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THE RELATIONSHIP BETWEEN BUILT ENVIRONMENT AND WALKING
ABSTRACT
The present study aimed to explore the possible relationship between the built environment
and walking behaviour among Malaysian residents. The objectives outlined are; i) to
determine the level of neighbourhood walkability in Johor Bahru as perceived by the
residents; ii) to gauge the residents¶ accumulated walking minutes per week for non-work
travel; and iii) to describe the relationship between the built environment attributes and
walking behaviour of the residents. Data were collected at a randomly chosen neighbourhood
area; Taman Pelangi, which is located at the heart of Johor Bahru City Centre; whereby 107
randomly selected samples (male = 64%, female = 36%) ranging from age 25 to 65 years old
responded on self-administered questionnaire surveys on built environment and walking.
From the findings, it is ascertained that only two out of five built environment attributes show
that the study area has high walkability characteristic including high mix of land-use diversity
and low traffic hazards. The remaining three attributes including the low access to services
and facilities; high safety fears; and high crime rate in the study area indicated quite the
opposite. Regarding the second objective, it is found that most samples (38%) walked 10 to
19 minutes per week for non-work travel. The measure of association (Gamma) was utilized
to achieve the third objective, and it is established that the three attributes which were
statistically proven to have influence on walking includes land-use mix diversity ( P = 0.010),
traffic hazard ( P = 0.016) and crime ( P = 0.066), while the remaining two attributes including
land-use mix access ( P = 0.137) and safety ( P = 0.351), are deemed as having no influence on
walking. Overall, although the study area was perceived as having low mix of land-use access
and triggered high safety fears among residents, most still reported to have walked 10 to 19
minutes per week for non-work travel. This is opposing the previous studies.
KEYWORDS: Built environment, walking behaviour, neighbourhood area and measure of
association.
1.0 INTRODUCTION
The Department of Statistics Malaysia and
Malaysia¶s Ministry of Works haverevealed that there is an astonishing
increase of private vehicle ownership from
9.6 persons per vehicle in 1974 to 1.7
persons per vehicle in 2005. The same
source also revealed that the total numbers
of registered vehicles increased from
1,090,279 in year 1974 to 15,026,660
vehicles in 2005. As depicted in Figure 1,
the numbers of motorcar increasesignificantly every year. The annual
growth of motorcars from year 1991 to
2002 is about 9.53%, while for
motorization level is 6.78%. Compared to
the population annual growth rate (2.57%),
the increase in motorcar ownership is
relatively higher (almost 10% per year).
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Figure 1: Motorization rate in Malaysia f rom
1991 to 2002
Many factors contribute to the
growth in the number of vehicles in
Malaysia, including increased population
size, disposable incomes, fuel subsidies,
and decentralization (Izatun Shari, 2009).
Moreover, the author continues to state
that the Malaysian government which
targeted the motor vehicle industry as a
key economic growth sector has further
encouraged the motorization rate.
This is particularly true in
Malaysia, as increasing disposable income
has made private motor vehicles more
affordable, leading to increased demand(Pucher et al ., 2005) hence, creating a
system that is moving away from
achieving pedestrian-friendly environment.
Besides, the planning and provision of
transportation infrastructure for urban
travel in Malaysia has been largely
oriented towards the needs of private car
users, which consequently, shaping a breed
of generation which dislikes walking.
To counter this problem of lowwalking activity among Malaysians, the
present study intend to find out which
factors would or would not promote
walking among Malaysians, taking a
neighborhood in the area of Johor Bahru as
the study boundary. Hence, this study
serves as the first step to understanding the
perception of the residence and how does
this perception influence their walking
behavior as a whole.
2.0 THEORITICAL FRAMEWORK
According to several previous studies on
human behavior, it can be concluded that
the behavior of a person is largely
influenced by logic and external factors;
one of which is a person¶s immediate
environment. For example, Borst et al.
(2009) has presented a model describing
the influence of environmental street
characteristics on the walking route choice
of elderly people.
Other examples including the
Behavioural Model of Environment
(BME) proposed by Moudon et al. (2003);
the Behavioural Framework studied by
Cao et al. (2009); Travel-To-School Mode
Choice Modelling studied by Muller et al.
