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Issue addressed: Growing evidence shows that higher-density, mixed-use, pedestrian-friendly neighbourhoods encourage active transport, including transport-related walking. Despite widespread recognition of the benefits of creating more walkable neighbourhoods, there remains a gap between the rhetoric of the need for walkability and the creation of walkable neighbourhoods. Moreover, there is little objective data to benchmark the walkability of neighbourhoods within and between Australian cities in order to monitor planning and design intervention progress and to assess built environment and urban policy interventions required to achieve increased walkability. This paper describes a demonstration project that aimed to develop, trial and validate a 'Walkability Index Tool' that could be used by policy makers and practitioners to assess the walkability of local areas; or by researchers to access geospatial data assessing walkability. The overall aim of the project was to develop an automated geospatial tool capable of creating walkability indices for neighbourhoods at user-specified scales. Methods: The tool is based on open-source software architecture, within the Australian Urban Research Infrastructure Network (AURIN) framework, and incorporates key sub-component spatial measures of walkability (street connectivity, density and land use mix). Results: Using state-based data, we demonstrated it was possible to create an automated walkability index. However, due to the lack of availability of consistent of national data measuring land use mix, at this stage it has not been possible to create a national walkability measure. The next stage of the project is to increase useability of the tool within the AURIN portal and to explore options for alternative spatial data sources that will enable the development of a valid national walkability index. Conclusion: AURIN's open-source Walkability Index Tool is a first step in demonstrating the potential benefit of a tool that could measure walkability across Australia. It also demonstrates the value of making accurate spatial data available for research purposes. SO WHAT?: There remains a gap between urban policy and practice, in terms of creating walkable neighbourhoods. When fully implemented, AURIN's walkability tool could be used to benchmark Australian cities against which planning and urban design decisions could be assessed to monitor progress towards achieving policy goals. Making cleaned data readily available for research purposes through a common portal could also save time and financial resources.
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Developing a research and practice tool to measure
walkability: a demonstration project
Billie Giles-Corti
A,B,F,G
, Gus Macaulay
A
, Nick Middleton
C
, Bryan Boruff
B,F
, Fiona Bull
B,F
,
Iain Butterworth
D
, Hannah Badland
A,F
, Suzanne Mavoa
A,F
, Rebecca Roberts
A,F
and Hayley Christian
B,E,F
A
McCaughey VicHealth Centre for Community Wellbeing, School of Population and Global Health,
The University of Melbourne, Level 5, 207 Bouverie Street, Carlton, Vic. 3010, Australia.
B
Centre for the Built Environment and Health, School of Population Health, University of Western Australia,
35 Stirling Highway, Crawley, WA 6009, Australia.
C
NJM Spatial, 11 Leon Road, Dalkeith, WA 6009, Australia.
D
North West Region Victorian Department of Health, 145 Smith Street, Fitzroy, Vic. 3065, Australia.
E
Telethon Kids Institute, University of Western Australia, PO Box 855, West Perth, WA 6872, Australia.
F
NHMRC CRE in Healthy Liveable Communities, School of Population and Global Health, University of Melbourne,
Level 5, 207 Bouverie Street, Carlton, Vic. 3010, Australia.
G
Corresponding author. Email: b.giles-corti@unimelb.edu.au
Abstract
Issue addressed: Growing evidence shows that higher-density, mixed-use, pedestrian-friendly neighbourhoods encourage
active transport, including transport-related walking. Despite widespread recognition of the benets of creating more walkable
neighbourhoods, there remains a gap between the rhetoric of the need for walkability and the creation of walkable
neighbourhoods. Moreover, there is little objective data to benchmark the walkability of neighbourhoods within and between
Australian cities in order to monitor planning and design intervention progress and to assess built environment and urban policy
interventions required to achieve increased walkability. This paper describes a demonstration project that aimed to develop,
trial and validate a Walkability Index Tool that could be used by policy makers and practitioners to assess the walkability of
local areas; or by researchers to access geospatial data assessing walkability. The overall aim of the project was to develop an
automated geospatial tool capable of creating walkability indices for neighbourhoods at user-specied scales.
Methods: The tool is based on open-source software architecture, within the Australian Urban Research Infrastructure Network
(AURIN) framework, and incorporates key sub-component spatial measures of walkability (street connectivity, density and land
use mix).
Results: Using state-based data, we demonstrated it was possible to create an automated walkability index. However, due to the
lack of availability of consistent of national data measuring land use mix, at this stage it has not been possible to create a national
walkability measure. The next stage of the project is to increase useability of the tool within the AURIN portal and to explore
options for alternative spatial data sources that will enable the development of a valid national walkability index.
Conclusion: AURINs open-source Walkability Index Tool is a rst step in demonstrating the potential benet of a tool that could
measure walkability across Australia. It also demonstrates the value of making accurate spatial data available for research purposes.
So what? There remains a gap between urban policy and practice, in terms of creating walkable neighbourhoods. When fully
implemented, AURINs walkability tool could be used to benchmark Australian cities against which planning and urban design
decisions could be assessed to monitor progress towards achieving policy goals. Making cleaned data readily available for
research purposes through a common portal could also save time and nancial resources.
Received 9 October 2014, accepted 13 October 2014, published online 8 December 2014
Introduction
Growing evidenceshows that higher-density, mixed-use, pedestrian-
friendly neighbourhoods encourage active transport, including
transport-related walking.
13
Encouraging active forms of transport,
including transport-related walking, has benets across multiple
portfolios, including health, the environment, transport and
Journal compilation Australian Health Promotion Association 2014 CSIRO Publishing www.publish.csiro.au/journals/hpja
Health Promotion Journal of Australia, 2014, 25, 160166
Research Methods
http://dx.doi.org/10.1071/HE14050
community.
