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spillover_risk.sh
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#!/bin/bash
############################################################################
#
# MODULE: spillover_risk.sh
# AUTHOR(S): Markus Metz
#
# PURPOSE: RVF spillover risk calculation after Hardcastle et al. 2020
# COPYRIGHT: (C) 2024 - 2025 by mundialis GmbH & Co. KG
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
#############################################################################
# ---------------------
# CONFIGURATION
# ---------------------
# setting environment, so that awk works properly in all languages
unset LC_ALL
LC_NUMERIC=C
export LC_NUMERIC
# set variables (map names for human population, livestock population and
# maxent suitability maps)
# mrt_ppp_2020_1km_Aggregated_UNadj: Estimated total number of people per grid-cell.
# https://hub.worldpop.org/geodata/summary?id=37504
HUMAN_POPABS="mrt_ppp_2020_1km_Aggregated_UNadj_shifted@WorldPop_Mauritania"
# all livestock added together (CTL, GTS, SHP)
LIVESTOCK_POPDENS="GLW4-2020.D-DA_livestock_zero_30arcsec@GLW_2020_Mauritania"
MAXENT_MODEL_VERSION="mv06"
REGION="aoi_buf_rast@RVF_Mauritania"
# ---------------------
# PROCESSING
# ---------------------
# set region
g.region raster=${REGION} -p
# -- number of humans and livestock at risk per pixel
# optional preparation if needed:
# convert human population density (number / km2) to absolute number of people
# r.mapcalc "human_pop_abs = $HUMAN_POPDENS * area() / 1000000.0"
# convert livestock population density (number / km2) to absolute number of livestock
r.mapcalc "livestock_pop_abs = \"${LIVESTOCK_POPDENS}\" * area() / 1000000.0"
# -- calculate natural log: loop over all years and months
# initialize overall minima and maxima for
# log of absolute number of humans at risk
# log of proportion of humans at risk
# log of absolute number of livestock at risk
# log of proportion of livestock at risk
TOTAL_LOG_H_ABS_MIN=0.0
TOTAL_LOG_H_ABS_MAX=0.0
TOTAL_LOG_H_PROP_MIN=0.0
TOTAL_LOG_H_PROP_MAX=0.0
TOTAL_LOG_L_ABS_MIN=0.0
TOTAL_LOG_L_ABS_MAX=0.0
TOTAL_LOG_L_PROP_MIN=0.0
TOTAL_LOG_L_PROP_MAX=0.0
FIRST=1
# use log(1 + x):
# output will be >= 0, no large negative values
# input can be zero
g.message "natural logs ..."
for YEAR in `seq 2019 2023` ; do
FIRSTMONTH=1
LASTMONTH=12
if [ $YEAR -eq 2019 ] ; then
FIRSTMONTH=3
fi
for MONTH in `seq $FIRSTMONTH $LASTMONTH` ; do
MONTH2D=`printf "%02d\n" $MONTH`
g.message "$YEAR $MONTH2D ..."
MAXENT_SUITABILITY="model_${MONTH2D}_${YEAR}_${MAXENT_MODEL_VERSION}@RVF_Mauritania_potential_risk_areas"
# absolute number of humans at risk
r.mapcalc "human_abs_risk_${YEAR}${MONTH2D} = $HUMAN_POPABS * $MAXENT_SUITABILITY" || exit 1
# proportion of humans at risk: MAXENT_SUITABILITY
# add livestock movement to livestock population
# wet season: June to October
# dry season: November to May
MOVEMENTMAP=""
if [ $MONTH -eq 11 ] || [ $MONTH -eq 12 ] || [ $MONTH -eq 1 ] || [ $MONTH -eq 2 ] || [ $MONTH -eq 3 ] || [ $MONTH -eq 4 ] || [ $MONTH -eq 5 ] ; then
