A GIS-Based Method for Analysing the Association Between School-Built Environment and Home-School Route Measures with Active Commuting to School in Urban Children and Adolescents

International Journal of Environmental Research and Public Health Article A GIS-Based Method for Analysing the Association Between School-Built Environment and Home-School Route Measures with Active Commuting to School in Urban Children and Adolescents Francisco Sergio Campos-Sánchez 1,* , Francisco Javier Abarca-Álvarez 1, Javier Molina-García2and Palma Chillón3 1Department of Urban and Spatial Planning, School of Architecture, University of Granada, 18009 Granada, Spain; [email protected] 2AFIPS research group, Department of Teaching of Musical, Visual and Corporal Expression, University of Valencia, Avda. dels Tarongers, 4, 46022 Valencia, Spain; [email protected] 3PROFITH ‘PROmoting FITness and Health through Physical Activity’ Research Group, Department of Physical Education and Sport, Faculty of Sport Sciences, University of Granada, 18071 Granada, Spain; [email protected] *Correspondence: [email protected] Received: 4 March 2020; Accepted: 26 March 2020; Published: 29 March 2020   Abstract: In the current call for a greater human health and well-being as a sustainable development goal, to encourage active commuting to and from school (ACS) seems to be a key factor. Research focusing on the analysis of the association between environmental factors and ACS in children and adolescents has reported limited and inconclusive evidence, so more knowledge is needed about it. The main aim of this study is to examine the association between different built environmental factors of both school neighbourhood and home-school route with ACS of children and adolescents belonging to urban areas. The ACS level was evaluated using a self-reported questionnaire. Built environment variables (i.e., density of residents, street connectivity and mixed land use) within a school catchment area and home-school route characteristics (i.e., distance and pedestrian route directness—PRD) were measured using a geographic information system (GIS) and examined together with ACS levels. Subsequently, the association between environmental factors and ACS was analysed by binary logistic regression. Several cut-offpoints of the route measures were explored using receiver operating characteristic (ROC) curves. In addition, the PRD was further studied regarding different thresholds. The results showed that 70.5% of the participants were active and there were significant associations between most environmental factors and ACS. Most participants walked to school when routes were short (distance variable in children: OR =0.980; p=0.038; and adolescents: OR =0.866; p<0.001) and partially direct (PRD variable in children: OR =11.334; p<0.001; and adolescents: OR =3.513; p<0.001), the latter specially for children. Mixed land uses (OR =2.037; p<0.001) and a high density of street intersections (OR =1.640; p<0.001) clearly encouraged adolescents walking and slightly discouraged children walking (OR =0.657, p=0.010; and OR =0.692, p=0.025, respectively). The assessment of ACS together with the environmental factors using GIS separately for children and adolescents can inform future friendly and sustainable communities. Keywords: active transportation; connectivity; logistic regression; pedestrian route directness; ROC curve; sustainable development goals; walkability Int. J. Environ. Res. Public Health 2020,17, 2295; doi:10.3390/ijerph17072295 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020,17, 2295 2 of 19 1. Introduction 1.1. Active Commuting to/from School (ACS) Contributes to the Sustainable Development Goals (SDGs) It has been known for decades that a crucial factor in achieving urban sustainability can be reducing society’s dependence on motor vehicles [ 1 ]. It is widely recognised that ACS (i.e., non-motorised travel, e.g., walking or cycling) reduces car use, thereby improving health and well-being as suggested by the latest systematic reviews existing in this regard [ 2 – 5 ]. The authors of these reviews examined many of the studies reporting evidence of the positive impact of ACS on physical activity (n=90), body weight (n=90) and cardiovascular health (n=12). Therefore, ACS is positively related to the Sustainable Development Goals (SDGs) of Agenda 2030 [ 6 ] and more specifically, it may contribute to both the SDG 3 ‘good health and well-being’, since practicing active commuting improves human health at every age; and the SDG 11 ‘sustainable cities and communities’ considering that active commuting can reduce the pollution in the cities. The present study focuses mainly on the relationship between the urban built environment and ACS, providing information about how to develop efficient urban