GRASS logo

NAME

r.estimap.recreation - Implementation of ESTIMAP to support mapping and modelling of ecosystem services (Zulian, 2014)

KEYWORDS

estimap, ecosystem services, recreation potential

SYNOPSIS

r.estimap.recreation
r.estimap.recreation --help
r.estimap.recreation [-refsip] [land=name] [natural=name[,name,...]] [water=name[,name,...]] [urban=name[,name,...]] [infrastructure=name[,name,...]] [recreation=name[,name,...]] [landuse=name] [suitability_scores=filename] [landcover=name] [land_classes=filename] [lakes=name] [lakes_coefficients=Coefficients[,Coefficients,...]] [coastline=name] [coastline_coefficients=Coefficients[,Coefficients,...]] [coast_geomorphology=name] [bathing_water=name] [bathing_coefficients=Coefficients[,Coefficients,...]] [protected=filename] [protected_scores=rules] [artificial=name] [artificial_distances=rules] [roads=name] [roads_distances=rules] [devaluation=name[,name,...]] [mask=name] [potential=name] [opportunity=name] [spectrum=name] [spectrum_distances=rules] [base=name] [base_vector=name] [aggregation=name] [population=name] [demand=name] [unmet=name] [flow=name] [supply=prefix] [use=prefix] [metric=Metric[,Metric,...]] [units=Units[,Units,...]] [timestamp=string] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

-r
Let the mobility function derive real numbers for the flow
-e
Match computational region to extent of land use map
-f
Filter maps in land and natural components before computing recreation maps
-s
Save temporary maps for debugging
-i
Print out citation and other information
-p
Print out results (i.e. supply table), don't export to file
--overwrite
Allow output files to overwrite existing files
--help
Print usage summary
--verbose
Verbose module output
--quiet
Quiet module output
--ui
Force launching GUI dialog

Parameters:

