Note: This document is for an older version of GRASS GIS that will be discontinued soon. You should upgrade, and read the current manual page.

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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] [region=name] [land=name] [natural=name[,name,...]] [water=name[,name,...]] [infrastructure=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] [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=filename] [use=filename] [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:

region=name
Input map to set computational extent and region
Name of input raster map
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
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]
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
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=filename
Output absolute file name for the supply table
Supply table CSV output file name
use=filename
Output absolute file name for the use table
Use table CSV output file name
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 definitions:
Recreation Potential
is the potential of the ecosystems to offer recreation opportunities. It depends on the properties and conditions of an ecosystem. The recreation potential map, derives by adding and normalizing maps of natural components that may provide recreation opportunities.
Recreation Demand
is the number of households using a recreation service. By definition, population data are required in order to quantify the demand.
Recreation (Opportunity) Spectrum
also mentioned as ROS, is the range of recreation opportunities categorized by their distance to roads and residential areas. It derives by combining the recreation potential and maps that depict access (i.e. infrastructure) and/or areas that provide opportunities for recreational activities.

An important category is the one with the Highest Recreation Spectrum. It includes areas of very high recreational value which, at the same time, are very near to access.

Potential | Opportunity Near Midrange Far
Near 1 2 3
Midrange 4 5 6
Far 7 8 9
Met Demand
is the part of the population that has access to high quality areas for daily recreation (population that lives within 4km from areas of high recreational value.
Unmet Demand
is the part of the population whose access to high quality areas for daily recreation is not guaranteed (population that lives beyond 4km from areas of high recreational value.
Actual Flow
is the spatial relationship between potential and demand. In the case of recreation services, the flow is the number of visits in the areas of interest for daily recreation.
The supply table
presents the contribution of each ecosystem type to the actual flow of outdoor recreation as measured by the number of potential visits to areas for daily recreation per year.
The Use table
presents the allocation of the service flow to the users (inhabitants).

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
Normalization
As part of the algorithm, 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 to derive a recreation potential map:

Area of interest Land suitability Water resources Protected areas

The maps shown above are available to download, among other sample maps, at: https://gitlab.com/natcapes/r.estimap.recreation.data.

Note, the prefix input_ in front of all maps is purposive in order to make the examples easier to understand. Similarly, all output maps and files will be prefixed with the string output_.

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

Before anything, we need to define the extent of interest by executing 

  g.region  raster=input_area_of_interest  -p
which returns 
  projection: 99 (ETRS89 / LAEA Europe)
  zone:       0
  datum:      etrs89
  ellipsoid:  grs80
  north:      2879700
  south:      2748850
  west:       4735600
  east:       4854650
  nsres:      50
  ewres:      50
  rows:       2617
  cols:       2381
  cells:      6231077

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 that add to the recreational value of the area. Pro-processing means here to derive a map that scores the given resources, in the context of recreation and the ESTIMAP algorithm.

Potential

To produce a recreation potential map, the simplest command requires the user to define the input map option land and name the output map via the 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=input_land_suitability  potential=output_potential
Example of a recreation potential output map

Example of a recreation potential output map

Note, this will process the map input_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 option mask: 

    r.estimap.recreation  land=input_land_suitability  mask=input_area_of_interest  potential=output_potential_1
Example of a recreation potential output map while using a MASK

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 output_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. Note also the --o flag: it is required to overwrite the already existing map named potential_1.

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

    r.estimap.recreation  land=input_land_suitability  water=input_water_resources  potential=output_potential_2
Example of a recreation potential output map while using a MASK, a land suitability map and a water resources map

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 output_potential_2.

Following, we provide a map of protected areas named input_protected_areas, we modify the output map name to output_potential_3 and execute the updated command: 

    r.estimap.recreation  land=input_land_suitability  water=input_water_resources  natural=input_protected_areas  potential=output_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

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. For example, we add a second map named input_bathing_water_quality to the water component and modify the output map name to output_potential_4: 

    r.estimap.recreation  land=input_land_suitability  water=input_water_resources,input_bathing_water_quality  natural=input_protected_areas  potential=output_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

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 (however, we will skip for now important cartographic elements). 

    d.rast  output_potential_4
    d.legend  -c  -b  output_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 output_potential_1, output_potential_2, output_potential_3 and output_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.