46
These benets include a reduced risk of chronic
disease by encouraging physical activity, as well as benets to air
quality, trafc congestion and reduced social isolation as a result
of encouraging alternative forms of transport to driving.
6
The
development of compact walkable environments is now actively
being encouraged by multiple sectors (health, transport and
land use planning), a policy direction recommended internationally
by the Organisation for Economic Co-operation and Development,
7
as well as in Australia in both the federal
8,9
and state
1012
levels.
Despite widespread recognition of the benets of creating more
walkable neighbourhoods, there remains a gap between the
rhetoric of the need for walkability and the creation of walkable
neighbourhoods in practice. Fuelled by public demands for
affordable housing and property industry demands for land
supply, low-density, single-use developments poorly served by
public transport continue to be built on Australias urban fringe.
13
Moreover, there is little objective evidence to benchmark the
walkability of neighbourhoods within (and between) Australian
cities to monitor progress towards creating more walkable areas
and to assess built environment and urban policy interventions
required to achieve increased walkability in new and established
areas. In order to provide these benchmarks, readily available and
consistent data on the walkability of Australian cities are needed.
In the past decade, there has been a rapid increase in the use of
Geographic Information Systems (GIS) in built environment and
physical activity research.
14
In the US, Frank et al.
15
pioneered the
use of GIS to capture neighbourhood walkability by combining
three subcomponent spatial measures: (1) residential density, (2)
street connectivity and (3) land use mix. The use and validity of
this walkability index have been replicated in studies globally,
16,17
including various studies across many Australian states, including
South Australia,
18
Western Australia (WA)
1921
and New South
Wales.
22
Context for this demonstration project
The population of the North West Region (NWR) of Melbourne
(Victoria; see Fig. 1) is growing rapidly, with increasing concerns
about the regions walkability and liveability.
23
Hence, this
demonstration project was undertaken in partnership with the
Department of Health (NWR) and the North West Regional
Management Forum, made up of key state and local government
organisations working in the region. The project was funded by
the Australian Urban Research Infrastructure Network (AURIN;
http://www.aurin.org.au, veried 23 October 2014), a $20 million
initiative funded by the Australian Governments Super Science
scheme. AURIN aims to provide built environment and
urban researchers, designers and planners with the technical
infrastructure to facilitate access to a distributed network of
aggregated datasets and information services, and to facilitate
data being accessed, interrogated, modelled and/or simulated
to assist in the improved design and management of Australian
cities.
0 4.25 25.5 34
km
Railway lines
North-West
Metropolitan Region
8.5 17
Fig. 1. Melbournes north-west metropolitan region.
Measuring walkability: a demonstration project Health Promotion Journal of Australia 161
Within this context, this project was one of AURINs rst
demonstration projects. There were two overall aims of the study:
(1) to create a tool that would assist in the translation of existing
research into policy and practice; and (2) to facilitate future built
environment research by overcoming problems associated with
poor access to spatial data for the development of walkability
indicators and a lack of available expertise in GIS to calculate
walkability measures.
The specic aims of the study were to: (1) develop, trial and validate
an automated open-source tool capable of creating walkability
indices at user-specied scales (i.e. suburb, Australian Bureau of
Statistics (ABS) Statistical Areas and user-specied road network
buffers) for any Australian urban area; (2) create a exible tool that
would enable walkability to be measured using existing data
available within the AURIN portal (e.g. public sector mapping
agencies (PSMA), ABS and other state and Federal agencies) or to
enable users to upload their own detailed data (e.g. land use,
street and/or pedestrian networks); and (3) to create a tool that
would allow researchers to upload geocoded addresses from
survey data to create user-specied service areas (e.g. 400, 800
or 1600 m) around these addresses, and to download associated
geospatial data for further interrogation. A service area
encompasses all accessible streets within a certain distance from
an address (e.g. a 400-m service area for an address includes all
the streets that can be reached within 400 m from that address).
Methods
Walkability index
Full details of the methods used, and decisions made, to create
the walkability indices used for this project have been described
elsewhere.
20
Briey, three environmental characteristics were used
to construct the walkability index: (1) street connectivity; (2)
residential or dwelling density (with the potential to generate
either a gross density or net density value); and (3) land use mix.
These built environment attributes were calculated for each
participants walkable service area level, dened in this study
as a street network buffer that could be walked briskly within
15 min,
20
or 1.6 km.
24
This buffer size has been used in all our
previous research
20,21
and is based on the 1996 US Surgeon
General Report.
24
It represents approximately how far an able-
bodied person could walk at a moderate to vigorous pace within
15 min, half the recommended level of daily physical activity for
adults.
20
Street connectivity measures the inter-connectedness of the street
network as a ratio of the count of three (or more)-way intersections
over the area (km
2
). Residential or dwelling density measures people
per unit area (hectares) or density of dwellings, respectively. Both
street connectivity and net dwelling density measures were based
on methods used by Frank et al.
15
and replicated in WA;
20
however,
the AURIN walkability tool also allows for the calculation of gross
density. Land use mix examines the heterogeneity of land uses (of
interest) within an area. The land use mix component of the
walkability measure used in the AURIN system was calculated using
a variant on the original formula used by Frank et al.
15
and is the
same as that used by Christian et al.
20
Land use mix (LUM) was calculated by rst extracting the area for
each relevant land use (i.e. residential; retail; ofce; health, welfare
and community; entertainment, culture and recreation) for each
service area. This was used to calculate the proportion of each land
use as the ratio of the area of a land use over the summed area of
all land uses of interest within a service area, as follows:
LUM ¼
X
n
i¼1
ðp
i
ln p
i
Þ= ln n
where p
i
is the proportion of a land use i and n is the number of
land uses.
