Toronto Metropolitan University
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A multi-dasymetric mapping approach for tourism

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posted on 2023-09-15, 16:28 authored by Eric de Noronha VazEric de Noronha Vaz, Ana Claudia Campos
The challenge of measuring at municipal level tourism density has been a daunting task for both statisticians and geographers. The reason of this is enforced by the fact that administrative areas, such as municipalities, tend to be large spatial administrative units, sharing a large demographic asymmetry of tourist demand within the municipality. The rationale is that geographic characteristics such as coastal line, climate and vegetation, play a crucial role in tourist offer, leaning towards the conclusion that traditional census at administrative level are simply not enough to interpret the true distribution of tourism data. A more quantifiable method is necessary to assess the distribution of socio-economic data. This is developed by means of a dasymetric approach adding on the advantages of multi-temporal comparison. This paper adopts a dasymetric approach for defining tourism density per land use types using the CORINE Land Cover dataset. A density map for tourism is calculated, creating a modified areal weighting (MAW) approach to assess the distribution of tourism density per administrative municipality. This distribution is then assessed as a bidirectional layer on the land use datasets for two temporal stamps: 2000 and 2006, which leads to (i) a consistent map on a more accurate distribution of tourism in Algarve, (ii) the calculation of tourism density surfaces, and (iii) a multi-locational and temporal assessment through density cross-tabulation. Finally a geovisual interpretation of locational analysis of tourism change in Algarve for the last decade is created. This integrative spatial methodology offers unique characteristics for more accurate decision making at regional level, bringing an integrative methodology to the forefront of linking tourism with the spatio-temporal clusters formed in rapidly changing economic regions.




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