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The built environment, walking, and physical activity: Is the environment more important to some people than others?



We examine whether specific types of people are more sensitive to the built environment when making a decision to walk or engage in other physical activity. Over 700 participants from 36 environmentally diverse, but equivalent-sized neighborhoods or focus areas responded to a survey, kept a travel diary, and wore an accelerometer for seven days. Subgroups defined by demographic and socioeconomic variables, as well as self reported health and weight status demonstrate that most subgroups of people walk more for transportation in high density areas. However, only the less healthy walked more overall in high density areas after controlling for sociodemographic characteristics and physical activity was remarkably similar among the groups and across different kinds of environments. While environmental interventions may not increase physical activity population wide, some populations – including some for whom interventions may be important such as the less healthy and the unemployed or retired – are more affected by these neighborhood environmental characteristics.
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The built environment, walking, and physical activity: Is the
environment more important to some people than others?
Ann Forsyth
, J. Michael Oakes
, Brian Lee
, Kathryn H. Schmitz
Department of City and Regional Planning, 106 West Sibley Hall, Cornell University, Ithaca, NY 14853, USA
Division of Epidemiology and Community Health, 1300 South Second Street, Suite 300, Minneapolis, MN 55454, University of Minnesota, Minneapolis, MN, USA
Division of Clinical Epidemiology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, 921 Blockley Hall,
423 Guardian Dr., Philadelphia, PA 19104-6021, USA
article info
Physical activity
Built environment
We examine whether specific types of people are more sensitive to the built environment
when making a decision to walk or engage in other physical activity. Over 700 participants
from 36 environmentally diverse, but equivalent-sized neighborhoods or focus areas
responded to a survey, kept a travel diary, and wore an accelerometer for seven days. Sub-
groups defined by demographic and socioeconomic variables, as well as self reported
health and weight status demonstrate that most subgroups of people walk more for trans-
portation in high density areas. However, only the less healthy walked more overall in high
density areas after controlling for sociodemographic characteristics and physical activity
was remarkably similar among the groups and across different kinds of environments.
While environmental interventions may not increase physical activity population wide,
some populations – including some for whom interventions may be important such as
the less healthy and the unemployed or retired – are more affected by these neighborhood
environmental characteristics.
Ó2008 Elsevier Ltd. All rights reserved.
1. Background
How do density and street pattern affect travel walking, leisure walking, total walking and physical activity for different
types of people? Work in the field of transportation has found people living in certain types of places walk more for travel
(Pucher and Renne, 2003; Transportation Research Board, 2005). In addition, there has been a great deal of recent interest in
the potential to leverage this situation to try to increase overall physical activity, an important public health aim (Handy
et al., 2002; Sallis et al., 2004).
This paper examines whether specific types of people are more sensitive to the built environment when making a deci-
sion to walk or engage in other physical activity. As non-motorized transportation receives a higher profile due to concerns
over issues such as energy and equity, such questions about the environmental supports for non-motorized modes are of
increasing interest to transportation professionals. The paper draws on the data set of the uniquely designed Twin Cities
Walking Study (TCWS) conducted in Minnesota, US. The study found that the built environment affected travel walking
but it did not clearly cause an increase in physical activity in the general population (Oakes et al., 2007; Forsyth et al.,
2007, 2008). However these general findings might mask important differences among groups of people. While not statis-
tically powered for extensive subgroup analyses, this paper uses these data to examine subgroups by: race, education,
sex, self reported health, work status, presence of children in the household, car ownership, and obesity.
1361-9209/$ - see front matter Ó2008 Elsevier Ltd. All rights reserved.
*Corresponding author.
E-mail address: (A. Forsyth).
Transportation Research Part D 14 (2009) 42–49
Contents lists available at ScienceDirect
Transportation Research Part D
journal homepage:
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Many earlier studies have examined the physical activity or walking behavior of different subpopulations distinguished
by age, ethnicity, income, sex, car ownership, or household size (Agrawal and Schimek, 2007; Bates et al., 2005; Plaut, 2005;
Simpson et al., 2003). Others have examined the effects of the built environment on walking and physical activity for par-
ticular populations such as all residents of sample neighborhoods or children. For adults these studies have typically but not
uniformly found that higher population densities, more connected street patterns, attractive destinations, and some pedes-
trian amenities support travel walking; effects on leisure and walking are more mixed (Badland and Schofield, 2005; Lee and
Moudon, 2006; McGinn et al., 2007; Plotnikoff et al., 2004).
However, few studies have examined these two issues together, that is whether measured local built environmental
features have different effects on different subgroups of adults living within the same neighborhoods (Papas et al., 2007;
Wendel-Vos et al., 2007). In addition, studies have often focused on a small subset of physical activity or walking behavior,
for example, on trips to work or on recreational walking.
There are some exceptions. A study of walking by 2650 Australian adults, using the International Physical Activity Ques-
tionnaire (IPAQ), found that education moderated the effects of environment on walking for transportation—those with 12 or
more years of education walked more for transportation in areas scoring high on a walkability index composed of housing
density, street pattern, land use, and retail dimensions; those with 10 or fewer years did not (Owen et al., 2007). An earlier
Australian study with 800 responses had found men more likely to walk in their neighborhood if they lived in a coastal area
but women were not so affected (Humpel et al., 2004). A study of over 3000 children and youth in Atlanta (ages 5–18), using
a two-day dairy to record if these youth had walked at all, found that white, high-income, and high-car households were
more affected by the built environment (Kerr et al., 2007). This paper expands this work.
