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REVIEW ARTICLE
Estimating temporary populations: a systematic
review of the empirical literature
Radoslaw Panczak 1✉, Elin Charles-Edwards1& Jonathan Corcoran1
ABSTRACT
The estimation of temporary populations is a well-established field, but despite growing
interest they are yet to form part of the standard suite of official population statistics. This
systematic review seeks to review the empirical literature on temporary population estima-
tion and identify the contemporary “state of the art”. We identify a total of 96 studies that
attempt to estimate or describe a method of estimation. Our findings reveal strong growth in
the number of studies in recent decades that in part has been driven by the rise in both the
type and availability of new sources of information, including mobile phone data. What
emerges from this systematic review is the lack of any “gold standard”data source or
methodology for temporary population estimation. The review points to a number of
important challenges that remain for estimating temporary populations, both conceptually
and practically. What remains is the need for clear definitions along with identification of
appropriate data and methods that are able to robustly capture and measure the diverse
array of spatial behaviours that drive temporary population dynamics. To our knowledge, this
is the first review on this topic that brings together literature from various disciplines and
collates methods used for estimation.
Background
Conventional population estimates capture a population at a single point in time. In doing
so, such estimates ignore the short-term dynamism of populations that are caused by
temporary population mobility, a territorial movement that does not result in a per-
manent change of usual residence (Bell and Ward, 2000). Temporary population movements are
defined as moves of one or more night’s duration but can be broadened to also include diurnal
movements, such as daily commuting (Smith, 1989).
1
Temporary population mobility can exert
substantive impacts on both the size and composition of populations at the small-area level
(Charles-Edwards and Panczak, 2018) impacting traffic, housing, retail sales, medical services
and emergency preparedness to name a select few (Smith, 1989; Smith and House, 2007). As a
consequence of these impacts, there exists a growing interest and need for temporary population
estimates across a broad range of purposes including the planning and delivery of goods and
services (Markham et al., 2013), fiscal equalisation (Graebert et al., 2014), retail analysis
(Soundararaj et al., 2019), transport demand (Toole et al., 2015), emergency preparedness (Gao
https://doi.org/10.1057/s41599-020-0455-y OPEN
1Queensland Centre for Population Research, School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD, Australia.
✉email: r.panczak@gmail.com
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1234567890():,;
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et al., 2014), and as better denominators for crime and epide-
miological models (Charles-Edwards et al., 2020)—for a full range
of purposes please refer to Supplementary Table 1, Part 1, ‘Source,
type, purpose main data and methods’.
Scholarship concerned with developing a conceptual basis for
temporary population estimates is longstanding with early work
first appearing in the United States in the 1950s (Foley, 1954;
Schmitt, 1956). This early work proposed building on approaches
previously used for the estimation of resident populations to
generate temporary population estimates. Schmitt (1956) was the
first to recognise the value of symptomatic data as a source for
such estimates. Several decades later, it was Smith (1987,1989)
that proposed a set of metrics (visitor-days and visitor-years) to
capture measures of temporary populations as well as high-
lighting the difficulty of validating temporary population esti-
mates. More recently, Bell (2000,2004) argued that estimates of
temporary populations are contingent on developing a rigorous
understanding of temporary population mobility. Here he drew
attention to four dimensions of temporary mobility (movement
intensity, duration, seasonality, and spatial impact), which he
argued drive change in the stock of temporary visitors at both the
origin and at the destination.
Despite growing interest in temporary population statistics,
they are not yet part of the standard suite of official population
statistics. Historically, this has been due to a lack of the requisite
data capturing temporary populations, but also nascent con-
ceptualisation of temporary populations along with a lack of
standard methodologies for their production. Indeed, Smith
(1989), in his seminal paper on the estimation of temporary
populations, noted that the development of a methodology
applicable across a wide range of populations and situational
contexts was ‘most likely impossible’(p. 430). At the time of
writing he could not have predicted the proliferation of geo-
located data from mobile phones and other emergent technolo-
gies that have arisen in the 21st Century with potential for the
sensing of temporary populations across large geographic areas
and in widely differing contexts. Set against this backdrop, the
current study assesses progress in the field of temporary popu-
lation statistics. To this end, we conduct a systematic review of
the international literature on the estimation of temporary
populations to identify the contemporary state of the art. In doing
so, we seek to understand the volume, geography, population
coverage along with the data and methods used to generate
temporary population estimates.
