mregions introduction

mregions is useful to a wide diversity of R users because you get access to all of the data MarineRegions has, which can help in a variety of use cases:

Visualize marine regions alone
Visualize marine regions with associated data paired with analysis
Use marine region geospatial boundaries to query data providers (e.g., OBIS (http://www.iobis.org))
Geocode - get geolocation data from place names
Reverse Geocode - get place names from geolocation data

Install

Stable version

install.packages("mregions")

Dev version

devtools::install_github("ropenscilabs/mregions")
install.packages("leaflet")

library("mregions")

Get list of place types

res <- mr_place_types()
head(res$type)
#> [1] "Town"                      "Arrondissement"           
#> [3] "Department"                "Province (administrative)"
#> [5] "Country"                   "Continent"

Get Marineregions records by place type

res1 <- mr_records_by_type(type = "EEZ")
head(res1)
#>   MRGID
#> 1  3293
#> 2  5668
#> 3  5669
#> 4  5670
#> 5  5672
#> 6  5673
#>                                                                                                                                                                                                             gazetteerSource
#> 1 Flanders Marine Institute (2016). Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 9. Available online at http://www.marineregions.org/. http://dx.doi.org/10.14284/242
#> 2 Flanders Marine Institute (2016). Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 9. Available online at http://www.marineregions.org/. http://dx.doi.org/10.14284/242
#> 3 Flanders Marine Institute (2016). Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 9. Available online at http://www.marineregions.org/. http://dx.doi.org/10.14284/242
#> 4 Flanders Marine Institute (2016). Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 9. Available online at http://www.marineregions.org/. http://dx.doi.org/10.14284/242
#> 5 Flanders Marine Institute (2016). Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 9. Available online at http://www.marineregions.org/. http://dx.doi.org/10.14284/242
#> 6 Flanders Marine Institute (2016). Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 9. Available online at http://www.marineregions.org/. http://dx.doi.org/10.14284/242
#>   placeType latitude longitude minLatitude minLongitude maxLatitude
#> 1       EEZ 51.46483  2.704458    51.09111     2.238118    51.87000
#> 2       EEZ 53.61508  4.190675    51.26203     2.539443    55.76500
#> 3       EEZ 54.55970  8.389231    53.24281     3.349999    55.91928
#> 4       EEZ 40.87030 19.147094    39.63863    18.461940    41.86124
#> 5       EEZ 42.94272 29.219062    41.97820    27.449580    43.74779
#> 6       EEZ 43.42847 15.650844    41.62201    13.001390    45.59079
#>   maxLongitude precision            preferredGazetteerName
#> 1     3.364907  58302.49   Belgian Exclusive Economic Zone
#> 2     7.208364 294046.10     Dutch Exclusive Economic Zone
#> 3    14.750000 395845.50    German Exclusive Economic Zone
#> 4    20.010030 139751.70  Albanian Exclusive Economic Zone
#> 5    31.345280 186792.50 Bulgarian Exclusive Economic Zone
#> 6    18.552360 313990.30  Croatian Exclusive Economic Zone
#>   preferredGazetteerNameLang   status accepted
#> 1                    English standard     3293
#> 2                    English standard     5668
#> 3                    English standard     5669
#> 4                    English standard     5670
#> 5                    English standard     5672
#> 6                    English standard     5673

Get a data.frame of region names

rnames <- mr_names("MarineRegions:iho")

Search region names

Either pass output of mr_names()

mr_names_search(rnames, "IHO")
#> # A tibble: 7 × 6
#>               layer    name_first name_second     id                      name mrgid
#>               <chr>         <chr>       <chr>  <chr>                     <chr> <chr>
#> 1 MarineRegions:iho MarineRegions         iho iho.34            Gulf of Mexico  4288
#> 2 MarineRegions:iho MarineRegions         iho iho.37           Rio de La Plata  4325
#> 3 MarineRegions:iho MarineRegions         iho iho.82                Ionian Sea  3351
#> 4 MarineRegions:iho MarineRegions         iho iho.65            Sea of Okhotsk  4309
#> 5 MarineRegions:iho MarineRegions         iho iho.69        Gulf of California  4314
#> 6 MarineRegions:iho MarineRegions         iho iho.95                 Timor Sea  4344
#> 7 MarineRegions:iho MarineRegions         iho iho.62 Seto Naikai or Inland Sea  4306

or don't (but then mr_names_search() call takes longer)

mr_names_search("iho", q = "Sea")
#> # A tibble: 74 × 6
#>                layer    name_first name_second     id                                      name mrgid
#>                <chr>         <chr>       <chr>  <chr>                                     <chr> <chr>
#> 1  MarineRegions:iho MarineRegions         iho  iho.1 Inner Seas off the West Coast of Scotland  4283
#> 2  MarineRegions:iho MarineRegions         iho  iho.2         Mediterranean Sea - Western Basin  4279
#> 3  MarineRegions:iho MarineRegions         iho  iho.3         Mediterranean Sea - Eastern Basin  4280
#> 4  MarineRegions:iho MarineRegions         iho  iho.4                            Sea of Marmara  3369
#> 5  MarineRegions:iho MarineRegions         iho  iho.5                                 Black Sea  3319
#> 6  MarineRegions:iho MarineRegions         iho  iho.6                               Sea of Azov  3320
#> 7  MarineRegions:iho MarineRegions         iho  iho.7        Irish Sea and St. George's Channel  2357
#> 8  MarineRegions:iho MarineRegions         iho iho.10                             Bay of Biscay  2359
#> 9  MarineRegions:iho MarineRegions         iho iho.11                                Celtic Sea  2351
#> 10 MarineRegions:iho MarineRegions         iho iho.14                                 North Sea  2350
#> # ... with 64 more rows

Get a region - geojson

res3 <- mr_geojson(key = "Morocco:dam")
class(res3)
#> [1] "mr_geojson"
names(res3)
#> [1] "type"          "totalFeatures" "features"      "crs"           "bbox"

Get a region - shp

res4 <- mr_shp(key = "Morocco:dam")
class(res4)
#> [1] "SpatialPolygonsDataFrame"
#> attr(,"package")
#> [1] "sp"

Get OBIS EEZ ID

mr_obis_eez_id("Bulgarian Exclusive Economic Zone")
#> [1] 71

Convert to WKT

From geojson or shp. Here, geojson

res7 <- mr_geojson(key = "Morocco:dam")
mr_as_wkt(res7, fmt = 5)
#> [1] "MULTIPOLYGON (((41.573732 -1.659444, 45.891882 ... cutoff

Dealing with bigger WKT

What if you're WKT string is super long? It's often a problem because some online species occurrence databases that accept WKT to search by geometry bork due to limitations on length of URLs if your WKT string is too long (about 8000 characters, including remainder of URL). One way to deal with it is to reduce detail - simplify.

install.packages("rmapshaper")

Using rmapshaper we can simplify a spatial object, then search with that.

shp <- mr_shp(key = "MarineRegions:eez_iho_union_v2", maxFeatures = 5)

Visualize

library(leaflet)
leaflet() %>%
  addTiles() %>%
  addPolygons(data = shp)

Simplify

library("rmapshaper")
shp <- ms_simplify(shp)

It's simplified:

library(leaflet)
leaflet() %>%
  addTiles() %>%
  addPolygons(data = shp)

map3