2. Methods
The TCWS involved 716 participants sampled from the 36 environmentally diverse, but equivalent-sized neighborhoods
or focus areas, with approximately equal numbers in each area. Focus areas were 805 805 m and were selected from part of
the Twin Cities metropolitan area for which particularly good GIS data were available. Within the target region, 130 such
focus areas were identified and stratified into high (>24.7 persons/ha or 10/acre), medium, or low (<12.4 persons/ha or
5/acre) gross population density categories and high (>3.2 ha or 8 acres), medium, and low (<2 ha or 5 acres) block size.
The extreme types were then identified – high density large block (HDLB), high density small block (HDSB), low density large
block (LDLB), and low density small block (LDSB). Nine of each type were randomly selected, and in each area approximately
20 participants were recruited. This was done in four equal waves over an eight month period of warmer weather from April
to November. Of respondents, 74% were randomly selected and the rest recruited through other means; overall demograph-
ics matched the census profiles of these areas (Oakes et al., 2008). Height and weight were measured in person; surveys were
conducted by phone with the respondent following a printed copy of the survey (Twin Cities Walking Study, 2005; Forsyth
et al., 2009).
The study was innovative in sample design and in using both ‘‘objective” or GIS-based measures of the built environment
along with survey-based data on environments, self reported and objective measures of physical activity, measured height
and weight, and survey measures of a number of social and psychological variables. Travel walking, leisure walking, and
walking were measured for a seven day period by IPAQ and a version of the National Household Travel Survey diary modified
to include leisure walking. Physical activity was measured by IPAQ and accelerometer (motion detector). This paper uses the
measures the authors considered to be most valid. While the IPAQ had limitations it clearly distinguished between travel or
leisure walking, measured in MET minutes or intensity by duration; this distinction between travel and leisure was more
complex in the diaries. Diaries were used to analyze total walking, measured in distance, and accelerometers for total phys-
ical activity measured in accelerometer counts.
Environmental features were measured at many geographical scales including the 36 focus areas, and individual circular
and street network buffers around all participants ranging from 200 m to 1600 m. While providing rich data, earlier work
found little variation in environmental effects by geography; here we present analysis in terms of the 805 805 m square
focus areas (Forsyth et al., 2007, 2008). While the larger study focused on many environmental variables, here we examine
the sampling variables—gross density and median block size. Data were collected in 2004 and analysis has been ongoing.
Given the highly skewed distributions of outcome variables, two approaches were employed: negative binomial (nbreg)
and ordinal logistic regression (ologit) although only the latter are reported.
3. Results
Table 1 reports the sample characteristics of subpopulations by race, education, sex, self reported health, presence of chil-
dren in the household, employment vs. unemployment or retirement, car ownership, and obesity. All were self reported with
the exception of obesity (body mass index > 30) – height and weight were measured in person and the index calculated from
those figures. Self reported health was assessed through an answer to: in general, would you say your health is: Excellent,
Very Good, Good, Fair, Poor. The first two were classed as healthy and the last three as not.
Tables 2a–d tabulate mean values of the outcome walking and activity variables by environmental and subgroup
characteristics. We do not present statistical tests of difference due to concerns about multiple comparisons and confounding.
A. Forsyth et al. / Transportation Research Part D 14 (2009) 42–49 43
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Table 2a reports results for mean travel walking measured by International Physical Activity Questionnaire. Some populations
appear to be more affected by the built environment in terms of walking. Most obvious is the situation that people without cars
walk a great deal for travel. Nonwhites walk a great deal for travel in areas with large blocks.
Table 2b presents results for the same populations and areas for leisure walking measured by IPAQ. There is generally
more leisure walking in low density areas, particularly for the college-educated, men, healthy, retired, and non-obese. Table
2c presents results for total walking measured by travel diary in miles. Again, those without cars stand out as walking more
as do a number of groups in high density, small block areas including whites, those with college degrees, males, the healthy,
those without children, the unemployed or retired, and the non-obese.
Table 2d then shows the results for the same areas and groups for total physical activity measured by accelerometer.
What is striking is the lack of difference between subpopulation groups. In addition, the high density small block areas
do not have higher total physical activity; those without cars do not have higher activity either though they had high levels
of walking.
Multivariate regression models suggest a more complex picture. These models were adjusted for age, self reported health,
measured body mass index (BMI), ethnicity, education, and household income. We examined two groups for each variable
Table 1
Sample characteristics of sub-populations.