The remainder of the paper is structured as follows. In the
section “Methods: Protocol and capture”, we detail the study
methods including information sources and search terms, elig-
ibility criteria and data extraction. In the section “Results and
discussion: Estimating temporary populations”, we report the
results of the review across a number of themes including: pub-
lication time frame, sources and reasons for estimates; data types;
estimation methods; the geography of estimates; the temporal
units of estimates; estimated population size and composition;
and reproducibility. In the final section of the paper we sum-
marise the current state of the art in the field.
Methods: Protocol and capture
The review was conducted using the principles of the PRISMA
Statement for Reporting Systematic Reviews (Moher et al., 2009)
(see Supplementary Table 1 online). In order to deal with com-
plexity and lack of standardisation in the field and to account for
the theory-building character of this work we additionally
adopted the “mixed-method review”methodology (Gough, 2015).
This approach allowed us to combine methods of a systematic
review (e.g. selection of keywords and sources and eligibility
criteria) with the openness and flexibility of traditional reviews.
No review protocol was used in this study.
The articles that constituted the review were captured via a
search of four databases; (1) Web of Science; (2) Scopus; (3)
Google Scholar; and, (4) arXiv. A set of keywords that collectively
encompass the breadth of synonyms associated with temporary
populations were used. More specifically, we employed a suite of
terms derived from the authors’collective knowledge of the field,
as well as key words that emerged during early stages of the
review process. Terms included: “temporary population”,
“working population”,“daytime population”,“mobile popula-
tion”,“service population”,“floating population”,“elusive popu-
lation”,“ambient population”,“seasonal population”,“non-
resident population”,“real-time census”,“social sensing”and
“spatiotemporal population”. We additionally scrutinised the
reference lists of included studies to ensure a full coverage of
articles. Final searches were conducted on 24 November 2017. In
order to enhance the currency and coverage of the articles cap-
tured principles of the living systematic review methodology were
adopted (Elliott et al., 2014). To this end updates from three
databases covering all original queries were configured along with
periodic manual searches on the fourth database (arXiv) to ensure
any new articles following the final search were included in the
review. The final inclusion of studies into the review was con-
ducted on 13 October, 2019. Supplementary Text 1 online con-
tains details of the full search strategy.
To supplement the key search terms listed above, inclusion and
exclusion criteria were developed. These were;
●Studies were included if they produced estimates or forecasts
of temporary populations.
●Studies were required to detail their methodology for at least
one geographical region and capture non-resident popula-
tions.
●Studies were excluded if they focused their analyses on certain
population bases (for instance, social media or mobile phone
users) without any extrapolation to the total population of a
given case study region.
●Studies examining tourist and crowd behaviours were
excluded as this is a distinct and voluminous literature
deemed extraneous to the core motivation of this study.
●Book reviews and magazines were excluded.
●Only studies published in English were included.
Each of the above criteria were designed to capture studies that
offered an in-depth empirical analysis of temporary population
estimation. Beyond the above criteria, there were no restrictions on
publication date, or publication status (for example, a published
journal article versus an unpublished thesis). In cases where the
same or a similar methodology was used across several studies, one
representative article was selected for inclusion in the review and
used to collect additional references for the supplementary materials.
We developed a data extraction template that was iteratively
revised during the extraction process to ensure applicability
across all included studies. One author (RP) extracted data from
each study that was subsequently split into two major groups:
1. Publication details: Publication metadata included: author
(s), title, source, year and publication type. We classified
studies according to the primary source of data: mobile
phone, remote sensing, social media, transport (e.g. cordon
counts, traffic and smart card data), official statistics (e.g.
national censuses, large scale or national demographic
surveys and population registers), surveys (e.g. time use
surveys, holiday accommodation occupancy surveys), Wi-
Fi and data from utility providers (e.g. electricity consump-
tion). Studies could belong to more than one category.
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2. Estimation: Characteristics of the temporary population
estimates reported in the study including whether estimates
captured daytime or overnight populations, the country and
region for which estimates were made, data sources,
methods and software employed, data and population size,
number and characteristics of the spatial and temporal
units, estimate validation and the purpose for which the
estimates were produced.
