Built environment determinants of pedestrians’ and bicyclists’ route choices on commute trips: Applying a new grid-based method for measuring the built environment along the route

Journal of Transport Geography 78 (2019) 56–69

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Built environment determinants of pedestrians’ and bicyclists’ route choices on commute trips: Applying a new grid-based method for measuring the built environment along the route

T

Satu Sarjala School of Architecture, Tampere University of Technology, P.O. Box 600, FI-33101 Tampere, Finland

ARTICLE INFO

ABSTRACT

Keywords: Active transport built environment route choice commute GPS GIS

To better understand the role of the built environment in route choices among pedestrians and bicyclists, this study explores the built environment distribution along commute routes in two neighbourhoods in Tampere, Finland. A new grid-based method is developed to enable a more sophisticated analysis of the built environment along the route. The data consist of 73 commute routes collected with the Sports Tracker® smartphone application, of which 18 are made by foot and 55 by bicycle. To find the most relevant spatial scale, the values of each built environment variable are calculated with five buffer sizes for each cell of the grid covering the research area. Five statistics describing the distribution of the built environmental variables along each shortest and actual route are then calculated and compared. The results show that besides determining whether a significant association is found, different spatial scales for the analysis produce dissimilar findings in strength and even the direction of the correlations found. The most significant associations are found mainly with the smallest buffer (15m). Of the five built environment statistics calculated, the mean value of most built environmental variables had the most consistent correlation with route choice. Also, standard deviation and the third quartile of several built environmental variables along the routes correlate with route choice. The most significant associations with route choice are found with intersection density, institutional land use, slope, and age of buildings.

1. Introduction Because of the global obesity epidemic and concerns about climate change, the focus of urban planning has gradually shifted toward improving walking and cycling networks and facilities and promoting them as possible replacements for cars for utilitarian trips. Walking and cycling for transport are demonstrated to provide population-level health benefits in, for example, reducing obesity and the risk of diabetes (Pucher et al., 2010). Replacing urban trips in private cars with active transport modes also has the potential for significant reduction of CO2 emissions (Woodcock et al., 2009). As documented in several studies, physical activity is significantly influenced by many features of the built environment (BE) (Ding et al., 2011; Van Holle et al., 2012). The ways in which BE affects route choice may differ depending on the purpose of the trip. For example, connectivity, land use mix, and traffic-related factors have been proven to have associations with walking for transport but not with recreational walking (McCormack and Shiell, 2011). Active commuting to work

provides a highly accessible form of daily physical activity for the adult population (Panter et al., 2011). According to the Finnish Transport Agency (2018) 15 % of all trips in Finland are commute trips, of which less than 20 % are currently made by foot or bicycle. Therefore, this study focuses only on the BE in relation to commute trips. Comparing the actual route with the shortest possible route is a commonly used method to study the BE characteristics of routes (Badland et al., 2010; Krenn et al., 2014; Winters et al., 2010a,b). However, previous studies on pedestrian and bicycle route choices focus mainly on network characteristics, such as sidewalks, connectivity, bike paths and intersections (Buehler and Dill, 2016; Sugiyama et al., 2012) rather than the BE surrounding the route. Some of the most consistently associated BE variables with pedestrian route choice are found to be the proximity to shops and street connectivity (Borst et al., 2009; Guo and Loo, 2013; Sugiyama et al., 2012), whereas for bicyclists, bicycle paths seem to be consistently preferred, while high traffic volumes, traffic lights and hilliness are avoided (Broach et al., 2012; Krenn et al., 2014; Winters et al., 2010a). However, the

E-mail address: [email protected]. https://doi.org/10.1016/j.jtrangeo.2019.05.004 Received 14 November 2018; Received in revised form 12 February 2019; Accepted 7 May 2019 Available online 27 May 2019 0966-6923/ © 2019 Published by Elsevier Ltd.

Journal of Transport Geography 78 (2019) 56–69

S. Sarjala

volume of evidence is insufficient and more research is needed on both pedestrian and bicycle route choice. Studying BE features affecting route choice of commuters using active transport modes may provide new tools for managing and predicting traffic flows of pedestrians and bicyclists. It may also give clues about what kind of commute routes encourage walking and cycling, which may help cities make the best use of their budgets for improving walking and cycling environments. In previous related studies, BE around routes is mainly studied with one elongated buffer around the whole route, the so called “sausage buffer”, which considers the route as one homogenous space, as it only allows calculation of the average values of each BE variable along the entire route (Broberg and Sarjala, 2015; Krenn et al., 2014; Winters et al., 2010a,b). However, route environments are rarely homogenous from start to end, and the sausage buffer method does not account for situations where route choice is associated with only the largest or smallest values or the variation of a BE variable along the route. Moreover, routes are not experienced as one space, but as a series of varying episodes (Aura, 1993). To overcome these issues, the route surroundings in this study are analysed using a grid-based method. This allows the routes to be studied as a series of spaces and, thus, provides more information about the distribution of BE characteristics along the routes. To find out whether the distribution of the BE variables is essential in route choice or whether calculating only the average values is an adequate method to study route environments, the quartiles and the standard deviation along with the mean values of the BE variables are calculated for each route. A crucial challenge in studies concerning the associations between BE and physical activity is the definition of the most relevant spatial scale (Brownson et al., 2009; Heath et al., 2006; Mitra and Buliung, 2012). The most relevant radius, or buffer size, for studying BE may differ according to the studied transport mode and purpose as well as the analysed variable (Brownson et al., 2009). Despite that, the implication of the spatial scale in analysing the BE, especially in studies concerning route environments, has not been widely studied. As knowledge of the most relevant spatial scale is very limited, various buffer sizes are used across studies for analysing the BE, which reduces the comparability of the findings and might lead to conflicting results (Wong et al., 2011). In this study, all BE variables are studied with several buffer sizes to find out the most relevant spatial scale for each BE feature with respect to studied transport modes.

Drivetime programme version 7.1 based on the route network data of the Topographic Database provided by the National Land Survey of Finland. The demographic information was collected using a Webropol survey and connected with the Sports Tracker data by usernames queried in the survey. 2.2. The grid-based method The entire research area was covered with a 20 × 20-metre grid, and the values of each BE variable were calculated with 15 m, 50 m, 100 m, 200 m, and 300 m buffers for each cell of the grid with MapInfo Professional version 12.5. The buffer sizes were chosen to cover and to go slightly beyond the buffer sizes used previously in studies concerning route environments and active transport, at both ends of the scale. In previous related studies the buffer sizes ranged mainly from approximately 18 m (60 feet in the original paper) (Culyba et al., 2018) to 250 m (Badland et al., 2010; Winters et al., 2010a,b). 2.3. Built environment characteristics Altogether 15 built environment variables were analysed. Variables concerning land use were extracted from the national SLICES database provided by the National Land Survey of Finland. The analysed land use variables included parks and recreational, residential, industrial, commercial, office, institutional, water and forest proportions, which were calculated as the area proportion of each land use cover within the buffer. For land use mix, six land uses were included in the equation: parks and recreational, residential, industrial, commercial, offices and institutional. The calculation of land use mix was based on the formula introduced by Frank et al. (2006):

Land use mix = A = (b1 /a)

A (ln (N ))

ln (b1/ a) + (b2/ a)

ln (b3 / a) + (b4 / a)

ln (b2 / a) + (b3 /a)

ln (b4/ a) + (b5 / a)

ln (b5 / a) + (b6 / a)

ln (b6 / a) b1 = total square metre of land use area in residential usesb2 = total square metre of land use area in commercial usesb3 = total square metre of land use area in office usesb4 = total square metre of land use area in recreational uses and parksb5 = total square metre of land use area in institutional usesb6 = total square metre of land use area in industrial usesln = natural logarithma = total square metre of land for all six land uses present in bufferN = number of land uses in the calculation Intersection density was calculated based on the route network data of the Topographic Database also provided by the National Land Survey of Finland. The density of intersections was calculated by dividing the number of all intersections within the buffer by the buffer area. The variables concerning buildings were extracted from the open GIS database of the Tampere City Region. The floor area was calculated as the combined floor area and the dwellings as combined number of dwellings in all buildings within the buffer. The number of storeys was calculated as the average number of storeys and the age of buildings as the average age of all buildings within the buffer. The slope data was calculated based on the laser scanned elevation model (2 × 2 m) provided by the National Land Survey of Finland. The slope on each point of the model was calculated with MapInfo Vertical Mapper version 3.7 and the average slope of all the points within the buffer was then calculated. With each environmental variable, the average, 1st quartile, median, 3rd quartile and standard deviation of all cells intersecting the route were calculated for each route (see Figure 2). The features of the actual and the shortest routes were compared with each other and the significance (p-value) of the differences was calculated with a paired ttest with 95% confidence interval.