policies that raise ACS levels and thus improve health and the environment. This relationship may be connected to the SDG global indicators provided for monitoring both SDGs 3 and SDGs 11 (see global indicator framework adopted by the General Assembly—A/RES/71/313 in https://unstats.un.org/sdgs/indicators/indicators-list/). Consequently, a link between ACS and SDGs can be found with several indicators of the SDGs 3, in goals 3.4. (to reduce cardiovascular disease and promote mental health and well-being), 3.6. (to reduce traffic accidents), 3.9. (to reduce air pollution), and with the SDGs 11 in goals 11.6 (improve air quality) and 11.a (to support environmental, social and economic linkages between urban, periurban and rural areas). 1.2. ACS Behaviour The health benefits of active commuting are widely recognized. As evidence of these benefits, the recent systematic review conducted by Larouche et al. (2014) [ 5 ] found support for active commuting in improving physical activity levels (49 studies), body weight (39 studies) and cardiovascular fitness (10 studies). Active commuting to school also promotes other benefits such as independent mobility, improved social relationships, mental health and connection to urban and natural environments in children and adolescents [ 7 ]. However, it appears that ACS rates are generally decreasing due to the increased use of motorized transport [ 8 – 10 ]. Therefore, studying ACS correlates and enforcing them through appropriate policies may help change this behaviour. A growing number of studies suggest the existence of multiple factors influencing the ACS of young people on school travels [ 11 ]. According to ecological models of health behaviour [ 12 ], multiple interaction levels between personal characteristics (i.e., intrapersonal), psychosocial (e.g., interpersonal modelling and social support) and environmental factors (e.g., neighbourhood and home, school and workplace environments) and policies (e.g., health care and transport policies, zoning codes and traffic demand) determine active living and physical activity. Specifically, correlates of ACS were studied including demographic, family, school and social factors [ 13 ]. Considering these factors, observed rates of ACS were higher in children than in adolescents [ 14 ], and in those of lower versus (vs.) higher socio-economic status [ 15 , 16 ]. Likewise, among others, parental concerns regarding personal and traffic safety [ 17 , 18 ], as well as safety perceptions of walking routes [ 19 ] also influence how young people travel to/from school. According to the systematic reviews from D’Haese et al. (2015) [ 20 ] and Wong et al. (2011) [ 21 ], many studies assessed environmental correlates of ACS in children/adolescents using objective methods such as Geographical Information Systems (GIS). Moreover, most studies analysed separately the home or school-built environment or the home-school route, but very few studied several built environments (e.g., [ 22 ]). In addition, since the built environment is highly contextual and differs in every country, more research is needed in Spain since there are few studies about the built environment Int. J. Environ. Res. Public Health 2020,17, 2295 3 of 19 and ACS [ 23 – 25 ]. For example, the study of Rodr í guez-L ó pez et al. (2017) [ 26 ], that used the same Spanish urban sample as in the current study, only studied the distance from home to school but they did not include any home or school neighbourhood variables. New research integrating built environment and route to/from school factors analyses would help to identify specific urban interventions of ACS for each population subgroup including children and adolescents. 1.3. Environmental Factors that May Influence ACS of Children and Adolescents The built environment may influence ACS across three general dimensions (i.e., density, diversity and design), which may be evaluated around the school, home or home-school routes [ 27 ]. For example, among others, (a) density has to do with compactness encouraging non-motorized travel to school and shorter school trips; (b) diversity may mean having mixed land use destinations; and (c) design features, including pedestrian/cycling infrastructure and gridded street patters, may increase destinations active accessibility. Other objective environmental measures based on street design may be topography, traffic safety and aesthetics [21,28]. The influence of these environmental dimensions and measures also depends on whether children or adolescents are studied, in addition to the type of built environment. For example, Huertas-Delgado et al. (2017) [ 18 ] reported barriers evidence of Spanish parents on traffic volume for children and distance to school for adolescents. Dangerous intersections and crime were reported as