Race White 579 82.0
Non-white 127 18.0
College degree Yes 319 45.2
No 387 54.8
Sex Male 247 35.2
Female 455 61.8
Self reported health Less than Healthy 294 41.7
Healthy 411 58.3
Have children Yes 309 44.3
No 389 55.7
Unemployed or retired Yes 208 29.5
No 497 70.5
Has a car in household Yes 660 93.75
No 44 6.25
Obese Yes 221 31.84
No 473 68.16
Table 2a
Bivariate results of travel walking by population group with travel walking measured via IPAQ in mean MET minutes per week
Overall High density
large block
High density
small block
Low density
large block
Low density
small block
Race White 224.77 239.21 302.47 130.10 252.26
Non-white 433.25 551.63 449.38 676.50 114.18
College degree Yes 258.65 305.61 321.34 153.96 279.75
No 266.06 324.07 359.79 156.26 183.99
Sex Male 319.47 347.30 407.18 207.61 327.82
Female 231.95 300.47 306.75 122.85 192.23
Self reported health Less than healthy 237.03 233.55 379.10 117.03 147.54
Healthy 281.78 384.05 312.14 172.06 288.20
Have children Yes 252.68 271.97 291.08 161.47 279.54
No 271.56 366.45 388.15 152.09 202.17
Unemployed or retired Yes 319.49 404.90 368.60 147.42 329.68
No 239.12 276.14 335.74 157.81 190.97
Has a car Yes 219.04 255.70 282.11 155.08 193.61
No 916.50 986.70 741.81 1544.40
Obese (BMI > 30) Yes 270.35 345.28 398.42 133.53 177.99
No 266.91 311.86 326.40 168.56 262.09
1 MET (metabolic equivalent) is the energy expenditure for sitting quietly, so MET minutes are a measure of intensity by duration. The Centers for
Disease Control and Prevention recommendation of 30 min of moderate physical activity (3–6 METS), five times per week is approximately 450–900 MET
minutes per week (Centers for Disease Control, 2006a,b).
44 A. Forsyth et al. / Transportation Research Part D 14 (2009) 42–49
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e.g. men and women separately, car owners and car free, etc. Table 3a presents significant results for travel walking
measured by IPAQ. As would be expected, basically all groups walk more for travel in high density areas—meaning that
the environment affects different groups in similar ways. Statistically significant odds ratios for density were in the range
of 1.78–2.45 meaning that groups were 1.78–2.45 times as likely to walk in higher density areas including: whites, males,
those without a college degree, the less healthy, those without children in the household, the unemployed and retired, those
with a car, those with a BMI under 30, and the obese. Block size was only significant for non whites with those in large block
areas walking more for travel. Odds ratios for all other groups were in the same direction (more density, more travel
walking) and several were close to significance including females, those with a college degree, and those with children.
There were no statistically significant results for leisure walking. This is in line with mixed findings in other studies and
likely reflects the strong role of social and psychological factors in leisure activity.
Table 3b presents results for walking measured by diary. Here the less healthy walk more in higher density areas, and the
unemployed and retirees walk more in large block areas. A number of other findings were close to significance with several
groups walking more in high density areas including whites, those without a college degree, the employed, those with cars,
and the obese.
Table 2c
Bivariate results of total miles by population group with total walking measured via seven day diary in mean miles walked per day.
Overall High density
large block
High density
small block
Low density
large block
Low density
small block
Race White 0.89 0.76 1.22 0.85 0.81
Non-white 0.68 0.72 0.75 0.41 0.56
College degree Yes 1.02 1.01 1.39 0.90 0.89
No 0.73 0.58 0.90 0.76 0.65
Sex Male 0.85 0.75 1.16 0.84 0.61
Female 0.85 0.74 1.01 0.83 0.85
Self reported health Less than healthy 0.72 0.65 0.94 0.55 0.64
Healthy 0.96 0.83 1.24 0.95 0.86
Have children Yes 0.77 0.70 0.82 0.84 0.73
No 0.91 0.81 1.27 0.82 0.79
Unemployed or retired Yes 0.92 0.87 1.14 0.87 0.77
No 0.83 0.69 1.06 0.81 0.78
Has a car Yes 0.79 0.65 0.95 0.83 0.75
No 1.81 1.80 1.85 1.62
Obese (BMI > 30) Yes 0.74 0.65 1.08 0.58 0.60
No 0.92 0.79 1.14 0.93 0.84
Table 2b
Bivariate results of leisure walking by population group with leisure walking measured via IPAQ in mean MET minutes per week
Overall High density
large block
High density
small block
Low density
large block
Low density
small block
Race White 345.44 287.67 282.73 397.27 392.68
Non-white 216.69 145.28 252.22 305.25 241.56
College degree Yes 373.83 297.05 267.83 442.57 448.50
No 279.67 223.58 277.84 341.18 291.63
Sex Male 362.68 298.36 268.26 421.24 474.45
Female 302.13 227.33 279.42 378.53 328.35
Self reported health Less than healthy 249.94 175.56 270.09 325.21 251.80
Healthy 374.40 315.25 281.28 422.75 449.27
Have children Yes 293.65 191.62 261.80 358.03 393.13
No 345.29 314.63 288.14 417.91 353.71
Unemployed or retired Yes 409.38 302.50 306.96 550.29 508.96
No 285.54 228.90 259.65 338.19 312.15
Has a car Yes 322.72 255.10 267.32 393.04 364.95
No 306.75 211.20 310.75 574.20
Obese (BMI > 30) Yes 243.05 173.68 285.45 324.99 174.48
No 363.28 286.44 279.84 428.02 454.36
See note in Table 2a defining METs.
A. Forsyth et al. / Transportation Research Part D 14 (2009) 42–49 45
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Table 3c presents significant results for physical activity measured by accelerometer. Whites and the non-obese were less
physically active overall in high density area, and men were less physically active in large block areas. This runs counter to
the hope that higher density areas have higher physical activity; instead results are insignificant or in the opposite direction.
Only men followed the pattern of being less active in less connected street patterns.