2. Materials and methods 2.1. Data collection The data was collected in Tampere, Finland, in three phases within the KÄPY project led by the UKK Institute for Health Promotion Research and funded by the Ministry of Education and Culture. The first and second data collections took place in the Hatanpää area in fall 2014 (n = 33) and spring 2015 (n = 29), where the participants worked in a total of nine companies or institutions. The third set of data was collected in Hervanta in the spring 2016 (n = 11), from employees of five separate workplaces. The selection and recruitment of the participants is described in more detail in the protocol article by Aittasalo et al. (2017). The route data of active commuters was collected with the Sports Tracker® GPS application, which the participants downloaded to their own smartphones to track their actual commute routes (see Fig. 1). The participants were first asked to invite the profile of KÄPY project as their Sports Tracker friend. The participants then tracked their routes from home to work or vice versa by carrying the smartphones with the Sports Tracker application on tracking mode during the commute. In the end, the participants shared the tracked commute routes with their Sports Tracker friends and named the route “KÄPY” in order for the researchers to find and download the right route data. The shortest possible route options were calculated with MapInfo 57

Journal of Transport Geography 78 (2019) 56–69

S. Sarjala

Fig. 1. Sports Tracker® route data screen shot. Table 1 Descriptive characteristics of the study participants (n = 73).

Gender Male Female Transport mode Walking Cycling Age (mean 42 years, 2 missing) 23-32 33-42 43-52 53-62 Time of data collection Fall 2014 Spring 2015 Spring 2016

Fig. 2. A route on the grid The BE statistics were calculated based on the grid cells intersecting the route (shaded).

3. Results

Number

Percent

47 26

64.4 35.6

18 55

24.7 75.3

10 27 28 6

14.1 38.0 39.4 8.5

33 29 11

45,2 39.7 15.1

Table 2 Comparison of the shortest and actual routes (km).

The data consisted of 73 routes of which 18 were made by foot and 55 by bicycle. The age of the participants ranged from 23 to 62 years, with an average of 42. Almost two thirds of the participants were male (n = 47, 64.4 %) and 26 were female (see Table 1). The actual routes were, on average, 730 m (13 %) longer than the shortest routes (p = 0,010). For pedestrian routes only, the actual routes were 230 m (8 %) longer than the shortest routes, but not significantly (p = 0,070), and for cyclists the difference was 900 m (14%, p = 0,017). When comparing the median route lengths, the combined actual routes were 360 m (8%) longer than the shortest routes, 130 m (5%) longer with pedestrians only and 390m (7%) longer with bicyclists only (see Table 2). One of the main findings of the study was the discovery that buffer size can, in general, determine whether or not a significant correlation is found between the BE variable and route choice. The results also alter significantly in strength and even direction according to the spatial scale used in the analysis. The most relevant buffer size, in terms of the statistical significance of the difference between the shortest and actual

All routes Shortest routes Actual routes Pedestrian routes Shortest routes Actual routes Cyclist routes Shortest routes Actual routes

Median

Mean

Std Dev

Range

4.39 4.75

5.44 6.17

3.57 4.58

0.29 – 17.85 0.29 – 24.28

2.38 2.51

3.05 3.28

2.53 2.96

0.29 – 9.88 0.29 – 11.94

5.32 5.71

6.22 7.12

3.53 4.63

1.71 – 17.85 1.73 – 24.28

routes, varied across different BE variables and transport modes. The most connections with route choice were found with the smallest buffer size (15 m), and with mean, 3rd quartile and standard deviation of the BE variable values along the routes. As shown in Table 3, the clearest associations with route choice concerning pedestrians, bicyclists or both combined were found with 58

59

Mean Quartile 1

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size p-value 0.995 0.554 0.895 0.958 0.708

200m Mean diff -0.000545 0.000931 0.004391 -0.00148 -0.00216 p-value 0.938 0.861 0.631 0.886 0.637

200m Mean diff 0.006802 -0.001989 0.003635 0.006305 0.008492

p-value 0.493 0.797 0.757 0.704 0.282

300m Mean diff 0.007365 -0.005936 0.012852 0.011975 0.008706

p-value 0.072 N/A N/A 0.041 0.375

50m Mean diff 0.001628 0 0.005559 0.019678 -0.009313 p-value 0.737 N/A 0.058 0.082 0.212

200m Mean diff 0.008002 0.001336 0.004013 0.012475 0.007290

p-value 0.192 0.483 0.258 0.278 0.269

300m Mean diff 0.007651 0.001665 0.006980 0.013067 0.007728

15m Mean diff 0.012575 0

p-value 0.078 N/A

50m Mean diff -0.014043 -0.002855 p-value 0.094 0.188

Residential proportion (Actual - Shortest Route)

100m p-value 0.258 N/A 0.169 0.083 0.641

Park and recreation proportion (Actual - shortest route)

15m Mean diff 0.011665 0 0 0.04035 0.009620

Park and recreation proportion (Actual - shortest route)

100m p-value 0.449 0.680 0.464 0.355 0.306

100m Mean diff -0.0167 -0.002765

p-value 0.157 0.564 0.105 0.207 0.145

100m Mean diff 0.000345 3.196E-06 0.007350 0.008386 -0.008494

p-value 0.393 0.190 0.267 0.431 0.220

300m Mean diff 0.000963 -0.000711 0.009098 0.004126 -0.000684

200m Mean diff -0.000384 0.000100 -0.00022 0.004027 -0.003229

p-value 0.018 0.322 0.148 0.033 0.003

p-value 0.890 0.882 0.935 0.443 0.382

50m Mean diff 0.002482 0.008940 0.003614 0.004964 -0.003014

300m Mean diff -0.000203 -0.000583 -0.00088 0.003612 -0.000849

p-value 0.839 0.150 0.815 0.823 0.686

p-value 0.038 0.640

15m Mean diff 0.003988 0 0 0.026074 -0.003013

200m Mean diff -0.009903 -0.004789

p-value 0.513 N/A N/A 0.185 0.788

p-value 0.124 0.444

50m Mean diff -0.002986 0 0.004482 0.008841 -0.0163

300m Mean diff -0.005958 -0.007061

p-value 0.554 N/A 0.161 0.463 0.046

Park and recreation proportion (Actual - shortest route)

p-value 0.928 0.321 0.088 0.344 0.117

15m Mean diff 0.03757 0.003555 0.026612 0.08102 0.03216

Forest proportion (Actual - shortest route)