land=name
Input map scoring access to and suitability of land resources for recreation
Arbitrary number of maps scoring access to and land resources suitability of land use classes to support recreation activities
natural=name[,name,...]
Input maps scoring access to and quality of inland natural resources
Arbitrary number of maps scoring access to and quality of inland natural resources
water=name[,name,...]
Input maps scoring access to and quality of water resources
Arbitrary number of maps scoring access to and quality of water resources such as lakes, sea, bathing waters and riparian zones
urban=name[,name,...]
Input maps scoring recreational value of urban surfaces
infrastructure=name[,name,...]
Input maps scoring infrastructure to reach locations of recreation activities
Infrastructure to reach locations of recreation activities [required to derive recreation spectrum map]
recreation=name[,name,...]
Input maps scoring recreational facilities, amenities and services
Recreational opportunities facilities, amenities and services [required to derive recreation spectrum map]
landuse=name
Input land features map from which to derive suitability for recreation
Input to derive suitability of land use classes to support recreation activities. Requires scores, overrides suitability.
suitability_scores=filename
Input recreational suitability scores for the categories of the 'landuse' map
Scores for suitability of land to support recreation activities. Expected are rules for `r.recode` that correspond to categories of the input 'landuse' map. If the 'landuse' map is given and 'suitability_scores not provided, the module will use internal rules for the CORINE land classes.
landcover=name
Input land cover map from which to derive cover percentages within zones of high recreational value
Input to derive percentage of land classes within zones of high recreational value.
land_classes=filename
Input reclassification rules for the classes of the 'landcover' map
Expected are rules for `r.reclass` that correspond to classes of the input 'landcover' map. If 'landcover' map is given and 'land_classess' not provided, the module will use internal rules for the Urban Atlas land classes
lakes=name
Input map of inland waters resources for which to score accessibility
Map of inland water resources to compute proximity for, score accessibility based on a distance function
lakes_coefficients=Coefficients[,Coefficients,...]
Input distance function coefficients for the 'lakes' map
Distance function coefficients to compute proximity: distance metric, constant, kappa, alpha and score. Refer to the manual for details.
Default: euclidean,1,30,0.008,1
coastline=name
Input sea coast map for which to compute proximity
Input map to compute coast proximity, scored based on a distance function
coastline_coefficients=Coefficients[,Coefficients,...]
Input distance function coefficients for the 'coastline' map
Distance function coefficients to compute proximity: distance metric, constant, kappa, alpha and score. Refer to the manual for details.
Default: euclidean,1,30,0.008,1
coast_geomorphology=name
Input map scoring recreation potential in coast
Coastal geomorphology, scored as suitable to support recreation activities
bathing_water=name
Input bathing water quality map
Bathing water quality index. The higher, the greater is the recreational value.
bathing_coefficients=Coefficients[,Coefficients,...]
Input distance function coefficients for the 'bathing_water' map
Distance function coefficients to compute proximity to bathing waters: distance metric, constant, kappa and alpha. Refer to the manual for details.
Default: euclidean,1,5,0.01101
protected=filename
Input protected areas map
Input map depicting natural protected areas
protected_scores=rules
Input recreational value scores for the classes of the 'protected' map
Scores for recreational value of designated areas. Expected are rules for `r.recode` that correspond to classes of the input land use map. If the 'protected' map is given and 'protected_scores' are not provided, the module will use internal rules for the IUCN categories.
Default: 11:11:0,12:12:0.6,2:2:0.8,3:3:0.6,4:4:0.6,5:5:1,6:6:0.8,7:7:0,8:8:0,9:9:0
artificial=name
Input map of artificial surfaces
Partial input map to compute proximity to artificial areas, scored via a distance function
artificial_distances=rules
Input distance classification rules
Categories for distance to artificial surfaces. Expected are rules for `r.recode` that correspond to distance values in the 'artificial' map
Default: 0:500:1,500.000001:1000:2,1000.000001:5000:3,5000.000001:10000:4,10000.00001:*:5
roads=name
Input map of primary road network
Input map to compute roads proximity, scored based on a distance function
roads_distances=rules
Input distance classification rules
Categories for distance to roads. Expected are rules for `r.recode` that correspond to distance values in the roads map
Default: 0:500:1,500.000001:1000:2,1000.000001:5000:3,5000.000001:10000:4,10000.00001:*:5
devaluation=name[,name,...]
Input map of devaluing elements
Maps hindering accessibility to and degrading quality of various resources or infrastructure relating to recreation
mask=name
A raster map to apply as a MASK
potential=name
Output map of recreation potential
Recreation potential map classified in 3 categories
opportunity=name
Output intermediate map of recreation opportunity
Intermediate step in deriving the 'spectrum' map, classified in 3 categories, meant for expert use
spectrum=name
Output map of recreation spectrum
Recreation spectrum map classified by default in 9 categories
spectrum_distances=rules
Input distance classification rules for the 'spectrum' map
Classes for distance to areas of high recreational spectrum. Expected are rules for `r.recode` that correspond to classes of the input spectrum of recreation use map.
Default: 0:1000:1,1000:2000:2,2000:3000:3,3000:4000:4,4000:*:5
base=name
Input base map for zonal statistics
base_vector=name
Name of input vector map
Input base vector map for zonal statistics
aggregation=name
Input map of regions over which to aggregate the actual flow
population=name
Input map of population density
demand=name
Output map of demand distribution
Demand distribution output map: population density per Local Administrative Unit and areas of high recreational value
unmet=name
Output map of unmet demand distribution
Unmet demand distribution output map: population density per Local Administrative Unit and areas of high recreational value
flow=name
Output map of flow
Flow output map: population (per Local Administrative Unit) near areas of high recreational value
supply=prefix
Output prefix for the file name of the supply table CSV
Supply table CSV output file names will get this prefix
use=prefix
Output prefix for the file name of the supply table CSV
Use table CSV output file names will get this prefix
metric=Metric[,Metric,...]
Distance metric to areas of highest recreation opportunity spectrum
Distance metric to areas of highest recreation opportunity spectrum
Options: euclidean, squared, maximum, manhattan, geodesic
Default: euclidean
units=Units[,Units,...]
Units to report
Units to report the demand distribution
Options: mi, me, k, a, h, c, p
Default: k
timestamp=string
Timestamp
Timestamp for the recreation potential raster map

Table of contents

DESCRIPTION

r.estimap.recreation is an implementation of the ESTIMAP recreation algorithm to support mapping and modelling of ecosystem services (Zulian, 2014).

The algorithm estimates the capacity of ecosystems to provide opportunities for nature-based recreation and leisure (recreation opportunity spectrum). First, it bases upon look-up tables, to score access to or the quality of natural features (land suitability, protected areas, infrastructure, water resources) for their potential to support for outdoor recreation (potential recreation). Second, it implements a proximity-remoteness concept to integrate the recreation potential and the existing infrastructure.

