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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 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.
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.
1–3
Encouraging active forms of transport,
including transport-related walking, has benefits 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, 160–166
Research Methods
http://dx.doi.org/10.1071/HE14050
community.
4–6
These benefits include a reduced risk of chronic
disease by encouraging physical activity, as well as benefits to air
quality, traffic 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
10–12
levels.
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 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 Australia’s 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)
19–21
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 region’s 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, verified 23 October 2014), a $20 million
initiative funded by the Australian Government’s 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. Melbourne’s north-west metropolitan region.
Measuring walkability: a demonstration project Health Promotion Journal of Australia 161
Within this context, this project was one of AURIN’s first
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 specifi c aims of the study were to: (1) develop, trial and validate
an automated open-source tool capable of creating walkability
indices at user-specified scales (i.e. suburb, Australian Bureau of
Statistics (ABS) Statistical Areas and user-specified road network
buffers) for any Australian urban area; (2) create a flexible 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-specified 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
Briefly, 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
participant’s ‘walkable’ service area level, defined 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 first extracting the area for
each relevant land use (i.e. residential; retail; office; 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
classification 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, CBEH’s 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 configured according
to the needs of the user. The code is available through the GitHub
software repository (https://github.com/gusmacaulay/walkability-
core, verified 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 CBEH’s ‘land use’ variables (derived using
the state-based Valuer General’s data) and the land use variables
derived using national data (based on ABS MESH block data),
although significant (P < 0.000), were considered unacceptably low
(r < 0.30). When combined into composite indices of walkability,
indices calculated using CBEH’s state-level data and those calculated
using AURIN’s national-level data were moderately correlated
(r = 0.7–0.8; 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 identified.
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 reflecting the fact that, in these
areas, there is reasonable connectivity, mixed use and higher
densities.
Discussion
AURIN’s 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 benefits 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 General’s
Office: ValSys database, rateable features,
dwellings
30
Land use mix 2012 Landgate: cadastre
29
2010 Victorian Valuer General’sOffice:
valuations database, land use
33
2011 ABS Mesh Blocks: land use
A,32
2012 Western Australia Valuer General’s
Office: ValSys database, rateable
features, land use
30
A
An ABS Mesh Block comprises approximately 30–60 dwellings.
Measuring walkability: a demonstration project Health Promotion Journal of Australia 163
user-friendly tool that can assess an area’s 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 flexibility:
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 floor 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 significant 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 General’sOffice.
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 Health’s 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 sufficiently flexible to facilitate further research in this area.
Because this project was a demonstration project, we have
identified 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 scientifically 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 insufficiently 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 General’s dataset for Victoria). The Valuer General’s
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 sufficiently 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
AURIN’s 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 area’s
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
identified through major international studies focused on
understanding the built environment.
17
Conclusions
The development of the AURIN open source Walkability Index Tool
is a first step in demonstrating the potential benefit of a tool that
could measure walkability across Australia. For example, it could be
used to benchmark Australian cities to influence 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 financial resources, allowing
researchers to focus on advancing measurement of neighbourhood
attributes. We have shown that AURIN’s 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 benefits of ‘walkability’
and to use evidence to influence 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 NHMRC–National 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
General’sOffice in Victoria, the Australian Bureau of Statistics and
the PSMA.
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