(2008) and Parks¶ et al. (2006) Land-use
Transportation and Air Quality
(LUTRAQ) survey. All these models have
been tested empirically and justified therelationship between behaviour and the
environment in which a person lives in.
Also, it signified that the external
stimulus has influence on one¶s decision
making. Hence, this theory; which
supports that external factor has its
influence on a person¶s behaviour, frames
the fundamental idea of the present study
and become the foundation of the research.
3.0 LITERATURE REVIEW
There are a growing number of studies
establishing the relationship between built
environment and walking. Most were done
through collecting the perceptions of the
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public on the walkability of their
neighbourhood area and the reports of
their walking behaviour, which is
considered as a subjective measure (Lin et
al., 2010). These studies include a fairly
large sample which was selected from avariety of neighbourhood with different
characteristics. This is to further increase
the reliability of evidence which shows the
relationship between environmental
features and walking.
Most of the time, a person will
make a decision whether to walk or not to
walk to a destination based on his or hers
external environment. Hence, their
perception towards the environment is
crucial in determining their walking
behaviour. There are a few validated
studies which successfully outlined the
influence of the residents¶ perceptions
towards their walking behaviour. Below is
a discussion on the perception of residents
towards the physical attributes which
encourages and discourages walking
among residents of a neighbourhood.
3.1 The Perception of Physical
Attributes that Encouraged
Walking
There were six previous studies which
verified that respondents report a higher
level of walking behaviour when they
perceive that the sidewalks are of high
quality and highly accessible (Addy et al.,
2004; Brownson et al., 2000; Chad et al.,
2005; De Bourdeaudhuij et al., 2003;
Duncan et al., 2005; and King et al.,2003).The findings are further supported by
Booth¶s et al. (2000) and Humpel¶s et al.
(2004) studies, whereby both established
that respondents are more likely to walk
when their neighbourhood area provides
good access to various desired
destinations.
The desired destinations include
shopping areas (Addy et al., 2004; De
Bourdeaudhuij et al., 2003; Duncan et al.,
2005; King et al., 2003; Van Lenthe et al.,
2005), recreation facilities (Chad et al.,
2005), parks and open spaces (Foster et al., 2004; King et al., 2003; Li et al.,
2005), as well as public transportation
stops (De Bourdeaudhuij et al., 2003).
Borst et al. (2009) justified that higher
diversity of landuse mix, results in higher
non-motorized movement, especially by
foot.
Another aspect which inspires
higher level of walking activity is the
aesthetics of a pathway. This is supported
by studies done by Brownson et al. (2004),
Humpel et al. (2004) and Troped et al.
(2003). Cerin et al. (2007) has presented
the results for the Hong Kong sample
which shows that the residents which has
reported higher walking activity would
also report higher residential density, land-
use mix diversity, access to services, street
connectivity, infrastructure and safety for
walking, and more parking difficulties, but
fewer hilly streets, cul-de-sacs, physical
barriers, and traffic hazards. Based on the
study done by Parks et al. (2006), it
supports that higher density results in
higher walking activity.
3.2 The Perception of Physical
Attributes that Discouraged
Walking
There are some studies which suggest a
few aspects resulted in refusal to walk.Booth et al. (2000) and Foster et al. (2004)
found out that safety fears emerged as the
ultimate barrier to walking. Loukaitou-
Sideris (2006) and Loukaitou-Sideris et al.
(2002) addressed the way the
environmental and social features trigger
the fear of crime among residents, which
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and Indian (14%). Of the original sample
of 107 respondents, all questionnaires
were used.
4 .3 Procedures
The Universiti Teknologi Malaysia (UTM)Skudai approved this study. After securing
other approvals from the Taman Pelangi
Security Guards, the inhabitants were
approached and asked to participate in this
study. The randomly selected samples then
completed the Built Environment and
Walking Survey questionnaire, which took
approximately 20 minutes each. To ensure
quality control for data entry, data were
100% verified through double checking
the entered data against the raw data.