The module offers two functionalities. One is the production of recreation related maps by using pre-processed maps that depict the quality of or the access to areas of recreational value. The other is to transform maps that depict natural features into scored maps that reflect the potential to support for outdoor recreational. Nevertheless, it is strongly advised to understand first the concepts and the terminology behind the algorithm, by reading the related sources.

Terminology
First, an overview of the terminology
Recreation Potential
is ...
Recreation Opportunity
is ...
Recreation (Opportunity) Spectrum
is ...
Demand Distribution
is ...
Unmet Demand Distribution
is ...
Mobidtty
is ...
Flow
is ...
Supply
is ...
Use
is ...
Recreation Potential

The recreation potential map, derives by adding and normalizing maps of natural components that may provide recreation opportunities. Components are user-defined, pre-processed, input raster maps, that score access to or quality of resources such as:

Alternatively, the module treats unprocessed maps, by providing a set of relevant scores or coefficients, to derive component maps required by the algorithm. FIXME 1. an ASCII file with a set of land suitability scores (see below) 2. a string listing a set of comma-separated scores for each raster category.. -- FIXME 3. in the case of the CORINE map, use of internal rules FIXME For example, a CORINE land cover map may be given to the 'landuse' input option along with a set of land suitability scores, that correspond to the CORINE nomenclature. The latter is fed as an ASCII file to the 'suitability_scores' input option.

Recreation Opportunity

...

Recreation Spectrum

The recreation (opportunity) spectrum map, derives by combining the recreation potential and maps that depict access (i.e. infrastructure) and/or areas that provide opportunities for recreational activities.

Explain here significance of areas with the Highest Recreation Spectrum.

Potential | Opportunity Near Midrange Far
Near 1 2 3
Midrange 4 5 6
Far 7 8 9
Flow, Supply and Use

By integrating maps of regions of interest and population, the module supports the production of a series of demand and flow maps as well as exporting related supply and use tables.

Mathematical Background

The following equation represents the logic behind ESTIMAP:

Recreation Spectrum = Recreation Potential + Recreation Opportunity
Remoteness and Proximity
The base distance function to quantify attractiveness, is:
( {Constant} + {Kappa} ) / ( {Kappa} + exp({alpha} * {Variable}) )
where
Accessibility
Normalization
Each component is normalized. That is, all maps listed in a given component are summed up and normalised. Normalizing any raster map, be it a single map or the sum of a series of maps, is performed by subtracting its minimum value and dividing by its range.

EXAMPLES

For the sake of demonstrating the usage of the module, we use the following "component" maps (available to download at: ...) to derive a recreation potential map.

Below, a table overviewing all input and output maps used or produced in the examples.

Input map name Spatial Resolution Remarks
area_of_interest 50 m A map that can be used as a 'mask'
land_suitability 50 m A map scoring the potential for recreation over CORINE land classes
water_resources 50 m A map scoring access to water resources
protected_areas 50 m A map scoring the recreational value of natural protected areas
distance_to_infrastructure 50 m A map scoring access to infrastructure
population_2015 1000 m The resolution of the raster map given to the 'populatio' input option will define the resolution of the output maps 'demand', 'unmet' and 'flow'
local_administrative_unit 50 m A rasterised version of Eurostat's Local Administrative Units map
Output map name Spatial Resolution Remarks
potential
50 m
potential_1 50 m
potential_2 50 m
potential_3 50 m
potential_4 50 m
spectrum 50 m
opportunity 50 m Requires to request for the 'spectrum' output
demand 1000 m Depends on the 'flow' map which, in turn, depends on the 'population' input map
unmet 1000 m Depends on the 'flow' map which, in turn, depends on the 'population' input map
flow 1000 m Depends on the 'population' input map
Output table name
supply NA

Example of a land suitability input map Example of a land suitability input map Example of a water resources input map Example of a protected areas input map

Before anything, we need to define the extent of interest using

g.region  raster=area_of_interest

Using pre-processed maps

The first four input options of the module are designed to receive pre-processed input maps that classify as either land, natural, water and infrastructure resources.