barriers for both age subgroups. Bringolf-Isler et al. [ 29 ] found positive associations between main street crossings along home-school routes and non-active commuting in children, while Timperio et al. (2006) [ 30 ] did not observe an association between the presence of busy roads (as barriers) along the home-school route and ACS. The latter authors also found a negative association between steep slope along the route to school and ACS in children but not in adolescents [ 30 ]. Mitra et al. (2010) [ 31 ] observed that the density of industrial (manufacturing/trade) and office employment had a high and negative association with ACS to/from school in adolescents, while retail and service employment had no association with ACS. In addition, there is evidence of more ACS trips for children attending schools located in lower socio-economic status (SES) neighbourhoods [ 23 , 32 ], and more obesity and body fat for adolescents from lower-SES home neighbourhoods [ 24 ]. However, Ikeda et al. (2018a) [ 32 ] found that ACS was negatively associated with school SES for youths. In addition, a short distance to school was found to be the strongest positive correlate with ACS in most cases [33–35]. 1.3.1. Built Environment Measures Traditionally in the field of urban design, transport and planning, three of the most relevant factors of the built environment commonly used as indicators of walkability are (i) density of residents, (ii) street connectivity and (iii) mixed land uses [ 16 , 21 , 23 , 32 , 36 – 38 ]. It may mean that the presence of people, a dense grid of well-connected streets, and the functional complexity of land uses can support walkability [ 38 ]. However, research focusing on children and adolescents reported limited evidence and non-conclusive associations of these three built environment factors with ACS [ 21 ]. For example, Ikeda et al. (2018a) [ 32 ] found association between increased street connectivity around schools and ACS for both children and youth. However, they also found a negative relationship between dwelling density and distance to school with ACS, this distance being the strongest predictor of ACS [ 32 ]. Larsen et al. (2009) [ 39 ] reported significant positive associations of land-use mix in the school-neighbourhood with ACS in children, but no relationship between them was found in the home-neighbourhood. McDonald (2007b) [ 40 ] reported no association between land-use mix and ACS in children. Moreover, Molina-Garc í a and Queralt (2017a) [ 23 ] found non-significant associations between school-neighbourhood walkability (as an index of residential density, land-use mix and street connectivity based on GIS data) and ACS. In contrast, ACS behaviour was more frequent in lower-walkable home-neighbourhoods among Spanish adolescents [ 24 ]. In addition, Queralt and Molina-Garc í a (2019) [ 41 ] observed a positive association between street Int. J. Environ. Res. Public Health 2020,17, 2295 4 of 19 connectivity and independent mobility to different destinations (not specifically to school) in Spanish adolescents’ home-neighbourhoods. From a scale perspective, these three built environment measures (i.e., density of residents, street connectivity and mixed land uses) are considered macro-scale features as they are urban morphology characteristics. However, there are also micro-scale features consisting of small environmental details that may also affect walkability [ 42 ] such as traffic calming features, aesthetics attributes, parking areas existence and pavement quality [ 21 ]. This study focused on macro-scalar attributes of the school-neighbourhood. 1.3.2. Home-School Route Measures Street connectivity is considered by planning to be a key factor in urban design. From an urban network perspective, the more the urban fabric looks like a dense and continuous grid, the higher the connectivity [ 43 , 44 ]. Low connectivity means that the origin-destination route through the street network is less direct and therefore the distance to be covered is increased. Origin-destination long distance discourages ACS and hence physical activity, which affects the scope of SDGs. Low connectivity may be due, for example, to low street network density, unlinks within the urban grid, the large size and length of blocks and the existence of cul-de-sacs, among others [43]. Two street connectivity measures were addressed in this study. The density of street intersections as a measure of the school-built environment and, in addition, the pedestrian route directness (PRD) as a performance measure of the home-school route of each child/adolescent (i.e., direct vs. indirect routes). In addition, a previous study indicated that the PRD variable is considered to be the best predictor of ACS among other connectivity variables such as street network density, connected