Last updated: 2021-05-14

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# load required libraries
library("sf")
library("terra")
library("wdpar")
library("tidyverse")

starttime<-Sys.time() # mark the start time of this routine to calculate processing time at the end

Introduction

Anthropogenic disturbance to the protected areas is a well-known issue to all environmentalists and those working on the conservation of protected areas. To analyze the effect of human population pressure towards the protected areas, it is of utmost importance to get the idea of the number of the human population residing within the protected areas and in the vicinity of those areas. A figure demonstrating the human population and the change on those figures throughout the years assist in making an empirical analysis of the human population pressure to the protected areas. For this, it is necessary to get the most reliable and high-quality datasets. WorldPop, which was initiated in 2013, offers easy access to spatial demographic datasets, claiming to use peer-reviewed and fully transparent methods to create global mosaics for the years 2000 to 2020, which we will use here in this analysis.

Datasource and Metadata Information

  • Dataset: - WorldPop Unconstrained Global Mosaics
  • Geographical Coverage: Global
  • Spatial resolution: 1Km
  • Temporal Coverage: 2000-2020
  • Temporal resolution: Annual Updates
  • Unit: number of people per grid cell
  • Data downloaded: 5th May, 2021
  • Metadata Link
  • Download Link

Processing Workflow

The purpose of this analysis is to compute the number of people residing in a particular protected area’s polygon of interest. In order to obtain the result, we need to go through several steps of processing as shown in the routine workflow:

Prepare population count raster

Among all the available global rasters for years 2000 to 2020 were downloaded, for this routine, we would only use for a particular year i.e. 2000.

# load raster land cover
pop <- rast("/home/rstudio/shared/datalake/mapme.protectedareas/input/world_pop/global_mosaic2000.tif")
# plot raster
plot(pop)

Prepare WDPA Polygon

Since we already prepared raster data for our analysis. Now, we will try to get the country level polygon data from wdpar package. wdpar is a library to interface to the World Database on Protected Areas (WDPA). The library is used to monitor the performance of existing PAs and determine priority areas for the establishment of new PAs. We will use Brazil - for other countries of your choice, simply provide the country name or the ISO name e.g. Gy for Guyana, COL for Colombia.

# fetch the raw data from wdpar of country
br_raw_pa_data <- wdpa_fetch("BRA")

Since there are multiple enlisted protected areas in Brazil, we want to compute zonal statistics only for the polygon data of: - Reserva Biologica Do Rio Trombetas - wdpaid 43 - Reserva Extrativista Rio Cajari - wdpaid 31776 and - Estacao Ecologica Do Jari - wdpaid 4891

For this, we have to subset the country level polygon data to the PA level.

# subset three wdpa polygons by their wdpa ids
bra<-
  br_raw_pa_data%>%
  filter(WDPAID %in% c(43, 31776, 4891))
# plot the sample polygon
plot(bra[1])

Crop the population count raster

As we completed raster and vector data preparation, the next step would be to clip the raster layer by the selected PA polygon both by its extent and mask layer.

# convert object bra to spatVector object
bra_v <- as(bra, "Spatial")%>%
  vect()
# crop raster using polygon extent
myCrop <- terra::crop(pop, bra_v)
# plot the data - shows the raster after getting cropped by the extent of polygon
plot(myCrop)

# crop raster using polygon mask
myMask <- mask(myCrop, bra_v)
# plot the data - shows the raster after getting cropped by the polygon mask
plot(myMask)

Rasterize the polygon layer

To compute the zonal statistics, it is necessary to rasterize the polygon layer. Doing so, values are transferred from the spatial objects to raster cells. We need to pass the extent layer and the mask layer to the rasterize function.

# rasterize
r <- rasterize(bra_v, myMask, bra_v$WDPAID, background=NA, update=FALSE, touches=is.lines(bra_v), cover=FALSE)

Compute zonal statistics

A zonal statistics operation is one that calculates statistics on cell values of a raster (a value raster) within the zones defined by another dataset [ArcGIS definition].

# zonal stats
zstats <- zonal(myMask, r, fun='sum', na.rm=T)
zstats
   zone global_mosaic2000
1    43          703.6494
2  4891           12.0065
3 31776         2908.8636

From the result of zonal statistics, we can see the number of people residing in the protected areas with WDPAIDs 43, 4891 and 31776. In a similar way, we can also compute the number of people in a particular region of interest for the desired year and analyze whether the population is following increasing or decreasing trend which helps scientific community to understand the demographic effects towards the protected regions.

In the end we are going to have a look how long the rendering of this file took so that people get an idea about the processing speed of this routine.

stoptime<-Sys.time()
print(starttime-stoptime)
Time difference of -5.410514 secs

References

[1] Worldpop.org. 2021. WorldPop. [online] Available at: https://www.worldpop.org/ [Accessed 14 May 2021].


sessionInfo()
R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.5     purrr_0.3.4    
 [5] readr_1.3.1     tidyr_1.1.3     tibble_3.1.1    ggplot2_3.3.1  
 [9] tidyverse_1.3.0 wdpar_1.3.0.1   terra_0.6-9     sf_0.9-3       

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6         countrycode_1.2.0  lubridate_1.7.8    lattice_0.20-41   
 [5] class_7.3-16       assertthat_0.2.1   rprojroot_1.3-2    digest_0.6.27     
 [9] utf8_1.2.1         cellranger_1.1.0   R6_2.5.0           backports_1.2.1   
[13] reprex_0.3.0       evaluate_0.14      e1071_1.7-3        httr_1.4.1        
[17] pillar_1.6.0       rlang_0.4.11       curl_4.3           readxl_1.3.1      
[21] rstudioapi_0.13    raster_3.1-5       blob_1.2.1         rmarkdown_2.2     
[25] rgdal_1.5-8        munsell_0.5.0      broom_0.7.6        compiler_4.0.0    
[29] httpuv_1.6.0       modelr_0.1.8       xfun_0.22          pkgconfig_2.0.3   
[33] htmltools_0.5.1.1  tidyselect_1.1.1   workflowr_1.6.2    codetools_0.2-16  
[37] fansi_0.4.2        withr_2.4.2        crayon_1.4.1       dbplyr_1.4.4      
[41] later_1.2.0        rappdirs_0.3.3     grid_4.0.0         jsonlite_1.7.2    
[45] gtable_0.3.0       lifecycle_1.0.0    DBI_1.1.0          git2r_0.27.1      
[49] magrittr_2.0.1     units_0.6-6        scales_1.1.1       KernSmooth_2.23-16
[53] cli_2.5.0          stringi_1.5.3      fs_1.5.0           promises_1.2.0.1  
[57] sp_1.4-2           xml2_1.3.2         ellipsis_0.3.2     generics_0.1.0    
[61] vctrs_0.3.8        tools_4.0.0        glue_1.4.2         hms_0.5.3         
[65] yaml_2.2.1         colorspace_2.0-0   rvest_0.3.5        classInt_0.4-3    
[69] knitr_1.28         haven_2.3.1