3

I'd like to know how can I export as a featureCollection the List (line 31) of the following script:

https://code.earthengine.google.com/5f453a2f3cf39c4be49ac4f4df47ac08

6
  • Thank you! I will try to fix this issue. I thought that these variables ab, cc, var, cn, si, wr should also be taken from squares. Anyway, this is all really great!
    – pmj
    May 10 at 19:26
  • 1
    In my Editing Note you have a solution.
    – xunilk
    May 10 at 21:33
  • Fantastic! My only remaining question, if possible, is why 'var squares = ee.FeatureCollection(list.map(computeSquares)).flatten();' increases the number of elements from 312 to 460.
    – pmj
    May 11 at 11:44
  • 1
    I know the answer. Most of the countries have one feature in each feature collection, however, some of them have 3, 6 or 8 features (i.e. Russia). You can corroborate that with 'size' method of ee.FeatureCollection. However, main issue is 'sum'. Values are strange in many case and, I think is a scale issue,
    – xunilk
    May 11 at 12:26
  • 1
    It could be 'crs'. Image and country bounds don't coincide. For this reason there are many features with zero population. My suggestion is to use my former approach here: gis.stackexchange.com/questions/395815/… . It works as expected.
    – xunilk
    May 11 at 13:53
3

I assume that "export" means to Google Drive. In this case, you can add following lines (complete code here) after line 31.

// Apply your function to each item in the list by using the map() function => 31 line
var squares = ee.FeatureCollection(list.map(computeSquares)).flatten();

//print(squares);

// Export the FeatureCollection to a SHP file.
Export.table.toDrive({
  collection: squares,
  folder: 'GEE_Folder',
  description:'squares',
  fileFormat: 'SHP'
});

//Map.addLayer(ee.FeatureCollection(squares));
//Map.centerObject(squares);

//--------------end function/map--------

It worked for me and in 12 minutes exported to Google Drive about 150 MB of this vector file (squares) as shapefile.

Editing Note:

Following code also exports fields as CSV.

//population
var pop = ee.Image("JRC/GHSL/P2016/POP_GPW_GLOBE_V1/2015");

//Boundaries
var worldcountries = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017');

var list = worldcountries.aggregate_array('country_na');

//print(list);

//------Function/map---------

var computeSquares = function(country) {

  var filterCountry = ee.Filter.inList('country_na',[country]);
  var bound = worldcountries.filter(filterCountry);

  var popcountry = pop.clip(bound);

  var sumpop =popcountry.reduceRegions({
    collection:bound,
    reducer: ee.Reducer.sum(),
    scale:250,
  
   });

  return sumpop;

};

// Apply your function to each item in the list by using the map() function.
var squares = ee.FeatureCollection(list.map(computeSquares)).flatten();

//print(squares);

// Export the FeatureCollection to a SHP file.
Export.table.toDrive({
  collection: squares,
  folder: 'GEE_Folder',
  description:'squares',
  fileFormat: 'SHP'
});

//Map.addLayer(ee.FeatureCollection(squares));
//Map.centerObject(squares);

//abbreviati; country_co; country_na; sum; system:index; and wld_rgn
var ab = squares.aggregate_array('abbreviati');
var cc = squares.aggregate_array('country_co');
var cn = squares.aggregate_array('country_na');
var sum = squares.aggregate_array('sum');
var si = squares.aggregate_array('system:index');
var wr = squares.aggregate_array('wld_rgn');

var count = squares.size();

var seq = ee.List.sequence(0, count.subtract(1));

var fields = seq.map(function(ele) {
  
  var list = ee.List([]);
  
  list = list.add(ab.get(ele))
    .add(cc.get(ele))
    .add(cn.get(ele))
    .add(sum.get(ele))
    .add(si.get(ele))
    .add(wr.get(ele));
  
  return list;
  
});

print("fields", fields);

var myFeatures = ee.FeatureCollection(fields.map(function(el){
  el = ee.List(el); // cast every element of the list
  return ee.Feature(null, {
    'abbreviati': ee.String(el.get(0)),
    'country_co': ee.String(el.get(1)),
    'country_na': ee.String(el.get(2)),
    'sum': ee.Number(el.get(3)),
    'system:index': ee.String(el.get(4)),
    'wld_rgn': ee.String(el.get(5))
  });
}));

//print(myFeatures);

// Export features, specifying corresponding names.
Export.table.toDrive(myFeatures,
"export_fields", //my task
"GEE_Folder", //my export folder
"fields_CSV",  //file name
"CSV");