The Walkability Index Tool has been developed to enable users
to: (1) select land use information provided by AURIN; (2) upload
their own land use information; or (3) construct their own land use
classication using tools within the AURIN portal (see description
below). The land use dataset must contain land use codes, from
which a subset of land use classes can be selected providing a range
of options for more experienced users to test various combinations.
Open source tool development, trial and validation
The project consisted of three phases, namely the development,
trial and validation of the Walkability Index Tool. Phase 1 involved
the development of an open-source web-based spatial analytical
tool to examine walkability at varying scales across Australia. As
noted above, the Walkability Index Tool was based on analytical
tools developed by the Centre for the Built Environment and Health
(CBEH) at The University of Western Australia, which were built
using ArcGIS software package
25
using state government datasets.
ArcGIS software is not open source; however, CBEHs tools, which
were written in the Python programming language, provided a
base structure for the migration of these tools to open source Java-
based programs. The Walkability Index Tool in the AURIN portal
was developed in the Java programming language using the open
source Geotools spatial library. The tool is designed as several
separate modular components (connectivity, density, land use mix,
land use priority allocation), which can be congured according
to the needs of the user. The code is available through the GitHub
software repository (https://github.com/gusmacaulay/walkability-
core, veried 23 October 2014).
Phase 2 trialled the Walkability Index Tool using national data for
WA and Victoria. The national datasets (Table 1) were used within
the AURIN portal to create walkability indices in Perth (WA) and
Melbourne (Victoria).
Phase 3 validated the open source Walkability Index Tool. This
was done opportunistically using WA data. We compared the
162 Health Promotion Journal of Australia B. Giles-Corti et al.
ArcGIS-based tools created by the CBEH for the RESIDE study
20
with
the AURIN Walkability Index Tool within the AURIN portal. The
ArcGIS-based tools were applied to WA data, whereas the AURIN
Walkability Index Tool was applied to national datasets to test the
ability of the AURIN tool to replicate the RESIDE walkability
measures using national data. The results were compared for the
three walkability subcomponents (i.e. land use mix, density and
street connectivity) and the composite Walkability Index using data
from a subset of RESIDE participants (n = 561).
Both versions of the Walkability Index Tool (i.e. the ArcGIS CBEH
RESIDE version and the AURIN open source version) were used to
create service areas within 1600 m of each RESIDE study participant.
Notably, the service areas differed slightly depending on which tool
was used. This was mostly due to the inability to replicate the ArcGIS
service area function (proprietary software) as an open source tool.
The approach used in the Neighbourhood Generator component
of the AURIN portal tool was based on a sausage buffer,
acknowledged as a valid and appropriate approach in health
research of this nature.
26
Correlations between the variables
produced using the ArcGIS CBEH Walkability Index method and
the AURIN open source Walkability Index Tool were examined
using SPSS (version 21.0.0.0).
Results
Table 2 shows the correlation between the Walkability Indices, as
well as the subcomponents created by CBEH using ArcGIS and
measures created using the AURIN open source Walkability Index
Tool. There was a high correlation (P < 0.000) between the
measures of connectivity and density (r 0.80). However, the
correlation between the CBEHs land use variables (derived using
the state-based Valuer Generals data) and the land use variables
derived using national data (based on ABS MESH block data),
although signicant (P < 0.000), were considered unacceptably low
(r < 0.30). When combined into composite indices of walkability,
indices calculated using CBEHs state-level data and those calculated
using AURINs national-level data were moderately correlated
(r = 0.70.8; P < 0.000). Nevertheless, given the poor validity of the
national land use mix data, at this stage it is not recommended
that the AURIN Walkability Index Tool be used with national data
until an appropriate source of data for the land use mix calculation
is identied.
Because of the poor quality of the national land use variable,
walkability in Victoria was assessed using Victorian data (rather
than using national data). For interest, Fig. 2 maps the walkability
of our study area, the north-west region of Melbourne, for ABS
Statistical Area 1 (referred to hereafter as SA1). SA1 is the second
smallest statistical unit of the ABS with, on average, ~400 people,
with densities of 3 dwellings per hectare.
In Fig. 2, walkability is presented as deciles of walkability, with
the more walkable areas indicated in shades of green (most
walkable = darkest green). Low walkable areas are shown in shades
of orange (least walkable = red). Fig. 2 shows that walkability varies
across the north-west Melbourne region. Most of the outer growth
areas generally exhibit lower walkability, whereas inner Melbourne
is generally shown in shades of green, indicating much higher
walkability. However, even in outer Melbourne there are some
areas with various shades of green reecting the fact that, in these
areas, there is reasonable connectivity, mixed use and higher
densities.
Discussion
AURINs open-source tool was developed to assist in translating
research on walkability into policy and practice, and to facilitate
future research. Although there is considerable discussion about
the benets of creating more pedestrian-friendly environments,
there is often a gap between policy and implementation. For
example, a recent WA study found that a state government
policy designed to create more walkable pedestrian-friendly
environments was only 50% implemented.
27
Having access to a
Table 1. Walkability subcomponent measure data sources
PSMA, public sector mapping agencies; ABS, Australian Bureau of Statistics
Walkability measure Western Australia Victoria National data
Street connectivity 2012 Landgate: road centre lines
28
2011 PSMA: transport and topography:
transport lines
31
2011 PSMA: transport and topography:
transport lines
31
Residential or dwelling
density as a net or
gross value
2012 Landgate: cadastre
29
2011 ABS Mesh Blocks: dwellings
32
2011 ABS Mesh Blocks: dwellings
32
2012 Western Australia Valuer Generals
Ofce: ValSys database, rateable features,
dwellings
30
Land use mix 2012 Landgate: cadastre
29
2010 Victorian Valuer GeneralsOfce:
valuations database, land use
33
2011 ABS Mesh Blocks: land use
A,32
2012 Western Australia Valuer Generals
Ofce: ValSys database, rateable
features, land use
30
A
An ABS Mesh Block comprises approximately 3060 dwellings.