Table 3a
Populations with significant effects of built environment in mean travel walking (MET minutes per week)
White OR Pvalue 95% CI No children OR Pvalue 95% CI
Lower Upper Lower Upper
High density 1.97 0.02 1.14 3.43 High density 2.17 0.02 1.14 4.15
Large block 0.81 0.42 0.47 1.37 Large block 0.74 0.36 0.39 1.41
Interaction 0.64 0.17 0.34 1.21 Interaction 0.75 0.46 0.34 1.63
N566 N375
1209.4 BIC 822.5
Non-white Unemployed or retired
High density 1.99 0.3 0.54 7.34 High density 1.81 0.03 1.07 3.08
Large block 6.81 0.03 1.21 38.26 Large block 0.78 0.38 0.44 1.36
Interaction 0.13 0.06 0.01 1.12 Interaction 0.69 0.29 0.34 1.38
N119 N487
BIC 280.4 BIC 1056.7
Male Has car
High density 1.78 0.02 1.12 2.83 High density 1.91 0.01 1.19 3.08
Large block 0.95 0.88 0.51 1.77 Large block 0.94 0.81 0.59 1.52
Interaction 0.7 0.38 0.32 1.54 Interaction 0.6 0.09 0.33 1.08
N239 N641
BIC 503.0 BIC 1362.1
No college degree Not obese
High density 2.07 0.03 1.06 4.05 High density 1.93 0.02 1.13 3.3
Large block 1.12 0.73 0.6 2.09 Large block 0.91 0.76 0.51 1.63
Interaction 0.44 0.04 0.2 0.95 Interaction 0.7 0.31 0.35 1.39
N373 N455
BIC 814.6 BIC 988.15
Less than healthy Obese
High density 2.45 0.05 1 5.97 High density 2.22 0.02 1.15 4.3
Large block 1.3 0.55 0.55 3.09 Large block 1.18 0.64 0.6 2.31
Interaction 0.37 0.06 0.13 1.03 Interaction 0.38 0.03 0.16 0.89
N284 N207
BIC 620.1 BIC 483.3
Significant results are in bold.
BIC = The Bayesian Information Criterion—a measure of model fit where lower is better.
Table 2d
Bivariate results of total physical activity by population group with total PA measured via accelerometer in mean thousands of counts per valid day
Overall High density
large block
High density
small block
Low density
large block
Low density
small block
Race White 224.57 228.08 210.10 217.75 241.50
Non-white 222.51 229.33 219.36 207.87 221.65
College degree Yes 227.58 229.80 209.27 228.93 238.88
No 221.43 227.45 215.01 205.00 238.37
Sex Male 217.82 218.66 213.69 209.16 232.91
Female 227.87 233.80 212.02 223.68 240.96
Self reported health Less than healthy 226.19 234.13 212.13 221.28 239.45
Healthy 222.69 223.64 212.91 215.55 238.09
Have children Yes 229.20 240.71 225.09 208.13 241.11
No 220.28 214.85 202.90 223.41 238.00
Unemployed or retired Yes 220.55 215.65 201.33 216.17 249.37
No 225.72 234.33 217.58 217.70 234.01
Has a car Yes 224.80 226.63 216.14 217.30 238.76
No 215.41 252.73 188.21 – 234.01
Obese (BMI > 30) Yes 226.60 223.09 217.86 228.48 239.61
No 222.87 231.74 208.56 209.44 240.34
Accelerometer total counts per valid day where counts are movement detected by the device and invalid days are those with <2 h of counts above
minimum threshold.
46 A. Forsyth et al. / Transportation Research Part D 14 (2009) 42–49
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Table 3c
Populations with significant environmental differences in mean accelerometer counts per valid day (P< 05)
White OR Pvalue 95% CI
Lower Upper
High density 0.66 0.04 0.45 0.98
Large block 0.75 0.16 0.51 1.12
Interaction 1.62 0.12 0.88 2.95
High density 0.69 0.3 0.35 1.38
Large block 0.53 0.02 0.31 0.91
Interaction 2.16 0.12 0.82 5.7
BIC 731.51
Not obese
High density 0.63 0.04 0.41 0.99
Large block 0.68 0.13 0.42 1.12
Interaction 2.04 0.04 1.03 4.04
BIC 1340.2
Significant results are in bold.
BIC = The Bayesian Information Criterion—a measure of model fit where lower is better.
Table 3b
Populations with significant effects of built environment on total walking (in miles) (P< 05)
Less than healthy 95% CI
OR Pvalue Lower Upper
High density 2.26 0.03 1.1 4.61
Large block 1.13 0.75 0.54 2.36
Interaction 0.56 0.21 0.23 1.38
High density 1.16 0.74 0.49 2.72
Large block 2.28 0.02 1.12 4.66
Interaction 0.52 0.21 0.19 1.45
BIC 428.74
Significant results are in bold.
BIC = The Bayesian Information Criterion—a measure of model fit where lower is better.
Fig. 1. Odds ratio by sub-population for density. Note: travel and leisure walk values for respondents with no car in the household not listed due to small
sample size.
A. Forsyth et al. / Transportation Research Part D 14 (2009) 42–49 47
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Figs. 1 and 2 present the results of Tables 3a–c in a graphical format, demonstrating higher odds ratios for travel walking.