//--------------end function/map--------

An extract of produced CSV can be observed as follows:

system:index,abbreviati,country_co,country_na,sum,wld_rgn,.geo
0_00000000000000000000,,CD,Chad,0.0,Africa,
1_00000000000000000014,Mal.,MI,Malawi,0.0,Africa,
2_00000000000000000015,Zam.,ZA,Zambia,0.0,Africa,
3_00000000000000000016,Zimb.,ZI,Zimbabwe,0.0,Africa,
4_00000000000000000017,Bots.,BC,Botswana,0.0,Africa,
5_00000000000000000018,Nam.,WA,Namibia,0.0,Africa,
6_00000000000000000019,Ang.,AO,Angola,0.0,Africa,
7_0000000000000000001a,Buru.,BY,Burundi,0.0,Africa,
8_0000000000000000001b,Rw.,RW,Rwanda,0.0,Africa,
9_0000000000000000001c,S. Afr.,SF,South Africa,0.0,Africa,
10_0000000000000000001d,Leso.,LT,Lesotho,0.0,Africa,
11_0000000000000000001e,Swaz.,WZ,Swaziland,0.0,Africa,
12_00000000000000000022,May.,MF,Mayotte,0.0,Africa,
13_00000000000000000094,,NG,Niger,0.0,Africa,
14_00000000000000000095,,SU,Sudan,0.0,Africa,
15_00000000000000000096,,LY,Libya,0.0,Africa,
16_00000000000000000097,,UU,Koualou Area,0.0,Africa,
17_00000000000000000098,Tun.,TS,Tunisia,0.0,Africa,
18_000000000000000000a8,,EG,Egypt,0.0,Africa,
19_000000000000000000a9,,EG,Bir Tawil,0.0,Africa,
20_000000000000000000aa,,SU,Halaib Triangle,0.0,Africa,
21_000000000000000000c0,Erit.,ER,Eritrea,0.0,Africa,
22_000000000000000000c1,Eth.,ET,Ethiopia,0.0,Africa,
23_000000000000000000c2,Dji.,DJ,Djibouti,0.0,Africa,
24_000000000000000000c3,Som.,SO,Somalia,0.0,Africa,
25_000000000000000000c4,S. Sudan,OD,South Sudan,0.0,Africa,
26_000000000000000000c5,,UU,Abyei Area,0.0,Africa,
27_000000000000000000c6,,KE,Kenya,0.0,Africa,
28_000000000000000000c7,Ug.,UG,Uganda,0.0,Africa,
29_000000000000000000c8,Tanz.,TZ,Tanzania,0.0,Africa,
30_000000000000000000c9,Moz.,MZ,Mozambique,0.0,Africa,
31_000000000000000000ca,Como.,CN,Comoros,0.0,Africa,
32_000000000000000000cb,Madag.,MA,Madagascar,0.0,Africa,
33_000000000000000000dc,C. Ver.,CV,Cabo Verde,0.0,Africa,
34_000000000000000000de,W. Sah.,WI,Western Sahara,0.0,Africa,
35_000000000000000000df,Maur.,MR,Mauritania,0.0,Africa,
36_000000000000000000e0,Mor.,MO,Morocco,0.0,Africa,
37_000000000000000000e1,Sp.,SP,Spain (Canary Is),0.0,Africa,
38_000000000000000000e2,Port.,PO,Portugal (Madeira Is),0.0,Africa,
39_000000000000000000e4,Sp.,SP,Spain (Africa),0.0,Africa,
40_000000000000000000e8,Alg.,AG,Algeria,0.0,Africa,
41_000000000000000000e9,,ML,Mali,0.0,Africa,
42_000000000000000000ea,Burk.,UV,Burkina Faso,0.0,Africa,
43_000000000000000000eb,,TO,Togo,0.0,Africa,
44_000000000000000000ec,,GH,Ghana,0.0,Africa,
45_000000000000000000ed,C. d Iv.,IV,Cote d'Ivoire,0.0,Africa,
46_000000000000000000ee,Gui.,GV,Guinea,0.0,Africa,
47_000000000000000000ef,Sen.,SG,Senegal,0.0,Africa,
48_000000000000000000f0,Gam.,GA,"Gambia, The",0.0,Africa,
49_000000000000000000f1,Gui.-Bis.,PU,Guinea-Bissau,0.0,Africa,
50_000000000000000000f2,S. Leo.,SL,Sierra Leone,0.0,Africa,
51_000000000000000000f3,Liber.,LI,Liberia,0.0,Africa,
52_000000000000000000f4,,BN,Benin,0.0,Africa,
53_000000000000000000f5,Nig.,NI,Nigeria,0.0,Africa,
54_000000000000000000f6,Camer.,CM,Cameroon,0.0,Africa,
55_000000000000000000f7,Equa. Gui.,EK,Equatorial Guinea,0.0,Africa,
56_000000000000000000f8,S. To. & Prin.,TP,Sao Tome & Principe,0.0,Africa,
57_000000000000000000f9,,GB,Gabon,0.0,Africa,
