The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Two differing resolutions of climate zone data have been included in this package.
These can be accessed with the parameter res in the RoundCoordinates() and LookupCZ() functions.
Course Resolution
Distance between data points, both latitude and longitude, is 0.5 degrees.
Latitude and longitude values are rounded to the nearest value ending in either 0.25 and 0.75.
Fine Resolution
Distance between data points, in both latitude and longitude, is 100 seconds.
Data originates from a 12960 x 6480 pixel image, and coordinates are rounded to the center coordinates of the nearest pixel.
An selection of example cities worldwide, and their reported climate zones from Wikipedia, have been included in this package in the dataframe kgcities.
Estimated climate zones for each city from both course and fine resolution datasets are queried, and results are shown in tabular format.
library("kgc")
print(head(kgcities))## loc rczd rcz lon lat
## 1 Puyo, Ecuador tropical rainforest Af -78.00111 -1.06666700
## 2 Medan, Indonesia tropical rainforest Af 98.66667 3.58333300
## 3 Davao, Philippines tropical rainforest Af 125.60000 7.06666700
## 4 Macapa, Brazil tropical monsoon Am -51.06639 0.03388889
## 5 Miami, Florida tropical monsoon Am -80.20889 25.77528000
## 6 Yangon, Myanmar tropical monsoon Am 96.18333 16.85000000 # Query Course Resolution
data <- data.frame(kgcities, rndCoord.lon = RoundCoordinates(kgcities$lon), rndCoord.lat = RoundCoordinates(kgcities$lat))
data <- data.frame(data,CZ.c=LookupCZ(data))
colnames(data)[which(colnames(data)=='rndCoord.lon')] <- 'rndCoord.lon.course'
colnames(data)[which(colnames(data)=='rndCoord.lat')] <- 'rndCoord.lat.course'
# Query Fine Resolution
data <- data.frame(data, rndCoord.lon = RoundCoordinates(kgcities$lon,res='fine',latlong='lon'), rndCoord.lat = RoundCoordinates(kgcities$lat,res='fine',latlong='lat'))
data <- data.frame(data,CZ.f=LookupCZ(data,res='fine'))
# Print Results
print(data[,c(1,3,8,11)])## loc rcz CZ.c CZ.f
## 1 Puyo, Ecuador Af Cfb Af
## 2 Medan, Indonesia Af Af Af
## 3 Davao, Philippines Af Af Af
## 4 Macapa, Brazil Am Am Am
## 5 Miami, Florida Am Am Am
## 6 Yangon, Myanmar Am Am Am
## 7 Monte Cristi, Dominican Republic As Aw Aw
## 8 Trincomalee, Sri Lanka As As As
## 9 Luperon, Dominican Republic As Af Af
## 10 Darwin, Australia Aw Aw Aw
## 11 Brasilia, Brazil Aw Aw Aw
## 12 Accra, Ghana Aw Aw Aw
## 13 Sabha, Libya BWh BWh BWh
## 14 Murcia, Spain BWh BSk BSk
## 15 Lima, Peru BWn Af Af
## 16 Niamey, Niger BSh BSh BSh
## 17 Alicante, Spain BSh BSk BSh
## 18 Piraeus, Greece BSh Csa Csa
## 19 Swakopmund, Namibia BWn BWk BWh
## 20 Walvis Bay, Namibia BWn BWk BWh
## 21 Lima, Peru BWn BWh BWh
## 22 Leh, India BWk Dwc BWk
## 23 Aktau, Kazakhstan BWk BSk BWk
## 24 Nukus, Uzbekistan BWk BWk BWk
## 25 Reno, Nevada BSk Csb Csa
## 26 Tabriz, Iran BSk BSk BSk
## 27 Zaragoza, Spain BSk BSk BSk
## 28 Beirut, Lebanon Csa Csa Csa
## 29 Adelaide, Australia Csa Csb Csb
## 30 Nice, France Csa Csb Csa
## 31 Porto, Portugal Csb Csb Csb
## 32 Cape Town, South Africa Csb Csb Csb
## 33 Potenza, Italy Csb Csb Cfb
## 34 Balmaceda, Chile Csc Cfc Csb
## 35 Haleakala Summit, Hawaii, USA Csc Af Ocean
## 36 New Delhi Cwa Cwa BSh
## 37 Hanoi, Vietnam Cwa Cwa Cwa
## 38 Kathmandu, Nepal Cwa Cwa Cwa
## 39 Da Lat, Vietnam Cwb Aw Cwb
## 40 Nairobi, Kenya Cwb Cfb Cfb
## 41 Mexico City, Mexico Cwb Cwb Cwb
## 42 El Alto, Bolivia Cwc Cwc ET
## 43 Durban, South Africa Cfa Cfa Cfa
## 44 Tokyo, Japan Cfa Cfa Cfa
## 45 Kutaisi, Georgia Cfa Cfa Cfa
## 46 Paris, France Cfb Cfb Cfb
## 47 Berlin, Germany Cfb Cfb Cfb
## 48 Port Elizabeth, South Africa Cfb Cfa BSh
## 49 Unalaska, Alaska, USA Cfc ET Cfc
## 50 Reykjavík, Iceland Cfc Cfc Cfc
## 51 Punta Arenas, Chile Cfc ET Cfc
## 52 Bishkek, Kyrgyzstan Dsa Dfb Dfa
## 53 Mus, Turkey Dsa Dsa Dsb
## 54 Cambridge, Idaho, USA Dsa Csb Csa
## 55 Sivas, Turkey Dsb Dsb Dsb
## 56 Bridgeport, California Dsb Csb Csb
## 57 Dras, India Dsb Dfc Dfc
## 58 Homer, Alaska, USA Dsc Dfc Dsc
## 59 Bodie, California, USA Dsc Csb Csb
## 60 Bohemia Mountain, Oregon, USA Dsc Csb Csb
## 61 Beijing, China Dwa Dwa BSk
## 62 Changchun, China Dwa Dwa Dwa
## 63 Pyongyang, North Korea Dwa Dwa Dwa
## 64 Vladivostok, Russia Dwb Dwb Dwb
## 65 Baruunturuun, Mongolia Dwb BSk BSk
## 66 Heihe, China Dwb Dwb Dwb
## 67 Yushu City, Qinghai China Dwc ET ET
## 68 Moron, Mongolia Dwc Dwc BSk
## 69 Lukla, Nepal Dwc ET Cwb
## 70 Seymchan, Russia Dwd Dfc Dfc
## 71 Oymyakon, Russia Dwd Dfd Dfd
## 72 Oral, Kazakhstan Dfa Dfa Dfa
## 73 Minneapolis, Minnesota, USA Dfa Dfa Dfa
## 74 Bucharest, Romania Dfa Cfa Cfa
## 75 Montpelier, Vermont, USA Dfb Dfb Dfb
## 76 Youngstown, Ohio, USA Dfb Dfb Cfb
## 77 Erzurum, Turkey Dfb Dfb Dsb
## 78 Aldan, Russia Dfc Dfc Dfc
## 79 Anchorage, Alaska, USA Dfc Dfc Dsc
## 80 Tromso, Norway Dfc Dfc Dfc
## 81 Yakutsk, Russia Dfd Dfd Dfc
## 82 Verkhoyansk, Russia Dfd Dfd Dfd
## 83 Ushuaia, Argentina ET ET ET
## 84 La Rinconada, Peru ET ET ET
## 85 Mt Rainier, Washington, USA ET Csb ET
## 86 Mount Ararat, Turkey EF Csa Ocean
## 87 Mount Everest, China/Nepal EF ET EF
## 88 Scott Base, Antarctica EF EF EFThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.