All analyses were conducted in R (R Core Team, 2019) version
3.6.1. Data management used several packages of the tidyverse
family (Wickham, 2019a,b; Wickham and Bryan, 2019; Wickham
et al., 2019; Wickham and Henry, 2019); descriptive statistics
were done using sjmisc package (Luedecke, 2020); reporting was
facilitated with knitr (Xie, 2019) accompanied by scales (Wick-
ham, 2018) and bibtex (Francois, 2017); and graphs were pro-
duced using the ggplot2 package (Wickham, 2016).
Results and discussion: Estimating temporary populations
Publications time frame, sources and reasons for estimates.
Collectively, our search strategy initially identified around 22,900
documents (Fig. 1). After removing duplicates, titles and abstracts
were screened for eligibility resulting in a reduction to 122 arti-
cles. The final stage involved a screening of full texts resulting in
the removal of a further 68 articles. An additional 26 articles were
identified from other sources, while 16 were identified by scan-
ning updates of the search results. A total of 96 studies were
selected for analysis (see Supplementary Text 2 online).
The 96 studies were published between 1925 and 2019 (median
publication year, 2011) (Table 1; see Supplementary Table 2
online). Notably, there has been exponential growth in the
number of studies since the first publication in 1925, with more
than a third of the studies (35.4%) published since 2015. A total of
55% of the studies were published as articles followed by 22% as
reports and 12% as conference proceedings or presentations.
Taken together articles focused on various aspects of temporary
population estimation, including emergency planning (n=22),
understanding service populations (n=8), epidemiology (n=4)
and commuting (n=3).
Data types.Official statistics, defined as large scale data collected
by national statistical agencies (such as censuses, population
surveys, administrative records or registers), formed the basis, or
were an important component of the largest number of studies
(Fig. 2). The first recorded report used data on place of work
collected at the 1921 Census data from England and Wales to
estimate daytime populations (Census of England and Wales
1921, 1925). Similar work was undertaken by the US Census
Bureau in the 1950s (Bureau of the Census 1956), and such
estimates continue to be produced in many countries around the
world. A more recent innovation are workday population esti-
mates, which combine estimates of working populations with
night-time usual resident counts. These estimates were produced
for England and Wales using data from the 2011 Census (Office
for National Statistics 2013); see also related work in Supple-
mentary Table 3 online]. In a similar vein, the US Census Bureau
has constructed commuter-adjusted population estimates using
data from the American Community Survey (McKenzie et al.,
2013).
Fig. 1 Flowchart of study selection.
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Census data have served as an input into a host of other
population estimates. Census-based studies from the United
States include the work from the Seattle City Planning
Commission (1951), Fulton (1984), Gober and Mings (1984),
Nelson and Nicholas (1992), McPherson and Brown (2003),
Kobayashi et al. (2011), Swanson and Tayman (2011), Hodur and
Bangsund (2015), Kim and Ahn (2017), Boeing (2018) and Esri
(2018). These papers used a range of methods to adjust Census
estimates at a variety of temporal and spatial scales, often
accompanied by other data sources including place of work,
transportation models or payroll data that helps to determine size
and location of daytime or seasonal populations. Census data
from the United Kingdom have formed a key input into the
recent work of Smith et al. (2005) and Martin et al., (2015).
Census data has also been used for the estimation and analysis
of temporary populations in a number of other countries. Taking
advantage of the availability of the place of residence and place of
enumeration data, Bell and Ward (1998); (see also subsequent
work in Supplementary Table 3 online) looked at the one-day
snapshot of the mobility of Australians across multiple census
years and spatial scales. Similarly, Taylor (1998) used census data
to estimate the mobility of indigenous Australians. Gao et al.,
(2014) and Qi et al., (2015) employed Chinese census data
adjusted with information for tourists, school and patient
populations extended with land use models to calculate daytime
population estimates. The main advantage of census data is that it
provides close to full enumeration of the population; however, it
captures only a snapshot of temporary populations on one day or
night, every 5 or 10 years. We know that many forms of
temporary population mobility are highly seasonal, thus census
data will not reflect the population present across other times of
the year. To address this deficit a number of studies have drawn
upon large administrative datasets such as health service
utilisation records (Markham et al., 2013), building (Greger,
2015) or second home registries (Adamiak et al., 2017), however,
these studies tend to target specific population groups or
geographical regions, and may not directly capture the mobility
of visitors. Notwithstanding these limitations, official statistics
remain a crucial component of many temporary population
estimates.
