An appraisal of the CORINE land cover database in airport catchment area analysis using a GIS approach
Pere SUAU-SANCHEZ
Department of Air , Martell House, Cranfield University, CranfTransportield, Bedfordshire MK43 0TR, United Kingdom
E-mail: p.suausanchez@cranfield.ac.uk
Guillaume BURGHOUWT
Airneth, SEO Economic Research, Roetersstraat 29, 1018 WB Amsterdam, TheNetherlands
E-mail: g.burghouwt@seo.nl
Montserrat PALLARESBARBERA
Department of Geography, Universitat Autònoma de Barcelona, Edifici B – Campus de la UAB, 08193 Bellaterra, Spain
E-mail: montserrat.pallares@uab.cat
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Journal of Air Transport Management -Volume 34 - January 2014 - Pages 12-16
Abstract
This paper presents a free available dataset, the CORINE land cover that helps dealing with the biases caused by pre-defined and heterogeneous census district boundaries in airport catchment area analysis in Europe. Using this dataset and a conventional GIS software it is possible to measure the size of the population within catchment areas at the same spatial level for all EU airports, allowing for consistent comparisons among airports. To illustrate the potential of the CORINE/GIS approach, the size of the population in the catchment areas of all European airports was determined. The empirical exercise has an aggregate perspective, but this database presents many other possibilities of analysis to perform in a case-by-case basis.
1. Introduction: the Modifiable Area Unit Problem
Catchment area analysis is a way of estimating “the geographic area from which a large proportion of an airport’s outbound passengers originate from, or inbound passengers travel to, and their geographic distribution within this area” (CAA, 2011, pp.5). Insight into the nature and size of the catchment area is important. The size of the originating market is a significant determinant of airport performance, in terms of its attractiveness to airlines, traffic throughput, connectivity and seat capacity offered (Dobruszkes et al., 2011; Fröhlich and Niemeier, 2011; Humphreys and Francis, 2002). Only airports with a substantial airline hub operation or a large inbound (tourism) market are able to grow beyond the size supported by the local originating market. Hence, airports use the catchment area potential in their marketing towards airlines. Catchment area analysis also helps policy makers in the forecasting of passenger demand (Lieshout, 2012).
Nevertheless, calculating the potential size of the catchment area is not as straightforward as it seems. The potential of an airport’s market will depend on basic features of the region where it is located (e.g., amount of population in the area, their propensity to fly, economic activities, airport access time), airport related factors (e.g., network supplied by the airlines) and airport competition. In addition, the depiction of airport catchment areas by drawing concentric circles around the airport based on maximum allowable access time has some important drawbacks. The discrete choice approach has been put forward as a better alternative (Lieshout, 2012). However, this approach is more demanding from a technical and data point of view, and will there be less suitable for analyses at higher geographical scales and for cases where passenger survey data is not available.
A problematic issue in the measurement of catchment area concerns the population in the catchment area. European studies considering population in the catchment area usually take the NUTS 3 level1 to aggregate population values around the airport (e.g., Papatheodorou and Arvanitis, 2009; Grosche et al., 2007). Two recent studies use lower levels of data aggregation than NUTS 3, Redondi et al. (2013) use municipality level units and Scotti et al. (2012) use zip codes, both represent an advance. Nevertheless, when aggregating point-based geospatial values –such as population– into pre-defined districts, results are influenced by the choice of the district boundaries, which becomes a source of statistical bias. The spatial analysis boundary problem is known as the Modifiable Area Unit Problem (MAUP) (Reynolds, 1998). In particular in multivariate analysis, results are likely to vary with the configuration of the zoning system and the level of aggregation of spatial units (Fotheringham and Wong, 1991). Such statistical biases may lead to non-accurate airport policy decisions This paper presents a free available dataset, the CORINE2 land cover that helps dealing with the biases caused by pre-defined and heterogeneous census district boundaries in airport catchment area analysis. We apply a methodology that uses conventional GIS (Geographical Information System) software and provides an appraisal of the use of the CORINE land cover database for catchment area analysis. The use of GIS in combination with the CORINE land cover database allows researchers and policy-makers dealing with catchment areas to assess their potential size at any geographical level in a relatively simple way. The approach allows researchers to measure population within the catchment area at the same spatial level for all EU airports. To show the potential of the database we calculated the population in the catchment areas of all European airports with scheduled traffic (N=459) at three geographical levels.
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NUTS stands for Nomenclature of Territorial Units for Statistics. It is a geocode standard for referencing the subdivisions of EU countries for statistical purposes.
2 CORINE stands for Coordination of Information on the Environment.
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