Figure

Attractiveness of regions by percentage of area covered by hot spots

Figure Created 16 Aug 2017 Last modified 11 Sep 2017

2 min read

Topics: Environment and health

In order the better capture the attractiveness, an indicator on hot spots have been applied. Hot spots are areas with significant higher concentration of pictures compared with the rest of the region and have also a significant spatial aggregation. It should be noted that the hot spot has a spatial extent, i.e. there is a high probability that people move inside the hot spot (in other words the same person will take pictures from different places inside the the hot spot). This also makes a difference with areas outside hot spots, there can still be a relatively high number of points where pictures are taken, but these points are not spatially clustered. Two type of hot spots have been identified: hot spots in rural areas and hot spots in urban areas.

https://eea.europa.eu./data-and-maps/figures/attractiveness-of-regions-by-percentage/attractiveness-of-nuts-3-regions/image_large

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Methodology:: Panoramio is a geolocation-oriented photo sharing mashup owned by Google. Accepted photos uploaded to the site can be accessed as a layer in Google Earth and Google Maps, with new photos being added at the end of every month. It is possible to access to the Panoramio photos database through a web API. The aim of this test analysis has been to link the number and density of photos taken with the N2000 network of protected sites. The different methodological steps carried out are detailed below:
Step 1: Downloading from Panoramio the photos database by different geographical windows within the study area*. Single photos details are obtained, including its location (latitude, longitude), owner, title or URL, amongst other.
Step 2: Merging of all photo database in one single file.
Step 3: Converting the photo database into a GIS point layer.
Step 4: Overlaying the photo point layer with the European Reference Grid at 1 km2
The identification of hot spots has been based on the The Getis-Ord Gi* statisti c (pronounced G-i-star) for each feature in a dataset. The resultant z-scores and p-values tell you where features with either high or low values cluster spatially. This tool works by looking at eac h feature within the context of neighboring features. A feature with a high value is interesting but may not be a statistically significant hot spot. To be a statistically significant hot spot, a feature will have a high value and be surrounded by other features with high values as well. The local sum for a feature and its neighbors is compared proportionally to the sum of all features; when the local sum is very different from the expected local sum, and that difference is too large to be the result of random chance, a statistically significant z-score results.
Units:: Percentage of area of NUTS3 covered by hot spots
Rights:: EEA standard re-use policy: unless otherwise indicated, re-use of content on the EEA website for commercial or non-commercial purposes is permitted free of charge, provided that the source is acknowledged (https://www.eea.europa.eu/legal/copyright). Copyright holder: European Environment Agency (EEA).
Owners:: European Environment Agency (EEA)
Figure type:: Map

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