Overall, while some populations appear quite sensitive to different environmental features in terms of walking for travel, a
finding of interest in the transportation field, these relationships were muted for other kinds of walking and overall physical
4. Conclusions
While the built environment alone is not an answer to the problem of obesity and related health concerns, the hypothesis
that it plays an important role in prevention remains appealing to scholars and policymakers. Overall, as predicted by trans-
portation research most subgroups of people in this study walked more for travel in high density areas. In the adjusted mod-
el, there were no significant relationships for leisure walking and the environmental features analyzed. Further, the
relationship between high density and total walking was significant only for the less healthy. In terms of physical activity,
the results were weak and mixed; whites and the non-obese was less physically active overall in such high density areas,
counter to proposals in the literature, and while men were less active in large block areas all other results were not statis-
tically significant.
The study has a number of limitations – a cross-sectional observational design, sample size limitations for subpopula-
tions, and analysis of only density and block size variables, measured at one geography, the focus area, rather than various
buffers. More work could investigate destinations and pedestrian infrastructure that had not proved significant for the gen-
eral population in being associated with overall physical activity but might prove important for subpopulations, as well as
other populations such as different age groups and dog walkers (Forsyth et al., 2007, 2008). It also does not account for self-
selection of those liking to walk into more walkable areas – however, accounting for this would have only reduced the mod-
est findings still further.
The results may point to the importance of the social environment over the built environment (Wendel-Vos et al., 2007).
Earlier bivariate analyses with the same data set had shown that questions about social life, though not about social cohe-
sion, were positively and significantly associated with total physical activity. Social life was measured in terms of ‘‘how many
days in the last month people had waved, said hello, stopped and talked to a neighbor, gone to a neighbor’s house, had a
neighbor over to socialize, gone somewhere with a neighbor, asked them for help, or sought advice” (Forsyth et al., 2008).
While representing only one study, these findings lend further support to the idea that individuals have a physical activity
budget and if they walk more they do less of other things (Rodríguez et al., 2006; Krizek et al., 2004; Forsyth et al., 2008).
However, the results indicate that certain populations are more affected by particular characteristics than others these in-
clude some populations for whom interventions may be important such as the less healthy. It follows that possible points of
intervention to promote increased overall physical activity for subgroups remain an important area of inquiry and policy
Thanks to the Active Living Research program of the Robert Wood Johnson Foundation that funded this study. We thank
Joel Koepp for valuable assistance.
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... However, such association tends to be insignificant in high-density cities such as Hong Kong (Cerin et al., 2013;Kamada et al., 2011). Additionally, empirical studies have found that the walking behaviors of disadvantaged populations tend to be less responsive to built-environment factors, and even demonstrate opposite responses to the expected effects of these factors (Adkins et al., 2017;Forsyth et al., 2009;Frank et al., 2008;Huang et al., 2022;Lovasi et al., 2008). Therefore, it is also important to obtain pedestrian demand on streets (Chen et al., 2020). ...
... Conversely, assuming equal population density between two areas, differences in pedestrian demand may reveal a difference in walking attractiveness. Furthermore, walking attractiveness may be different for certain groups of people, such as older or disabled pedestrians (Adkins et al., 2017;Forsyth et al., 2009;Frank et al., 2008;Lovasi et al., 2008). Therefore, we constructed a new walking index using the ratio of observed pedestrian demand of a given group and the residential population of that group with the following equation: ...
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As an emerging and freely available urban big data, Street View Imagery (SVI) has proven to be a useful resource to examine various urban phenomena in human behavior, the built environment and their interactions. However, due to technical limitations, previous studies often focused on general pedestrians and ignored certain population subgroups such as older adults. In this study, we develop an innovative method for detecting older pedestrians using SVI. We adopted transfer learning to train a model which can accurately detect older pedestrians on SVI with an accuracy of 87.1%. Using Hong Kong as a case study, we created a dataset consisting of 72,689 street view panoramas and detected 7,763 older pedestrians and 29,231 non-older pedestrians. We further visualized the distribution of detected older pedestrians and found a significant spatial discrepancy between older pedestrians and residential population of older adults. To account for this spatial discrepancy, this study proposed a novel index to assess pedestrian demand and walking environment based on the ratio of the number of pedestrians and the residential population. We also found pedestrian demand assessed with this index has a stronger correlation with the built environment compared with population-level travel survey. This novel approach can be used to assess pedestrian demand for older adults, as well as aging-friendly walking environment.
... Accordingly, the quality of the pedestrian network is arguably one of the most important parameters for sustainable urban development and sustainable mobility ( Forsyth, Oakes, Lee, & Schmitz, 2009 ;Lilasathapornkit, Rey, Liu, & Saberi, 2022 ). A pedestrian network can be understood as a structure within an urban space, which consists of interconnected streets with elements of accessibility and connectivity ( Fonseca, Fernandes, & Ramos, 2022 ;Gaglione, Cottrill, & Gargiulo, 2021 ;Jabbari et al., 2021 ;Pearce, Matsunaka, & Oba, 2021 ). ...
... In addition, the physical environment is expressed through the structural characteristic of the space, which influences the overall perception of walkability. For this reason, many pedestrian studies in the literature refer to behavioral experiences related to the physical environment ( Bahrainy & Khosravi, 2013 ;Forsyth et al., 2009 ;Gaglione, Gargiulo, & Zucaro, 2022 ;Gilderbloom et al., 2015b ;Lamíquiz & López-Domínguez, 2015 ;Nasir, Lim, Nahavandi, & Creighton, 2014 ). It is important to check the results of the pedestrian models and compare them with real pedestrian behavior. ...