MOVEMENTMAP="Current_dry_1km_nodata_scaled2@livestock_movement_Mauritania"
fi
if [ $MONTH -eq 6 ] || [ $MONTH -eq 7 ] || [ $MONTH -eq 8 ] || [ $MONTH -eq 9 ] || [ $MONTH -eq 10 ] ; then
MOVEMENTMAP="Current_wet_1km_nodata_scaled2@livestock_movement_Mauritania"
fi
# modified number of livestock: more movement -> more animals
r.mapcalc "livestock_abs_move_${YEAR}${MONTH2D} = livestock_pop_abs * (1 + $MOVEMENTMAP)" || exit 1
# absolute number of livestock at risk
r.mapcalc "livestock_abs_risk_${YEAR}${MONTH2D} = livestock_abs_move_${YEAR}${MONTH2D} * $MAXENT_SUITABILITY" || exit 1
# proportion of livestock at risk: MAXENT_SUITABILITY
# natural log of humans at risk
r.mapcalc "human_abs_risk_log_${YEAR}${MONTH2D} = log(1 + human_abs_risk_${YEAR}${MONTH2D})" || exit 1
r.mapcalc "human_prop_risk_log_${YEAR}${MONTH2D} = log(1 + $MAXENT_SUITABILITY)" || exit 1
# natural log of livestock at risk
r.mapcalc "livestock_abs_risk_log_${YEAR}${MONTH2D} = log(1 + livestock_abs_risk_${YEAR}${MONTH2D})" || exit 1
r.mapcalc "livestock_prop_risk_log_${YEAR}${MONTH2D} = log(1 + $MAXENT_SUITABILITY)" || exit 1
# minimum and maximum of these 4 logs across all pixels, months, and years
if [ $FIRST -eq 1 ] ; then
FIRST=0
eval `r.info -s human_abs_risk_log_${YEAR}${MONTH2D}`
TOTAL_LOG_H_ABS_MIN=$min
TOTAL_LOG_H_ABS_MAX=$max
eval `r.info -s human_prop_risk_log_${YEAR}${MONTH2D}`
TOTAL_LOG_H_PROP_MIN=$min
TOTAL_LOG_H_PROP_MAX=$max
eval `r.info -s livestock_abs_risk_log_${YEAR}${MONTH2D}`
TOTAL_LOG_L_ABS_MIN=$min
TOTAL_LOG_L_ABS_MAX=$max
eval `r.info -s livestock_prop_risk_log_${YEAR}${MONTH2D}`
TOTAL_LOG_L_PROP_MIN=$min
TOTAL_LOG_L_PROP_MAX=$max
else
eval `r.info -s human_abs_risk_log_${YEAR}${MONTH2D}`
TOTAL_LOG_H_ABS_MIN=`echo $TOTAL_LOG_H_ABS_MIN $min | awk '{printf "%g\n", ($1 < $2 ? $1 : $2)}'`
TOTAL_LOG_H_ABS_MAX=`echo $TOTAL_LOG_H_ABS_MAX $max | awk '{printf "%g\n", ($1 > $2 ? $1 : $2)}'`
eval `r.info -s human_prop_risk_log_${YEAR}${MONTH2D}`
TOTAL_LOG_H_PROP_MIN=`echo $TOTAL_LOG_H_PROP_MIN $min | awk '{printf "%g\n", ($1 < $2 ? $1 : $2)}'`
TOTAL_LOG_H_PROP_MAX=`echo $TOTAL_LOG_H_PROP_MAX $max | awk '{printf "%g\n", ($1 > $2 ? $1 : $2)}'`
eval `r.info -s livestock_abs_risk_log_${YEAR}${MONTH2D}`
TOTAL_LOG_L_ABS_MIN=`echo $TOTAL_LOG_L_ABS_MIN $min | awk '{printf "%g\n", ($1 < $2 ? $1 : $2)}'`
TOTAL_LOG_L_ABS_MAX=`echo $TOTAL_LOG_L_ABS_MAX $max | awk '{printf "%g\n", ($1 > $2 ? $1 : $2)}'`
eval `r.info -s livestock_prop_risk_log_${YEAR}${MONTH2D}`
TOTAL_LOG_L_PROP_MIN=`echo $TOTAL_LOG_L_PROP_MIN $min | awk '{printf "%g\n", ($1 < $2 ? $1 : $2)}'`
TOTAL_LOG_L_PROP_MAX=`echo $TOTAL_LOG_L_PROP_MAX $max | awk '{printf "%g\n", ($1 > $2 ? $1 : $2)}'`
fi
done
done
# -- scale the log maps to be between 0 and 10 using the overall minima and maxima
# and calculate geometric mean
g.message "rescale log maps ..."
for YEAR in `seq 2019 2023` ; do
FIRSTMONTH=1
LASTMONTH=12
if [ $YEAR -eq 2019 ] ; then
FIRSTMONTH=3
fi
for MONTH in `seq $FIRSTMONTH $LASTMONTH` ; do
MONTH2D=`printf "%02d\n" $MONTH`
g.message "$YEAR $MONTH2D ..."
