GPS-based measurement of geographic spillovers in tourism – example of Polish districts

Abstract

ABSTRACTThe agglomeration phenomenon in tourism often spreads beyond the borders of territorial units what is referred to as geographic spillovers. However, the measurement of spatial concentration of tourism demand and economic activity is usually based on statistics collected within regional administrative boundaries and omits the spatial interdependency between neighboring regions. Recognition of such spatial interdependency in the standard procedure to define neighborhood relies on the distance between geometric means (centroids) of territorial units which, however, rarely reflects real ‘centers’ of tourism agglomerations and leads to errors and biased results. Hence, we propose to modify the measures of the neighborhood with the use of GPS coordinates of tourism firms and attractions in order to designate their regional central tendencies and thus to correct (shift in space) localization of centroids of territorial units. We test the usefulness of the new approach to obtain a more precise measurement of spatial concentration when tourism spills over beyond the boundaries of territorial units using the example of Polish districts. We employ the exploratory spatial data analysis (spatial statistics) and spatial regression models – to assess the difference between using traditional centroids and GPS coordinates in defining neighborhood and determining spillover effects in regional analysis. Furthermore we apply the new method into the model of tourism potential in order to identify spillover effects in Polish regions. We use the data collected by Central Statistical Office (tourists staying overnight in 379 districts in 2014) and by Polish Tourist Organization (14,390 GPS coordinates of individual entities: tourism firms and attractions). The neighborhood determined with the use of GPS coordinates to measure the distance between centers of tourism agglomerations eliminates the dependence of the results on the administrative boundaries – but only to some degree. The challenge is to identify tourism agglomeration phenomenon as such, based on the mobility of tourists in space.