We can remove all of the potential maps via 

    g.remove raster pattern=output_potential* -f

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 input_distance_to_infrastructure, is defined as an additional input:

Example of an input map showing distances to infrastructure

Example of an input map showing distances to infrastructure

Naturally, we need to define the output map option spectrum too: 

  r.estimap.recreation  \
    land=input_land_suitability \
    water=input_water_resources,input_bathing_water_quality \
    natural=input_protected_areas \
    infrastructure=input_distance_to_infrastructure \
    spectrum=output_spectrum

or, the same command in a copy-paste friendly way for systems that won't understand the special \ character: 

    r.estimap.recreation  land=input_land_suitability  water=input_water_resources,input_bathing_water_quality  natural=input_protected_areas  infrastructure=input_distance_to_infrastructure  spectrum=output_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

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 an infrastructure map, while asking for the spectrum output, the command will abort and inform about.

The image of the spectrum map was produced via the following native GRASS GIS commands 

    d.rast  output_spectrum
    d.legend  -c  -b  output_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. As such, and by design, it requires to also request for the output option spectrum. Be aware that this design choice is applied in the case of the unmet output map option too. Building upon the previous command, we add the opportunity output option: 

  r.estimap.recreation --o \
    mask=input_area_of_interest \
    land=input_land_suitability \
    water=input_water_resources,input_bathing_water_quality \
    natural=input_protected_areas \
    infrastructure=input_distance_to_infrastructure \
    opportunity=output_opportunity \
    spectrum=output_spectrum

or, the same command in a copy-paste friendly way: 

    r.estimap.recreation  --o mask=input_area_of_interest  land=input_land_suitability  water=input_water_resources,input_bathing_water_quality  natural=input_protected_areas  infrastructure=input_distance_to_infrastructure  opportunity=output_opportunity  spectrum=output_spectrum

We also add the --o overwrite flag, because existing output_spectrum map will cause the module to abort.

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

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

The image of the opportunity map was produced via the following native GRASS GIS commands 

  d.rast  output_opportunity
  d.legend  -c  -b  output_opportunity  at=0,20,0,1  border_color=white
  d.text  text="Opportunity"  bgcolor=white

More input maps

To derive the outputs met demand distribution, unmet demand distribution 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)

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

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

In this example command, we remove the previously added opportunity and spectrum output options, and logically add the demand output option: 

  r.estimap.recreation --o \
    mask=input_area_of_interest \
    land=input_land_suitability \
    water=input_water_resources,input_bathing_water_quality \
    natural=input_protected_areas \
    infrastructure=input_distance_to_infrastructure \
    population=input_population_2015 \
    base=input_local_administrative_units \
    demand=output_demand

Of course, the maps output_opportunity and output_spectrum still exist in our data base, unless explicitly removed.

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

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

In the following example, we add unmet output map option. In this case of the unmet distribution map too, by design the module requires the user to define the demand output map option. 

r.estimap.recreation --o \
    mask=input_area_of_interest \
    land=input_land_suitability \
    water=input_water_resources,input_bathing_water_quality \
    natural=input_protected_areas \
    infrastructure=input_distance_to_infrastructure \
    population=input_population_2015 \
    base=input_local_administrative_units \
    demand=output_demand \
    unmet=output_unmet_demand
Example of an 'unmet demand' output map while using a MASK and inputs for land suitability, water resources, natural resources, infrastructure, population and base

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

It is left as an exercise to the user to create screenshots of the met, the unmet demand distribution and the flow output maps. For example, is may be similar to the command examples that demonstrate the use of the commands d.rast, d.legend and d.text, that draw the potential, the spectrum and the opportunity maps.