Potential

To compute a potential output map, the simplest possible command call requires the user to define the input map option land and define a name for the output map option potential. Using a pre-processed map that depicts the suitability of different land types to support for recreation (here the map named land_suitability) the command to execute is:

r.estimap.recreation  land=land_suitability  potential=potential

Example of a recreation potential output map

Note, this will process the input map land_suitability over the extent defined previously via g.region, which is the standard behaviour in GRASS GIS.

To exclude certain areas from the computations, we may use a raster map as a mask and feed it to the input map option mask:

r.estimap.recreation  land=land_suitability  mask=area_of_interest  potential=potential_1

Example of a recreation potential output map while using a MASK
The use of a mask (in GRASS GIS' terminology known as MASK) will ignore areas of No Data (pixels in the area_of_interest map assigned the NULL value). Successively, these areas will be empty in the output map potential_1. Actually, the same effect can be achieved by using GRASS GIS' native mask creation module r.mask and feed it with a raster map of interest. The result will be a raster map named MASK whose presence acts as a filter. In the following examples, it becomes obvious that if a single input map features such No Data areas, they will be propagated in the output map.

Nonetheless, it is good practice to use a MASK when one needs to ensure the exclusion of undesired areas from any computations. Also, note the --o flag: it is required to overwrite the already existing map named potential_1.

Next, we add a water component, a map named water_resources, modify the output map name to potential_2 and execute again, without a mask:

r.estimap.recreation  land=land_suitability  water=water_resources  potential=potential_2

Example of a recreation potential output map while using a MASK, a land suitability map and a water resources map
At this point it becomes clear that all NULL cells present in the "water" map, are propagated in the output map potential_2.

Following, we provide a map of protected areas named protected_areas, modify the output map name to potential_3 and repeat the command execution:

r.estimap.recreation  land=land_suitability  water=water_resources  natural=protected_areas  potential=potential_3

Example of a recreation potential output map
while using a MASK, a land suitability map, a water resources map and a natural
resources map

While the land option accepts only one map as an input, both the water and the natural options accept multiple maps as inputs. In example, we add a second map named bathing_water_quality to the water component and modify the output map name to potential_4:

r.estimap.recreation  land=land_suitability  water=water_resources,bathing_water_quality  natural=protected_areas  potential=potential_4

In general, arbitrary number of maps, separated by comma, may be added to options that accept multiple inputs.

Example of a recreation potential output map
while using a MASK, a land suitability map, two water resources maps and a natural
resources map

This example, features also a title and a legend, so as to make sense of the map.

d.rast  potential_4
d.legend  -c  -b  potential_4  at=0,15,0,1  border_color=white
d.text  text="Potential"  bgcolor=white

The different output map names are purposefully selected so as to enable a visual comparison of the differences among the differenct examples. The output maps potential_1, potential_2, potential_3 and potential_4 range within [0,3]. Yet, they differ in the distribution of values due to the different set of input maps.

All of the above examples base upon pre-processed maps that score the access to and quality of land, water and natural resources. For using raw, unprocessed maps, read section Using unprocessed maps.

Spectrum

To derive a map with the recreation (opportunity) spectrum, we need in addition an infrastructure component. In this example a map that scores distance to infrastructure (such as the road network) named distance_to_infrastructure is defined as an input:

Example of an input map showing distances to infrastructure
Naturally, we need to define the output map option spectrum too:
r.estimap.recreation  \
  land=land_suitability \
  water=water_resources,bathing_water_quality \
  natural=protected_areas \
  infrastructure=distance_to_infrastructure
  spectrum=spectrum  \
or, the same command in a copy-paste friendly way:
r.estimap.recreation  land=land_suitability  water=water_resources,bathing_water_quality  natural=protected_areas  infrastructure=distance_to_infrastructure  spectrum=spectrum

Example of a recreation spectrum output map
while using a MASK, a land suitability map, a water resources map and a natural
resources map
Missing to define the infrastructure map, the command will abort and inform about.