4 .4 Measures
The 48-item Built Environment and
Walking Questionnaire contained
information from three domains:
demographics, built environment attributes
and walking report. The five built
environment attributes including the land-
use mix diversity, land-use mix access,
safety, crime and traffic hazard was taken
from previously published research (the
NEWS Questionnaire Survey) which has
strong reliability and validity. Besides that,
the walking report was adapted from
previous research by Cerin et al., 2006.
Other items were developed for use in this
study. The description of items relevant to
this study is provided below.
4.4.1 DemographicAge, gender, and race were included as
demographic variables in the study and the
questions were stated straight forwardly.
The samples were asked to write down
their age and gender on the space
provided, while race was assessed through
the question of µWhat is your race?¶ and
the answering categories include µMalay¶,
µChinese¶, µIndian¶ and µOthers¶.
4.4.2 Built Environment
The NEWS Questionnaire Survey is a 68-
item instrument which measures the perceived attributes of local environment
and is hypothesized to be used for
assessing physical activity; especially
walking. The subscales were perceived to
be related to walking and other physical
activities that are discussed in urban
planning literatures (Frank et al., 2003).
The questionnaire assesses the
characteristics of residential density; land
use mix diversity; land use mix access;
street connectivity; walking facilities;
aesthetics; pedestrian traffic safety and
crime safety.
All of these subscales, except for
the residential density item and the land
use mix-diversity item, were measured by
a 4-point Likert type scale which ranges
from 1 (strongly disagree) to 4 (strongly
agree). The residential density item
questions about the frequency of various
types of residences available within the
perceived neighbourhood.
The possible answer ranges from
single-family detached homes to 13-storey
or higher apartments and condominiums. It
is measured by a 5-point Likert type scale
(1 = none; to 5 = all). On the other hand,
the land use mix-diversity item was
assessed by the walking proximity from
home to various types of stores and
facilities, with responses ranged from 5 (1to 5 minutes walking distance) to 1 (> 30
minutes of walking distance).
In the present study, only five out
of eight of the attributes were adapted due
to time and manpower constraint. The five
attributes were land use mix diversity; land
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use mix access; pedestrian traffic safety
and crime safety.
In order to assess the land-use mix
diversity of the study area, all of the
respondents were asked to select the
perceived minutes of walking from their houses to the given services and facilities.
There are a total of 23 types of services
and facilities including supermarket,
hardware store, vegetable market and
library. The minutes of walking was
categorized into five choices ranging from
1 (>31 minutes) to 5 (<5 minutes).
Land-use mix access was assessed
through asking the respondents to rate four
questions in this part, for example, µI can
do most of my shopping at local stores¶.
The rating ranged from 1 (Strongly
Disagree) to 4 (Agree). The residents¶
perceived safety level of their
neighborhood was assessed through
questions including µThe sidewalks in my
neighborhood are well maintained¶ and
µMy neighborhood is well lit at night¶.
Consequently, the ten questions regarding
safety level were ranked by the
respondents and the choices ranged from 1
(Strongly Disagree) to 4 (Agree).
On the other hand, six questions
were asked in order to determine the
residents¶ perceived traffic hazard level of
their immediate neighborhood areas. The
questions asked including µThe speed of
traffic on the street I live in is usually
slow¶ and µWhen walking in my
neighborhood there are a lot of exhaust
fumes¶ were ranked with four choices (1for µStrongly Disagree¶ to 4 for µAgree¶).
Lastly, all respondents answered this
section which consists of only four
questions and it aims to find out the
perceived crime level of the neighborhood
area. For example, they were asked to rank
hypothetical situations such as µThere is a
high crime rate in my neighborhood¶ and
µThe high crime rate in my neighborhood
makes it unsafe to go for a walk at night¶
through a scale ranging from 1 (Strongly
Disagree) to 4 (Agree).
4.4.3 Walking Behaviour
The walking behavior of the respondents
was gained through asking them to report
their accumulated minutes of walking for
non-work travel per week. The choices
ranged from 1 (less than 4 minutes) to 5
(more than 30 minutes).
4 .5 Data Analysis
Gamma test was used to measure the
strength of relationship between the five
built environment attributes and the
residents¶ walking behaviour.