Measuring walkability: a demonstration project Health Promotion Journal of Australia 163
user-friendly tool that can assess an areas walkability may assist
both policy makers and advocates to evaluate policy as well as
advocate for better walkability outcomes.
This demonstration project aimed to explore whether it was
possible to create a tool that could be used by policy makers and
practitioners to benchmark neighbourhood walkability within and
between Australian cities, against which policy objectives and built
environment interventions could be measured and monitored
over time. Moreover, for researchers, our aim was to develop a tool
that could facilitate national- and state-level built environment
research by providing access to cleaned spatial data to be used
either within the AURIN portal or to enable walkability measures to
be downloaded for analysis with other datasets (e.g. travel behaviour
or health data).
A key feature of the AURIN Walkability Index Tool is its exibility:
it can be used to assess the overall walkability of cities, suburbs
or neighbourhoods, or even to compare the walkability of
neighbourhoods surrounding key destinations (e.g. schools, train
stations or aged care facilities). However, the tool is also designed
to facilitate research by enabling other spatial measures to be
added (e.g. access to public transport, retail oor area, block size)
or different algorithms for developing subcomponents of the
Table 2. Correlations between the Centre for the Built Environment and Health (CBEH) state-based walkability measures and the Australian Urban
Research Infrastructure Network (AURIN) national walkability measures
Note, all correlations were signicant at the 0.01 level (two-tailed). PSMA, public sector mapping agencies
Indicator National
PSMA
connectivity
PSMA gross
density
AURIN transport
land use mix
B
AURIN transport
walkability index
AURIN recreation
land use mix
B
AURIN recreation
walkability index
State
CBEH connectivity 0.999
CBEH net density 0.804
CBEH transport land use mix
A
0.270
CBEH transport walkability index 0.724
CBEH recreation land use mix
A
0.242
CBEH recreation walkability index 0.765
A
Developed using data from the Western Australia Valuer GeneralsOfce.
B
Developed using Australian Bureau of Statistics Mesh block data.
Walkability index deciles
1 (Low)
10 (High)
2
3
4
5
6
7
8
9
9 13.5 18
km
Fig. 2. The walkability of the Victorian Department of Healths north-west metropolitan region.
164 Health Promotion Journal of Australia B. Giles-Corti et al.
walkability index (e.g. the land use mix variable) to be tested. In
so doing, we have created a tool that facilitates access to data and
is sufciently exible to facilitate further research in this area.
Because this project was a demonstration project, we have
identied several limitations, particularly related to the data that
could be used to create walkability indices. First, at this stage it is
not possible to create a scientically valid national walkability
index. Despite several attempts, a comparable land use mix variable
could not be derived using national data (e.g. PSMA and Mesh
Block data, or a combination of the two). This appears to be
because the available datasets are insufciently precise to measure
the variety of land uses in an area (e.g. ABS Mesh Block data). Hence,
in this project it was necessary to use state-based data (in this case
the Valuer Generals dataset for Victoria). The Valuer Generals
dataset is very useful for mapping land use because it is dynamic
and is regularly updated in response to changing land features
and land sales. However, variations in land use codes applied by
different states may restrict the ability to accurately compare
results across the country. A national source of land use data that
is consistent across states and sufciently precise (i.e. cadastre-
level land use) is needed to advance the development of national
walkability measures. Therefore, sourcing such data or modifying the
coding of future national datasets warrants further exploration.
Identifying an appropriate source of land use data is a priority in
the next stage of this project.
Second, the tool is a measure of neighbourhood walkability and
its use must be restricted to urban environments in cities and
regional towns. For rural areas, measuring and mapping walkability
is likely to provide skewed results, especially when calculating
deciles of walkability for the composite measures (which is a
relative measure). In Fig. 1, we restricted SA1s to those with
densities of at least three houses per hectare. However, this may
have limitations and another approach may have been preferable
to exclude large, low-density SA1s. This requires further exploration.
Third, the establishment of the AURIN portal is a new way of
working with urban spatial data and has been built from the
ground up. At this stage, the AURIN portal and the Walkability
Index Tool are not user friendly, requiring expertise to navigate.
Hence, a second phase of the project has been funded by AURIN
to improve the useability of the tool. This will involve workshops
with user groups to obtain their feedback and working with
AURINs technical team to incorporate these ideas to build a more
user-friendly portal and the Walkability Index Tool.
Fourth, the walkability index we have developed includes only
the variables frequently used in walkability tools in the US and
Australia (i.e. land use mix, density and street connectivity). This
may not include all the variables that may contribute to an areas
walkability (e.g. access to public transport, block length, topography).
However, AURIN did not aim to fund new research. Rather, its aim
was to fund demonstration projects that could show the value of
making data available for urban policy making and research using
tools already shown to be effective in previous research. Further
research could explore the value of adding these additional variables
into the walkability indices.
Finally, a major challenge for comparing cities across Australia is the
need for consistent and reliable national data. In some cases, the
required spatial data are not available. If national datasets are
available made up of state data, the data are often collected
inconsistently across the various jurisdictions. This is an impediment
to undertaking national research. Moreover, in some cases there are
restrictions on the use of data. These restrictions hamper urban
research, and increasing access to data through portals such as
AURIN is to be encouraged. This problem represents an even
bigger problem when undertaking international research, a
challenge that is beyond the scope of this paper but has been
identied through major international studies focused on
understanding the built environment.