The image above, was produced via the following native GRASS GIS commands

d.rast  spectrum
d.legend  -c  -b  spectrum  at=0,30,0,1  border_color=white
d.text  text="Spectrum"  bgcolor=white
Opportunity
The opportunity map is actually an intermediate step of the algorithm. The option to output this map opportunity is meant for expert users who want to explore the fundamentals of the processing steps. Hence, it requires to define the output option spectrum map as well. Building upon the previous command, we add the opportunity output option:
r.estimap.recreation  \
  mask=area_of_interest \
  land=land_suitability \
  water=water_resources,bathing_water_quality \
  natural=protected_areas \
  spectrum=spectrum  \
  infrastructure=distance_to_infrastructure \
  opportunity=opportunity
or, the same command in a copy-paste friendly way:
r.estimap.recreation  mask=area_of_interest  land=land_suitability  water=water_resources,bathing_water_quality  natural=protected_areas  spectrum=spectrum  infrastructure=distance_to_infrastructure  opportunity=opportunity

Example of a recreation spectrum output map
while using a MASK, a land suitability map, a water resources map and a natural
resources map

More input maps

To derive the outputs met demand distributiom, unmet demand distributiom and the actual flow, additional requirements are a population map and one of boundaries, as an input to the option base, within which to quantify the distribution of the population. Using a map of administrative boundaries for the latter option, serves for deriving comparable figures across these boundaries. The algorithm sets internally the spatial resolution of all related output maps demand, unmet and flow to the spatial resolution of the population input map.

Population

Fragment of a population map (GHSL, 2015)
In this example, the population map named population_2015 is of 1000m^2.

Local administrative units

Fragment of a local administrative units input map
The map named local_administrative_units serves in the following example as the base map for the zonal statistics to obtain the demand map.

Demand

r.estimap.recreation --o \
  mask=area_of_interest \
  land=land_suitability \
  water=water_resources,bathing_water_quality \
  natural=protected_areas \
  infrastructure=distance_to_infrastructure \
  demand=demand \
  population=population_2015 \
  base=local_administrative_units
or, the same command in a copy-paste friendly way:
r.estimap.recreation --o  mask=area_of_interest  land=land_suitability  water=water_resources,bathing_water_quality  natural=protected_areas  infrastructure=distance_to_infrastructure  demand=demand  population=population_2015  base=local_administrative_units

Example of a demand distribution output map while using
a MASK
and inputs for
land suitability,
water resources,
natural resources,
infrastructure,
population
and base

Unmet Demand

r.estimap.recreation --o \
  mask=area_of_interest \
  land=land_suitability \
  water=water_resources,bathing_water_quality \
  natural=protected_areas \
  infrastructure=distance_to_infrastructure \
  demand=demand \
  unmet=unmet_demand \
  population=population_2015 \
  base=local_administrative_units
or, the same command in a copy-paste friendly way:
r.estimap.recreation --o  mask=area_of_interest  land=land_suitability  water=water_resources,bathing_water_quality  natural=protected_areas  infrastructure=distance_to_infrastructure  demand=demand  unmet=unmet_demand  population=population_2015  base=local_administrative_units

Example of an 'unmet demand' output map while using
a MASK
and inputs for
land suitability,
water resources,
natural resources,
infrastructure,
population
and base

Flow

The flow bases upon the same function used to quantify the attractiveness of locations for their recreational value. It includes an extra score term.

The computation involves a distance map, reclassified in 5 categories as shown in the following table. For each distance category, a unique pair of coefficient values is assigned to the basic equation.

Distance Kappa Alpha
0 to 1 0.02350 0.00102
1 to 2 0.02651 0.00109
2 to 3 0.05120 0.00098
3 to 4 0.10700 0.00067
>4 0.06930 0.00057

Note, the last distance category is not considered in deriving the final "map of visits". The output is essentially a raster map with the distribution of the demand per distance category and within predefined geometric boundaries

r.estimap.recreation --o \
  mask=area_of_interest \
  land=land_suitability \
  water=water_resources,bathing_water_quality \
  natural=protected_areas \
  infrastructure=distance_to_infrastructure \
  mobility=mobility \
  population=population_2015 \
  base=local_administrative_units
or, the same command in a copy-paste friendly way:
r.estimap.recreation --o  mask=area_of_interest  land=land_suitability  water=water_resources,bathing_water_quality  natural=protected_areas  infrastructure=distance_to_infrastructure  mobility=mobility  population=population_2015  base=local_administrative_units

Example of a mobility output map while using
a MASK
and inputs for
land suitability,
water resources,
natural resources,
infrastructure,
population
and base

