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Fry, Richard, Orford, Scott, Rogers, Sarah, Morgan, Jennifer and Fone, David 2020. A best practice
framework to measure spatial variation in alcohol availability. Environment and Planning B: Urban
Analytics and City Science 47 (3) , pp. 381-399. 10.1177/2399808318773761 file
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A best practice framework to measure spatial variation in alcohol availability
1
This is a pre-copy-editing, author-produced PDF of an article accepted following peer review
for publication in Environment and Planning B: Urban Analytics and City Science
A best practice framework to measure spatial variation in alcohol availability
Fry, R.
1
, Orford, S.
2
, Rogers, S.
1
, Morgan, J.
2
, Fone, D.
2
1
Swansea University
2
Cardiff University
Abstract
Alcohol outlet density (AOD) and alcohol related harms are an internationally reported
phenomenon. There are multiple methods described in the literature to measure AOD, but
with very little commentary on the geographical underpinnings of the methods. In this paper,
we present a framework to help practitioners and researchers choose the most appropriate
spatial method of measuring AOD. The framework includes components on theoretical
geography, statistical implications and practical considerations, with an emphasis on
population level exposure. We describe the CHALICE AOD measurement method which
investigated the relationships between AOD and population harm (Fone et al. 2016). The
CHALICE method is compared to four other methods found in the published literature. We
demonstrate the impact of methodological choices (e.g. network vs. Euclidean distances) on
resulting AOD scores. We conclude that wherever possible the best practice approach to
modelling AOD should be used to facilitate flexibility in subsequent statistical analysis and
improve the transparency of the results.
Keywords
Alcohol Outlet Density; GIS; Framework; Alcohol Related Harm; Public Health
A best practice framework to measure spatial variation in alcohol availability
2
Introduction
The impact of alcohol outlet density (AOD) on health is an internationally reported
phenomenon with recent studies reporting on density measures from New Zealand (Cameron
et al., 2015), Australia (Livingston, 2014; Morrison et al., 2015), Scotland (Richardson et al.
2015), South Africa (Leslie et al., 2015) and the USA (Brenner et al., 2015; Cederbaum et al.,
2015; Cook et al., 2014; Parker, 2014). Their aims are to better understand the link between
AOD and the wide range of harms resulting from substantial levels of excess alcohol
consumption (Anderson, 2011; Campbell et al., 2009; World Health Organisation, 2017). As
the environment in which an individual resides has been demonstrated to be a key influencer
on individual behaviour in relation to alcohol use (Dahlgren and Whitehead, 2007), AOD
potentially impacts population health. Policy interventions which modify our environment to
reduce AOD by restricting the number of alcohol outlets in a geographic area requires robust
evidence to stand up to challenges from the retail sector and the multibillion pound alcohol
industry (e.g. The Scottish Parliament 2014).
Producing robust evidence linking AOD and health outcomes is not straight-forward, in part
because there is no agreed approach to measure AOD. Multiple approaches have been
reported in the literature (e.g. Fone et al., 2016; Grubesic et al., 2016; Richardson et al., 2015).
Two main issues can be identified here. The first is that any measure of AOD is based on
models, which are necessarily simplifications of reality. Good quality research should include
a statement of the limitations, or abstraction from reality but these statements are not always
evident, particularly with regard to the limitations of underlying AOD measurements. The
second is that alternative spatial models may produce different, and sometimes conflicting,
results and are often chosen in relation to the outcomes under investigation (e.g. alcohol
A best practice framework to measure spatial variation in alcohol availability
3
related harms, violence or consumption) making comparisons of outcome measures difficult
if not impossible. The limitations of AOD measurement methods need to be clearly
understood to facilitate statistical analysis and interpretation of results when analysing the
associations between AOD and outcomes.
In this paper, we present a best practice framework that will allow researchers and policy
makers to decide what makes a good spatial model of AOD given the circumstances or setting
of the research. Recent work by Grubesic et al. (2016) compares alcohol access in Seattle,
finding gravity model-based approaches to modelling access the most balanced approach.
We add to this work, through the development of a conceptual framework which can be used
to decide which AOD measurement is the most appropriate and to help researchers to define
the strengths and limitations of a method. We compare the different methods, like Grubesic
et al. (2016), but at a national population-level and add stratification by urban-rural
classifications and deprivation to investigate how the social and geographic morphologies
may influence AOD measurements. We illustrate the framework by comparing the main
measures of AOD reported in the literature to a high-resolution household level method
developed as part of the CHALICE project, which investigated the relationships between AOD
and population alcohol-related harm (Fone et al. 2016). We will focus on methods that
produce consistent and theoretically sound spatial models, which best capture the
environment in which an individual resides. Having a consistent spatial model is key to
understanding the other social processes influencing alcohol related health.
A best practice framework to measure spatial variation in alcohol availability
4
Alcohol outlet density in the literature
AOD measurements can be broadly split into population-based measures and geography-
based measures.. The main population-based measures are 1) counts of outlets per capita in
a population-based administrative unit (Gruenewald & Remer 2006; Treno et al. 2007;
Lapham et al. 2004; Cameron et al. 2015) and 2) counts of outlets per km
2
of a geographical
unit (Morrison et al. 2015; Yu et al. 2008; Pollack et al. 2005). These methods are less
concerned with local variation in AOD and more concerned with a per capita or per area unit
measure of AOD and assume a) that access is equal across a study area and b) the population
is unaffected by the constraints imposed by artificial boundaries (Richardson et al. 2015). The
most widely reported geography-based measures are 1) counts per walking or driving
eighourhood uffer zoe Hukle et al. 8; Pollak et al. 5) and 2) Kernel Density
Estimate measures (KDE), which model distance decay within user-defined neighbourhoods
(Richardson et al. 2015; Major et al. 2014; Berke et al. 2010). These methods measure AOD
(to varying degrees of sophistication), modelling spatial heterogeneity as a fundamental
component of the density measure. They typically use a Geographic Information System (GIS)
to define a local neighbourhood around a population centre – either a household or a census
tract centroid. Other measures of alcohol outlet availability described in previous research
were calculated but are not presented here because they do not result in an area-based
density score; for instance, outlets per road distance (e.g. Yu et al. 2008; Yu et al. 2009; Cohen
et al. 2006) do not consider population distribution and assume equity of access across an
area. Nearest outlet to a home or population centre (Day et al., 2012; Halonen et al., 2013)
have also been excluded as they do not result in an outlet density measure. This literature