100m Mean diff -5.11E-05 0.003964 -0.00128 -0.000773 -0.002082

Forest proportion (Actual - shortest route)

p-value 0.662 0.115 0.929 0.592 0.737

Cyclists

50m Mean diff 0.004248 0.008633 -0.001076 0.009536 0.002184

Pedestrians

p-value 0.003 0.226 0.119 0.009 0.001

p-value 0.866 0.877 0.194 0.617 0.868

p-value 0.262 0.244

100m Mean diff -0.002496 4.242E-06 0.006809 0.000492 -0.0129

p-value 0.929 0.608 0.702 0.406 0.768

100m Mean diff -0.00315 0.003183 0.001146 -0.008199 -0.006567

200m Mean diff -0.002949 0.001886 0.004638 -0.004028 -0.005646

p-value 0.058 0.331 0.516 0.119 0.094

p-value 0.735 0.776 0.689 0.751 0.306

50m Mean diff 0.009643 0.007696 -0.015407 0.023503 0.018066

300m Mean diff -0.001132 0.000998 0.007870 0.001557 -0.003757

p-value 0.479 0.522 0.279 0.363 0.182

200m Mean diff -0.003128 -0.000304 -0.001606 0.001262 -0.006672

p-value 0.059 N/A N/A 0.123 0.080

p-value 0.313 0.645 0.639 0.832 0.129

50m Mean diff 0.015729 0 0.008851 0.052792 0.012124

300m Mean diff -0.002773 -0.001319 -0.003452 0.000517 -0.003655

p-value 0.204 N/A 0.210 0.056 0.481

15m Mean diff 0.027617 0

p-value 0.081 N/A

50m Mean diff -0.005342 -0.006274

100m Mean diff -0.013538 -0.015206

p-value 0.261 0.271 0.200 0.913 0.283

100m Mean diff 0.009026 0 0.009005 0.032505 0.005043

p-value 0.873 0.867 0.358 0.874 0.450

100m Mean diff 0.009417 0.00635 -0.008696 0.021916 0.011622

(continued on next page)

p-value 0.723 0.396

Residential proportion (Actual - Shortest Route)

p-value 0.565 0.322 0.203 0.961 0.040

15m Mean diff 0.035122 0 0 0.083951 0.048223

Park and recreation proportion (Actual - shortest route)

p-value 0.772 0.670 0.926 0.651 0.305

15m Mean diff 0.035634 0.002798 0.007819 0.080493 0.041495

Forest proportion (Actual - shortest route)

Forest proportion (Actual - shortest route)

15m Mean diff 0.03709 0.003368 0.021978 0.08089 0.03446

Pedestrians

Pedestrians & cyclists

Table 3 Differences in built environment distribution along actual vs. shortest routes

S. Sarjala

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60

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Median Quartile 3 SD

0.190 0.692 0.001

0.005815 -0.0371 -0.0131

0.666 0.048 0.025

200m Mean diff -0.011369 -0.015386 -0.014626 -0.002496 0.005624

p-value 0.208 0.266 0.348 0.910 0.612

300m Mean diff -0.009638 -0.019023 -0.018131 0.000545 0.004553

p-value 0.005 N/A N/A N/A 0.001

50m Mean diff 0.002516 0 0 -0.001414 0.006569 p-value 0.276 N/A N/A 0.465 0.275

200m Mean diff -0.002941 -0.000859 0.003133 -0.001666 -0.004551

p-value 0.498 0.331 0.513 0.856 0.264

300m Mean diff -0.006865 -0.002453 -0.003409 -0.00703 -0.007699

15m Mean diff 0.002503 0 0 0 0.01608

p-value 0.110 N/A N/A N/A 0.014

50m Mean diff -0.002325 0 0 -0.001284 -0.001836 p-value 0.308 N/A N/A 0.182 0.749

Commercial proportion (Actual - shortest route)

100m p-value 0.929 N/A 0.346 0.718 0.825

Industrial proportion (Actual - Shortest Route)

15m Mean diff 0.0072 0 0 0 0.02785

Industrial proportion (Actual - Shortest Route)

100m p-value 0.269 0.429 0.854 0.335 0.718

Residential proportion (Actual - Shortest Route)

0.007549 0.007204 0.02755

Residential proportion (Actual - Shortest Route)

Table 3 (continued)

100m Mean diff -0.003022 0 0 -0.002623 -0.004228

p-value 0.127 0.278 0.402 0.422 0.063

100m Mean diff 0.000663 0 -0.000763 0.005111 -0.000665

p-value 0.284 0.173 0.148 0.973 0.570

-0.007188 -0.031 -0.01317

-0.011082 -0.0263 -0.001321

0.232 0.038 0.756

-0.004652 -0.014263 0.000348

200m Mean diff 0.000599 -0.000213 0.002328 0.005249 -0.001341

p-value 0.339 N/A 0.835 0.997 0.028

p-value 0.777 0.319 0.165 0.221 0.602

50m Mean diff -0.016891 -0.001736 0.003192 -0.041149 -0.01839

300m Mean diff 8.414E-05 -0.000385 0.002306 0.003042 -0.001796

p-value 0.093 0.297 0.828 0.080 0.007

p-value 0.205 N/A N/A 0.301 0.413

15m Mean diff 0.00538 0 0 0 0.02327

200m Mean diff -0.004182 0 -0.000491 -0.00308 -0.007322

p-value 0.038 N/A N/A N/A 0.003

p-value 0.051 N/A 0.223 0.191 0.086

50m Mean diff 0.002321 0 0 -0.000507 0.004574

300m Mean diff -0.003371 -3.88E-05 -0.000202 -0.004008 -0.005308

p-value 0.437 N/A N/A 0.322 0.464

Industrial proportion (Actual - Shortest Route)

p-value 0.770 N/A 0.167 0.093 0.876

15m Mean diff 0.007652 0 0.000646 8.113E-05 0.02082

Residential proportion (Actual - Shortest Route)

0.578 0.038 0.008

p-value 0.058 0.175 0.755 0.092 0.100

100m Mean diff 0.000727 0 -0.000345 0.005817 -0.001572

p-value 0.968 0.594 0.208 0.442 0.397

100m Mean diff -0.017768 0.001306 -0.011178 -0.03454 -0.01618

0.521 0.138 0.921

200m Mean diff -0.009423 -0.001321 -0.009923 -0.0341 -0.003594

0.185 0.402 0.002

p-value 0.243 0.851 0.380 0.026 0.414

0.013829 -0.024704 0.003022

300m Mean diff -0.004754 -0.003147 -0.00024 -0.019109 -0.001027

0.671 0.348 0.797

200m Mean diff 0.001757 -9.09E-07 0.002064 0.007512 -0.00029

p-value 0.068 N/A N/A N/A 0.078

p-value 0.471 0.322 0.206 0.125 0.927

50m Mean diff 0.004423 0 0 -0.004185 0.012666

300m Mean diff 0.002358 0.000292 0.00418 0.006338 0.000135

p-value 0.454 N/A N/A 0.597 0.421

15m Mean diff -0,003391 0 0 0 -0,002934

p-value 0,187 N/A N/A N/A 0,779

50m Mean diff -0,0127 0 0 -0,00577 -0,0242

100m Mean diff -0,011456 0 0 -0,013261 -0,019719

p-value 0.313 0.639 0.040 0.153 0.956

100m Mean diff 0.000467 0 -0.002041 0.002955 0.002106

p-value 0.462 0.638 0.978 0.102 0.794

0.005001 -0.020143 -0.003959

(continued on next page)

p-value 0,028 N/A N/A 0,137 0,037

Commercial proportion (Actual - shortest route)

p-value 0.773 N/A 0.160 0.061 0.744

15m Mean diff 0.012757 0 0 0 0.041843

Industrial proportion (Actual - Shortest Route)

p-value 0.076 0.791 0.452 0.065 0.004

0.028642 0.028971 0.04813

Residential proportion (Actual - Shortest Route)

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Mean Quartile 1 Median

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

200m Mean diff -0,009801 0 -0,001884 -0,009122 -0,016701

p-value 0,075 N/A 0,256 0,149 0,076

p-value 0.177 N/A N/A 0.854 0.008

50m Mean diff -0.003608 0 -0.002373 -0.002858 0.002183

200m Mean diff -0.002593 -0.003172 -0.008331 -0.007776 0.003031

p-value 0.727 0.536 0.293 0.597 0.597

300m Mean diff 7.192E-06 -3.42E-05 -0.001971 0.002741 0.002062

p-value 0.282 N/A 0.287 0.715 0.643

300m Mean diff -0,007653 -0,000101 0,000842 -0,011984 -0,012197

p-value 0.001 N/A N/A 0.350 0.000

50m Mean diff 0.0121 0 0.000288 0.012790 0.02249 p-value 0.008 N/A 0.321 0.076 0.003