MAXENT_SUITABILITY="model_${MONTH2D}_${YEAR}_${MAXENT_MODEL_VERSION}"
# scale natural log of absolute number of humans at risk
r.mapcalc "human_abs_risk_log_scaled_${YEAR}${MONTH2D} = ((human_abs_risk_log_${YEAR}${MONTH2D} - $TOTAL_LOG_H_ABS_MIN) / ($TOTAL_LOG_H_ABS_MAX - $TOTAL_LOG_H_ABS_MIN)) * 10.0" || exit 1
# scale natural log of proportion of humans at risk
r.mapcalc "human_prop_risk_log_scaled_${YEAR}${MONTH2D} = ((human_prop_risk_log_${YEAR}${MONTH2D} - $TOTAL_LOG_H_PROP_MIN) / ($TOTAL_LOG_H_PROP_MAX - $TOTAL_LOG_H_PROP_MIN)) * 10.0" || exit 1
# scale natural log of absolute number of livestock at risk
r.mapcalc "livestock_abs_risk_log_scaled_${YEAR}${MONTH2D} = ((livestock_abs_risk_log_${YEAR}${MONTH2D} - $TOTAL_LOG_L_ABS_MIN) / ($TOTAL_LOG_L_ABS_MAX - $TOTAL_LOG_L_ABS_MIN)) * 10.0" || exit 1
# scale natural log of proportion of livestock at risk
r.mapcalc "livestock_prop_risk_log_scaled_${YEAR}${MONTH2D} = ((livestock_prop_risk_log_${YEAR}${MONTH2D} - $TOTAL_LOG_L_PROP_MIN) / ($TOTAL_LOG_L_PROP_MAX - $TOTAL_LOG_L_PROP_MIN)) * 10.0" || exit 1
# geometric mean for humans at risk
r.mapcalc "human_geomean_${YEAR}${MONTH2D} = sqrt(human_abs_risk_log_scaled_${YEAR}${MONTH2D} * human_prop_risk_log_scaled_${YEAR}${MONTH2D})" || exit 1
# geometric mean for livestock at risk
r.mapcalc "livestock_geomean_${YEAR}${MONTH2D} = sqrt(livestock_abs_risk_log_scaled_${YEAR}${MONTH2D} * livestock_prop_risk_log_scaled_${YEAR}${MONTH2D})" || exit 1
# geometric mean for humans and livestock at risk -> final spillover potential
r.mapcalc "spillover_geomean_${YEAR}${MONTH2D} = sqrt(human_geomean_${YEAR}${MONTH2D} * livestock_geomean_${YEAR}${MONTH2D})" || exit 1
# without zero values
r.mapcalc "spillover_geomean_nozero_${YEAR}${MONTH2D} = if(spillover_geomean_${YEAR}${MONTH2D} == 0, null(), spillover_geomean_${YEAR}${MONTH2D})" || exit 1
done
done
# -- quintile ranking for each spillover value from all geographic units (pixels), months, and years, excluding 0 cells
# r.univar with all spillover_geomean_* maps
# the commandline must not become too long !
MAPLIST=`g.list rast mapset=. pattern=spillover_geomean_nozero_* separator=comma`
eval `r.univar -ge map=$MAPLIST percentile=20,40,60,80`
RULESFILE=`g.tempfile pid=$$`
# write rules for r.recode to file
echo "0:${percentile_20}:1
${percentile_20}:${percentile_40}:2
${percentile_40}:${percentile_60}:3
${percentile_60}:${percentile_80}:4
${percentile_80}:10:5" >$RULESFILE
# assign coded quintile to each pixel according to the quintile its value falls into
# loop over all monthly maps and recode (with copy)
g.message "Recode spillover to quintiles ..."
for YEAR in `seq 2019 2023` ; do
FIRSTMONTH=1
LASTMONTH=12
if [ $YEAR -eq 2019 ] ; then
FIRSTMONTH=3
fi
for MONTH in `seq $FIRSTMONTH $LASTMONTH` ; do
MONTH2D=`printf "%02d\n" $MONTH`
g.message "$YEAR $MONTH2D ..."
r.recode input=spillover_geomean_${YEAR}${MONTH2D} output=spillover_quintile_${YEAR}${MONTH2D} rules=$RULESFILE || exit 1
done
done
# average quintile for each pixel and month across all years
g.message "average quintile per month over all years ..."
for MONTH in `seq 1 12` ; do
MONTH2D=`printf "%02d\n" $MONTH`
g.message "$MONTH2D ..."
# all maps over all years for this month: average quintile:
# synoptic spillover potential for each pixel and month across all years
MAPLIST=`g.list rast mapset=. pattern=spillover_quintile_????${MONTH2D} separator=comma`
r.series input=$MAPLIST method=average output=spillover_quintile_month_${MONTH2D} || exit 1
done
# average geomean for each pixel and month across all years
g.message "average geomean per month over all years ..."
for MONTH in `seq 1 12` ; do
MONTH2D=`printf "%02d\n" $MONTH`
g.message "$MONTH2D ..."
# all maps over all years for this month: average geomean:
# synoptic spillover potential for each pixel and month across all years
MAPLIST=`g.list rast mapset=. pattern=spillover_geomean_????${MONTH2D} separator=comma`
r.series input=$MAPLIST method=average output=spillover_geomean_month_${MONTH2D} || exit 1
done
exit 0