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The design of urban spaces that foster sustainable practices requires new analytical and structural approaches to spatial planning. An appropriate pedestrian network could significantly contribute to sustainable urban development goals, particularly by promoting sustainable mobility and pedestrian friendliness. With such goals, several attempts have been made to develop suitable models for pedestrian networks. However, something that is missing from the current literature is a framework that incorporates the main findings of the various studies as an integrated concise concept of the pedestrian network. To address this knowledge gap, this paper reviews studies on pedestrian networks and evaluates this concept based on the systematic 3W1H analysis method, which asks where, what, who, and how. In essence, the following questions are thus analyzed: Where is the pedestrian network located, What criteria play a role in the pedestrian network's performance, Who uses the pedestrian network, and How can the pedestrian network be analyzed? In this context, a systematic literature review is carried out by investigating studies conducted during the period 2001 to 2023 that appear in the Scopus database. The paper presents the results of the review of a selection of 67 papers dealing with pedestrian networks. Findings show that different models have been developed based on particular characteristics. Overall, researchers aimed to identify the most suitable network based on specific criteria for optimizing the walking experience in urban areas. By synthesizing the findings reported in these papers, this paper arguably contributes to a more comprehensive understanding of pedestrian networks, provides insights into the prioritization of design phases, facilitates the use of pedestrian network assessment models for future research , and creates a bigger picture for urban planners with a multidimensional view to a new sustainable urban structure.
... External factors like road characteristics, built environment, land use characteristics, socioeconomic characteristics, and demographic characteristics at a stop, along a route or in the network also influence public transportation ridership (Pulugurtha and Agurla 2012;Guerra 2014;Bhattacharjee and Goetz 2016). Factors such as pedestrian-friendly intersections, walk and bike connectivity, value of riders' travel time and savings, and safety at public transportation stops influence ridership (Khattak and Rodriguez 2005;Kim et al. 2007;Forsyth et al. 2009;Choi et al. 2012;Chepuri et al. 2020;Pulugurtha and Srirangam 2022). The findings from past research also indicate that safety and accessibility to bus transit systems play a vital role in increasing the use of such systems, i.e., ridership (Pulugurtha et al. 1999(Pulugurtha et al. , 2011Pulugurtha and Vanapalli 2008). ...
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The focus of this study is to examine the association between bus transit reliability and the number of boarding passengers at bus-stop level using data obtained from the Charlotte Area Transit System (CATS) in the city of Charlotte, North Carolina, USA for the year 2017. The on-time performance percentage was computed and used as bus transit reliability at bus-stop level. Two different thresholds were considered to compute the on-time performance measure. The ridership data was processed to compute the average number of boarding passengers per bus at bus-stop level. The findings indicate that the day of the week, time of the day, direction of travel, and the type of bus-stop influence the association between the on-time performance percentage and the average number of boarding passengers per bus.
... Besides the basic prerequisites for walking such as security, shade, and connectivity, communal and personal needs are important for tourists walking (Ram & Hall, 2018). Since populations such as the less healthy and the unemployed or retired are more affected by environmental characteristics, the promotion of overall physical activity is an important area of inquiry and policy (Forsyth, et al., 2009). The rapid industrial and economic development in many emerging industrialized countries impacted the limited outdoor activity of children from urban areas affecting their interactions with the natural environment and overall physical inactivity as the fourth leading risk (Fang, et al., 2017). ...
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Since the medical system is poorly prepared to use walking as a therapy for keeping mental well-being and taking into consideration people's needs for activities, the advantages of nature and active holidays on the mountain, rural and non-polluted urban areas are a basis for the development of strategies of the walking tourism management. The purpose of the research is to observe the possibility of managing walking tourism based on the needs of tourists for daily indoor and outdoor activities to achieve sustainable local economic development in the area of the tourist destination. The research was conducted using a descriptive method by the questionnaire-based survey. Results indicated that the management of walking tourism should take into account the differences that exist concerning indoor and outdoor activities between tourists of gender, age, marital status, number of children, education, work status, and annual personal investment for tourist travel and vacation. In the last decade, an increase in the number of walking strategies in the cities of highly developed countries has been identified, as well as the incorporation of this strategy into development policies and plans. For efficient and effective management of walking tourism, authors recommend embedding walking tourism in the sustainable local economic development strategy that will contribute to the creation of enabling business environment for the development of all types of tourism destinations through the "demand-driven" tourism offer based on sustainable using natural resources, local infrastructure, and building the capacities of the workforce.
... Walkability is one of the parameters of a sustainable environment: it makes for an improved environmental experience, offers social possibilities [24,30], and contributes to physical activities [31,32]. Quantitatively, it was found that multiple intersections improved walkability [33,34], as did better access to public transportation [35]. ...
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We have identified a change in rural towns these days. They are transforming from agricultural towns to settlements of a rural quality of life and scenic resources, threatened by densification and development processes. This article aims to outline tools for future rural renewal, focusing on rural areas and emphasizing the village center. We use existing physical analysis tools for urban renewal and apply them on rural regeneration, using an ideological type of rural development area, the moshav, and adapting the tools to two typical physical/geometrical models for moshavs : concentric and linear. Our effort will focus on qualitative and quantitative values for renewal, with a special emphasis on examining ideological rural settlements, which were motivated by agriculture and cultivating the family lot, and resulted in the establishment of rural settlements organized and governed by state institutions, while the original visions have changed, as have the original ideas. In this article, we will review the cooperative and agricultural ideology that founded and nourished the establishment of the rural settlements, as well as how the towns are currently developing, where smaller and smaller percentages of the residents work in agriculture. Lots meant for agriculture are sold to the highest bidder, and people who are not part of the community build houses there, changing the settlement's character and visibility. Considering these threats, the tools outlined in this article for rural renewal will meet the need for maintaining the agricultural-rural character and its humble nature, as well as for densification and attracting additional employment.