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=input_area_of_interest \
    land=input_land_suitability \
    water=input_water_resources,input_bathing_water_quality \
    natural=input_protected_areas \
    infrastructure=input_distance_to_infrastructure \
    population=input_population_2015 \
    base=input_local_administrative_units \
    flow=output_flow
Example of a flow output map while using a MASK and inputs for land suitability, water resources, natural resources, infrastructure, population and base

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

If we check the output values for the output_flow map, they are rounded by the module automatically to integers! Here the first few lines reporting areal statistics on the output_flow map: 

    r.stats output_flow -acpln --q |head

returns 

    52  125000000.000000 50000 1.72%
    53  191000000.000000 76400 2.63%
    54  303000000.000000 121200 4.17%
    55  392000000.000000 156800 5.39%
    56  196000000.000000 78400 2.69%
    57  178000000.000000 71200 2.45%
    58  286000000.000000 114400 3.93%
    59  185000000.000000 74000 2.54%
    60  207000000.000000 82800 2.85%
    61  176000000.000000 70400 2.42%

If the user wants the real numbers, that derive from the mobility function, the -r flag comes in handy: 

  r.estimap.recreation --o -r \
    mask=input_area_of_interest \
    land=input_land_suitability \
    water=input_water_resources,input_bathing_water_quality \
    natural=input_protected_areas \
    infrastructure=input_distance_to_infrastructure \
    population=input_population_2015 \
    base=input_local_administrative_units \
    flow=output_flow

Querying again areal statistics via 

    r.stats output_flow -acpln --q |head
returns 
    52-52.139117 from  to  50000000.000000 20000 0.69%
    52.139117-52.278233 from  to  11000000.000000 4400 0.15%
    52.278233-52.41735 from  to  39000000.000000 15600 0.54%
    52.41735-52.556467 from  to  32000000.000000 12800 0.44%
    52.556467-52.695583 from  to  7000000.000000 2800 0.10%
    52.695583-52.8347 from  to  9000000.000000 3600 0.12%
    52.8347-52.973817 from  to  25000000.000000 10000 0.34%
    52.973817-53.112933 from  to  13000000.000000 5200 0.18%
    53.112933-53.25205 from  to  28000000.000000 11200 0.38%
    53.25205-53.391167 from  to  92000000.000000 36800 1.26%

Supply and Use

The module outputs by request the supply and use tables in form of CSV files. Here is how: 

  r.estimap.recreation --o -r \
  mask=input_area_of_interest \
  land=input_land_suitability \
  water=input_water_resources,input_bathing_water_quality \
  natural=input_protected_areas \
  infrastructure=input_distance_to_infrastructure \
  population=input_population_2015 \
  base=input_local_administrative_units \
  supply=output_supply \
  use=output_use

Not surprisingly, the above command fails! It however informs that a land cover map and corresponding reclassification rules, for the classes of the landcover map, are required. Specifically, the algorithm's designers developed a MAES land classes scheme. The "translation" of the CORINE land classes (left) into this scheme (classes after the = sign) are for example: 

  1 = 1 Urban
  2 = 1 Urban
  3 = 1 Urban
  4 = 1 Urban
  5 = 1 Urban
  6 = 1 Urban
  7 = 1 Urban
  8 = 1 Urban
  9 = 1 Urban
  10 = 1 Urban
  11 = 1 Urban
  12 = 2 Cropland
  13 = 2 Cropland
  14 = 2 Cropland
  15 = 2 Cropland
  16 = 2 Cropland
  17 = 2 Cropland
  18 = 4 Grassland
  19 = 2 Cropland
  20 = 2 Cropland
  21 = 2 Cropland
  22 = 2 Cropland
  23 = 3 Woodland and forest
  24 = 3 Woodland and forest
  25 = 3 Woodland and forest
  26 = 4 Grassland
  27 = 5 Heathland and shrub
  28 = 5 Heathland and shrub
  29 = 3 Woodland and forest
  30 = 6 Sparsely vegetated land
  31 = 6 Sparsely vegetated land
  32 = 6 Sparsely vegetated land
  33 = 6 Sparsely vegetated land
  34 = 6 Sparsely vegetated land
  35 = 7 Wetland
  36 = 7 Wetland
  37 = 8 Marine
  38 = 8 Marine
  39 = 8 Marine

We save this into a file named corine_to_maes_land_classes.rules and feed it to the land_classes option, then re-execute the command: 

  r.estimap.recreation --o -r \
  mask=input_area_of_interest \
  land=input_land_suitability \
  water=input_water_resources,input_bathing_water_quality \
  natural=input_protected_areas \
  infrastructure=input_distance_to_infrastructure \
  population=input_population_2015 \
  base=input_local_administrative_units \
  landcover=input_corine_land_cover_2006 \
  land_classes=corine_to_maes_land_classes.rules \
  supply=output_supply \
  use=output_use

This time it works. Here the first few lines from the output CSV files: 

    head -5 output_*.csv
returns 
  ==> output_supply.csv <==
  base,base_label,cover,cover_label,area,count,percents
  3,,1,723.555560,9000000.000000,9,7.76%
  3,,3,246142.186250,64000000.000000,64,55.17%
  3,,2,21710.289271,47000000.000000,47,40.52%
  1,,1,1235.207129,11000000.000000,11,7.97%

  ==> output_use.csv<==
  category,label,value
  3,,268576.031081
  4,,394828.563827
  5,,173353.69508600002
  1,,144486.484126

Using other land cover maps as input, would obviously require a similar set of land classes translation rules.

