added more maps for visualization

Dockerfile

@@ -9,6 +9,7 @@ RUN pip install -r requirements.txt
 
 RUN mkdir ./pages
 COPY /pages ./pages
+COPY /data ./data
 
 ENV PROJ_LIB='/opt/conda/share/proj'
 

data/forest_cover.csv

@@ -0,0 +1,177 @@
+sum,GDP_MD_EST,ISO_A2,POP_RANK,ISO_A3,CONTINENT,POP_EST,INCOME_GRP,SUBREGION,system:index,NAME
+1973.1843137254896,25810.0,TJ,13,TJK,Asia,8468555,5. Low income,Central Asia,00000000000000000068,Tajikistan
+20752.63529411765,21010.0,KG,13,KGZ,Asia,5789122,5. Low income,Central Asia,00000000000000000069,Kyrgyzstan
+100135.97254901967,40000.0,KP,15,PRK,Asia,25248140,5. Low income,Eastern Asia,0000000000000000005f,North Korea
+29323.02745098038,628400.0,BD,17,BGD,Asia,157826578,5. Low income,Southern Asia,00000000000000000063,Bangladesh
+96498.0235294118,71520.0,NP,15,NPL,Asia,29384297,5. Low income,Southern Asia,00000000000000000065,Nepal
+6108.8941176470535,64080.0,AF,15,AFG,Asia,34124811,5. Low income,Southern Asia,00000000000000000067,Afghanistan
+122397.35294117646,58940.0,KH,14,KHM,Asia,16204486,5. Low income,South-Eastern Asia,0000000000000000005a,Cambodia
+541900.3882352939,311100.0,MM,16,MMR,Asia,55123814,5. Low income,South-Eastern Asia,0000000000000000005d,Myanmar
+89095.79999999994,1929000.0,KR,16,KOR,Asia,51181299,1. High income: OECD,Eastern Asia,00000000000000000060,South Korea
+382913.33333333355,4932000.0,JP,17,JPN,Asia,126451398,1. High income: OECD,Eastern Asia,0000000000000000009a,Japan
+842.7372549019608,297000.0,IL,13,ISR,Asia,8299706,1. High income: OECD,Western Asia,0000000000000000004c,Israel
+128465.37647058828,460700.0,KZ,14,KAZ,Asia,18556698,3. Upper middle income,Central Asia,00000000000000000005,Kazakhstan
+250.01960784313727,94720.0,TM,13,TKM,Asia,5351277,3. Upper middle income,Central Asia,0000000000000000006a,Turkmenistan
+3066764.247058823,2.114E7,CN,18,CHN,Asia,1379302771,3. Upper middle income,Eastern Asia,000000000000000000af,China
+1956.278431372548,85160.0,LB,13,LBN,Asia,6229794,3. Upper middle income,Western Asia,0000000000000000004d,Lebanon
+82.0,86190.0,JO,14,JOR,Asia,10248069,3. Upper middle income,Western Asia,00000000000000000053,Jordan
+231450.07450980396,1670000.0,TR,16,TUR,Asia,80845215,3. Upper middle income,Western Asia,0000000000000000007c,Turkey
+28086.474509803924,167900.0,AZ,13,AZE,Asia,9961396,3. Upper middle income,Western Asia,00000000000000000090,Azerbaijan
+388.25882352941176,3600.0,-99,10,-99,Asia,265100,3. Upper middle income,Western Asia,0000000000000000009e,N. Cyprus
+28879.027450980393,1459000.0,IR,16,IRN,Asia,82021564,3. Upper middle income,Southern Asia,0000000000000000006b,Iran
+261376.3803921569,1161000.0,TH,16,THA,Asia,68414135,3. Upper middle income,South-Eastern Asia,0000000000000000005b,Thailand
+322948.2549019607,863000.0,MY,15,MYS,Asia,31381992,3. Upper middle income,South-Eastern Asia,00000000000000000093,Malaysia
+3274.227450980392,202300.0,UZ,15,UZB,Asia,29748859,4. Lower middle income,Central Asia,00000000000000000006,Uzbekistan
+117907.9725490196,37000.0,MN,12,MNG,Asia,3068243,4. Lower middle income,Eastern Asia,00000000000000000061,Mongolia