node ratio, intersection density and link-node ratio [43]. The PRD measure was used in Portland (USA) to fix the maximum length of urban blocks, with a limit value to consider the routes as direct of PRD =1.5 [ 44 ]. Taking into account the study of Randall and Baetz (2001) [ 1 ], values for PRD =1.4–1.5 mean neighbourhood-grid street patterns and relatively small blocks. In contrast, values for PRD =1.63–1.88 show irregular streets and cul-de-sacs existence. The INDEX model sets values for PRD =1.2–1.5 as direct routes while values for PRD =1.6–1.8 indicate indirect routes [ 45 ]. Timperio et al. (2006) [ 30 ] classified the pedestrian routes of children to school as direct when they were <1.6 and indirect when they were ≥ 1.6. For these authors, negative correlates of ACS included parental perceptions of children, no lights or crossings to use, a busy road barrier, a steep incline route to school and a good connectivity [ 30 ]. In addition, until the beginning of the 21st century the PRD measure had not yet been used by local governments [44]. The greater the number of shorter and a priori more direct routes (i.e., less home-school distance) the higher the ACS level. Apart from route measures, ACS may also depend on other factors such as traffic level [ 17 – 19 ], which will be discussed regarding these measures. Dill’s research (2004) [ 43 ] gathered international evidence on the use of the PRD measure to show how direct/indirect are certain origin-destination routes. Nevertheless, how direct/indirect home-school routes are for Spanish children/adolescents in urban areas have not yet been studied. Knowing the association between ACS and how direct/indirect the home-school routes are as well as other environmental factors (i.e., density of residents, density of street intersections and mixed land uses) may help urban policy and planning decision-makers on the built environment encouraging walkability to/from school. The main aim of the work was to examine the association between different environmental factors of both school-neighbourhood and home-school route with ACS in Spanish children and adolescents of urban areas. In addition, the work aimed to identify threshold data for route variables, i.e., the overall threshold distance for participants’ ACS, as well as the overall threshold PRD and separately by participants’ age group. This study can be useful for urban policy and planning for the development of built environment interventions improving ACS levels, thus helping the scope of related SDGs. Int. J. Environ. Res. Public Health 2020,17, 2295 5 of 19 2. Materials and Methods 2.1. Study Sample and Design The participants’ data came from a cross-sectional study conducted in November 2012. The participants were primary school children (7–11 years old) and secondary school adolescents (12–18 years old) initially belonging to 26 schools of cities with >20,000 inhabitants of the southeast of Spain distributed in the provinces of Granada, Almeria and Murcia. These schools were recruited as a sample of convenience. Initially, 4777 students agreed to participate in the study by completing a questionnaire. It included questions such as personal data (e.g., age, family postal address, gender and school) and how participants commuted to/from school during the study week (i.e., the question from which the number of times a week participants walked to/from school was known). The questionnaire was approved by the Ethics Committee on Human Research of the University of Granada (Spain). All schools involved in the study were informed of the purpose of the questionnaire. Each school informed the participants and their parents in order to obtain their acceptance and written consent. 2.2. Active Mode of Commuting to/from School The participants completed a self-reported questionnaire helped by the teachers. The questions on the mode of transport to the school were set with the support of the systematic review on this respect by Herrador-Colmenero et al. (2014) [ 46 ] and were previously validated by the study of Chill ó n et al. (2017) [ 47 ]. The most important question asked regarding this work was about the mode of weekly travel (5 days, 2 possible times per day) to/from school. This question identified the number of times (0–10) that participants went to/from school and in which mode (i.e., active vs. non-active). The answer options were (a) walking or cycling (both coded as active commuting), although cycling was not included in the sample because of the low sample, or (b) using car, motorbike or bus (coded as non-active commuting). The sample of the study was very biased to the right. In other words, many participants reported reaching the maximum value of ACS (ACS =10) in their travels to/from school from home. In the dichotomous recoding process, active participants (1) were considered to be those who reported an ACS value of [4–10], and non-active participants (0) those who reported an ACS value of [0–3], according to the study by Chill ó n et al. (2014) [ 48 ]. In addition, socio-demographic data were reported in the questionnaire such as the participants’ age group and gender, among others. 