All in one call

Of course it is possible to derive all output maps with one call:
r.estimap.recreation --o  \
  mask=area_of_interest  \
  land=land_suitability  \
  water=water_resources,bathing_water_quality  \
  natural=protected_areas  \
  infrastructure=distance_to_infrastructure  \
  potential=potential  \
  opportunity=opportunity  \
  spectrum=spectrum  \
  demand=demand  \
  unmet=unmet_demand  \
  mobility=mobility  \
  population=population_2015  \
  base=local_administrative_units
  timestamp='2018'
or, the same command in a copy-paste friendly way:
r.estimap.recreation --o  mask=area_of_interest  land=land_suitability  water=water_resources,bathing_water_quality  natural=protected_areas  infrastructure=distance_to_infrastructure  potential=potential  opportunity=opportunity  spectrum=spectrum  demand=demand  unmet=unmet_demand  mobility=mobility  population=population_2015  base=local_administrative_units  timestamp='2018'

Note the use of the timestamp parameter! This concerns the spectrum map. If plans include working with GRASS GIS' temporal framework on time-series, this will be useful.

Supply and Use tables

The module can export supply and use tables, in form of CSV files, given the identically named supply and use file name output options are defined. In order to extract a supply table, the module requires maps that enable the estimation of the actual flow and how each different ecosystem type contributes, in terms of its areal extent, to this flow. The dependencies to extract a supply table are the following: Practically and in terms of components (that is pre-processed maps), the module requires at minimum the following input options and the output option supply

An example command to derive a supply table is:

r.estimap.recreation  \
  land=land_suitability  \
  infrastructure=distance_to_infrastructure  \
  population=population_2015  \
  base=local_administrative_units  \
  landcover=corine_land_cover_2006  \
  aggregation=regions  \
  supply=supply
or, instead of the land component, only using the water component
r.estimap.recreation  \
  water=water_resources  \
  infrastructure=distance_to_infrastructure  \
  population=population_2015  \
  base=local_administrative_units  \
  landcover=corine_land_cover_2006  \
  land_classes=corine_accounting_to_maes_land_classes.rules \
  aggregation=regions  \
  supply=supply
or, instead, using only the natural component:
r.estimap.recreation  \
  natural=protected_areas  \
  infrastructure=distance_to_infrastructure  \
  population=population_2015  \
  base=local_administrative_units  \
  landcover=corine_land_cover_2006  \
  land_classes=corine_accounting_to_maes_land_classes.rules  \
  aggregation=regions  \
  supply=supply
Here a "real" example:
r.estimap.recreation --o  mask=area_of_interest  land=land_suitability  water=water_resources,bathing_water_quality  natural=protected_areas  infrastructure=distance_to_infrastructure  potential=potential  opportunity=opportunity  spectrum=spectrum  demand=demand  unmet=unmet_demand  population=population_2015  base=local_administrative_units timestamp='2018'  landcover=corine_land_cover_2006  aggregation=regions  land_classes=../categories_and_rules/corine_accounting_to_maes_land_classes.rules  supply=supply  use=us
which will output the following supply table
base,base_label,cover,cover_label,area,count,percents
3,Region 3,1,355.747658,6000000.000000,6,6.38%
3,Region 3,3,216304.146140,46000000.000000,46,48.94%
3,Region 3,2,26627.415787,46000000.000000,46,48.94%
1,Region 1,1,1466.340177,11000000.000000,11,9.09%
1,Region 1,3,13837.701610,10000000.000000,10,8.26%
1,Region 1,2,105488.837775,88000000.000000,88,72.73%
1,Region 1,4,902.359018,13000000.000000,13,10.74%
1,Region 1,7,53.747332,4000000.000000,4,3.31%
4,Region 4,1,26884.220460,65000000.000000,65,28.26%
4,Region 4,3,291863.216396,70000000.000000,70,30.43%
4,Region 4,2,48260.411774,92000000.000000,92,40.00%
4,Region 4,4,477.251251,7000000.000000,7,3.04%
2,Region 2,1,1113.270785,11000000.000000,11,10.19%
2,Region 2,3,157977.541352,58000000.000000,58,53.70%
2,Region 2,2,7701.208609,29000000.000000,29,26.85%
2,Region 2,4,3171.919491,15000000.000000,15,13.89%
5,Region 5,1,27748.714430,37000000.000000,37,44.58%
5,Region 5,3,133262.033972,31000000.000000,31,37.35%
5,Region 5,2,2713.756942,15000000.000000,15,18.07%
5,Region 5,4,677.823622,5000000.000000,5,6.02%
6,Region 6,1,14377.698637,31000000.000000,31,57.41%
6,Region 6,3,56746.359740,14000000.000000,14,25.93%
6,Region 6,2,4117.270100,13000000.000000,13,24.07%
The use table can be requested via the use output option.