5.0 RESULTS
The null hypothesis of the research is that
none of the built environment attributes
has influence towards the residents¶
walking behaviour. Hence, P -values that
are less than = 0.10 means that it rejects
the null hypothesis, and vice versa.
Table 3: T e results of t e measure of
association between t e built environment
attributes and t e respondents¶ walking
behaviour using the Gamma test
Built
Environment
Attributes
P -
value
Verdict
(10%
signif icance
level)
R eject /
Accept
Null
Hypothesis
Diversity 0.010 P <0.10 RejectAccess 0.137 P >0.10 Accept
Saf ety 0.351 P >0.10 Accept
Traff ic 0.016 P <0.10 Reject
Crime 0.066 P <0.10 Reject
The result shows that there are
three attributes which significantly
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influences the residents¶ walking
behaviour and the other two does not have
influence towards the residents¶ walking
behaviour. According to Table 3, the
attributes which have P -values less than
= 0.10, including land-use mix diversity ( P = 0.010), traffic hazard ( P = 0.016) and
crime ( P = 0.066), are statically proven as
having influence on walking behaviour.
On the other hand, the attributes which
have P -values more than = 0.10,
including land-use mix access ( P = 0.137)
and also safety ( P = 0.351), are deemed as
having no significant relationship with
walking behaviour.
6.0 DISCUSSION
This study was prompted by the fact that
the Malaysian lifestyle is in a wrench;
including the ever increasing private
vehicle ownership per person, the
provision of low quality walking facilities
and the negligence of pedestrianism in the
policy making process. All of which has
resulted in disinterest of its population to
walk, and thus, bringing an abundance of
negative externalities, for example,
increase in pollution emission and the loss
of valuable time on road due to severe
traffic congestion, just to name a few.
To counter this problem of low
walking activity among Malaysians, the
present study intend to find out which
factors would or would not promote
walking among Malaysians, taking aneighborhood in the area of Johor Bahru as
the study boundary. Hence, this study
serves as the first step to understanding the
perception of the residence and how does
this perception influence their walking
behavior as a whole.
The first objective of the research
is to determine the level of neighborhood
walkability in Johor Bahru as perceived by
the residents in the study area. This
objective aimed to discover the
neighbourhood walkability through the perception of the residents by answering
the questions posed during the
questionnaire survey. The answers
gathered would be compared against the
previous studies of related topics and the
determination of the neighbourhood
walkability would be presented
accordingly.
Studies have showed that high mix
of land-use diversity (Cerin et al., 2007;
Parks et al., 2006; Crane, 1996; Ewing,
1999); high accessibility to the services
and facilities (Crane, 1996; Ewing, 1999;
Cerin et al., 2007; Borst et al., 2009; Chad
et al., 2005; Parks et al., 2006; Addy et al.,
2004; De Bourdeaudhuij et al., 2003;
Duncan et al., 2005; King et al., 2003;
Van Lenthe et al., 2005; Foster et al.,
2004; Li et al., 2005); low crime level
(refer Loukaitou-Sideris, 2006; Loukaitou-
Sideris et al., 2002); low safety fears
(Crane, 1996; Ewing, 1999; Addy et al.,
2004; Brownson et al., 2000; Chad et al.,
2005; De Bourdeaudhuij et al., 2003;
Duncan et al., 2005; and King et al.,2003;
Booth et al., 2000; Humpel et al., 2004)
and low traffic hazards (Booth et al.,
2000; Foster et al., 2004; Parsons et al .,
1993; Sarkar, 1993; Pikora et al ., 2002;
Ewing, 1999) are the signs of a highly
walkable neighbourhood area.In order to gauge the data from the
residents, the NEWS questionnaire survey
was employed, whereby they would have
to answer five domains including land-use
mix diversity, land-use mix access, crime,
traffic hazards, and safety fears. The result
of the analysis is that, the study area has a
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high mix of land-use diversity; low access
to services and facilities; high safety fears;
low traffic hazards and high crime rate in
the study area as perceived by the
residents. This means that only two out of
five attributes mentioned above is agreeingthat the study area is of high walkability.
