ. Urban form, individual spatial footprints, and travel: examination of space-use behavior. Transp Res Rec J Transp Res Board. 2008;2082:98–106.View ArticleGoogle Scholar
- Buck C, Pohlabeln H, Huybrechts I, De Bourdeaudhuij I, Pitsiladis Y, Reisch L, et al. Development and application of a moveability index to quantify possibilities for physical activity in the built environment of children. Health Place. 2011;17:1191–201.View ArticlePubMedGoogle Scholar
- McNeill LH, Kreuter MW, Subramanian SV. Social environment and physical activity: a review of concepts and evidence. Soc Sci Med. 2006;63:1011–22.View ArticlePubMedGoogle Scholar
- Spittaels H, Verloigne M, Gidlow C, Gloanec J, Titze S, Foster C, et al. Measuring physical activity-related environmental factors: reliability and predictive validity of the European environmental questionnaire ALPHA. Int J Behav Nutr Phys Act. 2010;7:48.View ArticlePubMed CentralPubMedGoogle Scholar
- Panter JR, Jones A. Attitudes and the environment as determinants of active travel in adults: what do and don’t we know? J Phys Act Health. 2010;7:551–61.PubMedGoogle Scholar
- Johnson JB, Omland KS. Model selection in ecology and evolution. Trends Ecol Evol. 2004;19:101–8.View ArticlePubMedGoogle Scholar
- Nakaya T, Fotheringham AS, Charlton M, Brunsdon C. Semiparametric Geographically Weighted Generalised Linear Modelling in GWR4.0, Proceedings of geocomputation. 2009.Google Scholar
- Nakaya T. Geographically weighted generalised linear modelling. In: Brunsdon C, Singleton A, editors. Geocomputation: A Practical Primer. London: Sage Publication; 2015. p. 217–20.Google Scholar
- Wendel-Vos GCW, Schuit AJ, de Niet R, Boshuizen HC, Saris WHM, Kromhout D. Factors of the physical environment associated with walking and bicycling. Med Sci Sports Exerc. 2004;36:725–30.View ArticlePubMedGoogle Scholar
- Wendel-Vos W, Droomers M, Kremers S, Brug J, van Lenthe F. Potential environmental determinants of physical activity in adults: a systematic review. Obes Rev Off J Int Assoc Study Obes. 2007;8:425–40.View ArticleGoogle Scholar
- Ogilvie D, Mitchell R, Mutrie N, Petticrew M, Platt S. Personal and environmental correlates of active travel and physical activity in a deprived urban population. Int J Behav Nutr Phys Act. 2008;5:43.View ArticlePubMed CentralPubMedGoogle Scholar
- Bopp M, Kaczynski AT, Besenyi G. Active commuting influences among adults. Prev Med. 2012;54:237–41.View ArticlePubMedGoogle Scholar
- Bopp M, Kaczynski AT, Campbell ME. Social ecological influences on work-related active commuting among adults. Am J Health Behav. 2013;37:543–54.View ArticlePubMedGoogle Scholar
- Sallis JF, Owen N, Fisher E. Ecological Models of Health Behavior. San Francisco: Jossey-Bass; 2008.Google Scholar
- Giles-Corti B, Donovan RJ. The relative influence of individual, social and physical environment determinants of physical activity. Soc Sci Med. 2002;54:1793–812.View ArticlePubMedGoogle Scholar
- Blainey SP. Forecasting the use of new local railway stations and services using GIS. phd. PhD thesis, University of Southampton; 2009.
- Lu GX, Sullivan J, Troy A. Impact of ambient built-environment attributes on sustainable travel modes: a spatial analysis in Chittenden county, Vermont. 91st Annual Meeting of the Transportation Research Board. Washington, DC. 2012;21.
- Reis RS, Hino AAF, Parra DC, Hallal PC, Brownson RC. Bicycling and walking for transportation in three Brazilian cities. Am J Prev Med. 2013;44:e9–17.View ArticlePubMedGoogle Scholar
- Parkes A, Kearns A. The multi-dimensional neighbourhood and health: a cross-sectional analysis of the Scottish Household Survey, 2001. Health Place. 2006;12:1–18.View ArticlePubMedGoogle Scholar
- Macintyre S, Ellaway A, Cummins S. Place effects on health: how can we conceptualise, operationalise and measure them? Soc Sci Med 1982. 2002;55:125–39.Google Scholar
- Lytle LA. Measuring the food environment: state of the science. Am J Prev Med. 2009;36(4 Suppl):S134–44.View ArticlePubMed CentralPubMedGoogle Scholar
- Shephard R, Vuillemin A. Limits to the measurement of habitual physical activity by questionnaires. Br J Sports Med. 2003;37:197–206.View ArticlePubMed CentralPubMedGoogle Scholar
- Andrews GJ, Hall E, Evans B, Colls R. Moving beyond walkability: on the potential of health geography. Soc Sci Med 1982. 2012;75:1925–32.Google Scholar
- Inoue S, Ohya Y, Odagiri Y, Takamiya T, Ishii K, Kitabayashi M, et al. Association between perceived neighborhood environment and walking among adults in 4 cities in Japan. J Epidemiol. 2010;20:277–86.View ArticlePubMed CentralPubMedGoogle Scholar
- Karusisi N, Thomas F, Méline J, Brondeel R, Chaix B. Environmental conditions around itineraries to destinations as correlates of walking for transportation among adults: the RECORD cohort study. PLoS One. 2014;9:e88929.View ArticlePubMed CentralPubMedGoogle Scholar
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.