100m p-value 0.182 N/A 0.241

200m Mean diff 0.013811 0.003275 0.00571

p-value 0.132 0.331 0.035

300m Mean diff 0.01144 0.001357 0.000586

Institutional proportion (Actual - shortest route)

15m Mean diff 0.01721 0 0 0.004951 0.04076

Institutional proportion (Actual - shortest route)

100m p-value 0.319 0.590 0.364 0.188 0.936

Offices proportion (Actual - shortest route)

15m Mean diff 0.004911 0 0 0.001725 0.01888

Offices proportion (Actual - shortest route)

100m p-value 0,065 N/A N/A 0,156 0,063

Commercial proportion (Actual - shortest route)

Table 3 (continued)

p-value 0.125 0.383 0.908

100m Mean diff 001084 0 0.001735 0.010856 0.01855

p-value 0.999 0.993 0.676 0.780 0.500

100m Mean diff -0.0066 -0.000209 -0.002232 -0.007995 -0.005856

p-value 0,068 0,331 0,655 0,072 0,061

200m Mean diff -0.004192 -0.001219 -0.00148 -0.003901 -0.004402

p-value 0,019 N/A N/A N/A 0,005

p-value 0.064 0.337 0.478 0.387 0.054

50m Mean diff 0,001054 0 0 0,000184 0,005482

200m Mean diff 0.00677 0.000370 0.003460 0.004472 0.01107

p-value 0.428 N/A N/A 0.239 0.128

p-value 0.042 0.826 0.064 0.473 0.009

50m Mean diff -0.003036 0 -0.000436 0.002366 -0.000133

300m Mean diff 0.004300 0.001063 -0.000855 0.006396 0.005602

p-value 0.351 N/A 0.655 0.694 0.980

300m Mean diff -0.002253 -0.000157 -0.001302 -0.000847 -0.002686

p-value 0,650 N/A N/A 0,322 0,401

15m Mean diff 0.013 0 0

p-value 0.004 N/A N/A

50m Mean diff 0.00998 0 0

p-value 0.017 N/A N/A

Institutional proportion (Actual - shortest route)

p-value 0.007 N/A 0.442 0.085 0.003

15m Mean diff 0.002830 0 0 0.001993 0.012465

Offices proportion (Actual - shortest route)

p-value 0.042 0.498 0.556 0.222 0.098

15m Mean diff 0,00443 0 0 0 0,0223

Commercial proportion (Actual - shortest route)

100m Mean diff 0.0094 0 0.000348

p-value 0.124 0.341 0.697 0.227 0.064

100m Mean diff -0.005635 -0.00037 0.000926 -0.001422 -0.0075

p-value 0.117 0.882 0.382 0.772 0.052

100m Mean diff -0,000262 0 0 0,000858 0,000841 p-value 0,293 N/A 0,364 0,643 0,375

p ≤ 0.001(bold) p ≤ 0.01 (bold, italic) p ≤ 0.05 (italic)

200m Mean diff -0,002343 0 -3,48E-05 -0,001103 -0,004252

200m Mean diff -0.0047 -0.00058 0.000762 -0.002632 -0.00683

p-value 0.269 N/A N/A 0.981 0.007

p-value 0.012 0.182 0.485 0.486 0.004

50m Mean diff -0.005357 0 -0.008289 -0.018822 0.009260

300m Mean diff -0.003 -0.000197 -0.001084 -0.002022 -0.00424

p-value 0.578 N/A 0.343 0.480 0.381

300m Mean diff -0,001969 -1,85E-05 -0,000544 -0,001398 -0,003054

p-value 0.017 N/A 0.890

15m Mean diff 0.030094 0 0 0.013251 0.06815

200m Mean diff 0.004471 -0.00058 0.002722

p-value 0.071 N/A N/A 0.514 0.021

p-value 0.176 0.768 0.243

50m Mean diff 0.018546 0 0.001170 0.015244 0.040056

p-value 0.488 0.490 0.586

100m Mean diff 0.015258 0 0.005974 0.009344 0.027124

p-value 0.018 0.709 0.403 0.384 0.006

100m Mean diff -0.009463 0.000283 -0.011885 -0.028082 -0.000698

p-value 0,311 0,322 0,377 0,543 0,414

(continued on next page)

300m Mean diff 0.001963 0.000966 -0.001326

p-value 0.177 N/A 0.331 0.542 0.060

Institutional proportion (Actual - shortest route)

p-value 0.066 0.322 0.741 0.789 0.048

15m Mean diff 0.011267 0 0 0.000905 0.03847

Offices proportion (Actual - shortest route)

p-value 0,914 N/A N/A 0,553 0,887

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Mean

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Quartile 3 SD

0.018258 0.019147

0.345 0.141

p-value 0.272 N/A N/A 0.287 0.656

50m Mean diff 0.006401 0 0.003769 0.010937 0.004410

200m Mean diff 0.007738 0.016482 0.022251 -0.000331 -0.006606

p-value 0.616 0.258 0.265 0.988 0.428

p-value 0.275 N/A N/A 0.321 0.130

50m Mean diff -0.006753 -0.0004 -0.00116 -0.012493 -0.0081

200m Mean diff -0.018945 -0.034021 -0.015741 -0.017711 0.006826

p-value 0.065 0.136 0.188 0.240 0.564

15m Mean diff 0.4841

p-value 0.013

50m Mean diff -0.293702

Slope (%) (Actual - shortest route)

100m p-value 0.150 0.537 0.215 0.132 0.606

Land use mix (Actual - shortest route)

15m Mean diff -0.00136 0 0 -0.000486 -0.004019

Land use mix (Actual - shortest route)

100m p-value 0.376 0.369 0.197 0.359 0.936

Water proportion (Actual - shortest route)

15m Mean diff 0.002217 0 0 0.001765 0.001893

Water proportion (Actual - shortest route)

0.604 0.121

p-value 0.116

300m Mean diff -0.016933 -0.0348 -0.000244 -0.014314 0.012539

p-value 0.149 0.321 0.841 0.373 0.015

300m Mean diff 0.002921 0.010341 0.003862 -0.002373 -0.005989

p-value 0.087 N/A 0.249 0.086 0.303

0.027161 0.013357

Institutional proportion (Actual - shortest route)

Table 3 (continued)

100m Mean diff -0.3792

p-value 0.061 0.046 0.984 0.286 0.212

100m Mean diff -0.007286 -0.008219 -0.001245 -0.014916 -0.001798

p-value 0.826 0.426 0.742 0.919 0.344

100m Mean diff 0.008811 0.001907 0.010706 0.01772 0.003736

0.094 0.158

200m Mean diff 0.006824 0.008338 0.011922 0.008541 8.053E-05

0.431 0.001

p-value 0.265 0.072 0.068 0.409 0.988

0.01199 0.01675

200m Mean diff -0.011358 -0.009565 -0.01446 -0.011291 0.002108

p-value 0.489 N/A N/A 0.322 0.866

p-value 0.078 0.455 0.107 0.087 0.633

50m Mean diff 0.004589 0 0.001430 0.005762 0.003634

p-value 0.03

15m Mean diff -0.000257 0 0 0 -0.001689

200m Mean diff -0.3398

p-value 0.835 N/A N/A N/A 0.545

p-value 0.017

50m Mean diff -0.001041 0 0.003272 0.005126 -0.004639

Land use mix (Actual - shortest route)

p-value 0.280 0.235 0.923 0.128 0.505

15m Mean diff 0.001112 0 0 0.000161 0.000793

Water proportion (Actual - shortest route)

p-value 0.082 0.357 0.057 0.046 0.455

0.002235 0.03179

300m Mean diff -0.2406

p-value 0.819 N/A 0.482 0.732 0.200

300m Mean diff -0.007863 -0.010543 0.001177 -0.01258 0.004670

p-value 0.156 N/A 0.259 0.239 0.469

300m Mean diff 0.001509 0.006940 0.001855 -0.004154 -0.001824

0.026 0.022

Institutional proportion (Actual - shortest route)