The second objective was to gauge
the residents¶ walking minutes per week
for non-work purposes. This is also
achieved through the questionnaire survey
session held on field. The residents were
given five ranges of choices; 1 (less than 4
minutes) to 5 (more than 30 minutes). The
result shows that a total of 38% of
respondents managed to walk 10 to 19
minutes per week, while 19% and 17% of
the respondents have walked 20 to 29
minutes per week and 5 to 9 minutes per
week respectively. Another 17% of them
have walked less than 4 minutes per week.
Lastly, only 9% of the residents have been
able to walk more than 30 minutes per
week for non-work travel.
The third objective of the study
aimed to describe the relationship between
the built environment attributes and the
residents¶ walking behaviour. In order to
achieve this objective, the measure of
association, which is the Gamma test, is
employed. Each and every one of the built
environment attributes were tested against
the residents¶ walking behaviour.
Therefore, the results were used to reject
or accept the null hypothesis outlined
earlier.
The present study found out thatare three attributes which significantly
influences the residents¶ walking
behaviour and the remaining two attributes
does not have influence towards the
residents¶ walking behaviour. The
attributes which have rejected the null
hypothesis and has P -values less than =
0.10, including land-use mix diversity ( =
0.010), traffic hazard ( = 0.016) and
crime ( = 0.066), are statically proven as
having influence on walking behaviour.
On the other hand, the attributes which
have accepted the null hypothesis and have P -values more than = 0.10, including
land-use mix access ( = 0.137) and also
safety ( = 0.351), are deemed as having
no significant relationship with walking
behaviour.
From the results shown, it is clear
that two of the attributes, including the
land-use mix access and safety fear have
differed from that of the previous research.
For the former attribute, although the
majority of the respondents have agreed
that they cannot do most of their shopping
at local stores, the self-reported minutes of
walking for non-work travel shows quite
the opposite, whereby most of them still
walks between 10 to 19 minutes per week.
This result is opposing the previous
research done by Booth et al. (2000),
Humpel et al. (2004), Addy et al. (2004),
Brownson et al. (2000), Chad et al. (2005),
De Bourdeaudhuij et al. (2003), Duncan et
al. (2005) and King et al. (2003), whereby
they all agreed that if the residents cannot
do most of their shopping at the local
stores, it would discourage walking.
As for the latter attribute, although
half of the respondents perceived their
neighbourhood to be unsafe, yet the
minutes of walking per week for non-work
purpose is still considerably high. The
result is opposing to previous researchfindings by Booth et al. (2000) and Foster
et al. (2004), whereby they certified that
safety fears is the ultimate barrier to
walking. Based on the discussion made, it
can be concluded that the research
objectives and research questions has been
achieved as well as answered accordingly.
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7.0 BIBLIOGRAPHY
Addy, C. L., Wilson, D. K., Kirtland, K.
A., Ainsworth, B. E., Sharpe, P.,
Kimsey, D. (2004). Association of
perceived social and physical
environmental supports with
physical activity and walking
behavior. American Journal of
Public Health, 94 (3), 440-443.
Babbie, E. (2002). The Basics of Social
Research. 2nd
Edition. Wadsworth Group.
Berrigan, D., Troiano, P. (2002). The
association between urban form
and physical activity in U.S. adults.
American Journal of Preventive
Medicine, 23 (2S).
Booth, M. L., Owen, N., Bauman, A.,
Clavisi, O., Leslie, E. (2000).
Social cognitive and perceived
environment influences associated
with physical activity in older
Australians. Preventive Medicine,
31 (1), 15-22.
Borst, H. C., Vries, S. I., Graham, J. M.
A., Dongen, J. E. F., Baker, I.,
Miedema, H. M. E. (2009).Influence of environmental street
characteristics on walking route
choice of elderly people. Journal of
Environmental Psychology, 29,
477-484.
Brownson, R. C., Chang, J. J., Eyler, A.
A., Ainsworth, B. E. (2004).
Measuring the environment for
friendliness towards physical
activity: Acomparison of thereliability of 3 questionnaires.
American Journal of Public Health,
94 (3), 473.
Cao, X., Mokhtarian, P. L., Handy, S. L.
