download-regions-observations.RmdRegions shown in the Biodiversité Québec atlas portal can be accessed
through the ratlas package and be used to filter downloaded
observations. Regions are organized by type (ie. Ecological Region,
Administrative Region, and Hexagonal Grid) and each region type is
organized by scale. It is recommended to explore regions using the
Biodiversité Québec Atlas web application to determine the region type
and scale of interest.
For this exercices we want to only return the Municipalities of Quebec. Specifically, we will extract observations for the city of Sherbrooke in 2002.
library(ratlas)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:ratlas':
#>
#> db_write_table
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, unionThe get_regions() function returns a data frame with the
regions available in the atlas portal. The function returns a data frame
with the following columns:
fid: the unique identifier of the regiontype: the type of region. Can be one of
cadre_eco, admin, and hex
scale: integer for the scale of the region. Example : 1
for the province of Quebec, 2 for the administrative regions, 3 for MRC,
4 for the Municipalitiesname: name of the region. Example : “Quebec”,
“Montreal”, “Laurentides”scale_desc: description of the scale.Running the function without any parameters will return all regions
in the atlas. This can be a very large dataset and may take a long time
to download. To explore the regions, it is recommended to use the
id parameter to return a specific region or the
type parameter to return a specific type of region. It is
also possible to exclude the geometry from the results by setting the
geometry parameter to FALSE.
regions_desc <- get_regions(geometry = FALSE)
head(regions_desc, 20) |> kable()| fid | type | scale | scale_desc | name |
|---|---|---|---|---|
| 17 | admin | 2 | Région administrative | Montérégie |
| 633273 | hex | 5 | 5 km | Nord-du-Québec |
| 698412 | hex | 5 | 5 km | Nord-du-Québec |
| 858059 | cadre_eco | 4 | Niveau 4 | Buttons du réservoir Gouin |
| 858060 | cadre_eco | 2 | Niveau 2 | Basses-terres du lac Témiscamingue |
| 858086 | cadre_eco | 4 | Niveau 4 | Buttons du réservoir Robert-Bourassa |
| 134 | admin | 4 | Territoire autochtone | Whapmagoostui |
| 698426 | hex | 5 | 5 km | Nord-du-Québec |
| 858113 | cadre_eco | 3 | Niveau 3 | Plateau septentrional de la Péninsule gaspésienne |
| 859302 | cadre_eco | 2 | Niveau 2 | Collines La Vérendrye |
| 859305 | cadre_eco | 2 | Niveau 2 | Péninsule de la Gaspésie |
| 698427 | hex | 5 | 5 km | Nord-du-Québec |
| 698428 | hex | 5 | 5 km | Nord-du-Québec |
| 698429 | hex | 5 | 5 km | Nord-du-Québec |
| 859733 | cadre_eco | 4 | Niveau 4 | Buttes du lac Mantouchiche |
| 859734 | cadre_eco | 4 | Niveau 4 | Buttons du lac Hore |
| 860092 | cadre_eco | 4 | Niveau 4 | Plaine bosselée du lac Atachikami |
| 693183 | hex | 5 | 5 km | Nord-du-Québec |
| 698430 | hex | 5 | 5 km | Nord-du-Québec |
| 698431 | hex | 5 | 5 km | Nord-du-Québec |
Let’s filter the results to only return the Municipalities of Quebec. And search the results for the city of Sherbrooke.
municipalities_desc <- get_regions(type = "admin", scale = 4, geometry = FALSE)
sherbrooke <- municipalities_desc |> filter(name == "Sherbrooke")
sherbrooke |> kable()| fid | type | scale | scale_desc | name |
|---|---|---|---|---|
| 749 | admin | 4 | Municipalité | Sherbrooke |
The database obs_regions_taxa_datasets_counts view
contains the number of observations for each region, taxa and datasets.
We can query it directly with the db_read_table()
function.
sherbrooke_count_response <- db_read_table(
table_name = "obs_regions_taxa_datasets_counts",
schema = "atlas_api",
fid = sherbrooke$fid
# id_taxa_obs = 735,
)
# Total number of observations for the city of Sherbrooke can be summed
sherbrooke_count <- sum(sherbrooke_count_response$count_obs)
print (paste("Number of observations in Sherbrooke:", sherbrooke_count))
#> [1] "Number of observations in Sherbrooke: 348534"We can now use the fid value for the city of Sherbrooke
to download observations for the year 2002. The
get_observations() function returns a data frame with the
observations for the specified region. Other filter parameters can be
used to further filter the results. For example, we can filter the
results to only return observations for a specific year.
sherbrooke_observations <- get_observations(region_fid = sherbrooke$fid, year = 2002)
# Print number of observations
print (paste("Number of observations in Sherbrooke in 2002:", nrow(sherbrooke_observations)))
#> [1] "Number of observations in Sherbrooke in 2002: 2764"