p-value 0.044

100m Mean diff -0.001676 -0.006733 0.010011 -0.01027 -0.001171

p-value 0.193 0.323 0.889 0.088 0.318

100m Mean diff 0.007373 1.212E-06 0.006683 0.015688 0.004707

p-value 0.811 0.161 0.752 0.697 0.736

0.011351 0.01575

-3.92E-05 0.00843

0.994 0.028

200m Mean diff 0.006525 0.005672 0.008542 0.011444 0.002268

p-value 0.407 N/A N/A 0.331 0.596

p-value 0.316 0.162 0.150 0.332 0.740

50m Mean diff 0.011939 0 0.010918 0.026748 0.006781

200m Mean diff -0.008875 -0.001562 -0.01404 -0.00919 0.000563

p-value 0.166 N/A N/A 0.331 0.095

p-value 0.263 0.919 0.215 0.211 0.900

50m Mean diff -0.024206 -0.001623 -0.014703 -0.0663 -0.0186

15m Mean diff 0.34886

p-value 0.298

50m Mean diff -0.418065

Slope (%) (Actual - shortest route)

p-value 0.813 0.297 0.482 0.370 0.672

15m Mean diff -0.004732 0 0 -0.001969 -0.011136

Land use mix (Actual - shortest route)

p-value 0.125 0.322 0.167 0.064 0.428

15m Mean diff 0.005595 0 0 0.006666 0.005255

Water proportion (Actual - shortest route)

0.065 0.009

100m Mean diff -0.420644

p-value 0.513 0.840 0.876 0.174 0.694

100m Mean diff -0.024427 -0.012762 -0.035637 -0.029111 -0.003712

p-value 0.885 0.256 0.862 0.696 0.947

100m Mean diff 0.013205 0.007733 0.023001 0.023961 0.000770

0.931 0.245

(continued on next page)

p-value 0.179

300m Mean diff -0.004894 -0.002617 0.001643 -0.012013 0.002095

p-value 0.061 0.331 0.443 0.047 0.017

300m Mean diff 0.001046 0.005826 0.001198 -0.004737 -0.000461

p-value 0.316 N/A 0.402 0.215 0.423

-0.000399 0.003064

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63

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Quartile 1 Median Quartile 3 SD

0.055 0.009 0.047 0.025

-0.136842 -0.149384 -0.386996 -0.255207

200m Mean diff -0.39 -0.246757 0.013914 -0.7218 -0.182139

p-value 0.025 0.157 0.96 0.049 0.311

p-value 0.034 N/A N/A 0.259 0.454

50m Mean diff 51.90758 -1.780822 -98.0137 7.876712 -197.022

200m Mean diff 1771.317 -1500.444 835.5 2963.277 4948.269

p-value 0.593 0.416 0.734 0.572 0.179

15m Mean diff 0.2676 0 0 0.013698 1.4787

p-value 0.001 N/A N/A 0.321 0

50m Mean diff -0.328452 0.356164 -0.712329 -2.69863 0.870711

Dwellings (#) (Actual - shortest route)

100m p-value 0.907 0.29 0.348 0.514 0.36

Floor area (m2) (Actual - shortest route)

15m Mean diff 32.3135 0 0 1.273972 89.46215

Floor area (m2) (Actual - shortest route)

100m p-value 0.055 0.219 0.788 0.193 0.235

Slope (%) (Actual - shortest route)

0.21145 0.49254 0.64973 0.33183

Slope (%) (Actual - shortest route)

Table 3 (continued)

p-value 0.739 0.321 0.587 0.184 0.38

300m Mean diff 1157.583 0.055555 98.38888 -5631.389 5989.932

p-value 0.667 0.986 0.474 0.958 0.391

300m Mean diff -0.3006 -0.176141 0.033218 -0.5182 -0.121048

0.242 0.446 0.171 0.06

100m Mean diff -3.11249 -0.780822 -7.60274 -7.09589 1.677525

p-value 0.829 1 0.974 0.579 0.287

100m Mean diff -201.4881 -223.137 -568.137 166.9178 -17.08534

p-value 0.019 0.302 0.836 0.029 0.437

-0.229454 -0.145569 0.26561 -0.2593

-0.2179 -0.047823 -0.55999 -0.206357

0.04 0.746 0.009 0.058

200m Mean diff -72.83708 -1123.74 -164.3288 625.7123 996.3379

p-value 0.025 0.122 0.035 0.053 0.023

p-value 0.957 0.137 0.88 0.734 0.447

50m Mean diff -0.253002 -0.138533 -0.103613 -0.345267 -0.225661

p-value 0.446 0.754 0.134 0.33 0.588

15m Mean diff 12.05476 0 0 0 27.86211

200m Mean diff -3.007715 -12.32877 -32.72603 -4.657534 23.8976

p-value 0.228 N/A N/A N/A 0.812

p-value 0.799 0.115 0.06 0.805 0.013

50m Mean diff -57.49815 -51.81818 -90.83636 -101.2364 -383.8897

Floor area (m2) (Actual - Shortest Route)

p-value 0.649 0.38 0.125 0.777 0.965

15m Mean diff 0.52837 0.199404 0.49138 0.73022 0.39263

Slope (%) (Actual - Shortest Route)

0.057 0.408 0.04 0.028

300m Mean diff -1.764431 -20.9589 -53.15068 5.273972 52.8062

p-value 0.617 0.321 0.509 0.529 0.105

300m Mean diff -2426303 -1087.589 -889.5068 -4277.151 2695.065

p-value 0.267 0.331 0.675 0.312 0.128

-0.097669 0.043684 -0.47634 -0.171128

p-value 0.099 0.131 0.437 0.093 0.064

0.248257 0.496089 0.403778 0.146082

200m Mean diff -0.323386 -0.208475 -0.068028 -0.507036 -0.214283

0.261 0.099 0.553 0.62

p-value 0.93 0.094 0.06 0.872 0.004

100m Mean diff -310.8007 -97.76364 -500.3818 -102.8364 -355.5278

15m p-value 0.914 0.497 0.648 0.229 0.21

200m Mean diff -676.3787 -1000.455 -491.5455 -139.3091 -297.0214

p-value 0.082 N/A N/A 0.268 0.41

p-value 0.644 0.221 0.688 0.938 0.815

50m Mean diff 386.2029 151.1111 -119.9444 341.2777 373.9627

p-value 0.075 0.108 0.697 0.053 0.108

-0.131676 -0.289238 -0.514502 -0.345485

15m Mean diff 0.52407 0 0 0.055555 1.70714

p-value 0.033 N/A N/A 0.331 0.011

50m Mean diff 0.536704 1.444444 -0.333333 -0.611111 2.471885

Dwellings (#) (Actual - shortest route)

p-value 0.506 0.731 0.231 0.868 0.33

Mean diff 94.2153 0 0 5.166666 277.6845

Floor area (m2) (Actual - shortest route)

100m Mean diff -0.36561 -0.219876 -0.174213 -0.570147 -0.268326

0.338 0.695 0.006 0.083

Slope (%) (Actual - shortest route)

100m Mean diff -2.525613 -3.222222 -14.11111 2.444444 4.916928

p-value 0.776 0.391 0.615 0.269 0.468

100m Mean diff 132.5225 -606.2222 -775.1667 991.1666 1017.044

p-value 0.151 0.562 0.735 0.033 0.124

-0.258719 -0.058046 -0.526676 -0.231666

(continued on next page)

p-value 0.832 0.331 0.941 0.869 0.221

300m Mean diff -700.8822 -1443.545 -1212.818 -3833.945 1616.745

p-value 0.265 0.699 0.751 0.346 0.532

300m Mean diff -0.220971 -0.071987 0.047109 -0.4627 -0.187518

0.503 0.272 0.303 0.286

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64

Mean Quartile 1 Median Quartile 3

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

Mean Quartile 1 Median Quartile 3 SD

Buffer size

200m Mean diff -4.573631 -26.11111 -53.33333 26 32.82968

p-value 0.858 0.245 0.334 0.451 0.087

300m Mean diff -11.6363 -36.44444 -87.27778 11.83333 53.27245

p-value 0.022 N/A N/A 0.321 0

50m Mean diff -0.008565 -0.015982 -0.12955 0.016068 0.089283 p-value 0.875 0.816 0.197 0.869 0.063

200m Mean diff 0.089057 0.002382 0.154671 0.264077 0.104209

p-value 0.195 0.951 0.116 0.077 0.084

300m Mean diff 0.102729 0.06403 0.139346 0.076216 0.044971

p-value 0.109 0.136 0.031 0.237 0.968

50m Mean diff -2.2292 -1.917808 -6.0548 -0.986301 0.730745 p-value 0.045 0.216 0.014 0.526 0.141

100m p-value 0.16 0.043 0.348 0.58

200m Mean diff -2.811021 -3.555556 -2.5 -1.333333

p-value 0.055 0.098 0.085 0.364

300m Mean diff -2.7919 -4.0556 -2.388889 -1.888889

Age of development (avg years) (Actual - shortest route)

15m Mean diff -3.028067 -3.573059 -7.4441 -3.894026 -0.072195

Age of development (avg years) (Actual - shortest route)

100m p-value 0.9 0.334 0.592 0.183 0.019

Number of storeys (avg #) (Actual - shortest route)

15m Mean diff 0.04088 0 0 0.013698 0.14051

Number of storeys (avg #) (Actual - shortest route)

100m p-value 0.799 0.729 0.346 0.863 0.344

Dwellings (#) (Actual - shortest route)

Table 3 (continued)

p-value 0.025 0.043 0.095 0.075

100m Mean diff -2.0081 -1.739726 -2.5753 -2.315068 -0.172877

p-value 0.142 0.16 0.262 0.42 0.442

100m Mean diff -0.023466 -0.1534 -0.061 0.084997 0.0926

p-value 0.801 0.228 0.334 0.886 0.181

200m Mean diff 0.012041 -0.048174 0.02567 0.1406 0.044874

p-value 0.015 N/A N/A N/A 0

p-value 0.743 0.088 0.559 0.046 0.062

50m Mean diff -0.611594 0 -0.836364 -3.381818 0.34669

300m Mean diff 0.013998 -0.030526 0.031693 0.043161 0.02992

p-value 0.554 N/A 0.395 0.165 0.763

200m Mean diff -1.85103 -1.493151 -1.9041 -1.780822 0.074371

p-value 0.543 N/A N/A N/A 0.004

p-value 0.01 0.175 0.017 0.051 0.885

50m Mean diff 0.009511 0.00606 -0.039394 -0.021812 0.086335

15m Mean diff -3.652211 -2.150168 -7.202525 -4.963636

p-value 0.084 0.294 0.08 0.127

50m Mean diff -2.332196 -1.472727 -4.8182 -1.818182

Age of development (avg years) (Actual - shortest route)

p-value 0.021 0.168 0.031 0.065 0.746

15m Mean diff 0.009515 0 0 0 0.09775

p-value 0.059 0.412 0.041 0.297

300m Mean diff -1.98024 -2.0274 -1.8356 -2.69863 -0.078067

p-value 0.877 0.919 0.742 0.839 0.062

Number of storeys (avg #) (Actual - shortest route)

p-value 0.653 0.024 0.304 0.29 0.009

15m Mean diff 0.18366 0 0 0 1.40394

Dwellings (#) (Actual - shortest route)

100m Mean diff -1.90919 -0.654545 -2.781818 -2.672727

p-value 0.003 0.032 0.027 0.002 0.871

100m Mean diff -0.027126 -0.1536 -0.058309 0.032571 0.048608

p-value 0.657 0.258 0.454 0.446 0.175

100m Mean diff -3.304559 0.018181 -5.472727 -10.21818 0.617357

200m Mean diff -2.495234 -7.818182 -25.98182 -14.69091 20.97434 p-value 0.853 0.299 0.084 0.514 0.062

300m Mean diff 1.466363 -15.89091 -41.98182 3.127272 52.6537

200m Mean diff -0.013163 -0.064719 -0.016548 0.100183 0.025455

p-value 0.009 N/A N/A 0.331 0.012

p-value 0.762 0.067 0.734 0.213 0.315

50m Mean diff -0.0638 -0.083333 -0.405 0.131812 0.098293

300m Mean diff -0.01504 -0.061471 -0.003539 0.032343 0.024995

p-value 0.593 0.703 0.025 0.567 0.477

p-value 0.065 0.655 0.054 0.077

15m Mean diff -1.09348 -7.722222 -8.138889 -0.722222 -1.425249

200m Mean diff -1.536846 -0.818182 -1.709091 -1.927273

p-value 0.814 0.294 0.181 0.941 0.674

p-value 0.066 0.526 0.075 0.085

50m Mean diff -1.914567 -3.277778 -9.833333 1.555555 2.114094

p-value 0.028 0.205 0.101 0.007

100m Mean diff -2.310475 -5.0556 -1.944444 -1.222222 1.111602

p-value 0.669 0.057 0.929 0.64 0.272

100m Mean diff -0.012283 -0.152651 -0.069221 0.245187 0.22701

p-value 0.947 0.242 0.083 0.929 0.011

(continued on next page)

300m Mean diff -1.7146 -1.363636 -1.654545 -2.96364

p-value 0.46 0.308 0.165 0.655 0.095

Age of development (avg years) (Actual - shortest route)

p-value 0.662 0.04 0.388 0.714 0.164

15m Mean diff 0.13673 0 0 0.055555 0.27115

Number of storeys (avg #) (Actual - shortest route)

p-value 0.456 0.99 0.252 0.233 0.87

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S. Sarjala

p ≤ 0.001(bold) p ≤ 0.01 (bold, italic) p ≤ 0.05 (italic)

0.278013 0.786

4. Discussion

0.353332

4.1. The grid-based method This study introduces a novel grid-based method for studying route environments. Compared to methods that have been commonly used, where the route is examined with so called sausage buffers (Badland et al., 2010; Dalton et al., 2013; Krenn et al., 2014; Panter et al., 2011; Winters et al., 2010a,b) or by calculating BE features by street segments or proportions of the route (Borst et al., 2009; Broach et al., 2012; Guo, 2009; Guo and Loo, 2013), the grid-based method enables a more precise examination of the distribution of the BE along the route. For instance, sometimes only the highest or lowest values of a BE variable are either preferred or avoided or the variation of the BE is a valued feature in route choice, even if the mean values of a variable show no correlation with route choice. This study demonstrates examples of situations where the grid-based method provides information that could not be detected previously. In most cases, however, if the connection is significant with one of the quartiles of a variable along the route, a significant connection seemed to also exist with the mean value. This supports the adequacy of the sausage buffer method. However, the mean value calculated with the grid-based method does not always fully correspond to the values calculated with the sausage buffer method, which brings challenges in comparing them. The difference is most clearly demonstrated with the calculation of land use mix. With the grid-based method the average land use mix along the route indicates the mean diversity of land uses at every spot along the route. Instead, land use mix calculated with the sausage buffer method actually indicates the evenness of the share of land uses along the whole route, which does not consider whether the land uses are actually diversely distributed or whether they come in larger single-land-use areas

1.575965 0.066 1.848629 0.292 SD

Age of development (avg years) (Actual - shortest route)

Table 3 (continued)

0.083

Age of development (avg years) (Actual - shortest route)