(2009). The relationship between
the built environment and non-
work travel: A case study of
Northern Californ
Transportation Research Part A ,
43, 548-559.
Carver, R., Kockelman, K. (2005). Travel
demand and the 3Ds: density,
diversity and design.Transportation Research
Development 2 (3), 199-219.
Cerin, E., Leslie, E., Toit, L., Owen,
Neville, and Frank, L. D. (2006).
Destinations that matter:
Associations with walking for
transport. Health & Place, 13, 713-
734.
Cerin, E., Macfarlane, D. J., Hin, H. K.,
and Kwok, C. A. C. (2007).
Measuring perceived
neighbourhood walkability in Hong
Kong. Cities, 34 (3) 209-217.
Craig, C. J., Brownson, R. C., Cragg, S.
E., Dunn, A. L. (2002). Exploring
the effect of the environment on
physical activity: a study
examining walking to work.
American Journal of Preventive
Medicine, 23 (2S).
Crane, R. (1996). On form versus function:
will the new urbanism reduce
traffic or increase it?. Journal of
Planning Educational Research,
15, 117-126.
De Bourdeaudhuij, I., Sallis, J. F., Saelens,
B. E. (2003). Environmental
correlates of physical activity in a
sample of Belgium adults.
American Journal of Health
Promotion, 18 (1).Department of Statistics Malaysia.
http://www.statistics.gov.my
Duncan, M., Mummery, K. (2005).
Psychosocial and environmental
factors associated with physical
activity among city dwellers in
8/6/2019 Relationship Between Built Environment and Walking
http://slidepdf.com/reader/full/relationship-between-built-environment-and-walking 10/12
regional Queensland. Preventive
Medicine, 40 (4), 363-372.
Economic Planning Unit Malaysia, Prime
Minister¶s Department Malaysia.
http://www.epu.gov.my/home
ESCAP ± United Nations Economic andSocial Commission for Asia and
the Pacific.
http://www.unescap.org/
Ewing, R. (1999). Pedestrian and transit-
friendly design: A primer for smart
growth. ICMA. Washington D. C.
Frank, L. D., Engelke, P. O., Schmid, T. L.
(2003). Health and community
design: The impact of built
environment on physical activity.
Wasington D. C.: Island.
Frank, L. D., Sallis, J. F., Conway, D.,
Chapman, J., Saelens, B.,
Bachman, W. (2006). Multiple
pathways from landuse to health:
walkability association with active
transportation, body mass index
and air quality. Journal of
American Planning A ssociation,
72, 75-87.
Heath, G. W., Brownson, R. C., Kruger, J.
(2006). The effectiveness of urban
design and land use and transport
policies and practices to increase
physical activity: A systematic
review. Journal of Physical Activity
and Health, 3(1), 55-76.
Holtzclaw, J. (1994). Using residential
patterns and transit to decrease
auto dependence and cost . Natural
Resources Defense Council, SanFrancisco, CA. American Journal
of Preventive Medicine, 23 (3),
187-194.
Humpel, N., Owen, N., Leslie, E. (2002).
Environmental factors associated
with adults; participation in
physical activity: A review.
American Journal of Preventive
Medicine, 22, 188-199.
Izatun Shari. (2009). A good report card
for PM . The star newspaper online.
http://thestar.com.my/news/story.as
p?file=/2009/7/11/nation/4274075&sec=nation
King, W. C., Branch, J. F., Belle, S.,
Killingsworth, R., Fenton, N.,
Kriska, A. (2003). The relationship
between convenience of
destinations and walking levels in
older women. American Journal of
Health Promotion, 18, 74-82.
Leslie, E., Saelens, B. E., Frank, L., Owen,
N., Bauman, A., Coffee, N., Hugo,
G. (2004). Residents¶ perception of
walkability attributes in objectively
different neighborhoods: a pilot
study. Health and Place, 11, 227-
236.
Li, F., Fisher, K. J., Brownson, R. C.,
Bosworth, M. (2005). Multilevel
modeling of built environment
characteristics related to
neighborhood walking activity in
older adults. Journal of Epidemiol
and Community Health, 59 (7),
558-564.