Using unprocessed input maps

The module offers a pre-processing functionality for all of the following input components:

A first look on how this works, is to experiment with the landuse and suitability_scores input options.

Let's return to the first example, and use a fragment from the unprocessed CORINE land data set, instead of the land_suitability map. This requires a set of "score" rules, that correspond to the CORINE nomenclature, to translate the land cover types into recreation potential.

Fragment from the CORINE land data base Legend for the CORINE land data base

In this case, the rules are a simple ASCII file (for example named corine_suitability.scores that contains the following

1:1:0:0
2:2:0.1:0.1
3:9:0:0
10:10:1:1
11:11:0.1:0.1
12:13:0.3:0.3
14:14:0.4:0.4
15:17:0.5:0.5
18:18:0.6:0.6
19:20:0.3:0.3
21:22:0.6:0.6
23:23:1:1
24:24:0.8:0.8
25:25:1:1
26:29:0.8:0.8
30:30:1:1
31:31:0.8:0.8
32:32:0.7:0.7
33:33:0:0
34:34:0.8:0.8
35:35:1:1
36:36:0.8:0.8
37:37:1:1
38:38:0.8:0.8
39:39:1:1
40:42:1:1
43:43:0.8:0.8
44:44:1:1
45:45:0.3:0.3
This file is provided in the suitability_scores option:
r.estimap.recreation  landuse=corine_land_cover_2006 suitability_scores=corine_suitability.scores  potential=potential_corine --o

Example of a recreation spectrum output map
while using a MASK, based on a fragment from the CORINE land data base
The same can be achieved with a long one-line string too:
r.estimap.recreation \
  landuse=corine_land_cover_2006 \
  suitability_scores="1:1:0:0,2:2:0.1:0.1,3:9:0:0,10:10:1:1,11:11:0.1:0.1,12:13:0.3:0.3,14:14:0.4:0.4,15:17:0.5:0.5,18:18:0.6:0.6,19:20:0.3:0.3,21:22:0.6:0.6,23:23:1:1,24:24:0.8:0.8,25:25:1:1,26:29:0.8:0.8,30:30:1:1,31:31:0.8:0.8,32:32:0.7:0.7,33:33:0:0,34:34:0.8:0.8,35:35:1:1,36:36:0.8:0.8,37:37:1:1,38:38:0.8:0.8,39:39:1:1,40:42:1:1,43:43:0.8:0.8,44:44:1:1,45:45:0.3:0.3"  potential=potential_1 --o
In fact, this very scoring scheme, for CORINE land data sets, is integrated in the module, so we obtain the same output even by discarding the suitability_scores option:
r.estimap.recreation  landuse=corine_land_cover_2006  suitability_scores=corine_suitability.scores  potential=potential_1 --o
This is so because CORINE is a standard choice among existing land data bases that cover european territories. In case of a user requirement to provide an alternative scoring scheme, all what is required is either of

REFERENCES

SEE ALSO

r.univar, r.mapcalc

AUTHOR

Nikos Alexandris

COPYRIGHT

Copyright 2018 European Union Licensed under the EUPL, Version 1.2 or – as soon they will be approved by the European Commission – subsequent versions of the EUPL (the "Licence"); You may not use this work except in compliance with the Licence. You may obtain a copy of the Licence at: https://joinup.ec.europa.eu/collection/eupl/eupl-text-11-12 Unless required by applicable law or agreed to in writing, software distributed under the Licence is distributed on an "AS IS" basis, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the Licence for the specific language governing permissions and limitations under the Licence. Consult the LICENCE file for details.

Last changed: $Date: 2017-11-03 18:21:39 +0100 (Fri, 03 Nov 2017) $

SOURCE CODE

Available at: r.estimap.recreation source code (history)


Main index | Raster index | Topics index | Keywords index | Graphical index | Full index

© 2003-2019 GRASS Development Team, GRASS GIS 7.8.2dev Reference Manual