17
Conclusions
The development of the AURIN open source Walkability Index Tool
is a rst step in demonstrating the potential benet of a tool that
could measure walkability across Australia. For example, it could be
used to benchmark Australian cities to inuence planning and
policy decisions at the local, state and national government levels
and against which progress could be assessed. It also demonstrates
the value of making accurate spatial data available for research
purposes. This will save time and nancial resources, allowing
researchers to focus on advancing measurement of neighbourhood
attributes. We have shown that AURINs open source Walkability
Index Tool can be used effectively using data sourced from states.
The next major research challenge is to upscale the Tool to measure
the walkability of all Australian capital cities, allowing comparison
within and between cities. However, beyond research, the policy
challenge is to transcend the rhetoric of the benets of walkability
and to use evidence to inuence decisions that create walkable
neighbourhoods that determine the health and well being of
Australians, as well as the environmental sustainability and
economic resilience of Australia.
Acknowledgements
This project was funded by the Australian Urban Research
Infrastructure Network (AURIN) and this funding is gratefully
acknowledged, together with the technical and leadership AURIN
teams members, particularly Professor Bob Stimson, Associate
Professor Chris Pettit, Dr Martin Tomko and Dr William Voorsluys for
their assistance with this project. Our industry partners for the
Place Health and Liveability Program, the Department of Health
NW Region and the NW Regional Management Forum (RMF) are
acknowledged. In particular, the assistance of the former Chair of
the NW RMF, Mr Jim Betts, in facilitating access to the data used in
Measuring walkability: a demonstration project Health Promotion Journal of Australia 165
this project is gratefully acknowledged. SM is supported by
Community Indicators Victoria, which is funded by VicHealth; BGC
is supported by a National Health and Medicine Research Council
(NHMRC) Principal Research Fellowship (#1004900); and HC is
supported by an NHMRCNational Heart Foundation Early Career
Fellowship (#1036350). Particular thanks to the open source
software providers through which the AURIN Walkability Index
Tool was developed (GeoTools, OpenLayer, JTS, GeoJSN). Finally,
this project would not have been possible without the support
of the GIS team at the University of Western Australia Centre for
the Built Environment and Health, who assisted with cleaning and
preparing data for this project (Sharyn Hickey and Bridget Beasley).
Moreover, they, together with other members of the RESIDE
study team, have contributed to intellectual capital that underpins
this project, and this expertise and contribution is gratefully
acknowledged. Data for this project were obtained from a range
of sources, including Landgate in Western Australia, the Valuer
GeneralsOfce in Victoria, the Australian Bureau of Statistics and
the PSMA.
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... Although one of the main objectives of modern planning is the evaluation of walkability in the neighborhoods, there are few studies that have quantitatively measured the level of walkability (Asadi-Shekari et al., 2013;Giles-Corti et al., 2014;Talavera-Garcia & Soria-Lara, 2015;Taleai & Yameqani, 2018). This quantitative measurement can be completely done by a spatial index, and such an index helps urban planners to evaluate and develop neighborhoods from a walkability perspective (Aghaabbasi et al., 2018). ...
... Based on this worthy study, several studies such as Badland et al. ( 2009), Kerr et al. (2013), Giles-Corti et al. (2014), and Bereitschaft, 2017 have been accomplished. Nevertheless, their study approaches are simple, and some essential criteria in walking index such as greenness, temperature, and accessibility are neglected. ...
... Finally, they found that among their parameters, green areas, beaches, and street connectivity have prominent roles in daily walking time. Additionally, Frank et al. (2005), Badland et al. (2009), andGiles-Corti et al. (2014) applied the entropy method for the evaluation of land use mix. In the land use mix, it is vital to consider the number and the expansion of the attractive areas in a neighborhood. ...
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Urbanization and modernization of services in the world triggers decrement in the physical activities of citizens. The most common solution for this decrement is improving walking capacity in neighborhoods to engage the citizens in walking activity. In this research, a novel model is presented to quantify walkability based on several necessary criteria and indicators considering the physical and non-physical conditions. For this model, promising sciences, including GIS, RS, and MCDM, are utilized to compute indicators and aggregate them in the form of a spatial index in a central region of Tehran, Iran, at the neighborhood scale. For evaluating the result, the obtained walkability level is compared to the physical structure of neighborhoods, previous works, housing price along with some other socio-economic parameters, and municipal action plan in the neighborhoods. These evaluations show that the outcome of the proposed model is a robust tool for walkability planning.
... Subjective evaluations of BE attributes and their influence on walkability Arellana et al., 2020;Kaczynski, 2010;Oyeyemi et al., 2017Oyeyemi et al., , 2019Pelclov a et al., 2013. Development of walkability assessment indexes/evaluations Frank et al., 2010;Giles-Corti et al., 2014;Lee et al., 2020;Lefebvre-Ropars et al., 2017;Rundle et al., 2019;Shammas & Escobar, 2019. Travel behavior/active travel Christiansen et al., 2014;Koohsari et al., 2016;Moran et al., 2017;Moran et al., 2018;Ramezani et al., 2021. ...