Lin, L., Moudon, A. V. (2010). Objective
versus subjective measures of the
built environment, which are most
effective in capturing the
associations with walking?. Health
and Place, 16, 339-348.
Litman, T. (2003). Measuring
transportation: Traffic, mobilityand accessibility. ITE Journal , 73
(10), 28-32.
Ministry of Science, Technology and
Innovation Malaysia.
http://www.mosti.gov.my/mosti/
Moudon, A., Lee, C. (2003). Walking and
bicycling: An evaluation of
8/6/2019 Relationship Between Built Environment and Walking
http://slidepdf.com/reader/full/relationship-between-built-environment-and-walking 11/12
environmental audit instruments.
American Journal of Health
Promotion, 18 (1).
Muller, S., Tscharaktschiew, S., Haase, K.
(2008). Travel to school mode
choice modelling and patterns of school choice in urban areas.
Journal of Transport Geography,
16, 342-357.
National Energy Balance, Malaysia
Energy Database and Information
System. (2003).
http://medis.ptm.org.my/
National Policy Energy Review -
Malaysia, prepared by EC-ASEAN
COGEN Programme. (2003).
http://www.cogen3.net/doc/policyre
view/NationalEnergyPolicyReview
Malaysia.pdf
Ninth Malaysia Plan 2006 ± 2010
Norusis, M. J. (1997). SPSS 6.1 Guide to
Data Analysis. New Jersey:
Prentice Hall
Parks, J. R., Schofer, J. L. (2006).
Characterizing neighbourhood
pedestrian environments with
secondary data. Transportation
Research Part D, 11, 250-263.
Kish, L. (1995). Survey Sampling. New
York: John Wiley & Sons.
Saelens, B. E., Sallis, J. F., Black, J. B,
Chen, D. (2003). Neighborhood-
based differences in physical
activity: An environment scale
evaluation. American Journal of
Public Health, 93 (9).
Rosenberg, D., Ding, D., Sallis, J. F., Kerr,J., Norman G. J., Durant, N.,
Harris, S. K., Saelens, B. E. (2009).
Neighborhood Environment
Walkability Scale for Youth
(NEWS-Y): Reliability and
relationship with physical activity.
Preventive Medicine, 49, 213-218.
Johor Bahru District Local Plan 2020.
Majlis Bandaraya Johor Bahru.
Transport Research Board and Institute of
Medicine. (2005). Does the built
environment influence the physical
activity?: Examining the evidence.Special Report 282. Washington D.
C.: National Academies Press.
Van Lenthe, C., De Bourdeaudhuij, I.,
Philipaerts, R., Sjostrom, M.,
Sallis, J. (2005). Reliability and
validity of of computerized and
Dutch version of the International
Physical Activity Questionnaire
(IPAQ). Journal of Physical
Activity and Health, 2, 63-75.
Parsons, B. Q., Douglas, Inc. (1993). The
pedestrian environment : Volume
4A.
Pikora, T., Bull, F., Jamrozik, K.,
Kruiman, M., Giles-Korti, B.,
Donovan, R. (2002). Developing a
reliable audit instrument to
measure physical environment for
physical activity.
Replogle, M. (1990). Computer
transportation models for landuse
regulation and master planning in
Montgomery County, Maryland.
Transportation Research Rec.,
1262, 91-100.
Sarkar, S. (1993). Determination of service
levels for pedestrians, with
European examples.
Transportation Research Rec.,
1405.
Srinivasan, S. (2002). Quantifying specialcharacteristics of cities. Urban
Studies, 39 (11), 2005-2028.
Troped, P. J., Saunders, R. P., Pate, R. R.,
Reininger, B., Addy, C. L. (2003).
Correlates of recreational and
transportation physical activity
among adults in a New England
8/6/2019 Relationship Between Built Environment and Walking
http://slidepdf.com/reader/full/relationship-between-built-environment-and-walking 12/12
community. Preventive Medicine,
37 (4), 304-310.
Vries, S. I., Bakker, I., van Mechelen, W.,
Hopman-Rock, M. (2007).
Determinants of active-friendly
neighborhoods for children: resultsfrom SPACE study. American
Journal of Health Promotion, 21
(4), 312-316.