The second most common group of studies drew on survey
data. Surveys vary widely with respect to the target population,
spatial coverage and sample size and include urban travel surveys,
destination surveys (Erickson, 1961; Happel and Hogan, 2002)
and large-scale household surveys (Wurtele and Wellisch, 1968;
Kavanaugh, 1990; Stutz et al., 1992; Roddis, 1996; Collins and
Greaves, 2007; Lau, 2009; Horanont and Shibasaki, 2010; Charles-
Edwards, 2011; Sekimoto et al., 2011; Himoto and Kimata, 2014;
Kashiyama et al., 2017). Surveys have the singular advantage of
being able to collect detailed information on the spatial and
temporal dynamics of mobility, including the timing and
duration of visits, as well as the characteristics of movers. They
can be used to target transient populations such as seasonal
snowbirds (Happel and Hogan, 2002) or indigenous Australians
(Warchivker et al., 2000) not captured in official statistics. The
major disadvantage of surveys are related to capturing rare events
through random sampling. Even in large-scale origin-based
surveys, sampling error can limit the utility of survey data for
estimating temporary populations in small areas and over short
time periods. Purposive surveys of accommodation providers or
particular target populations fare better, but are usually limited
with respect to both population and geographic coverage.
The third largest category of studies, and one that experienced
the fastest growth in recent years, is work based on mobile phone
data. Some of the early attempts to use this emergent source of
data in tracking large-scale population distribution changes came
from the SENSEable City Laboratory and their work in Milan
(Ratti et al., 2006) and Rome (Reades et al., 2007). This was
followed by work on seasonal residents in Estonia (Silm and
Ahas, 2010). However these first studies did not attempt to
Table 1 Selected characteristics of included studies.
Characteristic Value n/median %/min; max
Year of publication 2011 1925; 2019
Search method Systematic 54 56.25
Manual 26 27.08
Update 16 16.67
Type of publication Article 53 55.21
Report 21 21.88
Conference 12 12.5
Thesis 7 7.29
Book chapter 3 3.12
Purpose of publicationaEmergency planning 22 22.92
Other 9 9.38
Service population 8 8.34
Epidemiology 4 4.16
Commuting 3 3.12
Main type of dataaOfficial statistics 53 55.20
Survey 40 41.68
Mobile phones 19 19.80
Transport 13 13.56
Other 11 11.44
Utilities 5 5.20
Remote sensing 2 2.08
Wi-Fi 2 2.08
Social media 1 1.04
Study region Single city 52 54.17
Country 17 17.71
Administrative region 14 14.58
Multiple cities 10 10.42
Regions for estimatesaAdministrative 55 57.30
Grid 28 29.16
Custom 8 8.34
Points 7 7.30
Building 3 3.12
Voronoi 2 2.08
Number of regions for
estimates
34 1; 52,000
Temporal unit of estimates Minute/hour 35 36.46
Daytime/nighttime 21 21.88
Daytime 14 14.58
Month 10 10.42
Other 7 7.29
Day 2 2.08
Weekday/weekend 2 2.08
Size of estimated population 355,100 1868;
53,349,074
Size of dataset 722,000 422;
55,963,096
aIndicates that the study can belong to more than one category.
Fig. 2 Cumulative distribution of year of publication across data types.
Studies can belong to more than one category; The ‘Social Media’category
has been excluded as it only has one member.
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extrapolate the mobile phone user base to the whole population
and the earliest such application was in response to the Haiti
earthquake (Bengtsson et al., 2011). This approach was followed
by large-scale estimates for Japan (Terada et al., 2013) and
analyses of temporary populations in France and Portugal
(Deville et al., 2014). The spatial coverage of mobile phone-
based studies often reflects the availability and accessibility of
such data. For instance, Italy and in particular Milan, have been
the foci for a number of studies given that local telecom providers
have offered access to the aggregated and anonymised call records
(Khodabandelou et al., 2016). Mobile phone data are also used in
parallel with other sources and methods for instance in
dasymetric interpolation (Järv et al., 2017) or transport survey
data (Lwin et al., 2016). While mobile phone data can provide
detailed information on short-term change in population
numbers at fine spatial resolutions they provide limited informa-
tion on the characteristics of movers. Finally, as a commercially
collected data set, they can be both difficult and costly to access.
