# FUTURES tutorial

r.futures.* is an implementation of FUTure Urban-Regional Environment Simulation (FUTURES) which is a model for multilevel simulations of emerging urban-rural landscape structure. FUTURES produces regional projections of landscape patterns using coupled submodels that integrate nonstationary drivers of land change: per capita demand (DEMAND submodel), site suitability (POTENTIAL submodel), and the spatial structure of conversion events (PGA submodel).

This tutorial shows how to prepare data and run the model. You can download sample dataset from here (TODO) and simulate urban growth in The Triangle, region in the Piedmont of North Carolina in the United States with rapidly growing cities Raleigh, Durham and Chapel Hill.

# Software

Required software includes:

- GRASS GIS 7
- r.futures addon
- R (≥ 3.0.2)
- SciPy (optional)

# Input data used in this tutorial

- digital elevation model (NED)
- NLCD 2001, 2011
- NLCD 1992/2001 Retrofit Land Cover Change Product
- transportation network (TIGER)
- county boundaries (TIGER)
- protected areas (Secured Lands)
- cities above 20000 (USGS)
- county population past estimates and future projections (NC OSBM) per county (nonspatial)

# Workflow

## Initial data preparation

First we will set computational region of our analyses to an extent covering our study area and so that the cells are aligned with one of our landuse rasters:

```
g.region n=1648005 s=1480695 e=1624815 w=1462965 align=landuse_2001 -p
```

We will derive urbanized areas from NLCD dataset for year 2001 and 2011 by extracting categories category 21 - 24 into a new binary map where developed is 1, undeveloped 0 and NULL (no data) is area unsuitable for development (water, wetlands, protected areas). First we will convert protected areas from vector to raster and set NULLs to zeros (for simpler raster algebra expression in the next step):

```
v.to.rast input=protected output=protected use=val
r.null map=protected null=0
```

And then create rasters of developed/undeveloped areas using raster algebra:

```
r.mapcalc "urban_2001 = if(landuse_2001 >= 21 && landuse_2001 <= 24, 1, if(landuse_2001 == 11 || landuse_2001 >= 90 || protected, null(), 0))"
r.mapcalc "urban_2011 = if(landuse_2011 >= 21 && landuse_2011 <= 24, 1, if(landuse_2011 == 11 || landuse_2011 >= 90 || protected, null(), 0))"
```

We will convert vector counties to raster with the values of the FIPS attribute which links to population file:

```
v.to.rast input=subregions_triangle type=area use=attr attribute_column=FIPS output=selected_counties_id
```

If we need to run FUTURES only for smaller number of counties, we can extract them based on the identifier:

```
v.to.rast input=subregions_triangle type=area where="FIPS in (37183, 37101)" use=attr attribute_column=FIPS output=selected_counties_id
```

## Potential submodel

### Predictors

#### Slope

We will derive slope in degrees from DEM using r.slope.aspect module:

```
r.slope.aspect elevation=DEM slope=slope
```

#### Distance from lakes/rivers

First we will extract category *Open Water* from 2011 NLCD dataset:

```
r.mapcalc "water = if(landuse_2011 == 11, 1, null())"
```

Then we compute the distance to water with module r.grow.distance and set color table to shades of blue:

```
r.grow.distance input=water distance=dist_to_water
r.colors -n map=dist_to_water color=blues
```

#### Distance from protected areas

We will use raster protected of protected areas we already created. We compute the distance to protected areas with module r.grow.distance and set color table from green to red:

```
r.grow.distance input=protected distance=dist_to_protected
r.colors map=dist_to_protected color=gyr
```

#### Forests

We will smooth the transition between forest and other land use, see NLCD category *Open Water*:

```
r.mapcalc "forest = if(landuse_2011 >= 41 && landuse_2011 <= 43, 1, 0)"
r.neighbors -c input=forest output=forest_smooth size=15 method=average
r.colors map=forest_smooth color=ndvi
```

#### Travel time to cities

Here we will compute travel time to cities (> population 20000)
as cumulative cost distance where cost is defined as travel time on roads.
First we specify the speed on different types of roads.
We copy the roads raster into our mapset so that we can change it
by adding a new attribute field `speed`. Then we assign speed values based on the type of road:

```
g.copy vector=roads_2015,roads
v.db.addcolumn map=roads columns="speed double precision"
v.db.update map=roads column=speed value=50 where="MTFCC = 'S1400'"
v.db.update map=roads column=speed value=100 where="MTFCC IN ('S1100', 'S1200')"
```