58_000000000000000000fa,Rep. of the Congo,CF,Rep of the Congo,0.0,Africa,
59_000000000000000000fb,Cen. Afr. Rep.,CT,Central African Rep,0.0,Africa,
60_000000000000000000fc,Dem. Rep. of the Congo,CG,Dem Rep of the Congo,0.0,Africa,
61_0000000000000000003e,H.K.,HK,Hong Kong,0.0,E Asia,
62_0000000000000000003f,,MC,Macau,0.0,E Asia,
63_00000000000000000040,Tai.,TW,Taiwan,0.0,E Asia,
64_00000000000000000041,,UU,Senkakus,0.0,E Asia,
65_00000000000000000042,,JA,Japan,1.2367127457851154E8,E Asia,
66_00000000000000000043,S. Kor.,KS,"Korea, South",0.0,E Asia,
67_00000000000000000044,N. Kor.,KN,"Korea, North",0.0,E Asia,
68_00000000000000000045,,UU,Korean Is. (UN Jurisdiction),0.0,E Asia,
69_00000000000000000046,,CH,China,4.13626724095709E8,E Asia,
69_00000000000000000048,,CH,China,1.5496324354785228E7,E Asia,
69_000000000000000000d5,,CH,China,6541.133977252492,E Asia,
69_000000000000000000d9,,CH,China,113014.24493963121,E Asia,
70_00000000000000000046,,CH,China,4.13626724095709E8,E Asia,
70_00000000000000000048,,CH,China,1.5496324354785228E7,E Asia,
70_000000000000000000d5,,CH,China,6541.133977252492,E Asia,
70_000000000000000000d9,,CH,China,113014.24493963121,E Asia,
71_00000000000000000046,,CH,China,4.13626724095709E8,E Asia,
71_00000000000000000048,,CH,China,1.5496324354785228E7,E Asia,
71_000000000000000000d5,,CH,China,6541.133977252492,E Asia,
71_000000000000000000d9,,CH,China,113014.24493963121,E Asia,
72_00000000000000000046,,CH,China,4.13626724095709E8,E Asia,
72_00000000000000000048,,CH,China,1.5496324354785228E7,E Asia,
72_000000000000000000d5,,CH,China,6541.133977252492,E Asia,
72_000000000000000000d9,,CH,China,113014.24493963121,E Asia,
73_00000000000000000129,,UU,Liancourt Rocks,0.0,E Asia,
74_0000000000000000000a,Rus.,RS,Russia,1.021918526464285E8,Europe,
74_00000000000000000011,Rus.,RS,Russia,5249239.941191522,Europe,
74_0000000000000000000f,Rus.,RS,Russia,2.6806786216873176E7,N Asia,
74_00000000000000000010,Rus.,RS,Russia,2725952.8460667613,N Asia,
74_00000000000000000122,Rus.,RS,Russia,1057696.7556383794,N Asia,
74_00000000000000000128,Rus.,RS,Russia,4781155.418985548,N Asia,
75_0000000000000000000c,Ukr.,UP,Ukraine,0.0,Europe,
76_0000000000000000000d,Rom.,RO,Romania,0.0,Europe,
77_0000000000000000000e,Mol.,MD,Moldova,0.0,Europe,
78_0000000000000000000a,Rus.,RS,Russia,1.021918526464285E8,Europe,
78_00000000000000000011,Rus.,RS,Russia,5249239.941191522,Europe,
78_0000000000000000000f,Rus.,RS,Russia,2.6806786216873176E7,N Asia,
78_00000000000000000010,Rus.,RS,Russia,2725952.8460667613,N Asia,
78_00000000000000000122,Rus.,RS,Russia,1057696.7556383794,N Asia,
78_00000000000000000128,Rus.,RS,Russia,4781155.418985548,N Asia,
79_00000000000000000012,Fin.,FI,Finland,5410341.968123293,Europe,
80_00000000000000000013,Nor.,NO,Norway,4835198.770938944,Europe,
.
.
.
2
  • Thanks so much! Is there a way to export 'squares' as a '.csv' file containing the following six fields: abbreviati; country_co; country_na; sum; system:index; and wld_rgn?
    – pmj
    May 10 at 14:44
  • 1
    If they are properties of these feature collection it is possible, I will see in only one feature for developing corresponding function for extracting them.
    – xunilk
    May 10 at 14:53

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