Transportation data, collected via cordon counts, or more
recently by smart cards, are an important source of information,
particularly for diurnal population estimates. Early work from the
United States can be traced back to Thornthwaite who estimated
the daytime population of a Central Business District using data
from a traffic survey (1929; cited in Wheeler and Brunn, 2002).
Others followed including Breese (1947), Sharp (1955)andWeir
(1960). The New York Regional Plan Association (1949), Seattle
City Planning Commission (1951) and Institute for Research in
Social Science at the University of North Carolina (1952) employed
various transportation surveys and cordon counts to estimate the
number of people in Central Business Districts. Similar work from
the United Kingdom was undertaken by Menzler (1952). More
recently and with the advent of public transport smart card data
(Ma et al., 2017) along with technologies such as number plate
recognition (Fehr & Peers, 2014) offer new data sources with utility
for temporary population estimation. These studies have the
advantage of longer collection periods, but tend to be limited to
relatively narrowly defined geographic regions, usually cities, and
tend to be employed for diurnal rather than seasonal population
estimates (see Fehr & Peers, (2014) for an exception).
Less common, yet still important, are studies based on a range
of symptomatic indicators including utilities data (Goldschmidt
and Dahl, 1976; Monmouth County Planning Board, 2008; Rigall-
I-Torrent, 2010; McKenzie and Canterford, 2016; Edmondson
and Nantucket Data Platform team, 2019), remote sensing data
(Taubenböck et al., 2007; Stathakis and Baltas, 2018) and Wi-Fi
data (Kontokosta and Johnson, 2017; Crols and Malleson, 2019).
At their most basic, these studies assume a constant relationship
between a region’s de facto population (including residents,
diurnal and overnight visitors) and a single symptomatic
indicator. Symptomatic variables theoretically enable the genera-
tion of continuous estimates of visitor populations in both a
timely and cost effective manner, but are contingent on
understanding the relationship between the symptomatic indi-
cator and populations, which may vary across space and time. In
addition, it can be difficult to identify appropriate symptomatic
indicators when estimates are sought for a large number of
regions. For example, a scoping study undertaken by the
Australian Bureau of Statistics was unable to identify any
symptomatic indicators that could be used to estimate visitor
populations across five Local Government Areas in Australia
(Lee, 1999). Contrary to our expectations, social media data were
only used in one study (Lwin et al., 2016). Here geotagged Twitter
data were used together with mobile phone records to predict
scaled counts from a large person trip survey.
Slightly more than one-third of studies (38.5%) drew on
multiple data sets. Census data was employed as a starting point,
for the calibration of models, or as a source of validation. For
instance, the seminal work of LandScan—capturing the ambient
population (an average population over a 24 hour period) at a
1 km resolution—(Bhaduri et al., 2007) used census data as one of
the main inputs and Ma et al. (2017) used smart card
transportation data to redistribute census (night-time) counts of
population across a city region. Studies also draw on multiple
data sources to estimate the components of temporary popula-
tions in particular regions (Edmondson and Nantucket Data
Platform team, 2019), while Rigall-I-Torrent (2010) combined
multiple symptomatic indicators with survey data to establish the
relationship between household size, type and period of the year
to produce population estimates. What emerges from this
systematic review is the lack of any “gold standard”data source
for temporary population estimation.
Estimation methods. Smith (1989) in his original study identified
two approaches for the estimation of temporary populations: the
‘direct’approach which draws on information collected directly
from temporary residents via censuses and surveys and the
‘indirect’which draws upon symptomatic variables that reflect
changes in temporary populations. This distinction is increasingly
fuzzy due to the emergence of new data sources such as mobile
phone and Wi-Fi data which can be both treated as either direct
or indirect sources depending on the methodology employed. In
addition, a number of studies combine both direct and indirect
sources to generate estimates for individual regions (Rigall-I-
Torrent, 2010) or to apportion temporary visitors across geo-
graphic areas (Lwin et al., 2016). Moreover studies are increas-
ingly drawing on sophisticated simulation and modelling
approaches to produce temporary population estimates (see Crols
and Malleson (2019) for an example).
