# Which map projection should I use for a country land cover map?

I am currently completing a piece of GIS work and I am a bit stuck, even after doing some reading.

What would the appropriate map projection and co-ordinate system be for a land cover map for a country the size of Kenya, for example?

Also, what would be the appropriate scale for the digital map data sets that would be used in this application?

• Umm these things totally depend upon the purpose of the map and the area you are trying to map. You say Kenya for example, but if this isnt where you are trying to do work, a CRS that works for Keyna will not work for say Arizona, etc... Also as to the purpose, why are you making this map, is distance more important than shape, or direction, etc... All these things come into play when choosing your CRS Jan 25, 2016 at 17:54

The purpose of the map here will really decide which CRS you will use. You basically have 2 options, a geographic coordinate system or a projected coordinate system and depending on what you want to accomplish will help you choose which system you want to make a map in.

http://www.geo.hunter.cuny.edu/~jochen/gtech201/lectures/lec6concepts/map%20coordinate%20systems/how%20to%20choose%20a%20projection.htm

http://www.geography.hunter.cuny.edu/~jochen/GTECH361/lectures/lecture04/concepts/Map%20coordinate%20systems/Map%20projections%20and%20distortion.htm

The web page above will help you decide what projection to pick depending upon the map you want to produce. Remember that projections all try to be as accurate as possible, but to do so they must distort either Area, Direction, Size, Distance (http://geokov.com/education/map-projection.aspx)

So if you are making a map of land cover in Kenya, here are some papers that specifically deal with which projection you should choose:

http://cegis.usgs.gov/projection/pdf/nmdrs.usery.prn.pdf

According to the results found from the twelve one-by-one degree polygons, the Robinson projection, a non-equal-area projection, showed the poorest estimation in terms of the percentage of areas represented after rasterization, an expected result. Three equal-area projection methods, the interrupted Goode homolosine, Mollweide, and equal-area cylindrical projections, showed little difference in area representation in spatial resolutions of one kilometer or less. However, at the spatial resolutions from one kilometer to eight kilometers, the Mollweide projection showed the best result. At the spatial resolution ranges from 16 km to 25 km, the Goode homolosine and equal-area cylindrical projections showed slightly better results than the Mollweide projection (the Mollweide projection tends to under-represent the original area at this spatial resolution range). The Robinson projection significantly over-represented the original area at the spatial resolution ranges of 16 kilometers or less and the over-representation reached about 10 percent. At the spatial resolution of eight km, all the global projections used in this study tend to overrepresent the original area at latitudes of 60 degrees or higher. The representation is most accurate in the Mollweide projection with Goode homolosine, equal-area cylindrical, and Robinson following in order of accuracy.

On a side note, Kenya is a good example here of a country that falls within multiple UTM zones (zones 36N, 37N, 36M, 37M) which makes things a little more difficult, if your data was coming from different UTM zones.

The lab below specifically deals with Kenya and different projections: