What is vespa-db
vespa-db is a database created by vespawatch to make Vespa velutina nest observations available in as near as real time as possible.
The platform contains observations from different sources, but currently only new observations are added from [waarnemingen.be)[https://waarnemingen.be/]. In order for an observation to be included in vespa-db, it needs to have the following properties:
- It has to currently be identified to Vespa velutina or a subspiecies thereof.
- It has to have the activity: Nest (no observations of individuals are included)
- It has to be validated by a validator, which means it has to have a photo. Automatically validated (based on location and likelyhood) observations are not included.
How did this package come to be
This package was initially written by @PietrH in the context of importing historical data into vespa-db. For matching and validation purposes it was convenient to wrap the api into functions so imports and matching could be quickly tested. This original code resides in the private repository: inbo/vespaR.
Later on a need arose to query the vespa-db api again, and the original code was moved here for more broad reuse.
The API
For an overview of all available API endpoints (certainly more than are wrapped here), see: https://db.vespawatch.be/swagger/.
Installation
You can install the development version of vespadb from GitHub with:
# install.packages("pak")
pak::pak("inbo/vespadb")Examples
There are two options to get data out of vespa-db. The fastest way is to fetch the most recent export file. This file is generated every day at around 4AM Brussels time.
library(vespadb)
get_vespadb_export()
#> # A tibble: 25,014 × 27
#> id observation_datetime latitude longitude province municipality anb
#> <dbl> <dttm> <dbl> <dbl> <chr> <chr> <lgl>
#> 1 14861 2025-04-11 10:59:00 51.0 3.38 West-Vlaand… Wingene FALSE
#> 2 14862 2025-04-11 00:00:00 51.1 4.62 Antwerpen Nijlen FALSE
#> 3 14863 2025-04-11 11:14:00 51.2 4.43 Antwerpen Antwerpen FALSE
#> 4 14865 2025-04-10 13:57:00 50.9 2.90 West-Vlaand… Ieper FALSE
#> 5 14866 2025-04-10 00:00:00 51.2 3.20 West-Vlaand… Brugge FALSE
#> 6 14867 2025-04-10 20:29:00 50.8 4.92 Vlaams Brab… Tienen FALSE
#> 7 14868 2025-04-10 20:01:00 51.1 3.56 Oost-Vlaand… Lievegem FALSE
#> 8 14869 2025-04-10 18:53:00 50.9 5.38 Limburg Hasselt FALSE
#> 9 14870 2025-04-10 17:40:00 51.1 4.46 Antwerpen Kontich FALSE
#> 10 14871 2025-04-10 16:07:00 50.9 4.36 Vlaams Brab… Grimbergen FALSE
#> # ℹ 25,004 more rows
#> # ℹ 20 more variables: nest_status <chr>, eradication_date <chr>,
#> # eradication_result <chr>, images <chr>, nest_type <chr>,
#> # nest_location <chr>, nest_height <chr>, nest_size <chr>,
#> # queen_present <lgl>, moth_present <lgl>, duplicate_nest <lgl>,
#> # other_species_nest <lgl>, notes <lgl>, source <chr>, source_id <dbl>,
#> # wn_id <dbl>, wn_validation_status <chr>, wn_cluster_id <dbl>, …Alternativly you can also send queries to the database direclty and get live data back. This is much slower.