0.59

-0.593252

0.339

-0.506296

0.392

-0.619387

0.27

intersection density, institutional, industrial and forest proportions, slope, age of development and number of dwellings and storeys in buildings. When looking at combined pedestrian and cycling routes, the most preferred characteristics were institutional and industrial land uses, slight hilliness, forests and dense housing, all the most significantly within 15 m along the route. The most avoided characteristics were intersections within 15 and 50 m, steep slopes within 200 to 300 m, old neighbourhoods within 300 m and the highest proportions of residential land use within 100 m from the route. Pedestrians most clearly seemed to prefer higher buildings and avoided intersections within 15 m, favoured newer neighbourhoods within 300 m and avoided hilliness within 200 to 300 m from the routes. The most obviously preferred BE characteristics for bicyclists were higher proportions of institutional land uses and forests as well as slight hilliness within 15 m from the route. Avoided characteristics were intersection density, most significantly within 15 to 50 m, and steepest slopes and oldest developments within 300 m from the route. The standard deviation regarding many land uses and characteristics of buildings was connected to route choice specifically in the immediate surroundings of the route. Standard deviation is used to quantify the amount of variation of a set of data values (Wikipedia contributors, 2018), and thus, may be interpreted as variation of the measured variable along the route. Pedestrians preferred routes that had more variation in the proportions of residential and office-dominated land uses, whereas bicyclists favoured routes that varied more in terms of industrial, commercial and institutional land uses and forests as well as number of dwellings and storeys in buildings. There were no or very few associations with route choice found with park and recreation areas, land use mix, water proportion and floor area. Also, commercial land use and offices did not have a clear positive of negative correlation with route choice, although the variation of their proportions seemed to play a role.

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Map 3. Example of route choices in respect to intersection density, 50-metre buffer.

(50 m and 200 m buffers) are avoided. Furthermore, only the steepest and longest slopes (300 m buffer) are avoided by cyclists, whereas pedestrians avoid only the most mixed uses of land (50 m buffer) and prefer larger areas with low land use mix (300 m buffer) and newer buildings (100 m buffer). Nevertheless, to thoroughly investigate whether the grid-based method reveals connections that the sausage buffer method does not show, it would be useful to compare both methods concurrently using the same route data in future studies.

Map 1. Example of route choices in respect to slope, 15-metre buffer.

4.2. Spatial scale A crucial problem in studies concerning the associations between BE and physical activity is the definition of the most relevant spatial scale (Brownson et al., 2009; Heath et al., 2006; Mitra and Buliung, 2012). This study confirms the findings of Mitra and Buliung (2012), as they discovered that the statistical significance, the coefficient and even the direction of the correlation vary according to the spatial scale used for the analysis. A majority of the associations between BE and route choice was found with a 15-metre buffer in this study. This is consistent with previous research, as Badland et al. (2010) found that bigger differences between the shortest and the actual routes are found with a 25-metre buffer, when compared with 100 and 250-metre buffers around the routes. However, there are some exceptions regarding the most relevant buffer size, as for example, the association with residential proportion was found to be the most significant with a 100 m buffer and age of development had the greatest association with route choice with a 300 m buffer. Even the direction of correlation changes with buffer size with some variables, such as hilliness, which was regarded as a pro with a 15 m buffer and a con with a 100–300 m buffer (see Map 1 and Map 2). Also, the number of storeys is a preferred feature with 15 m and 200 m buffer sizes, but an avoided feature with a 100 m buffer. Even though for the majority of the variables the immediate surroundings of the route have the most significant connections with route choice, the results with bigger buffer sizes seem to have a higher level of agreement with previous studies. This might be due to the scale effect, which decreases the sample variance with an increase in the scale of the analysis. This leads to more correlation between separate BE variables with bigger buffer sizes and less reliable statistical results when using small spatial scale (Mitra and Buliung, 2012). Perhaps the consistency of the results is therefore compromised between studies using smaller buffer sizes, and correspondingly, with larger spatial scale the more generalised values of the BE produce more reliable results. However, the consistency of the results with different spatial scales should be reviewed more closely before proper conclusions can be made.

Map 2. Example of route choices in respect to slope, 300-metre buffer.

along the route. Because of the incomparability between the methods with some BE variables, it is impossible to comprehensively draw conclusions about the adequacy of the sausage buffer method in examining the BE along routes. Perhaps studies with small sample sizes require a more detailed inspection of the BE enabled by the grid-based method in order to find correlations as opposed to studies with large sample sizes. The ability to calculate standard deviation of the BE along the route, is a major benefit of the grid-based method. It also has a significant relevance with several BE variables regarding route choice, especially when looking at the immediate surroundings of the route. For instance, the standard deviation of institutional and forest proportions, as well as number of dwellings and storeys, are all significantly higher on actual than shortest routes of combined pedestrian and cyclist routes within 15-metre buffer. With some of the BE variables, standard deviation can even be the only statistic that has a significant association with route choice. For example, the standard deviation of residential, commercial and office proportions within the 15m buffer had significant connections with combined pedestrians’ and bicyclists’ route choice, even though other statistics of the variables show no associations with it. Quartiles of the environmental variables along the routes are also proven to be important in defining route choice. With several BE variables, only the lowest or highest proportions or densities are avoided or preferred, although there might not be any difference in mean or median values between shortest and actual routes. As seen in Table 3, the route sections with the highest proportions of park and recreation areas (15 m buffer) as well as water (100 m buffer) are preferred, whereas only the highest proportions of residential land uses

4.3. Built environment associations with route choice Although land use mix is regarded as a pedestrian and bike-friendly 66

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feature (Saelens et al., 2003) and it is also one of the key components of the walkability index (Leslie et al., 2007), almost no significant association is found between land use mix and route choice in this study, apart from a relatively weak but consistent negative connection with pedestrian route choice. In some previous studies route environments with high land use mix are also reported to decrease the likelihood of walking (Dalton et al., 2013; Panter et al., 2011) and cycling (Panter et al., 2011). Nevertheless, Krenn et al. (2014) found that routes with mixed land uses are, in fact, preferred by bicyclists. It is also noted by Krenn et al. (2014) that the result is highly affected by the definition of land use mix, which is defined only partly similarly with this study. However, the most plausible explanation for the conflicting finding is the difference in research methods. Krenn et al. (2014) studied the route environment with a “sausage buffer”, which calculates the proportions of land uses along the whole route at once. As already explained in chapter 4.1, this way the route is considered very mixed even if the different land uses are located sequentially along the route, so that in fact, the different land uses are not experienced at the same time but one after another. With the grid-based method the proportions of land uses are calculated for each cell of the grid separately, which then indicates how mixed the land uses are around every spot along the route. The combination of the findings might suggest that cyclists do not prefer nor avoid places with high land use mix, but rather look for routes with variation in land uses along the route. This interpretation gets support from the observation that the standard deviation of several land use proportions is positively associated with route choice. Intersection density was found to have the most significant association with route choice in this study, as both pedestrians and cyclists avoid routes with high intersection densities (see Map 3). Considering the undisputed finding of this study, the results in previous related studies are surprisingly diverse. Pedestrians, for instance, are reported to both avoid (Borst et al., 2009) and prefer (Guo, 2009) routes with high intersection densities. The latter result prominently conflicts with the findings of this study, even though definition of intersection density and the focus of the studies on commute trips are similar. However, Guo (2009) had both start and endpoints predefined, which reduces the number of and, thus, the variation between different route choices which, in turn, increases the correlation between BE variables and might lead to bias. Even though the workplaces are also predefined in this study, the variation of the BE is much higher, since the home locations vary. Bicyclists, on the other hand, are found to avoid being exposed to motorized traffic in general (Broach et al., 2012), whereas Winters et al. (2010b) found no connection between route choice of bicyclists and intersection density, defined as the ratio of 4-way intersections to all intersections. The different definition of intersection density might explain the conflicting finding with this study. However, corresponding to the 250-metre buffer used by Winters et al. (2010b), the association between intersection density and route choice of bicyclists has only a fairly low significance with 200-300m buffer sizes in this study.