... The widely replicated index of Frank et al. (2010) was based on five uses (residential, retail, recreational, office and institutional), but the review identified studies using a number ranging from three (Taleai & Yameqani, 2018) to 17 land uses (Hanibuchi et al., 2012). The analyzed documents globally showed that mixed land uses providing nonresidential activities (shops, restaurants, offices, banks, etc.) are correlated to Adams et al., 2014Adams et al., , 2015Awuor & Melles, 2019;Bhadra et al., 2015;B€ odeker, 2018;Boulange et al., 2018;Bracy et al., 2014;Cerin et al., 2007;Chandrabose et al., 2019;Christiansen et al., 2014;Colley et al., 2019;Cook et al., 2013;Creatore et al., 2016;De Sa & Ardern, 2014;Deng et al., 2020;Dias et al., 2020;Dygryn et al., 2010;Esteban-Cornejo et al., 2016;Fan et al., 2018;Foster et al., 2021;Frank et al., 2005Frank et al., , 2010Gebel et al., 2009;Giles-Corti et al., 2014;Glazier et al., 2014;Hill et al., 2012;Howell et al., 2019;Huang et al., 2019;Kenyon & Pearce, 2019;Kerr et al., 2013Kerr et al., , 2014Koohsari et al., 2016Koohsari et al., , 2018Kozo et al., 2012;Laatikainen et al., 2018;Learnihan et al., 2011;Lee et al., 2020;Macdonald et al., 2016;Marshall et al., 2009;Mayne et al., 2013, 2017, Mayne et al., 2019McDonald et al., 2012;Mooney et al., 2020;Moran et al., 2017Moran et al., , 2018Oliver et al., 2015;Pouliou et al., 2014;Qureshi & Ho, 2014;Ramezani et al., 2021;Reyer et al., 2014;Ribeiro & Hoffimann, 2018;Roberts et al., 2015;Rub ın et al., 2015;Shashank & Schuurman, 2019;Taleai & Amiri, 2017;Todd et al., 2016;Van Dyck et al., 2012;Wang et al., 2017;Ye, 2020;Ye et al., 2017;Zhou et al., 2020. Survey: perceived residential density/ types of residences in an area Gebel et al., 2009;Kaczynski, 2010;Leslie et al., 2005; pedestrian-friendly environments and high levels of physical activity Kaczynski, 2010;Lovasi et al., 2011), and walking (Carlson et al., 2018;Clark et al., 2014;Fan et al., 2018). ...
... Street network connectivity increases walkability in two ways: more interconnected streets provide more potential routes for walking and shorter distances to destinations (Tsiompras & Photis, 2017). Street connectivity is often described by measurable properties of the street network, but there is no accepted Boulange et al., 2018;Bracy et al., 2014;Chandrabose et al., 2019;Christiansen et al., 2014;Clark et al., 2014;Colley et al., 2019;Cook et al., 2013;Creatore et al., 2016;Cruise et al., 2017;Dygryn et al., 2010;Ellis et al., 2016;Fan et al., 2018;Foster et al., 2021;Frank et al., 2010;Gebel et al., 2009;Giles-Corti et al., 2014;Glazier et al., 2014;Habibian & Hosseinzadeh, 2018;Hanibuchi et al., 2012;Hankey et al., 2012;Hill et al., 2012;Howell et al., 2019;James et al., 2015James et al., , 2017Kenyon & Pearce, 2019;Kerr et al., 2013;Kerr et al., 2014;Koohsari et al., 2015Koohsari et al., , 2016Koohsari et al., , 2018Kozo et al., 2012;Laatikainen et al., 2018;Learnihan et al., 2011;Leslie et al., 2005;Liao et al., 2020;Macdonald et al., 2016;Mayne et al., 2013Mayne et al., , 2017Mayne et al., , 2019Nichani et al., 2020;Oliver et al., 2015;Oluyomi et al., 2014;Orstad et al., 2018;Pereira et Impedance Impedance Network spatial analysis Kartschmit et al., 2020. method for assessing it (Ellis et al., 2016). ...
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Walking is a sustainable mode of transport and a healthy way of doing physical activity. Walkability is a concept that has gained enormous popularity in recent years due to its potential to promote more sustainable urban environments and healthy lifestyles. This paper provides a literature review to analyse the influence of built environment attributes on walkability. The Scopus and Web of Science databases were chosen to survey the peer-reviewed documents published up to June 2020. A total of 132 documents were selected by the search. The review of these 132 documents showed that various built environment attributes were differently analysed and assessed. More specifically, the search identified 32 built environment attributes that were assessed by using 63 measures. Intersection density, residential density and land use mix were the most used attributes for assessing walkability, namely by using objective methods, such as ratios and spatial score tools. In turn, attributes related to streetscape design and security were much less adopted in walkability assessments. This paper provides additional insights into how built environment attributes influence walkability and identifies gaps and issues that should be analysed in-depth in the future. The review could be helpful for researchers and urban planners in developing walkability studies and in defining policies to improve walkability.
... The maximum walkable distance around a primary school was set to 800 m and the walkable distance around a secondary school set to 1600 m (a street network buffer that could be walked briskly between 5 and 15 min), based on previously published research works [49]. ArcGIS 10.5.1 with Network Analyst extension-a commonly used online cloudbased mapping and analysis technique-was used to identify all pedestrian-accessible roads around each school and assign the road classification/road type to each road segment. ...
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A better understanding of the physical activity (PA) infrastructure in schools, the walkability of neighborhoods close to schools, and the food environments around schools, particularly in rural, socioeconomically challenged areas such as the North-West (NW) of Tasmania, could be important in the wider effort to improve the health of school-age children. Accordingly, this research aimed to assess PA resources, walkability, and food environments in and around schools in three socioeconomically disadvantaged, regional/rural Local Government Areas (LGAs) of Tasmania, Australia. A census of schools (including assessment of the PA infrastructure quality within school grounds), a walkability assessment, and a census of food outlets surrounding schools (through geospatial mapping) were executed. Most of the schools in the study region had access to an oval, basketball/volleyball/netball court, and free-standing exercise equipment. In all instances (i.e., regardless of school type), the quality of the available infrastructure was substantially higher than the number of incivilities observed. Most schools also had good (i.e., within the first four deciles) walkability. Numerous food outlets were within the walking zones of all schools in the study region, with an abundance of food outlets that predominantly sold processed unhealthy food.