get_vespadb_obs(
municipality_id = 32,
min_observation_datetime = "2025-07-01T00:00:00",
max_observation_datetime = "2025-07-25T00:00:00"
)
#> # A tibble: 38 × 30
#> id created_datetime modified_datetime location$type source source_id
#> <int> <chr> <chr> <chr> <chr> <lgl>
#> 1 40399 2025-07-03T04:00:31 2025-07-03T04:00:34 Point Waarne… NA
#> 2 40466 2025-07-04T04:00:05 2025-08-10T00:45:30 Point Waarne… NA
#> 3 40881 2025-07-08T02:00:19 2025-07-08T02:00:21 Point Waarne… NA
#> 4 41064 2025-07-10T02:00:11 2025-07-29T11:11:37 Point Waarne… NA
#> 5 40612 2025-07-06T02:00:08 2025-07-25T00:13:13 Point Waarne… NA
#> 6 40604 2025-07-06T02:00:08 2025-07-06T02:00:11 Point Waarne… NA
#> 7 44148 2025-08-05T02:02:02 2025-08-05T02:02:03 Point Waarne… NA
#> 8 41066 2025-07-10T02:00:11 2025-07-22T14:09:04 Point Waarne… NA
#> 9 41061 2025-07-10T02:00:11 2025-07-22T14:13:14 Point Waarne… NA
#> 10 41074 2025-07-10T02:00:14 2025-07-10T02:00:17 Point Waarne… NA
#> # ℹ 28 more rows
#> # ℹ 25 more variables: location$coordinates <list>, nest_height <chr>,
#> # nest_size <chr>, nest_location <chr>, nest_type <chr>,
#> # observation_datetime <chr>, eradication_date <chr>, municipality <int>,
#> # queen_present <lgl>, moth_present <lgl>, province <int>, images <list>,
#> # municipality_name <chr>, notes <lgl>, eradication_result <chr>,
#> # wn_id <int>, wn_validation_status <chr>, anb <lgl>, visible <lgl>, …In this example we fetch data for the first 25 days of July 2025, for the municipality of Leuven.
To get an overview of the available parameters that you can use to query our observation data, see get_observation_parameters().
get_observation_parameters()
#> # A tibble: 16 × 11
#> name `in` description required type format default items$type example
#> <chr> <chr> <chr> <lgl> <chr> <chr> <int> <chr> <list>
#> 1 municipal… query municipali… FALSE stri… <NA> NA <NA> <NULL>
#> 2 province_… query province_id FALSE stri… <NA> NA <NA> <NULL>
#> 3 min_creat… query min_create… FALSE stri… <NA> NA <NA> <NULL>
#> 4 max_creat… query max_create… FALSE stri… <NA> NA <NA> <NULL>
#> 5 min_modif… query min_modifi… FALSE stri… <NA> NA <NA> <NULL>
#> 6 max_modif… query max_modifi… FALSE stri… <NA> NA <NA> <NULL>
#> 7 min_obser… query min_observ… FALSE stri… <NA> NA <NA> <NULL>
#> 8 max_obser… query max_observ… FALSE stri… <NA> NA <NA> <NULL>
#> 9 anb query anb FALSE stri… <NA> NA <NA> <NULL>
#> 10 nest_type query nest_type FALSE stri… <NA> NA <NA> <NULL>
#> 11 visible query visible FALSE stri… <NA> NA <NA> <NULL>
#> 12 nest_stat… query nest_status FALSE stri… <NA> NA <NA> <NULL>
#> 13 ordering query Which fiel… FALSE stri… <NA> NA <NA> <NULL>
#> 14 dist query Represents… FALSE numb… float 1000 <NA> <NULL>
#> 15 point query Point repr… FALSE array <NA> NA float <int>
#> 16 cursor query The pagina… FALSE stri… <NA> NA <NA> <NULL>
#> # ℹ 2 more variables: maxItems <int>, minItems <int>You can also get an overview of the municipality codes, or query a municipality code directly with the function municipalities:
municipalities("Leuven")
#> [1] 32
municipalities(c("Hasselt", "Leuven"))
#> [1] 174 32
municipalities()
#> # A tibble: 285 × 2
#> municipality id
#> <chr> <int>
#> 1 Aalst 138
#> 2 Aalter 195
#> 3 Aarschot 95
#> 4 Aartselaar 4
#> 5 Affligem 27
#> 6 Alken 116
#> 7 Alveringem 294
#> 8 Antwerpen 271
#> 9 Anzegem 126
#> 10 Ardooie 10
#> # ℹ 275 more rows