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:
Stable version
install.packages("mregions")
Dev version
devtools::install_github("ropenscilabs/mregions")
install.packages("leaflet")
library("mregions")
res <- mr_place_types()
head(res$type)
#> [1] "Town" "Arrondissement"
#> [3] "Department" "Province (administrative)"
#> [5] "Country" "Continent"
res1 <- mr_records_by_type(type = "EEZ")
head(res1)
#> MRGID gazetteerSource
#> 1 3293 Maritime Boundaries Geodatabase, Flanders Marine Institute
#> 2 5668 Maritime Boundaries Geodatabase, Flanders Marine Institute
#> 3 5669 Maritime Boundaries Geodatabase, Flanders Marine Institute
#> 4 5670 Maritime Boundaries Geodatabase, Flanders Marine Institute
#> 5 5672 Maritime Boundaries Geodatabase, Flanders Marine Institute
#> 6 5673 Maritime Boundaries Geodatabase, Flanders Marine Institute
#> 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
rnames <- mr_names()
Either pass output of mr_names()
mr_names_search(rnames, "IHO")
#> # A tibble: 5 x 4
#> name
#> <chr>
#> 1 MarineRegions:iho
#> 2 MarineRegions:iho_quadrants_20150810
#> 3 World:iosregions
#> 4 MarineRegions:eez_iho_union_v2
#> 5 Belgium:vl_venivon
#> # ... with 3 more variables: title <chr>, name_first <chr>,
#> # name_second <chr>
or don't (but then mr_names_search() call takes longer)
mr_names_search("IHO")
#> # A tibble: 5 x 4
#> name
#> <chr>
#> 1 MarineRegions:iho
#> 2 MarineRegions:iho_quadrants_20150810
#> 3 World:iosregions
#> 4 MarineRegions:eez_iho_union_v2
#> 5 Belgium:vl_venivon
#> # ... with 3 more variables: title <chr>, name_first <chr>,
#> # name_second <chr>
res3 <- mr_geojson(name = "Turkmen Exclusive Economic Zone")
class(res3)
#> [1] "mr_geojson"
names(res3)
#> [1] "type" "totalFeatures" "features" "crs"
#> [5] "bbox"
res4 <- mr_shp(name = "Belgian Exclusive Economic Zone")
class(res4)
#> [1] "SpatialPolygonsDataFrame"
#> attr(,"package")
#> [1] "sp"
res6 <- rnames[grepl("eez", rnames$name, ignore.case = TRUE), ]
mr_obis_eez_id(res6$title)
#> [1] 218 1 2 3 5 9 10 11 13 16 18 21 24 28 29 30 33
#> [18] 34 35 36 37 41 42 43 46 51 56 64 67 68 69 70 71 74
#> [35] 78 82 84 85 91 92 93 94 95 97 99 100 101 104 105 106 107
#> [52] 108 112 113 114 115 118 120 124 130 134 137 141 147 149 151 153 154
#> [69] 158 164 166 167 169 171 172 174 177 184 189 191 193 194 195 196 198
#> [86] 200 201 203 204 205 206 209 210 211 212 213 217 223 226 145 143 179
#> [103] 39 38 181 133 110 216 231 180 183 31 32 44 47 48 53 102 202
#> [120] 7 8 12 17 19 40 86 88 222 178 73 72 75 76 61 63 66
#> [137] 96 103 89 146 155 150 152 156 161 173 111 116 117 129 139 168 14
#> [154] 20 22 23 25 27 207 49 190 6 119 126 122 127 227 228 214 15
#> [171] 123 182 136 132 131 77 121 165 52 188 199 208 238 239 59 185 45
#> [188] 54 192 65 237 197 135 50 79 60 62 162 159 98 220 221 219 176
#> [205] 175 163 138 224 187 241 81
From geojson or shp. Here, geojson
res7 <- mr_geojson(key = "MarineRegions:eez_33176")
mr_as_wkt(res7, fmt = 5)
#> [1] "MULTIPOLYGON (((41.573732 -1.659444, 45.891882 ... cutoff
Using Well-Known Text. Both shp and geojson data returned from region_shp() and region_geojson(), respectively, can be passed to as_wkt() to get WKT.
shp <- mr_shp(name = "Belgian Exclusive Economic Zone")
wkt <- mr_as_wkt(shp)
library('httr')
library('data.table')
args <- list(scientificname = "Abra alba", geometry = wkt, limit = 100)
res <- httr::GET('http://api.iobis.org/occurrence', query = args)
xx <- data.table::setDF(data.table::rbindlist(httr::content(res)$results, use.names = TRUE, fill = TRUE))
xx <- xx[, c('scientificName', 'decimalLongitude', 'decimalLatitude')]
names(xx)[2:3] <- c('longitude', 'latitude')
Plot
library('leaflet')
leaflet() %>%
addTiles() %>%
addCircleMarkers(data = xx) %>%
addPolygons(data = shp)
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(name = "Dutch Exclusive Economic Zone")
Visualize
leaflet() %>%
addTiles() %>%
addPolygons(data = shp)
Simplify
library("rmapshaper")
shp <- ms_simplify(shp)
It's simplified:
leaflet() %>%
addTiles() %>%
addPolygons(data = shp)