+4.96470588235294,21220.77,PS,12,PSE,Asia,4543126,4. Lower middle income,Western Asia,0000000000000000004f,Palestine
+526.2941176470589,596700.0,IQ,15,IRQ,Asia,39192111,4. Lower middle income,Western Asia,00000000000000000057,Iraq
+1903.9843137254904,50280.0,SY,14,SYR,Asia,18028549,4. Lower middle income,Western Asia,0000000000000000006c,Syria
+6942.3803921568615,26300.0,AM,12,ARM,Asia,3045191,4. Lower middle income,Western Asia,0000000000000000006d,Armenia
+56540.65490196079,37270.0,GE,12,GEO,Asia,4926330,4. Lower middle income,Western Asia,00000000000000000091,Georgia
+2.0,73450.0,YE,15,YEM,Asia,28036829,4. Lower middle income,Western Asia,0000000000000000009c,Yemen
+565477.1882352942,8721000.0,IN,18,IND,Asia,1281935911,4. Lower middle income,Southern Asia,00000000000000000062,India
+35028.84705882357,6432.0,BT,11,BTN,Asia,758288,4. Lower middle income,Southern Asia,00000000000000000064,Bhutan
+29292.000000000025,988200.0,PK,17,PAK,Asia,204924861,4. Lower middle income,Southern Asia,00000000000000000066,Pakistan
+53333.545098039234,236700.0,LK,15,LKA,Asia,22409381,4. Lower middle income,Southern Asia,0000000000000000008a,Sri Lanka
+1665379.6392156864,3028000.0,ID,17,IDN,Asia,260580739,4. Lower middle income,South-Eastern Asia,00000000000000000008,Indonesia
+11541.101960784317,4975.0,TL,12,TLS,Asia,1291358,4. Lower middle income,South-Eastern Asia,00000000000000000018,Timor-Leste
+226315.57254901956,40960.0,LA,13,LAO,Asia,7126706,4. Lower middle income,South-Eastern Asia,0000000000000000005c,Laos
+243985.99215686263,594900.0,VN,16,VNM,Asia,96160163,4. Lower middle income,South-Eastern Asia,0000000000000000005e,Vietnam
+225550.2117647059,801900.0,PH,17,PHL,Asia,104256076,4. Lower middle income,South-Eastern Asia,00000000000000000092,Philippines
+28136.929411764693,1127000.0,TW,15,TWN,Asia,23508428,2. High income: nonOECD,Eastern Asia,0000000000000000008b,Taiwan
+0.0,667200.0,AE,13,ARE,Asia,6072475,2. High income: nonOECD,Western Asia,00000000000000000054,United Arab Emirates
+0.0,334500.0,QA,12,QAT,Asia,2314307,2. High income: nonOECD,Western Asia,00000000000000000055,Qatar
+0.0,301100.0,KW,12,KWT,Asia,2875422,2. High income: nonOECD,Western Asia,00000000000000000056,Kuwait
+3.0,173100.0,OM,12,OMN,Asia,3424386,2. High income: nonOECD,Western Asia,00000000000000000058,Oman
+0.0,1731000.0,SA,15,SAU,Asia,28571770,2. High income: nonOECD,Western Asia,0000000000000000009d,Saudi Arabia
+2280.4666666666667,29260.0,CY,12,CYP,Asia,1221549,2. High income: nonOECD,Western Asia,0000000000000000009f,Cyprus
+8098.266666666667,33730.0,BN,10,BRN,Asia,443593,2. High income: nonOECD,South-Eastern Asia,00000000000000000094,Brunei
+2319213.4862745088,66010.0,CD,16,COD,Africa,83301151,5. Low income,Middle Africa,0000000000000000000b,Dem. Rep. Congo
+140506.57254901945,30590.0,TD,14,TCD,Africa,12075985,5. Low income,Middle Africa,0000000000000000000f,Chad
+616192.0666666667,3206.0,CF,13,CAF,Africa,5625118,5. Low income,Middle Africa,00000000000000000042,Central African Rep.
