Abstract
One of the key strengths of SMILE is its facility to combine spatial data from many diverse sources in order to examine economic, social and environmental issues of importance to the rural economy in Ireland. Indeed, spatial context lies at the heart of many aspects of the rural economy and SMILE, as a modelling and data infrastructure, can be usefully applied in conjunction with techniques such as geographic information systems (GIS) analysis and microeconometrics to examine such issues. One such area of interest is rural tourism. Rural tourism is now an important contributor to rural development in Ireland given the long term decline of agriculture, particularly in its potential for stimulating employment and providing a viable option for off-farm diversification.
Keywords
- Geographic Information System
- Negative Binomial Model
- Discrete Choice Model
- Rural Tourism
- Conditional Logit Model
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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- 1.
In the recreation demand modelling literature, such models are routinely referred to as random utility (maximisation) models (RUMs).
- 2.
According to Hanley et al. (2003: 12), “whether to use objective or subjective measures of these characteristics has proved another tricky issue”.
- 3.
In a recreation demand modelling context, this estimate could then be combined with an estimate of average consumer surplus per visit to site j in order to derive the total amenity value of the site. The focus here, however, is on estimating the demand across sites in order to consider the spatial pattern of rural tourism and recreation activities.
- 4.
See Hynes et al. (2007) for a full discussion of the data gathering exercise as well as a discussion of the descriptive statistics for the sample.
- 5.
In the first instance the proportion of kayakers in each county was estimated using survey data from the ICU and then multiplied by an estimate of the total number of kayakers in Ireland to develop estimates of the total number of kayakers by county. The next step involved allocating the kayakers in each county by ED within that county based on the spatial distribution of the total (working-age) population in Ireland in 2002 by ED using small area population statistics data from the CSO. Specifically, the proportion of a county’s (working-age) population in each ED was used to allocate kayakers, hence effectively matching the proportion of a county’s kayakers in each ED to the proportion of a county’s (working-age) population in each ED. The next step involves assigning personal, socio-economic and whitewater kayaking-specific characteristics to each individual kayaker in the synthetic population and this was done by replicating profiles from the Hynes et al. (2007) survey and randomly allocating them to individuals in the synthetic population.
- 6.
Kayakers are concentrated, as expected, in the principal urban areas, where populations are greatest and university and non-university kayaking clubs tend to be located. The correlation between the estimated number of kayakers per county and the actual number of kayaking clubs per county is 0.82.
- 7.
A centroid of an ED is defined as its geometric centre. It can be thought of as the point within an ED on which it would balance when placed on a needle, assuming that the ED was a smooth flat surface.
- 8.
Overdispersion in the data can easily be tested for in the estimation process.
- 9.
LR test of α = 0: chibar2(1) = 5014.83; Prob > = chibar2 = 0.000.
- 10.
A test for IIA is presented subsequently.
- 11.
All models were estimated using NLOGIT, which can take account of outcomes data when in the form of frequency counts. The frequencies must be non-negative integers for all choice outcomes and may be equal to zero.
- 12.
These four rivers were: The Liffey, Clifden Play Hole, The Boyne, and The Boluisce. The χ2 statistics from the tests on the other seven rivers ranged from 1.46 to 25.53.
- 13.
It should be stressed that the nests in Fig. 13.2 are somewhat arbitrary and numerous other combinations or groupings or rivers are also possible. For example, The Curragower and Clare Glens could be considered as part of the ‘West’ group or a two-nest approach focussed on an East–west division could also be examined. Overall it is obvious however that these nests are likely to differ across individuals depending on geographical location of individuals and this reduces the validity of the results of the nested logit model in Table 13.4. However, it is also worth noting that they do not differ greatly from the RPL estimates.
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Cullinan, J., Hynes, S., O’Donoghue, C. (2013). Modelling the Spatial Pattern of Rural Tourism and Recreation. In: O'Donoghue, C., Ballas, D., Clarke, G., Hynes, S., Morrissey, K. (eds) Spatial Microsimulation for Rural Policy Analysis. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30026-4_13
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