Now we rasterize the selected road types using the speed values from the attribute table as raster values.

```
v.to.rast input=roads type=line where="MTFCC IN ('S1100', 'S1200', 'S1400')" output=roads_speed use=attr attribute_column=speed
```

We set the rest of the area to low speed and recompute the speed as time to travel through a 30m cell in minutes:

```
r.null map=roads_speed null=5
r.mapcalc "roads_travel_time = 1.8 / roads_speed"
```

Finally we compute the travel time to larger cities using r.cost:

```
r.cost input=roads_travel_time output=travel_time_cities start_points=cities_20000
r.colors map=travel_time_cities color=byr
```

#### Road density

We will rasterize roads and use moving window analysis (r.neighbors) to compute road density:

```
v.to.rast input=roads_2015 output=roads use=val
r.null map=roads null=0
r.neighbors -c input=roads output=road_dens size=25 method=average
```

#### Distance to interchanges

We will consider TIGER roads of type `Ramp` as interchanges, rasterize them and compute euclidean distance to them:

```
v.to.rast input=roads_2015 where="MTFCC = 'S1630'" output=interchanges use=val
r.grow.distance -m input=interchanges distance=dist_interchanges
```

### Development pressure

Computing development pressure with r.futures.devpressure:

```
r.futures.devpressure -n input=urban_2011 output=devpressure_0_5 method=gravity size=20 gamma=0.5 scaling_factor=0.001
```

When gamma increases, development influence decreases more rapidly with distance. Size is half the size of the moving window. To explore the effects of these parameters, you can view the development pressure as a surface with draped urban_2011 raster over it.

TODO We should derive more layers with different gamma parameters for the potential statistical model.

### Rescaling variables

First we will look at the ranges of our predictor variables by running a short Python code snippet in Python shell:

```
for name in ('slope', 'dist_to_water', 'dist_to_protected', 'forest_smooth', 'travel_time_cities', 'road_dens', 'dist_interchanges', 'devpressure_0_5'):
minmax = grass.raster_info(name)
print name, minmax['min'], minmax['max']
```

We will rescale some of our input variables:

```
r.mapcalc "dist_to_water_km = dist_to_water / 1000"
r.mapcalc "dist_to_protected_km = dist_to_protected / 1000"
r.mapcalc "dist_interchanges_km = dist_interchanges / 1000"
r.mapcalc "road_dens_perc = road_dens * 100"
r.mapcalc "forest_smooth_perc = forest_smooth * 100"
```

### Sampling

To sample only in the analyzed counties, we will clip development layer:

```
r.mapcalc "urban_2011_clip = if(selected_counties_id, urban_2011)"
```

To estimate the number of sampling points, we can use r.report to report number of developed/undeveloped cells and their ratio.

```
r.report map=urban_2011 units=h,c,p
```

We will sample the predictors and the response variable with 10000 random points in undeveloped areas and 2000 points in developed area:

```
r.sample.category input=urban_2011_clip output=sampling sampled=selected_counties_id,devpressure_0_5,slope,road_dens_perc,forest_smooth_perc,dist_to_water_km,dist_to_protected_km,dist_interchanges_km,travel_time_cities npoints=10000,2000
```

The attribute table can be exported as CSV file (not necessary step):

```
v.db.select map=sampling columns=urban_2011_clip,selected_counties_id,devpressure_0_5,slope,road_dens_perc,forest_smooth_perc,dist_to_water_km,dist_to_protected_km,dist_interchanges_km,travel_time_cities separator=comma file=samples.csv
```

### Development potential

Now we find best model for predicting urbanization using r.futures.potential which wraps an R script.

```
r.futures.potential input=sampling output=potential.csv columns=devpressure_0_5,slope,road_dens_perc,forest_smooth_perc,dist_to_water_km,dist_to_protected_km,dist_interchanges_km,travel_time_cities developed_column=urban_2011_clip subregions_column=selected_counties_id min_variables=4
```