2.3. Built Environmental Variables All the built environmental variables (i.e., school-built environment measures and home-school route measures) were calculated using a spatial analysis with the software QGIS V.3.4. The input data came from the spatial analysis of (i) the school-built environment, as well as (ii) the home-school route. Both data were collected at the participant level to create environmental exposure variables to be used in further analyses. The urban street network vector data as a base map for developing the subsequent spatial analysis was obtained from the road network digital information of the CNIG (National Centre for Geographic Information, as translated from its Spanish acronym) (date of the GIS data source: 2017). Figure 1 shows a workflow scheme of the method. 2.3.1. School-Built Environment The built environment of each school (i.e., buffer in term of spatial analysis) was defined as the area between the school and the threshold distance covered in every direction through the street network. This distance is what a pedestrian (child or adolescents in this case) is willing to walk. Several built environments were identified depending on the threshold distance that the participants are willing to cover in a predominantly active way. In order to identify the different environments (n=2), the threshold distances for the children/adolescents from urban areas collected in the research of Rodr í guez-L ó pez et al. (2017) [ 26 ] were used as follows: 1250 m for children and 1350 m for adolescents. Int. J. Environ. Res. Public Health 2020,17, 2295 6 of 19 Int. J. Environ. Res. Public Health 2019, 16, x 6 of 19 Figure 1. Method workflow. Abbreviations: CNIG: National Centre for Geographic Information. ATOM Inspire: National cadastral service. INE: National Statistics Institute. DERA: Reference Spatial Data of Andalusia (Spain). OSM: Open Street Map. PRD: Pedestrian route directness. ORS: Open Route Service. SDGs: Sustainable Development Goals. GIS: Geographic Information System. ACS: Active commuting to school. Additional note: The literature review was a source of processed data since it allowed finding previous studies useful to define both the analysis variable of mixed land use and the built environment catchment area. 2.3.1. School-Built Environment The built environment of each school (i.e., buffer in term of spatial analysis) was defined as the area between the school and the threshold distance covered in every direction through the street network. This distance is what a pedestrian (child or adolescents in this case) is willing to walk. Several built environments were identified depending on the threshold distance that the participants are willing to cover in a predominantly active way. In order to identify the different environments (n = 2), the threshold distances for the children/adolescents from urban areas collected in the research of Rodríguez-López et al. (2017) [26] were used as follows: 1250 m for children and 1350 m for adolescents. The density of residents consists on the ratio of the number of residents to land area (area values given in hectares) of the school-neighbourhood buffer (i.e., residents variable). The density of street intersections as a connectivity measure consists on the number of street intersections to land Figure 1. Method workflow. Abbreviations: CNIG: National Centre for Geographic Information. ATOM Inspire: National cadastral service. INE: National Statistics Institute. DERA: Reference Spatial Data of Andalusia (Spain). OSM: Open Street Map. PRD: Pedestrian route directness. ORS: Open Route Service. SDGs: Sustainable Development Goals. GIS: Geographic Information System. ACS: Active commuting to school. Additional note: The literature review was a source of processed data since it allowed finding previous studies useful to define both the analysis variable of mixed land use and the built environment catchment area. The density of residents consists on the ratio of the number of residents to land area (area values given in hectares) of the school-neighbourhood buffer (i.e., residents variable). The density of street intersections as a connectivity measure consists on the number of street intersections to land area of the school-neighbourhood buffer (i.e., intersections variable). These methods for measuring the density of residents and the density of street intersections were used before by other authors [ 21 , 29 , 49 ]. The mixed land uses (i.e., the integration level of land uses in a given area) were measured by a mixed-use diversity index (i.e., mixed uses variable). It captures how evenly the square footage of several urban land uses (i.e., residential, industrial, retail, office, public service and recreational) is distributed within each school-built environment. Therefore, it could be said that it is an indicator of urban dynamism or vitality in terms of diversity and functional urban complexity. Int. J. Environ. Res. Public Health 2020,17, 2295 7 of 19 The number of residents and the urban land use areas of each built environment were obtained from the ATOM Inspire national cadastral service (date of the GIS data source: 2018). This was done by means of disaggregation and aggregation operations based on available information on the number of dwellings per building, types of land use and resident population data. The latter came from the available information in the census section of the National Statistics Institute (INE) (date of the GIS data source: 2014). Although there are several methods for analysing the diversity of mixed land uses, e.g., [ 21 , 50 , 51 ], here the mixed-use diversity index (processed as a z-value, i.e., a normalised value) was obtained by the procedure from International Physical Activity and the Environment Network (IPEN; www.ipenproject.org) methodology, based on the method of Frank et al. (2005) [ 52 ]. The DERA (Reference Spatial Data of Andalusia; date of the GIS data source: 2017) as well as vector data from Open Street Map (OSM; date of the GIS data source: 2017) collaborative database were used to measure the recreational area (i.e., parks, gardens, playgrounds, sport fields and other open spaces) within this index. The higher this mixed-use diversity index value is the more mixed the land use, which a priori is positive to increase the ACS levels. 2.3.2. Home-School Route The distance between home (identified by family postal address of each participant) and school was calculated using the shortest distance on the street network between both (i.e., distance variable), as previous studies did (e.g., [ 22 , 30 ]). The Pedestrian Route Directness refers to the ratio between the shortest distance of the home-school route through the street network and the home-school straight-line distance (i.e., the Euclidean distance) (i.e., PRD variable). The geolocation of homes and schools (i.e., the spatial georeferencing of their postal addresses) was carried out using MMQGIS, a set of Python plugins developed by Michael Minn. Previously, the participants’ family postal addresses, originally compiled in an Excel sheet, were exported to CSV format. This plugin used Google Maps web service as well as an application programming interface (API) key that enabled the geolocation process and allowed to examine the home-school routes. The shortest routes between children/adolescents’ homes and schools, as well as the buffers (i.e., isochrones in terms of time) used in the spatial analysis of the different variables, were obtained using the Open Route Service (ORS) plugin supported by the Heidelberg Institute for Geoinformation Technology (HeiGIT). The geoprocessing operations performed using both plugins were implemented in the GIS used. 2.4. Statistical Analysis The statistical analysis was developed using SPSS 23 software (SPSS Inc., Chicago, IL, USA). All of the above-mentioned environmental variables (as independent or predictive variables) were continuous and not normal (bilateral asymptotic significance <0.05 after Kolmogorov-Smirnov test). Their influence on ACS was studied using binary logistic regression (BLR). This is a type of statistical analysis commonly used in the association between the built environment and ACS (e.g., [ 26 , 34 ]). In BLR, the probabilities described by a single dependent variable are modelled based on several predictive variables using a logistic function. The Intro method was used. It consists of a non-automatic procedure by which all variables are entered in a single step. The dependent variable (ACS variable) was recoded as a dichotomous categorical variable, i.e., active or walkers (4-10 active travels/week) vs. non-active, passive or motorised commuters (0-3 active travels/week). In addition, due to the low sample size of the school-built environment variables (n <30) (i.e., residents, intersections and mixed uses variables), these variables were also categorised into dummy variables using the median as the cut-offpoint. The route predictors (i.e., distance and PRD) remained as numerical variables. The statistical analysis was developed in several steps as follows. (1) A first BLR (model 1) was carried out for all participants by adding every environmental predictive variable (i.e., residents, intersections, mixed uses, distance and