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Physical activity could improve individual health and reduce the risk of all-cause mortality. However, for health-promoting urban environments, some questions require further exploration. For instance, how urban form facilitates or constrains outdoor activities? How local meteorology modulates the urban form-outdoor exercise relationship? In this study, we apply a crowdsourced database, Strava, for outdoor exercisers in Atlanta, Georgia, to investigate the synergic effects of meteorological factors on the urban form-outdoor exercise relationship by developing two groups of models; one considers wind factors, and the other does not. The results show that the wind-related group outperforms their counterparts, especially for commute exercisers (R² = 0.77 vs. R² = 0.39), males (R² = 0.51 vs. R² = 0.39), and age groups of 13–19 (R² = 0.61 vs. R² = 0.25), demonstrating that incorporating local meteorological factors into urban form modeling can better reveal outdoor activity patterns. Besides, the urban form could impact the location preferences of individual exercisers, and such impact varies among different subgroups (e.g., seniors consider convenience, safety, and comfort more than young exercisers do). In addition, places become attractive for outdoor exercisers only when multiple urban form requirements are met (e.g., accessibility to public parks and proximity to residential communities). Finally, according to the non-monotonic and marginal effects, the impacts of urban form and meteorological factors on trip volume are only evident within specific ranges. These findings could help decision-makers make informed plans to promote more active and healthier communities.
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A heritage city is an urban agglomeration with one or more World Heritage Sites (Roders 2010; Roders and Van Oers 2011). Most heritage sites are car-free destinations in China because the roads in heritage cities are generally narrow. This study examines the ratings of environmental factors and perception of the environment of Gulangyu, China, by both residents and visitors and analyses how different groups perceive the environment while walking. The purpose of this research is to create a more functional walking environment and to achieve a balance between the needs of both groups. Two analytical methods - correlation and logistic regression - were used to analyse the environmental factors and walking perceptions using SPSS software. The logistic regression analysis suggests creating a more pedestrian-friendly environment for residents. Attention should be paid to five factors: comfort level, lighting, building maintenance, commercial attractiveness, and historic buildings. The four factors significantly impacting visitors' walking experience are road cleanliness, the indicating system, building facades along the street, and walking pleasure. The differences in the walking perceptions of residents and visitors suggest that the different purposes of walking for different types of people can lead to different concerns and experiences of the environment.
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Many agree that increasing physical activity will improve public health. This paper reports on empirical findings on the relationship between the density of the residential environment, walking and total physical activity. Using multiple objective and self-reported measures for 715 participants in the US, and improved techniques for sampling and analysis, it finds that density is associated with the purpose of walking (travel, leisure) but not the amount of overall walking or overall physical activity, although there are sub-group differences by race/ ethnicity. Overall, higher densities have many benefits in terms of efficient use of infrastructure, housing affordability, energy efficiency and possibly vibrant street life. But higher densities alone, like other built environment features, do not appear to be the silver bullet in the public health campaign to increase physical activity.
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The 2001 National Household Travel Survey (NHTS) confirms most of the same travel trends and variations among socioeconomic groups documented by its predecessors, the Nationwide Personal Transportation Surveys (NPTS) of 1969, 1977, 1983, 1990, and 1995. The private car continues to dominate urban travel among every segment of the American population, including the poor, minorities, and the elderly. By comparison, public transport accounts for less than 2% of all urban travel. Even the lowest-income households make only 5% of their trips by transit. The most important difference in the 2001 NHTS is the doubling in modal share of walk trips in cities, due to a much improved survey technique that captured previously unreported walks. While the private car dominates travel, there are important variations in auto ownership and travel behavior by income, race, ethnicity, sex, and age. Overall, the poor, racial and ethnic minorities, and the elderly have much lower mobility rates than the general population. Moreover, the poor, blacks, and Hispanics are far more likely to use transit than other groups. Indeed, minorities and low-income households account for 63% of the nation's transit riders. Different socioeconomic groups also have different rates of carpooling, taxi use, bicycling, and walking. In addition, they travel different distances and at different times of day. Many of these socioeconomic variations in travel behavior have important consequences for public policy.
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Do people walk more, or less, depending on the physical character of their residential areas rather than merely their individual characteristics? This paper reports findings for the Twin Cities, Minnesota, about how walking and total physical activity are affected by street pattern, `pedestrian-oriented' infrastructure and amenities, and mixed use or destinations—in shorthand, design and destinations. The effects of density are dealt with in less depth. Like earlier studies, it finds that walking for specific purposes (i.e. travel or leisure) varies in relation to the physical characteristics of places. However, this study using multiple measures of overall walking and physical activity suggests that socially similar people do the same total amount of physical activity in different kinds of places and that level of activity is, on average, low.