All in one call

Of course it is possible to derive all output maps with one call: 

  r.estimap.recreation --o \
    land=input_land_suitability \
    natural=input_protected_areas,input_urban_green  \
    water=input_water_resources,input_bathing_water_quality \
    infrastructure=input_distance_to_infrastructure \
    landcover=input_corine_land_cover_2006 \
    land_classes=corine_to_maes_land_classes.rules \
    mask=input_area_of_interest \
    potential=output_potential \
    opportunity=output_opportunity \
    spectrum=output_spectrum \
    base=input_local_administrative_units \
    aggregation=input_regions \
    population=input_population_2015 \
    demand=output_demand \
    unmet=output_unmet_demand \
    flow=output_flow \
    supply=output_supply \
    use=output_use \
    timestamp='2015'

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

Vector map

A vector input map with the role of the base map, can be used too.

Example of a vector map showing local administrative units

Example of a vector map showing local administrative units 

    r.estimap.recreation --o -r \
    mask=input_area_of_interest \
    land=input_land_suitability \
    water=input_water_resources,input_bathing_water_quality \
    natural=input_protected_areas \
    infrastructure=input_distance_to_infrastructure \
    population=input_population_2015 \
    base=input_local_administrative_units \
    base_vector=input_vector_local_administrative_units \
    landcover=input_corine_land_cover_2006 \
    land_classes=corine_to_maes_land_classes.rules \
    supply=output_supply \
    use=output_use

This command will also: 

    spectrum_sum
    demand_sum
    unmet_sum
    flow_sum
    flow_1_sum
    flow_2_sum
    flow_3_sum
    flow_4_sum
    flow_5_sum
    flow_6_sum

all of which are of double precision.

For example, the 

    v.db.select input_vector_local_administrative_units columns=lau2_no_name,spectrum_sum,demand_sum,unmet_sum,flow_sum where="flow_sum IS NOT NULL" |head

following the analysis, returns 

    lau2_no_name|spectrum_sum|demand_sum|unmet_sum|flow_sum
    801 Bad Erlach|22096|2810||700
    841 Leopoldsdorf|8014|1800||426
    630 Rabensburg|23358|8474||1546
    468 Maissau|73168|6650||2580
    10 Müllendorf|19419|1902||718
    544 Straß im Straßertale|57314|4368||1471
    67 Forchtenstein|53009|272||848
    460 Guntersdorf|27408|12183||1955
    103 Sankt Andrä am Zicksee|45130|3833||1926

Here the vector map used for administrative boundaries with the sum of flow for each unit:

Example of a vector map showing the flow per unit

Example of a vector map showing the flow per unit

and the corresponding unmet demand, based on the analysis

Example of a vector map showing the unmet demand in concerned units

Example of a vector map showing the unmet demand in concerned units

In the latter screenshot, the units bearing the unmet demand results, are not the same as the raster map previously shown. The different results are due to the -r flag used in this last analysis. The -r flag will round up floating point values during computations, thus the results with or without it will differ. The reason to use, in this last example the -r flag, was to have short integer numbers to print as labels inside the units (in the vector map).

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=input_corine_land_cover_2006  suitability_scores=corine_suitability.scores  potential=output_potential_corine
Example of a recreation spectrum output map while using a MASK, based on a fragment from the CORINE land data base

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=input_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

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=input_corine_land_cover_2006  \
    suitability_scores=suitability_of_corine_land_cover.scores \
    potential=output_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

Author

Nikos Alexandris

Licence

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.

SOURCE CODE

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

Latest change: Monday Jan 30 19:52:26 2023 in commit: cac8d9d848299297977d1315b7e90cc3f7698730

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