PRD). The age group variable (children vs. adolescents) was additionally added to the model as a categorical predictor in order Int. J. Environ. Res. Public Health 2020,17, 2295 8 of 19 to check its statistical significance and thus its applicability as a sample adjust variable for further analysis. In addition, the regression coefficients (i.e., the odds ratio—OR—as the ratio of the odds of ACS in the presence of exposure or independent variables) of the predictive variables obtained by including into the model the age group variable and without including it were compared to check for confounding effects. (2) The multicollinearity of these predictive variables was checked (i) by requesting correlation matrix in the BLR (i.e., no collinearity when correlation coefficients between predictors <0.80 and the standard errors <2.0 [ 53 ]); and (ii) using a multiple linear regression model (MLR) and requesting the collinearity diagnostics as recommended by SPSS manual (i.e., no collinearity when the variance inflator factor (VIF) is higher than 1.0 and the condition index <20 [54]). (3) Considering the results of the previous step, the association between the environmental predictors and the ACS variable was examined using a second BLR (model 2) separately for children and adolescents. (4) The threshold PRD of ACS for all participants and separately for children/adolescents, and the threshold distance of ACS for all participants were calculated using a receiver operating characteristics (ROC) curve analysis, which have already been studied previously (e.g., [ 1 , 26 ]). ROC curve analysis was widely used in several scientific fields when the evaluation of discrimination performance was of interest in the research [ 55 ]. The larger the area under the curve is (values between 0 and 1), the more discriminatory the test and the better the analysis model. Cut-offpoints between active vs. non-active participants for these variables were obtained using the Youden Index or J , where J =sensitivity +specificity – 1. This is the vertical distance between the ROC curve and the diagonal line [ 56 ]. The cut-offpoint for the threshold values mentioned above was obtained from the maximum value of J (i.e., maximum vertical distance). The ROC curve model was considered valid when the area under the curve ≥ 0.5 and the value 0.5 is outside the 95% CI (confidence interval). Otherwise the model was considered only exploratory. Once the significance variables were determined, an additional bivariate correlation analysis was carried out in order to discard multicollinearity between some of them. (5) In addition, the cross-table analysis allowed us to know the participants ACS level considering threshold PRD for all participants and for active vs. non-active participants. In addition, cross-table analysis made it possible to know the participants ACS level according to different ranges of PRD identifying to what extent the routes were direct or indirect [ 1 , 43 ] for different participants subgroups. 3. Results Participants were excluded from the analyses if they (i) did not complete the questionnaire regarding the number of total active travels to the school (n=720); (ii) reported commuting to/from school by bike (n=16); (iii) did not provide or did not write down correctly the information of their family postal address, which would ensure its correct georeferencing and therefore the correct calculation of the home-school distance; and (iv) were lost or null cases (n=1073). The final sample for the analyses was 2968 participants from 24 schools (8 primary schools for children, 14 secondary schools for adolescents and 2 primary-secondary schools for children and adolescents). This sample did not filter some possible cases of young people who mistakenly wrote down a different address than the family postal address, which in some isolated cases could be very far from the urban areas of the study. Table 1shows the participant characteristics according to socio-demographic variables (e.g., age and gender) and mode of travel to/from school (i.e., active vs. non-active). There was a higher percentage of active participants than non-active, more adolescents than children and slightly more men than women. There were more active children than non-active, and more active adolescent than non-active. The percentage of active adolescents over the total adolescent participants was higher than the percentage of active children over the total children participants. There were the same number of non-active women and men. However, the percentage of active men over the total male Int. J. Environ. Res. Public Health 2020,17, 2295 9 of 19 participants was slightly higher than the percentage of active women over the total female participants. Additionally, Table 2shows the descriptive statistics of