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Estimating visitor numbers and spending is conceptually simple, yet the actual collection of the data required to estimate and identify visitor spend, particularly at a local level, is complex (Wilton and Nickerson, 2006). Visitor expenditure is considered to be an important indicator of the economic benefits of tourism (Frechtling, 2006), and is one of the main drivers of employment in tourism (Ashworth and Johnson, 1990). As such, data on visitor expenditure is important, supporting policy makers at a national or regional level. Given that the tourist sector is difficult to identify on the supply side, much of the insight into visitor numbers (and their associated expenditure) has to be inferred from the demand side. This section first considers the range of national surveys available, allowing overall trends, visitor numbers and headline expenditure to be identified. Section 3.3.2 extends the discussion, noting that robust and timely data collection does not exist at the sub-regional level.

3.3.1

Visitor surveys in the UK

Three major national surveys provide headline figures on tourist numbers and their associated spend in the UK (international tourists) or Great Britain (domestic overnight and day visitors). In turn, these surveys feed into a series of economic impact models, used by regional and local tourist boards, counties and local authorities in order to understand the

impacts of tourism (see section 3.4). This section briefly reviews the key national surveys available in the UK or Great Britain, allowing identification of visitor numbers and expenditure associated with inbound visitors (section 3.3.1.1), domestic overnight visitors (section 3.3.1.2) and day visitors (section 3.3.1.3).

3.3.1.1 International Passenger Survey

Traditionally, organisations such as the Office for National Statistics (ONS) have placed importance on understanding expenditure associated with inbound visitors, and their impact on national or regional economies (as inbound international tourism represents a major source of income from foreign exchange) (Buccellato et al., 2010b). Since 1961, the International Passenger Survey (IPS) has provided information on both inbound and outbound international tourism and is based on an interview administered questionnaire completed with a sample of around 0.2% of all travellers passing through the UK’s main airports and ferry terminals (ONS, 2013). This survey provides data at a regional level relating to the number and type of inbound visitors, along with their trip characteristics and estimated expenditure, and is based on around 250,000 interviews per annum.

IPS data is not directly used in this thesis since broad trends in seasonal and spatial patterns have been derived from the UKTS/GBTS (section 3.3.1.2), as domestic visitors have a greater influence on grocery expenditure at the resort or destination level. Nonetheless, IPS data is incorporated within outputs used in Chapters 5 and 8 based on the ‘Cambridge Model’ (which is itself introduced in section 3.4.2.2).

3.3.1.2 United Kingdom/Great Britain Tourism Survey

The United Kingdom Tourism Survey (UKTS) provided data on domestic overnight trips undertaken by UK residents between the years 1989 and 2010. Based initially on a telephone survey, the methodology changed significantly in 2005 with the introduction of face-to-face interviews, carried out in the respondents’ home (of which around 2,000 were carried out each week) (TNS, 2010a). With a sample of around 100,000 respondents per year (Visit England, 2010), participants were asked to recall specific characteristics of up to three recent domestic trips. During analysis and reporting, the data were weighted to account for the demographic, socio-economic and geographic characteristics of the population as a whole. In 2011 the UKTS was replaced with the Great Britain Tourism Survey (GBTS) following the withdrawal of Northern Ireland from the data collection and reporting process. The survey methodology, sample size, analysis and reporting remained unchanged however, with the exception that published results no longer include Northern Ireland. Within this thesis, data from both the UKTS and GBTS are utilised in order to make inferences about the seasonal distribution of visits. Within Cornwall (Chapter 5), the year 2010 is considered and data is thus drawn from the UKTS. Within Kent (Chapter 8) the year 2011 is modelled and, as such, data is drawn from the GBTS. As the data used has been extracted from regional

tables (related to either the South West or South East region) and does not include Northern Ireland, the terms UKTS and GBTS can be used interchangeably within this thesis.

The UKTS/GBTS survey data are an immensely valuable resource to researchers and provide information on the volume and value of domestic trips. These trips are broken down by geographic hierarchy to regions and can be analysed against a broader set of variables (such as the group size, the mode of transport used or the region of origin of the respondent). Headline results are also reported on a monthly basis (based on the respondents self-reported month that a trip began). As such, seasonal distribution of trips (by month) by trip purpose, region visited or accommodation used can be identified. Unfortunately, however, and in common with most visitor survey data, the UKTS does not directly collect expenditure data on grocery spend (which is instead included alongside other forms of expenditure in the ‘other shopping’ category and not uniquely identifiable). Thus, whilst the UKTS/GBTS can be utilised to make inferences about trip distribution and broad expenditure associated with those visits, the level of insight provided is too broad to identify important forms of visitor consumption such as food and drink purchased from grocery stores.

