Recreation
For recreation, VITO has developed a method based on the attractiveness characteristics of the area and recreational facilities on the one hand and the average number of visits to the open space that Belgians make, to estimate the average number of visits per area per year. These numbers do not take into account any recreational pressure on the areas. The attractiveness score calculates a score based on the nature, agriculture and variety present in an area. In addition, there are a number of other elements that positively or negatively influence attractiveness. A number of score cards have been developed for this purpose. For more information, we refer to De Nocker et al. 2016, which can be found in the background documents of the Nature Value Explorer.
Score for relief​
Areas are more attractive if they show some relief. On this map, a storage score has been given to each cell where positive relief can be found according to the landscape features map of the Real Estate Agency. This map is a strong simplification of the relief.
Table: Relief factors
| Share zone with relief | storage factor |
|---|---|
| +30% | 0.50 |
| 20-30% | 0.38 |
| 10-20% | 0.25 |
| 1-10% | 0.13 |
| 0-0.1% | 0 |
Source: inspired by the Spanish landscape rating system (CPSS, 2005)
Score for cultural-historical value​
Areas are more attractive if they contain cultural-historical heritage, and this effect is greater if there are more or larger objects involved. This map shows the storage factor for the presence of cultural-historical heritage as indicated on the map with heritage values (protected landscapes, city and village views, monuments) (Immovable Heritage Agency, AGIV). There was no comparable map for Brussels.
Table: Addition factors for heritage values
| Share of zone with historical heritage values | storage factor |
|---|---|
| +30% | 0.50 |
| 20-30% | 0.38 |
| 10-20% | 0.25 |
| 1-10% | 0.13 |
| 0-0.1% | 0 |
Source: inspired by the Spanish landscape rating system (CPSS, 2005)
Score for horizon pollution​
Open green space is considered less attractive if there is horizon pollution in the immediate vicinity in the form of high-voltage lines and wind turbines. To calculate this reduction factor, the presence of wind turbines and high-voltage lines within 2.5 km of a cell was taken into account. Tall green (> 3m) in the area was also taken into account, which ensures that further disturbances are hidden from view.
Table: Score for the presence of horizon pollution
| Windmill distance | 0-1% high green | 1-10% tall green | 10-25% tall green |
|---|---|---|---|
| 0.5km | -0.25 | -0.25 | - |
| 0.5-1km | -0.125 | -0.125 | - |
| 1-2.5 km | -0.06 | -0.06 | - |
| Distance high voltage line 380kV | 0-1% tall green | 1-10% tall green | 10-25% tall green |
|---|---|---|---|
| 0.5km | -0.25 | -0.25 | -0.125 |
| 0.5-1km | -0.125 | -0.125 | - |
| 1-2.5 km | -0.06 | -0.06 | - |
Source: The structure and weights have been simplified from the Dutch experience GIS-2 (Crommentuijn, 2007)
Score for noise level​
Open green space is perceived as less attractive if there is noise pollution. We apply this for the cells that, according to the noise map, fall within the contours with a noise level of 55 dB or more. In practice, these are mainly areas adjacent to busy roads such as highways and railways. We use the noise maps for Flanders for this.
If there is noise pollution according to the noise maps, the cell on this map will receive a score -0.25. This score is based on a Flemish survey on the choice of area for recreation (De Valck et al. 2017) where quieter areas were rated higher.
Path density score​
We assess the accessibility of an area for recreation based on the presence and density of roads and paths. An area with more paths gets a higher score. This reflects, firstly, that an area (relative to its size) offers more possibilities and opportunities for longer routes, more variety, and more opportunity for different types of activities. Paths that are part of a signposted route and are closer to a cell (near path) are given a higher weight. In this way, for larger areas (+ 50 ha), the scores may differ between cells of the same area, and parts with relatively more paths and/or more routes receive a higher score. For more information about the allocation, we refer to De Nocker et al. 2016.
The different areas are then divided into 5 equal classes according to their path density score, and receive the scores below. Areas without a path receive a 0 score. Military domains also receive a 0 score.
Table: Path density score
| Group | Indicator (m/ha) | score |
|---|---|---|
| 5 | highest quintile | 0.5 |
| 4 | 4th quintile | 0.4 |
| 3 | Middle | 0.3 |
| 2 | 2nd quintile | 0.2 |
| 1 | lowest quintile | 0.1 |
| Inaccessible | 0 | 0 |
Expected number of visits​
Based on the average number of visits per resident (35 per year) and per type of visit, and the number of residents, the total visits to be spent per cell are first calculated. The average number of visits per inhabitants and the breakdown are mainly based on VITO surveys (Liekens, 2009, Liekens, 2013, Liekens 2012, De Valck, 2016 and De Valck, 2017) and verified with the results from previous surveys (Beyst, 2012 ), with detailed surveys on travel behavior (Janssens, 2010) and day trips (WES, 2014).
The number of tourists is based on tourist counts and includes the number of overnight stays for leisure purposes (e.g. no business overnight stays), and increased for overnight stays in guest rooms (+ 9.2%). For the coast area, the number of overnight stays has been increased for second homes and for direct rental.
The number of visits to open green space per tourist and per day is based on surveys among tourists regarding the type of activities (Nijs, 2014; WES, 2014; Knowledge Center for Tourism Limburg, 2012; Vlaeminck, 2015; Westtoer 2012). In total, this results in 13.4 million visits per year, or approximately 2.1 visits per inhabitant from Flanders.
Through an allocation mechanism, the total number of visits per cell is distributed over the available green space around the cell, depending on the proximity, size and quality of the green space supply. The calculation of the allocation per cell is done via a specifically designed GDX script (Van der Meulen, 2016).
The maps show the expected number of visits per hectare for the respective activity: walking, cycling, walking with preliminary transport, visits by tourists. The data is used to evaluate the "recreation" service.