Research into the effects of neighborhood environments on social and behavioral characteristics is threatened by inattention to some methodological obstacles, including the recruitment of a representative sample of resident participants. Although sampling is relatively straightforward, actual recruitment is not. The authors present the recruitment experience of the Twin Cities Walking Study addressing two questions: (a) Are randomly selected participants different from those selected by convenience? and (b) How well does the realized sample match known demographic characteristics of target neighborhoods? Of 716 total participants nested in 36 neighborhoods, 74% were randomly recruited. Socioeconomic status was positively correlated with random recruitment; nonrandom volunteers were more likely to be non-White females of lower socioeconomic status. Multivariate analyses, using propensity scores, show randomly selected and volunteer participants to be similar. The final mixed sample represented the target neighborhoods well. Supplementing a random sample with volunteers may yield a representative sample exchangeable at the group level.
If neighborhood design can support or undermine active lifestyles, then residents of new urbanist neighborhoods can be expected to exhibit higher levels of physical activity than residents of conventional communities. This study compared various measures of physical activity for residents of a new urbanist neighborhood to those for a group of conventional suburban neighborhoods in central North Carolina, finding no statistically significant differences, even after adjusting for individual and household characteristics. However, we did detect differences in where people were physically active. Residents of the new urbanist neighborhood were more likely to be physically active in their neighborhood than were residents of conventional suburbs. This difference was due to their walking more for utilitarian purposes, as distinct from walking for leisure. Despite the limitations of a quasi-experimental research design, our results raise questions regarding new urbanism's ability to raise residents' overall levels of physical activity.
Purpose: Ecological models highlight the importance of environmental influences. We examined associations of coastal versus noncoastal location and perceived environmental attributes with neighborhood walking, total walking, and total activity. Methods: Telephone interviews with 800 faculty and general staff of an Australian university. Results: Men were significantly more likely to walk in their neighborhood if they lived in a coastal location (odds ratio [OR] = 1.66), and they highly rated environmental "aesthetics" (OR = 1.91), "convenience" of facilities (OR = 2.20), and "access" to facilities (OR = 1.98). For women, neighborhood walking was associated with high ratings of "convenience" (OR = 3.78) but was significantly less likely if they had high ratings for "access" (OR = 0.48). For total walking and total physical activity, few significant associations emerged. Conclusions: Environmental attributes were related to walking in the neighborhood but not to more general activity indices. Understanding gender-specific environmental correlates of physical activity should be a priority.
This paper examines data about walking trips in the US Department of Transportation’s 2001 National Household Travel Survey. The paper describes and critiques the methods used in the survey to collect data on walking. Using these data, we summarize the extent of walking, the duration and distance of walk trips, and variations in walking behavior according to geographic and socio-demographic factors. The results show that most Americans do not walk at all, but those who do average close to thirty minutes of walking a day. Walk trips averaged about a half-mile, but the median trip distance was a quarter of a mile. A significant percentage of the time Americans’ walk was spent traveling to and from transit trips. Binary logit models are used for examining utility and recreational walk trips and show a positive relationship between walking and population density for both. For recreational trips, this effect shows up at the extreme low and high ends of density. For utility trips, the odds of reporting a walk trip increase with each density category, but the effect is most pronounced at the highest density categories. At the highest densities, a large portion of the effect of density occurs via the intermediary of car ownership. Educational attainment has a strong effect on propensity to take walk trips, for both for utility and recreation. Higher income was associated with fewer utility walk trips but more recreational trips. Asians, Latinos, and blacks were less likely to take utility walk trips than whites, after controlling for income, education, density, and car ownership. The ethnic differences in walking are even larger for recreational trips.
This paper looks at pedestrian travel in Atlanta by US youths aged 5–18 years. Relationships between five urban form variables and walking in specific demographic subgroups are assessed using stratified logistic models and controlling for participant demographics. All five urban form and recreation measures were related to walking among whites, but only land use mix and access to recreation spaces were significantly related to walking in non-whites. There were more significant urban form physical activity associations in high-income than in low-income households. More urban form variables were related to walking in households with 3 or more cars than in households with no cars. Living in mixed use-areas and having access to recreational space were related to youth walking for transport in 11 of 13 population subgroups studied.
Non-motorized forms of commuting include bicycling, walking to work and working at home and have the potential for reducing environmental damage. These non-motorized modes are analyzed empirically using US journey to work data. Higher salary income and more expensive housing are associated with greater propensity to work at home, but lower propensity to walk or bicycle. College education is in several cases associated with greater propensity to use non-motorized modes. There are sharp differences in the likelihood of using non-motorized modes across the sub-regions within the metropolitan area. Car ownership, race, gender, and various locational and neighborhood features are shown to affect modal choices regarding non-motorized alternatives, in comparison with car commuting.
The urban environment and modes of transport are increasingly being linked to physical activity participation and population health outcomes. Much of the research has been based on either health or urban design paradigms, rather than from collaborative approaches. Previous health reviews in the urban design area have been constrained to perceptions of the neighborhood or walking behaviors, consequently limiting the understanding of built environment influences on physical activity modalities. This review focuses on existing evidence surrounding various urban design factors and physical activity behaviors. Based on the available evidence, fostering suitable urban environments is critical to sustaining physical activity behaviors. In turn, these environments will provide part of the solution to improving population health outcomes. Key urban design features attributable to transport-related physical activity are density, subdivision age, street connectivity, and mixed land use. Future directions for research include consistent use of transport and health measurement tools, an enhanced understanding of traffic calming measures, and further collaborative work between the health, transport, and urban design sectors. Presenting these findings to transport and urban design audiences may influence future practice, thereby increasing the sustainability of health-related physical activity at the population level.