The rising number of homes and apartments rented out through Airbnb and similar peer-to-peer accommodation platforms cause concerns about the impact of such activity on the tourism sector and property market. To date, spatial analysis on peer-to-peer rental activity has been usually limited in scope to individual large cities. In this study, we take into account the whole territory of Spain, with special attention given to cities and regions with high tourist activity. We use a dataset of about 250 thousand Airbnb listings in Spain obtained from the Airbnb webpage, aggregate the numbers of these offers in 8124 municipalities and 79 tourist areas/sites, measure their concentration, spatial autocorrelation, and develop regression models to find the determinants of Airbnb rentals’ distribution. We conclude that apart from largest cities, Airbnb is active in holiday destinations of Spain, where it often serves as an intermediary for the rental of second or investment homes and apartments. The location of Airbnb listings is mostly determined by the supply of empty or secondary dwellings, distribution of traditional tourism accommodation, coastal location, and the level of internationalization of tourism demand.

Vegetation covers were one of the first land covers to receive special attention when thematic Land Use Cover (LUC) maps first appeared. Interest in this subject has remained strong since then because of the valuable information that these datasets provide for monitoring forests, deforestation and climate change, among other issues. A wide variety of thematic LUC datasets characterizing vegetation covers are currently available. In this chapter, we review eleven of these datasets, most of which provide long series of LUC maps, so permitting the study of LUC change. In thematic terms, most of the maps provide information on the vegetation or tree cover fraction per pixel, so characterizing the vegetation covers on Earth in great detail. A specific dataset has been found that maps mangrove distribution across the globe at 30 m for one date (1997/00). It is not included in this review because of its high specificity, which means it is only of interest to certain communities of users. Of all the products reviewed here, the World’s Forests 2000 is probably the most basic, providing information about three wooded cover categories for the year 1995/96 at a spatial resolution of 1 km. SYNMAP is a very specific thematic map designed to meet the needs of the carbon cycle and vegetation modelling community, which was produced at a spatial resolution of 1 km and with a legend of 48 categories. Among the maps providing information on the fraction of vegetation cover per pixel, the Hybrid Forest Mask 2000 (1 km) and the PTC Global Version (500 m–1 km) offer relatively coarse resolutions and few points in time: just one date in the former (2000) and two in the latter (2003, 2008). The Forests of the World 2010 is also available for just one year (2010), albeit at a more detailed spatial resolution (250 m). Various datasets provide information on the cover fraction for long periods of time at medium and high spatial resolutions. FCover provides the longest time series (1999-present) at 1 km, although since 2014 this dataset is also available at 300 m. Modis VCF also offers a long data series (2000–2019) at a spatial resolution of 250 m. MEaSUREs Vegetation Continuous Fields (VCF) is another thematic LUC dataset providing information on the tree cover fraction of the earth surface for a very long time period: 1982–2016. However, it is not reviewed here because of its coarse spatial resolution (around 5.6 km at the Equator). At very detailed spatial resolutions, GFCC30TC Landsat VCF (30 m) provides data on the cover fraction for four different points in time, between 2000 and 2015. It also gives information on forest change for two periods (1990–2000/2000–2005) through the associated GFCC30FCC dataset. The Hansen forest map (30 m) also provides one of the longest time series, from 2000 to 2019. Global FNF is the dataset with the highest resolution (25 m) of all those reviewed. It is available for two periods of time: 2007–2010 and 2015–2017. In thematic terms, however, this dataset is less detailed, in that it only differentiates between forest and non-forest covers. TanDEM-X Forest/Non-Forest also provides information on the forest extent at high spatial resolution (50 m). However, the map is only available for one point in time. Like Global FNF, it was also obtained from the classification of radar data.

CORINE Land Cover is one of the most relevant Land Use Cover (LUC) databases in Europe because of its degree of detail and long time series. Although some studies have assessed the uncertainty and inconsistencies of the database for specific years and periods, no work has been found that analyses all the available CORINE time series (1990–2018). In this study, we analyse the inconsistencies of the CORINE time series for a specific Spanish region (Asturias). To this end, we compare and analyse the CORINE status layers (CLC) and the CORINE layers of changes (CHA) for each of the mapped periods: 1990–2000, 2000–2006, Citation2006 –2012, 2012–2018. Results show how CLC and CHA layers provide different information, especially after the change of production of CORINE Spain in 2012. The last two CORINE editions (2012, 2018) show a lot of technical changes that make the use of CLC layers very uncertain. In addition, mixed categories, whose definition is imprecise and, therefore, more uncertain, are behind most of detected change in both types of CORINE layers (CHA, CLC).