... These studies researching the environmental conditions of spaces are mostly aiming to measure or audit walkability quantitatively. Some of the most common methods used in studies are audit tools like Walkscore (Duncan et al. 2011), Irvine-Minnesota Inventory (Day et al. 2006), 'Walkability Index Tool' (Giles-Corti et al. 2014) and The Global Walkability Index (Krambeck 2006) which was later adopted for local contexts (Leather et al. 2011;Yusuf and Waheed 2015). Some of these studies associated walkability audits with GIS based approaches (Cubukcu et al. 2015;Lee and Talen 2014) and indicators based on 7Cs (Moura, Cambra, and Gonçalves 2017). ...
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This study seeks answers to the research question ‘How can walkability of urban spaces be analysed with a multidimensional approach by using mobile methods?’ The research consists of a literature review, a field study in the city centre of Delft conducted as Go-Along walks, which provide better insight in capturing the experience of walking in situ, and evaluation of design scenarios that were developed according to the outcomes of the field study. As a result, the study emphasizes the strong inter-relations between metrics for a walkable place and the necessity to discuss walkability multi-dimensionally.
... A review of the literature on community vitality reveals a wide variety of conceptualizations. A positive relationship exists between community vitality and each of the following dimensions: community well-being (Mouratidis & Poortinga, 2020;Syhlonyk & Seasons, 2020); quality of life (Skevington & Böhnke, 2018;Li et al., 2017;Giles-Corti et al., 2014); sustainability (Bakar et al., 2015;Li et al., 2017;Smith & Miller, 2013;Molavi & Jalili, 2016); health (Andazola et al., 2019;Forsyth, 2020); and sense of community (Kee & Nam, 2016). ...
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How can the efforts of philanthropic agencies be better supported to promote citywide community well-being? To address this question, this article uses a citywide, engagement-driven initiative led by local practitioners that took place in Detroit between 2016 and 2019. This article is based on the initiative’s two main outcomes, namely the identification of seven elements of an effective community development system and a vitality framework designed to measure community progress and success. The author of this article conducted participant observation, interviews, and a literature review, as well as site visits to, and case studies on, four best-practice cities, and then used the outcomes to validate the results of the initiative. Informed by the outcomes of the initiative and the research, the article suggests how best to utilize the seven community development system elements and the vitality success framework effectively to support philanthropy that promotes community well-being. This article focuses on theory-building in Detroit and calls for empirical research to further validate the findings. The article provides useful insights into the benefits of community-based research and citywide engagement as essential components of an effective community development system that can coordinate philanthropic practice more effectively to promote community well-being.
... Walkability/walk score-type measures [13] This measure considers build-in-environment and accessibility from an origin to a destination Walk Score [16] Walk Score considers the distance to the closest destination in each land use category. It is based on the gravity-based model Walkability Index (WI) [17], [18] The WI is calculated from the sum of the z-scores of the four mentioned urban form measures. ...
... To assess the walkability of AUs for stratification and selection, all countries except for Malaysia, India and Nigeria used GIS data to construct a walkability index that was a composite of residential density, intersection density and land use mix, similar to what has been used in earlier studies. [33][34][35] Malaysia used a composite measure of residential and intersection density, but did not have GIS-based land uses. India and Nigeria did not have GIS data, but instead categorised areas as low or high walkable based on judgments by study investigators and local land-use experts who were familiar with the walkability index (table 3). ...
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Problem: Localities and states are turning to land planning and urban design for help in reducing automobile use and related social and environmental costs. The effects of such strategies on travel demand have not been generalized in recent years from the multitude of available studies.Purpose: We conducted a meta-analysis of the built environment-travel literature existing at the end of 2009 in order to draw generalizable conclusions for practice. We aimed to quantify effect sizes, update earlier work, include additional outcome measures, and address the methodological issue of self-selection.Methods: We computed elasticities for individual studies and pooled them to produce weighted averages.Results and conclusions: Travel variables are generally inelastic with respect to change in measures of the built environment. Of the environmental variables considered here, none has a weighted average travel elasticity of absolute magnitude greater than 0.39, and most are much less. Still, the combined effect of several such variables on travel could be quite large. Consistent with prior work, we find that vehicle miles traveled (VMT) is most strongly related to measures of accessibility to destinations and secondarily to street network design variables. Walking is most strongly related to measures of land use diversity, intersection density, and the number of destinations within walking distance. Bus and train use are equally related to proximity to transit and street network design variables, with land use diversity a secondary factor. Surprisingly, we find population and job densities to be only weakly associated with travel behavior once these other variables are controlled.Takeaway for practice: The elasticities we derived in this meta-analysis may be used to adjust outputs of travel or activity models that are otherwise insensitive to variation in the built environment, or be used in sketch planning applications ranging from climate action plans to health impact assessments. However, because sample sizes are small, and very few studies control for residential preferences and attitudes, we cannot say that planners should generalize broadly from our results. While these elasticities are as accurate as currently possible, they should be understood to contain unknown error and have unknown confidence intervals. They provide a base, and as more built-environment/travel studies appear in the planning literature, these elasticities should be updated and refined.Research support: U.S. Environmental Protection Agency.