the predictive variables. Table 1. Sample cases descriptive frequencies separately by participants’ subgroups. Sample Cases All n=2968 (100.0%) Active n=2091 (70.5%) Non-Active n=877 (29.5%) Children 826 (100.0%) 561 (67.9%) 265 (32.1%) Adolescents 2142 (100.0%) 1530 (71.4%) 612 (28.6%) Male 1508 (100.0%) 1070 (71.0%) 438 (29.0%) Female 1460 (100.0%) 1022 (70.0%) 438 (30.0%) Table 2. Environmental variables descriptive statistics separately by participants’ subgroups. Participants’ Subgroups Statistics Environmental Variables ACS (nº of Active Travels/Week) Residents (nº of Residents/Buffer) Intersections (nº of Street Crossings/Buffer) Mixed Uses (Index) Distance (km) PRD (Index) All Mean 6.53 158.83 3.74 0.00 2.93 1.28 Median 10.00 158.91 3.75 -0.06 0.80 1.24 SD 4.36 57.31 1.24 0.87 9.80 0.23 Min 0.00 66.51 1.44 -1.43 0.01 0.03 Max 10.00 271.61 6.63 2.00 112.61 4.57 Active Mean 9.18 156.12 3.68 0.03 1.55 1.30 Median 10.00 158.91 3.75 -0.06 0.62 1.26 SD 1.73 59.46 1.31 0.85 7.86 0.25 Min 4.00 66.51 1.44 -1.43 0.01 0.03 Max 10.00 271.61 6.63 2.00 111.44 4.57 Non-active Mean 0.20 165.28 3.90 -0.08 6.23 1.24 Median 0.00 170.66 3.74 -0.08 2.99 1.20 SD 0.61 51.29 1.03 0.89 12.74 0.17 Min 0.00 66.51 1.44 -1.43 0.04 0.55 Max 3.00 271.61 6.63 2.00 112.61 3.45 Children Mean 6.12 156.84 3.78 0.30 2.16 1.30 Median 9.00 136.17 3.76 0.30 0.69 1.25 SD 4.35 64.39 1.49 1.07 7.86 0.22 Min 0.00 66.51 1.44 -1.28 0.03 0.93 Max 10.00 271.61 6.63 2.00 85.74 3.13 Adolescents Mean 6.68 159.59 3.73 -0.12 3.23 1.28 Median 10.00 158.91 3.74 -0.06 0.86 1.23 SD 4.36 54.33 1.13 0.74 10.44 0.24 Min 0.00 67.46 1.56 -1.43 0.01 0.03 Max 10.00 234.50 6.54 1.19 112.61 4.57 Notes: Mean =The sum of the values of a data set divided by the number of the values; SD =Standard deviation; Median =The value separating the higher half from the lower half of a data sample; ACS =Active commuting to and from school; PRD =Pedestrian route directness; Min =Minimum value; Max =Maximum value. The results of the first BLR (model 1) showed that all predictive variables were found to be significant (p ≤ 0.05), except for the residents variable (p=0.1). Subsequently, the statistical significance of the age group variable was verified (p<0.001) (goodness of fit was 72.9% for all participants; cut-off value for the classification of cases p=0.50, i.e., cases with predicted values above the cut-offvalue for classification have the event or result modelled, while cases with predicted values below the cut-off value do not have the event or result, with pbeing a cut-offvalue between 0.01 and 0.99), this variable being accepted as an adjust variable for the BLR of model 2. In addition, the difference between the ORs of the predictive variables obtained by BLR (model 1) including the age group variable and without including it was found to be about or less than 10% [57]. The correlation matrix in BLR of model 1 showed correlation coefficients between predictors <0.80 and standard errors were <2.0. Despite this, the collinearity diagnostics from a MLR were requested to contrast them with each other. Regarding the latter, some of the collinearity results were considered Int. J. Environ. Res. Public Health 2020,17, 2295 16 of 19 Author Contributions: Conceptualization, F.S.C.-S., J.M.-G. and P.C.; methodology, F.S.C.-S., J.M.-G. and P.C.; software, F.S.C.-S., J.M.-G. and P.C.; validation, F.S.C.-S., F.J.A.- Á ., J.M.-G. and P.C.; formal analysis, F.S.C.-S., J.M.-G. and P.C.; investigation, F.S.C.-S., J.M.-G. and P.C.; resources, F.S.C.-S., J.M.-G. and P.C.; data curation, F.S.C.-S., J.M.-G. and P.C.; writing—original draft preparation, F.S.C.-S., J.M.-G. and P.C.; writing—review and editing, F.S.C.-S., F.J.A.- Á ., J.M.-G. and P.C.; visualization, F.S.C.-S., J.M.-G. and P.C.; supervision, F.S.C.-S., F.J.A.- Á ., J.M.-G. and P.C. All authors have read and agree to the published version of the manuscript. Funding: This research was funded by the Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund (DEP2016-75598-R, MINECO/FEDER, UE). Additionally, this study takes place thanks to funding from the University of Granada, Plan Propio de Investigaci ó n 2016 (Excellence actions: Unit of Excellence on Exercise and Health -UCEES-), and the Junta de Andaluc í a, Consejer í a de Conocimiento, Investigación y Universidades, European Regional Development Fund (ref. SOMM17/6107/UGR). Conflicts of Interest: The authors declare no conflict of interest. References 1. Randall, T.A.; Baetz, B.W. Evaluating pedestrian connectivity for suburban sustainability. J. Urban Plan. Dev. 2001,127, 1–15. [CrossRef] 2. Lee, M.C.; Orenstein, M.R.; Richardson, M.J. Systematic review of active commuting to school and children physical activity and weight. J. Phys. Act. Health 2008,5, 930–949. 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