As previously widely confirmed, steep slopes are avoided on both walking and cycling routes (Borst et al., 2009; Broach et al., 2012). Also, Winters et al. (2010a) found that hilly routes reduce the odds of cycling versus driving. However, according to Winters et al. (2010b) hilliness does not correlate with cyclists’ route choice when only slopes steeper than 10% calculated on 100 m road segments are included. A likely explanation for the lack of association may be the use of 100 m road segments, as in this study the negative associations of hilliness are found only with larger spatial scales. However, with the buffer method used in this study the slope is actually calculated within the whole buffer area around the route and not just along the route itself. Even though the average slope of the environment surrounding the route probably correlates strongly with the slope of the route itself this might still partly explain why the associations between slope and route choice are not more obvious with all buffer sizes in this study. Even though the negative effect of hilliness on mode and route choice is largely confirmed, this study brings a new aspect to the understanding of the role of small-scale hilliness, as slopes measured within 15 m around the route are actually preferred over flatter routes. Such findings have not been reported previously. However, Borst et al. (2009) found that hilliness increases the perceived attractiveness of the route, but the result was opposite to objective measures. Perhaps people’s perception of attractive routes concern only small hills, but not long, steep ones. More studies are required to confirm the positive association of small hills with route choice. The associations of land uses with route choice are not clear and seem to vary depending on buffer size and transport mode. Also, in previous studies the results concerning land uses have often been mixed. For example, parks, recreation areas and forests are rather common BE variables studied in relation to route choice, but the results vary from avoiding parks on walking routes (Borst et al., 2009) to preferring green, recreation and sports areas on cyclists’ routes (Krenn et al., 2014). Consistent with this study, Winters et al. (2010b) found no connection between green areas or parks and recreational land uses and route choice of bicyclists. According to Krenn et al. (2014), however, forest proportion is not associated with route choice of bicyclists, which in this study is a significantly preferred land use for bicyclists with a 15metre buffer (see Map 4). Perhaps local differences in the landscapes of Graz, Austria, and Tampere, Finland, explain why Krenn et al. (2014) found parks and recreation areas a significant factor in route choice, whereas forests play a bigger role in the Finnish context. When it comes to commercial land uses, the results of this study are generally contradict previous studies. In this study, commercial land uses are avoided by pedestrian commuters (50m buffer), but preferred by cyclists (15m buffer). In previous studies, however, shops and retail frontage en route are found to be preferred features on pedestrian routes (Borst et al., 2009; Guo and Loo, 2013), whereas cyclists are found to avoid commercial land uses (Krenn et al., 2014) or to react neutrally to them (Winters et al., 2010b). The conflicting results might be explained by trip purpose, as most previous studies include many types of utilitarian trips, whereas this study concerns only commute trips. However, in this study the association between commercial land use and route choice is consistent and at least nearly statistically significant only with pedestrians at all buffer sizes. But with cyclists even the direction of the connection alters between buffer sizes and is nowhere near statistical significance with buffer sizes other than 15 metres. Some of the BE variables have, apparently, not been addressed in previous studies concerning route environments. Interestingly, these variables seem to have distinct associations with route choice. A noteworthy discovery is the clear positive correlation of institutional proportion with commuter route choice, especially with cyclists (see Map 5). This might be due to taking children to day care or school on the way to work. When planning bicycle networks for commuters, this should be taken into consideration by locating walking and cycling trails near kindergartens and schools.

Map 4. Example of route choices in respect to forests, 15-metre buffer. 67

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2012). Since bike paths and other bicycle facilities may intercorrelate with some of the BE variables, such as slope or age of development, they can potentially be the actual explaining factor in route choice instead of some of the studied variables. The workplaces are located at two rather small areas geographically, which might cause some bias in the results. The home locations of the participants are, however, located all over Tampere and its nearby municipalities, which in turn, creates more environmental variation along the routes and, thereby, reduces correlation between BE variables. The network data used for calculating the shortest routes does not include all the informal paths and trails available to pedestrians and cyclists. It is also possible for pedestrians and cyclists to take shortcuts even through areas not meant for transport, such as lawns, forests or fields. This probably is the main reason why the actual tracked route was, in fact, shorter than the shortest calculated path in ten cases out of 73. Other possible causes for the problem are faults in the calculation of some of the shortest routes or errors in the GPS routes. However, some of the findings might partially be a result of using shortcuts rather than a clear determinant of route choice. For example, larger proportion of forest, slight hilliness, or fewer intersections could be characteristics of informal shortcuts, and the association may be weaker if informal shortcuts were included in the calculation of the shortest routes. Therefore, the results of the study need to be interpreted as a comparison of actual routes and shortest official routes instead of shortest possible routes.

Map 5. Example of route choices in respect to institutional land use, 15-metre buffer.

Another interesting finding is that, as with slope, the number of storeys also correlates with route choice in opposite directions according to buffer size. With a 15m buffer the correlation is positive, but with 50-100m buffers the correlation is mostly negative. Then again, with a 200m buffer the higher buildings are preferred en route. However, consistently across all buffer sizes and transport modes, variation in building heights is a preferred feature on commute routes. Also, the age of buildings has not been addressed in previous studies. Surprisingly, it seems to be associated with route choice at larger spatial scales, as both pedestrians and cyclists prefer newer neighbourhoods within 300 metres on their route to work (see Map 6). However, this might be due to bicycle and pedestrian facilities being better within newer neighbourhoods compared to older environments and not commuters being drawn, for example, to newer architecture. Some of the findings might be enhanced and even some new correlations could be found by excluding the overlapping sections of the shortest and actual routes from the analysis. However, to maintain the comparability to previous studies and methods the entire routes were included in the analysis.

6. Conclusion One of the main purposes of this study was to evaluate the usability of the grid-based method to analyse route environments. The most obvious advantage of the method is that environmental variation is made visible by calculating standard deviation of the BE variables along the route. In addition, the roles of the highest or lowest quartiles of certain BE variables can be analysed with the method. These qualities cannot be observed with previously used methods for analysing route environment. However, most connections found between route choice and the lowest or highest quartile of a BE variable were also found with the mean value of the variable although, as mentioned in chapter 4.1, this observation requires more research. In addition, compared to the conventional “sausage buffer” method, the grid-based analysis method is more complicated and includes more phases. Therefore, the analysis is more time consuming and requires more resources. Most of the statistically significant associations are found with the smallest (15m) buffer size. However, the office area proportion (with cyclists) and the age of buildings have the most significant association with route choice at 200 and 300m buffer sizes. Slope also has a negative correlation with route choice with the largest buffer sizes, but a positive correlation with 15m buffers. Therefore, a universal conclusion about the most relevant buffer size cannot be drawn. Intersections and long, steep slopes are features that are avoided. On the other hand, institutional land uses, slight hilliness, dwellings, forests and high buildings, as well as variation in land uses are preferred features along the commute routes. More research is needed especially on the effect of land use mix, slight hilliness, commercial and institutional land uses, and building characteristics, such as heights and ages, on route choice.

5. Limitations The most obvious limitation of this study is the small sample size, especially with pedestrian routes, which reduces the reliability of the results. However, the purpose of the study is mainly in testing the new grid-based method, for which the sample size is adequate. Due to the area-based composition of the grid-based method and its inherent constraints that limit the use of data in line format, this study concentrates merely on the BE characteristic surrounding the route. It does not account for network properties such as cycling infrastructure of the routes, although it is demonstrated to be an important determinant of cyclists’ route choices (Van Holle et al., 2012; Broach et al.,

Declaration of interest None. Acknowledgements Map 6. Example of route choices in respect to the age of buildings, 300-metre buffer.

Many thanks to Professor Minna Aittasalo for coordinating the KÄPY project and to Johanna Tiilikainen for her efforts in data 68

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collection. The helpful consultation of Kari Tokola with the statistics and Anssi Joutsiniemi with GIS analyses must not go unmentioned. I also thank Professor Ari Hynynen for his advice during the project. This study was funded by the Ministry of Education and Culture, Finland and the Finnish Cultural Foundation. The funding sources were not involved in the collection, analysis or interpretation of the data, in the writing and submission process of the article or at any other stage of the research.

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