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It has long been recognised that urban form impacts on health outcomes and their determinants. There is growing interest in creating indicators of liveability to measure progress towards achieving a wide range of policy outcomes, including enhanced health and wellbeing, and reduced inequalities. This review aimed to: 1) bring together the concepts of urban 'liveability' and social determinants of health; 2) synthesise the various liveability indicators developed to date; and 3) assess their quality using a health and wellbeing lens. Between 2011 and 2013, the research team reviewed 114 international academic and policy documents, as well as reports related to urban liveability. Overall, 233 indicators were found. Of these, 61 indicators were regarded as promising, 57 indicators needed further development, and 115 indicators were not useful for our purposes. Eleven domains of liveability were identified that likely contribute to health and wellbeing through the social determinants of health. These were: crime and safety; education; employment and income; health and social services; housing; leisure and culture; local food and other goods; natural environment; public open space; transport; and social cohesion and local democracy. Many of the indicators came from Australian sources; however most remain relevant from a 'global north' perspective. Although many indicators were identified, there was inconsistency in how these domains were measured. Few have been validated to assess their association with health and wellbeing outcomes, and little information was provided for how they should be applied to guide urban policy and practice. There is a substantial opportunity to further develop these measures to create a series of robust and evidence-based liveability indices, which could be linked with existing health and wellbeing data to better inform urban planning policies within Australia and beyond.
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Over the last 15 years, a growing body of Australian and international evidence has demonstrated that urban design attributes are associated with a range of health outcomes. For example, the location of employment, shops and services, provision of public and active transport infrastructure and access to open space and recreational opportunities are associated with chronic disease risk factors such as physical activity levels, access to healthy food, social connectedness, and air quality. Despite the growing knowledge base, this evidence is not being consistently translated into urban planning policy and practice in Australia. Low-density neighbourhoods with poor access to public transport, shops and services continue to be developed at a rapid rate in the sprawling outer suburbs of Australian cities. This paper provides an overview of the evidence of the association between the built environment and chronic diseases, highlighting progress and future challenges for health promotion. It argues that health promotion practitioners and researchers need to more closely engage with urban planning practitioners, policymakers and researchers to encourage the creation of healthy urban environments through integrated transport, land use and infrastructure planning. There is also a need for innovative research to evaluate the effectiveness of policy options. This would help evidence to be more effectively translated into policy and practice, making Australia a leader in planning healthy communities.
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Purpose: Evaluate the implementation of a government planning policy (Liveable Neighbourhoods Guidelines) and its impacts on residents' walking behaviors. Design: Cross-sectional study of participants from the RESIDential Environments project (RESIDE). Setting: Nineteen "liveable" and 17 "conventionally designed" housing developments across Perth, Western Australia. Subjects: Five hundred ninety-four participants from RESIDE who resided in 36 housing developments. Measures: Developed in geographic information systems to assess the on-ground implementation of 43 policy requirements. Policy compliance was defined as the degree to which construction of the developments adhered to the standards outlined. Walking behaviors were measured using the Neighborhood Physical Activity Questionnaire. K-means cluster analyses identified groups of homogeneous developments with respect to policy implementation. Analysis: Logistic regression with generalized estimating equations estimated the odds ratios (ORs) and 95% confidence intervals (95% CIs) for the likelihood of undertaking any and ≥ 60 minutes of transport and recreational walking associated with (1) policy compliance and (2) different clusters of developments. Results: There were few significant differences in on-ground outcomes between the two development types. Despite incomplete implementation, the odds of walking for transport increased with overall levels of policy compliance (OR = 1.53, 95% CI 1.13-2.08) and compliance with the community design (OR = 1.3, 95% CI 1.13-1.42), movement network (OR = 2.49, 95% CI 1.38-4.50), and lot layout elements (OR = 1.26, 95% CI 1.06-1.50). Conclusion: Consistent with the aims of the policy, residents in walkable (i.e., liveable) neighborhoods may be more physically active.
Article
Background: National and international strategies to increase physical activity emphasize environmental and policy changes that can have widespread and long-lasting impact. Evidence from multiple countries using comparable methods is required to strengthen the evidence base for such initiatives. Because some environment and policy changes could have generalizable effects and others may depend on each country's context, only international studies using comparable methods can identify the relevant differences. Methods: Currently 12 countries are participating in the International Physical Activity and the Environment Network (IPEN) study. The IPEN Adult study design involves recruiting adult participants from neighborhoods with wide variations in environmental walkability attributes and socioeconomic status (SES). Results: Eleven of twelve countries are providing accelerometer data and 11 are providing GIS data. Current projections indicate that 14,119 participants will provide survey data on built environments and physical activity and 7145 are likely to provide objective data on both the independent and dependent variables. Though studies are highly comparable, some adaptations are required based on the local context&period; CONCLUSIONS&colon; This study was designed to inform evidence-based international and country-specific physical activity policies and interventions to help prevent obesity and other chronic diseases that are high in developed countries and growing rapidly in developing countries.
Article
Physical activity is usually done in specific types of places, referred to as physical activity environments. These often include parks, trails, fitness centers, schools, and streets. In recent years, scientific interest has increased notably in measuring physical activity environments. The present paper provides an historical overview of the contributions of the health, planning, and leisure studies fields to the development of contemporary measures. The emphasis is on attributes of the built environment that can be affected by policies to contribute to the promotion of physical activity. Researchers from health fields assessed a wide variety of built environment variables expected to be related to recreational physical activity. Settings of interest were schools, workplaces, and recreation facilities, and most early measures used direct observation methods with demonstrated inter-observer reliability. Investigators from the city planning field evaluated aspects of community design expected to be related to people's ability to walk from homes to destinations. GIS was used to assess walkability defined by the 3Ds of residential density, land-use diversity, and pedestrian-oriented designs. Evaluating measures for reliability or validity was rarely done in the planning-related fields. Researchers in the leisure studies and recreation fields studied mainly people's use of leisure time rather than physical characteristics of parks and other recreation facilities. Although few measures of physical activity environments were developed, measures of aesthetic qualities are available. Each of these fields made unique contributions to the contemporary methods used to assess physical activity environments.