The earliest method of deriving temporary population statistics
was to use direct estimates, either from the Census (Census of
England and Wales 1921, 1925), survey data (Thornthwaite, 1929)
or by combining multiple formerly disparate sources of data
(Breese, 1947). These studies often took advantage of availability of
information about the workplaces of individuals (Office for
National Statistics, 2013), their status of residence (Gober and
Mings, 1984) or place of enumeration (Bell and Ward, 1998)to
distinguish between two or more states of population distribution
such as nighttime and daytime populations, or visitors and usual
residents. An allied approach involves the scaling of population
counts from one or more sources to the whole population based on
expansion factors. This approach was used by both early survey-
based studies (New York Regional Plan Association, 1949;Foley,
1952) and more contemporary analyses based on mobile phone or
Wi-Fi data. In the latter case, various strategies of deriving factors
were adopted, some based on the penetration rate of mobile data
providers (Bengtsson et al., 2011), night-time Census populations
(Deville et al., 2014) or ancillary survey data (Kontokosta and
Johnson, 2017). Studies either used a single factor for an entire
region (Thomas et al., 2017) or multiple factors, varying each
geographically (Batran et al., 2018) or over population subgroups
(Picornell et al., 2018).
Temporary populations have also been estimated using
population accounts or component-based approaches. These
methods used generic or area-specific equations to derive counts
of temporary residents from usual residents by subtracting
departures and adding arrivals to a spatial unit at a given time
(Yong li, 1998). For instance Journey-to-Work data (from the
Census) has been used to estimate the working population
(McKenzie et al., 2013), second home users and usage could be
used to capture seasonal populations in specific areas (Adamiak
et al., 2017) or sales tax data could be used to derive equivalent
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residents (Thakur, 2018). Additionally, separate estimates have
been derived for specific subgroups of the population (Swanson
and Tayman, 2011).
As discussed previously, symptomatic data such as electricity
usage have been used to track seasonal variation in populations
across specific regions (see Goldschmidt and Dahl, 1976). An
allied strand of research draws on symptomatic data to
redistribute temporary populations from larger to smaller spatial
units. These include dasymetric techniques commonly used for
mapping small area resident populations. The LandScan model
(Bhaduri et al., 2007) for instance has the largest spatial coverage
providing gridded population estimates of the nighttime and
daytime population for the United States. Martin et al.’s(2015)
model for Southampton on the other hand is an example of a
more spatially and temporally detailed approach that can be
adapted to various grid sizes and temporal resolutions. This
model has also been implemented in other locations and settings
(see Supplementary Table 3 online). The redistribution approach
usually uses ancillary data such as land use (Batista e Silva et al.,
2017) or building type (Greger, 2015) as well as data from
Censuses, surveys, mobile phones (Järv et al., 2017), transporta-
tion (Ma et al., 2017) and social media (Lwin et al., 2016) as input.
Finally, a number of studies employ some form of modelling or
simulation. These include agent-based simulation (Walker and
Barros, 2012; Kashiyama et al., 2017; Crols and Malleson, 2019),
cellular automaton models (Khakpour and Rød, 2016), and
neural networks (Liu et al., 2018; Chen et al., 2018).
The geography of estimates. Only 18% of studies generated
estimates for all geographic units within an entire country (Fig.
3). The United States was the best represented with a total of five
studies, followed by Australia (n=3) and England and Wales
(n=2). Only one developing country, Nepal, (Wilson et al., 2016)
was represented and one study reported estimates from two
countries (Deville et al., 2014). Studies covering a part of the
country were again most frequent in the United States (n=32).
This was followed by China, Australia, Japan and the United
Kingdom as well as a handful of developing nations (namely,
Haiti, Bangladesh, Mozambique, Myanmar and Vietnam). A little
more than half (54%) of the studies were focused on a single city.
Two types of spatial unit for the estimates dominated; 57% of
studies used some form of administrative unit (for example, a
statistical division, a county, a township or metropolitan region)
and 29% used grids of varying sizes. Other types of spatial unit
included points, buildings, or some custom units such as Voronoi
polygons. A number of studies (n=11) reported estimates for
more than one type of spatial unit.