3.3.1.3 England Leisure Visits/Great Britain Day Visits Survey

Day visitors are a subset of visitors that have received little attention within data collection. With the exception of a national survey carried out in 2005, little insight into these forms of visitors, or their associated seasonal or spatial characteristics, was undertaken in the 2000s. The 2005 England Leisure Visits Survey (ELVS) was led and coordinated by Natural England and carried out with the support of a number of national park authorities in England. Whilst the survey was comprehensive (for example considering both the characteristics of visitors themselves and the trips they were making), it was based on telephone interviews for only 23,500 households (Natural England, 2005). The ELVS did not form part of the national tourism data collection and was heavily focussed on particular forms of tourism (predominantly to national parks and the countryside) due to the nature of survey sponsors. It was not until 2011 that the Great Britain Day Visitor Survey (GBDVS) was launched. The GBDVS provides the most comprehensive source of data available related to day visitors. It is based on an online survey of around 35,000 households, weighted to account for the geodemographic and socio-economic characteristics of all households and further informed by around 6,000 face-to-face household interviews (Visit England, 2013). Outputs are reported by region visited and type of activity (again by month) but, in common with the key surveys of overnight visitors, expenditure data on groceries is not explicitly collected or reported. Nonetheless, the GBTS (and in some cases the ELVS) provides useful insight into the day visitor sector and is used to inform subsequent modelling in Chapters 5 and 8.

3.3.2

Understanding tourism at the sub-regional level

Whilst comprehensive and robust, these national sample surveys offer a very limited insight into the nature of tourism at a sub-regional (county) or local authority district level, and even less so within individual resorts or destinations (Beatty et al., 2010; TIU, 2011). This lack of focus on local outputs is surprising given that tourism activities have traditionally clustered around (and been dependent upon) key resorts, destinations and amenities, resulting in very localised (and often highly seasonal) economic impacts. Consequently, Bryan et al. (2006) note that decisions about service provision within tourist areas are often being made with very little knowledge of the extent to which visitor spending supports local economies. As such, firms and local development authorities may be making decisions about service provision with little knowledge of local visitor numbers, their seasonal distribution or the local impacts of visitor spend (see also Jones and Munday (2009)).

The lack of knowledge and insight into small-area visitor numbers is in stark contrast to the importance placed on understanding other population sub-groups. Visitors (in the form of tourists) are one component of local populations, which will also be made up of local residents alongside other visitors for work (commuters), education or other leisure and personal reasons. A robust and well-developed infrastructure exists for collecting information on small-area residential populations (via the decadal census). The census provides a snapshot of small-area populations resident in households or other similar establishments. Almost all conventional approaches to population modelling and resource allocation are based on residential locations, with sub-district population counts disseminated through a series of specially constructed hierarchical zones, the smallest representing an Output Area (OA) containing an average of 124 households (Vickers and Rees, 2006). OAs are built around residential addresses and are an important spatial scale for local-level analysis and decision making, yet very limited data relating to tourism is collected at this level. Census-derived local population estimates (and decisions about service provision that are based on them) fail to account for short-term population fluctuations driven by an influx of workers, students or visitors to particular areas at certain times of the day or at specific times of the year.

Smith and Fairburn (2008) attempted to produce a National Population Database (NPD) for use by the UK Health and Safety Executive (HSE). They attempted to incorporate populations not enumerated by the census, accounting for spatial clusters of population around schools, airports, hospitals, prisons, workplaces and leisure facilities. Smith and Fairburn (2008) attempted to account for some forms of visitor population that may be present within a destination, for example via incorporation of some forms of visitor accommodation (hotels, guest houses and some campsites and holiday parks). Only accommodation listed and clearly identifiable from the Ordnance Survey ‘MasterMap - AddressLayer 2’ product were incorporated and they noted two major obstacles. First, the address listings contained only a small proportion of the total accommodation stock that was

thought to exist; second, having identified specific sites, they suggested that “there is no obvious way of populating such features” (Smith and Fairburn, 2008, p51). Their experience in handling tourist populations within the NPD highlights that identifying the potential accommodation stock and populating that stock (based on some form of overall capacity and seasonal distribution) is a challenging and previously unaccomplished task. The handling of visitors and associated seasonal fluctuations within small area population geographies is a weakness identified in international contexts too. For example, Bhaduri (2008) notes the difficulties and challenges encountered in identifying daytime populations in the US, especially where these are made up of an influx of tourists or visitors with seasonal differences in numbers or distribution.

In spite of the obvious difficulties, Martin et al. (2009) note that there are clear arguments for understanding seasonal population movements resulting from tourism, citing a number of useful applications which range from hazard exposure (see for example Smith et al. (2013)), emergency service response (a useful example is provided by Ahola et al. (2007) through to service demand forecasting. In addressing this issue, however, Martin et al. (2010), p2 note that “a current area of deficiency is detailed counts for visitor numbers to residential and leisure facilities”. Consequently, Cockings et al. (2010) claim that even relatively modest advances in the availability of data suitable for understanding spatial and temporal population fluctuations driven by tourism would represent advances in understanding small- area populations.

Section 3.4 considers further the need for data suitable for identifying small-area visitor numbers, their seasonal and spatial distribution and subsequent expenditure.