+689332.0705882346,150600.0,TZ,16,TZA,Africa,53950935,5. Low income,Eastern Africa,00000000000000000001,Tanzania
+40917.45098039216,4719.0,SO,13,SOM,Africa,7531386,5. Low income,Eastern Africa,0000000000000000000c,Somalia
+123668.54901960802,152700.0,KE,15,KEN,Africa,47615739,5. Low income,Eastern Africa,0000000000000000000d,Kenya
+214618.89803921583,28330.0,ZW,14,ZWE,Africa,13805084,5. Low income,Eastern Africa,00000000000000000030,Zimbabwe
+86470.96862745093,21200.0,MW,14,MWI,Africa,19196246,5. Low income,Eastern Africa,00000000000000000047,Malawi
+799483.6039215687,35010.0,MZ,15,MOZ,Africa,26573706,5. Low income,Eastern Africa,00000000000000000048,Mozambique
+24092.22745098036,7892.0,BI,14,BDI,Africa,11466756,5. Low income,Eastern Africa,0000000000000000004b,Burundi
+389489.87843137246,36860.0,MG,15,MDG,Africa,25054161,5. Low income,Eastern Africa,0000000000000000004e,Madagascar
+7.0,9169.0,ER,13,ERI,Africa,5918919,5. Low income,Eastern Africa,00000000000000000099,Eritrea
+364336.48235294106,174700.0,ET,17,ETH,Africa,105350020,5. Low income,Eastern Africa,000000000000000000a3,Ethiopia
+194680.8470588235,84930.0,UG,15,UGA,Africa,39570125,5. Low income,Eastern Africa,000000000000000000a6,Uganda
+22382.450980392154,21970.0,RW,14,RWA,Africa,11901484,5. Low income,Eastern Africa,000000000000000000a7,Rwanda
+476560.6666666666,20880.0,SS,14,SSD,Africa,13026129,5. Low income,Eastern Africa,000000000000000000ae,S. Sudan
+95910.89803921568,38090.0,ML,14,MLI,Africa,17885245,5. Low income,Western Africa,00000000000000000034,Mali
+0.0,16710.0,MR,12,MRT,Africa,3758571,5. Low income,Western Africa,00000000000000000035,Mauritania
+95704.25490196078,24310.0,BJ,14,BEN,Africa,11038805,5. Low income,Western Africa,00000000000000000036,Benin
+4012.4980392156863,20150.0,NE,14,NER,Africa,19245344,5. Low income,Western Africa,00000000000000000037,Niger
+46974.909803921604,11610.0,TG,13,TGO,Africa,7965055,5. Low income,Western Africa,0000000000000000003a,Togo
+235165.07450980353,16080.0,GN,14,GIN,Africa,12413867,5. Low income,Western Africa,0000000000000000003d,Guinea
+30668.317647058837,2851.0,GW,12,GNB,Africa,1792338,5. Low income,Western Africa,0000000000000000003e,Guinea-Bissau
+93131.07450980389,3881.0,LR,12,LBR,Africa,4689021,5. Low income,Western Africa,0000000000000000003f,Liberia
+73541.76862745106,10640.0,SL,13,SLE,Africa,6163195,5. Low income,Western Africa,00000000000000000040,Sierra Leone
+30147.921568627477,32990.0,BF,15,BFA,Africa,20107509,5. Low income,Western Africa,00000000000000000041,Burkina Faso
+5629.392156862744,3387.0,GM,12,GMB,Africa,2051363,5. Low income,Western Africa,00000000000000000050,Gambia
+0.0,906.5,EH,11,ESH,Africa,603253,5. Low income,Northern Africa,00000000000000000002,W. Sahara
+262065.66274509794,35980.0,GA,12,GAB,Africa,1772255,3. Upper middle income,Middle Africa,00000000000000000044,Gabon
+1017770.9764705884,189000.0,AO,15,AGO,Africa,29310273,3. Upper middle income,Middle Africa,0000000000000000004a,Angola
+6207.75294117647,130800.0,TN,14,TUN,Africa,11403800,3. Upper middle income,Northern Africa,00000000000000000051,Tunisia
+28528.49411764706,609400.0,DZ,15,DZA,Africa,40969443,3. Upper middle income,Northern Africa,00000000000000000052,Algeria
+147.09019607843138,90890.0,LY,13,LBY,Africa,6653210,3. Upper middle income,Northern Africa,000000000000000000a2,Libya