There was a large variation in the number of spatial units used
to produce estimates ranging from 1 to 52,000 with a median
value of 34. A total of 10 studies reported on a single spatial unit
including islands (Edmondson and Nantucket Data Platform
team, 2019), a single administrative region (e.g. Fehr & Peers,
2014), a city (e.g. Goldschmidt and Dahl, 1976) and a university
campus (Charles-Edwards and Bell, 2013). Studies reporting
estimates for larger numbers of units provided estimates at the
building level (Ara, 2014), high-resolution grids (Adamiak et al.,
2017) or administrative areas (e.g. McKenzie et al., 2013), and
were often alongside national or large sub-national area
coverages. Half of the studies (n=47) did not report the exact
number of spatial units for which estimates were produced.
Temporal units of estimates. No study revealed efforts to pro-
duce and maintain an ongoing series of estimates. In contrast to
the majority of studies presenting ad hoc analyses using oppor-
tunistic data sources only a few analyses and associated estimates
received any form of update. Examples of these studies include,
ESRI Daytime Population (Esri, 2018) and analyses of Australian
Census from 1991 (Bell and Ward, 1998) to 2018 (Charles-
Edwards and Panczak, 2018).
The majority of studies (77%) report on daytime (or both
daytime and seasonal) populations and this reflected in the choice
of the temporal units used in the estimates. A total of 36% of
studies used minutes or hours as their temporal unit, with
seconds as the most common unit that was split between daytime
and night-time estimates (22%) or daytime only (15%) (Fig. 4).
Estimated population size and composition. There was a large
variation in the sizes of estimated populations, ranging from
1,868 to 53,349,074 with a median value of 355,100. The size of
dataset used for estimation ranged from 422 to 55,963,096 with
median value of 722,000. Combining the size of the dataset and
the size of estimated populations, largest populations were used
or estimated when mobile phone data were employed, followed
by smaller amount of transport-based studies (Fig. 5). Other large
studies used monthly sales tax data from counties in North
Carolina to derive “equivalent residents”surplus to the perma-
nent population.
In terms of composition, the majority of studies focused on
total populations present or working populations. For those
studies concerned with population estimates of summer resort
towns the total population was extended to include temporary or
seasonal visitors (Erickson, 1961; Goldschmidt and Dahl, 1976).
Fig. 3 Country of temporary population estimates and national coverage.
Study of Deville et al. (2014) describes two countries. Fig. 4 Temporal units used for estimates.
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The majority of studies (71%) did not report on any
characteristics of estimated populations. This was often driven
not by the theoretical or practical considerations but rather by data
availability. For instance, many studies drawing on mobile phone
data did not have access to any individual-level information (such
as, age, sex, reason for visitation). Some studies sought to capture
specific groups of temporary movers including retired individuals
undertaking seasonal movements (Rose and Kingma, 1989; Happel
and Hogan, 2002), school pupils (Campbell, 2010), second home
users (Adamiak et al., 2017), indigenous populations (Warchivker
et al., 2000), or on functional areas such as a university campus
(Charles-Edwards and Bell, 2013).
Reproducibility. 61% of studies did not report estimates nor
indicate where they could be obtained. Similarly, 95% of studies
did not report on the type of software and/or code used to pro-
duce the estimates. In some cases, particularly when relatively
simple methods were used (for example, equation based), this
does not limit reproducibility or replicability of the studies or the
possibility of their implementation across different situational
contexts. However, for the remaining studies, particularly those
based on proprietary data, the prospects of generating estimates
across different areas allied with their validation are limited. More
particularly, validation of the estimates was not common practice
with only 36% reporting some attempt at validation with a lack of
suitable data often quoted as a main reason for not undertaking
the task.
Conclusions
This study sought to systematically review the empirical lit-
erature on temporary population estimation to identify the
contemporary state of the art. To this end we revealed an
interest and demand for temporary population estimates that is
approaching 100 years in the making. However, it has been over
the past two decades where there has been strong growth in the
number of studies that is in part driven by the rise in both the
type and availability of emerging sources of ICT-based data.
This growth is likely to continue, set against the broader
backdrop of ‘big data’these new databases will need to be
assessed and evaluated for their relevance for temporary
population estimation along with their capacity to either unveil
previously unknown temporary population dynamics or act as
additional information source to bolster existing sources (both
traditional and emergent). To this end recent studies using
credit card information (see for example, De Montjoye et al.,
2015; Whitaker, 2019) would seem one of a number of pro-
mising new sources of data with the potential to reveal
important dimensions of population mobility. That said,
despite the rise in the number of studies using emergent sources
of information, there remains no single source of data for the
estimation of temporary populations. Rather traditional sources
of information including the census and survey data remain an
important backbone through which temporary population
estimates are founded.
Notwithstanding the growing prevalence of temporary population
studies, a disconnect currently exists with the lack of availability of
such estimates through national statistical agencies. To this end the
translation of temporary population studies into standardised pro-
ducts appears yet to occur in lieu of rising demand for such infor-
mation that reflect the so far unresolved conceptual and
methodological challenges. Often, in the absence of official estimates,
cities and regions generate their own estimates (Lamb, 1999;Mon-
mouth County Planning Board, 2008;Fehr&Peers,2014;Hodurand
Bangsund, 2015; Edmondson and Nantucket Data Platform team,
2019;Harrisetal.,2019). However, it would appear that this situation
is likely to change with the recent emergence of major coordinated
programs of research focused on temporary population estimates to
include ENhancing ACTivity and population mapping (ENACT)
(Batista e Silva et al., 2017) and the Population 24/7 (Martin et al.,
2015) projects (see also the subsequent work in Supplementary Table
3 online). It is likely that through these dedicated research programs
the conceptual and methodological challenges will be systematically
addressed and resolved.
It is important to highlight two interrelated limitations of the
current study. The first pertains to the fragmented nature of the
temporary population literature and its presence across multi-
ple academic domains. For instance 28.1% of studies included
in this review were found outside of the literature searches.
Both the range and number of keywords (13 individual key
words) used to capture studies of temporary populations
reviewed here underscore this issue with the result of generat-
ing a large amount of false positive findings during the litera-
ture search. Second, there is a relatively large volume of grey
literature represented. To this end, 27% of studies included in
this study were published as reports. As such, this makes it
difficult to systematically capture, classify as well as monitor the
development in this field.
In sum, there is little doubt that the field of temporary population
estimation will continue to grow however there remains a number
of important challenges. Conceptually there is need for the research
community and national statistical agencies to develop and agree on
clear definitions that enable temporary populations to be classified
in order to differentiate them from permanent residents. Practically,
there remains much work to complete that comprehensively cap-
tures and measures the diverse array of spatial behaviours that
involve different spatial circuits which are undertaken for a myriad
of different purposes. Coordinated efforts that address both of these
remaining challenges will ensure that we progress towards a more
robust conceptual and empirical framework for the estimation of
temporary populations.
Data availability
The datasets generated during and/or analysed during the current
study are available in the Zenodo repository, https://doi.org/
10.5281/zenodo.3521082.
Received: 20 November 2019; Accepted: 1 April 2020;
Published online: 06 May 2020
Fig. 5 Size of population by main data type. The first category used. Box
width is proportional to the total number of studies. Studies that did not
report on an estimated population size the size of dataset was used
whenever possible.
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Notes
1 Whilst we adopt the definition of temporary populations by Smith (1989) for reasons
of inclusiveness and simplicity, we recognise there exists a number of alternate
definitions. This is because human spatial mobility is continuous through space and
time (de Gans, 1994). For practical purposes it is necessary to delimit temporal and
spatial bounds to facilitate measurement and analysis (Gober and Mings, 1984).
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Acknowledgements
The authors wish to thank the Australian Bureau of Statistics (ABS), in particular -
Denise Carlton, Andrew Howe, Anthony Grubb, Glen West, Beidar Cho and Karen
Ecclestone of ABS for their input on various aspects of the paper. However, the inter-
pretations of the analysis are solely those of the authors and do not necessarily reflect the
views and opinions of the ABS or any of their employees. We would like to also thank the
authors that responded to queries about details of their work: Christoph Aubrecht, Filipe
Batista e Silva, Andrew Collins, Donald G. Janelle, Nik Lomax, David Martin, Chris
Needham, Andy Newing, Paola Pucci, Stanley Smith and Martijn Tennekes. This
research was funded by the Australian Government through the Australian Research
Council Linkage project scheme, LP160100305.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1057/s41599-
020-0455-y.
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