+268120.8549019611,739100.0,ZA,16,ZAF,Africa,54841552,3. Upper middle income,Southern Africa,00000000000000000019,South Africa
+29542.023529411763,35900.0,BW,12,BWA,Africa,2214858,3. Upper middle income,Southern Africa,00000000000000000031,Botswana
+22104.011764705883,25990.0,NA,12,NAM,Africa,2484780,3. Upper middle income,Southern Africa,00000000000000000032,Namibia
+416817.4078431374,77240.0,CM,15,CMR,Africa,24994885,4. Lower middle income,Middle Africa,00000000000000000039,Cameroon
+339031.50196078385,30270.0,CG,12,COG,Africa,4954674,4. Lower middle income,Middle Africa,00000000000000000043,Congo
+674879.6392156859,65170.0,ZM,14,ZMB,Africa,15972000,4. Lower middle income,Eastern Africa,00000000000000000046,Zambia
+0.0,3345.0,DJ,11,DJI,Africa,865267,4. Lower middle income,Eastern Africa,000000000000000000a4,Djibouti
+258.0,12250.0,-99,12,-99,Africa,3500000,4. Lower middle income,Eastern Africa,000000000000000000a5,Somaliland
+52409.36862745098,39720.0,SN,14,SEN,Africa,14668522,4. Lower middle income,Western Africa,00000000000000000033,Senegal
+440389.7137254898,1089000.0,NG,17,NGA,Africa,190632261,4. Lower middle income,Western Africa,00000000000000000038,Nigeria
+192655.00784313737,120800.0,GH,15,GHA,Africa,27499924,4. Lower middle income,Western Africa,0000000000000000003b,Ghana
+326596.7568627449,87120.0,CI,15,CIV,Africa,24184810,4. Lower middle income,Western Africa,0000000000000000003c,Côte d'Ivoire
+48230.79215686285,176300.0,SD,15,SDN,Africa,37345935,4. Lower middle income,Northern Africa,0000000000000000000e,Sudan
+17965.80392156863,282800.0,MA,15,MAR,Africa,33986655,4. Lower middle income,Northern Africa,000000000000000000a0,Morocco
+13790.498039215687,1105000.0,EG,16,EGY,Africa,97041072,4. Lower middle income,Northern Africa,000000000000000000a1,Egypt
+230.07450980392161,6019.0,LS,12,LSO,Africa,1958042,4. Lower middle income,Southern Africa,0000000000000000001a,Lesotho
+19150.749019607858,11060.0,SZ,12,SWZ,Africa,1467152,4. Lower middle income,Southern Africa,00000000000000000049,eSwatini
+24780.376470588264,31770.0,GQ,11,GNQ,Africa,778358,2. High income: nonOECD,Middle Africa,00000000000000000045,Eq. Guinea
+267359.1333333333,1052000.0,PL,15,POL,Europe,38476269,1. High income: OECD,Eastern Europe,00000000000000000071,Poland
+50601.12549019604,267600.0,HU,13,HUN,Europe,9850845,1. High income: OECD,Eastern Europe,00000000000000000073,Hungary
+47924.498039215716,168800.0,SK,13,SVK,Europe,5445829,1. High income: OECD,Eastern Europe,00000000000000000097,Slovakia
+80054.7960784314,350900.0,CZ,14,CZE,Europe,10674723,1. High income: OECD,Eastern Europe,00000000000000000098,Czechia
+523977.40784313745,2699000.0,FR,16,FRA,Europe,67106161,1. High income: OECD,Western Europe,0000000000000000002b,France
+97047.2039215687,416600.0,AT,13,AUT,Europe,8754413,1. High income: OECD,Western Europe,00000000000000000072,Austria
+326140.5058823526,3979000.0,DE,16,DEU,Europe,80594017,1. High income: OECD,Western Europe,00000000000000000079,Germany
+43740.43529411765,496300.0,CH,13,CHE,Europe,8236303,1. High income: OECD,Western Europe,0000000000000000007f,Switzerland
+2756.811764705884,58740.0,LU,11,LUX,Europe,594130,1. High income: OECD,Western Europe,00000000000000000080,Luxembourg
+23679.67843137255,508600.0,BE,14,BEL,Europe,11491346,1. High income: OECD,Western Europe,00000000000000000081,Belgium
+19362.078431372556,870800.0,NL,14,NLD,Europe,17084719,1. High income: OECD,Western Europe,00000000000000000082,Netherlands
+365233.3411764709,364700.0,NO,13,NOR,Europe,5320045,1. High income: OECD,Northern Europe,00000000000000000015,Norway
+798572.674509804,498100.0,SE,13,SWE,Europe,9960487,1. High income: OECD,Northern Europe,0000000000000000006e,Sweden
+68737.8039215687,38700.0,EE,12,EST,Europe,1251581,1. High income: OECD,Northern Europe,00000000000000000078,Estonia
+30787.376470588217,322000.0,IE,13,IRL,Europe,5011102,1. High income: OECD,Northern Europe,00000000000000000085,Ireland
+20033.8862745098,264800.0,DK,13,DNK,Europe,5605948,1. High income: OECD,Northern Europe,0000000000000000008d,Denmark
+133826.63137254893,2788000.0,GB,16,GBR,Europe,64769452,1. High income: OECD,Northern Europe,0000000000000000008e,United Kingdom
+0.0,16150.0,IS,10,ISL,Europe,339747,1. High income: OECD,Northern Europe,0000000000000000008f,Iceland
+685101.3490196083,224137.0,FI,13,FIN,Europe,5491218,1. High income: OECD,Northern Europe,00000000000000000096,Finland
+76612.15686274512,290500.0,GR,14,GRC,Europe,10768477,1. High income: OECD,Southern Europe,0000000000000000007b,Greece
+60048.12941176472,297100.0,PT,14,PRT,Europe,10839514,1. High income: OECD,Southern Europe,00000000000000000083,Portugal
+260862.47843137264,1690000.0,ES,15,ESP,Europe,48958159,1. High income: OECD,Southern Europe,00000000000000000084,Spain
+209196.27450980392,2221000.0,IT,16,ITA,Europe,62137802,1. High income: OECD,Southern Europe,0000000000000000008c,Italy
+24713.15686274515,68350.0,SI,12,SVN,Europe,1972126,1. High income: OECD,Southern Europe,00000000000000000095,Slovenia
+2.0806459717647053E7,3745000.0,RU,17,RUS,Europe,142257519,3. Upper middle income,Eastern Europe,00000000000000000012,Russia
+229999.52941176464,165400.0,BY,13,BLR,Europe,9549747,3. Upper middle income,Eastern Europe,0000000000000000006f,Belarus
+168833.43529411775,441000.0,RO,15,ROU,Europe,21529967,3. Upper middle income,Eastern Europe,00000000000000000075,Romania
+84391.92156862754,143100.0,BG,13,BGR,Europe,7101510,3. Upper middle income,Eastern Europe,0000000000000000007a,Bulgaria
+67439.36470588231,85620.0,LT,12,LTU,Europe,2823859,3. Upper middle income,Northern Europe,00000000000000000076,Lithuania
+94398.06274509801,50650.0,LV,12,LVA,Europe,1944643,3. Upper middle income,Northern Europe,00000000000000000077,Latvia
+57478.76862745106,42530.0,BA,12,BIH,Europe,3856181,3. Upper middle income,Southern Europe,000000000000000000a8,Bosnia and Herz.
+18038.858823529412,29520.0,MK,12,MKD,Europe,2103721,3. Upper middle income,Southern Europe,000000000000000000a9,Macedonia
+62806.71764705889,101800.0,RS,13,SRB,Europe,7111024,3. Upper middle income,Southern Europe,000000000000000000aa,Serbia
+14540.960784313726,10610.0,ME,11,MNE,Europe,642550,3. Upper middle income,Southern Europe,000000000000000000ab,Montenegro
+280463.75294117647,352600.0,UA,15,UKR,Europe,44033874,4. Lower middle income,Eastern Europe,00000000000000000070,Ukraine
+9963.156862745098,18540.0,MD,12,MDA,Europe,3474121,4. Lower middle income,Eastern Europe,00000000000000000074,Moldova
+19149.58823529412,33900.0,AL,12,ALB,Europe,3047987,4. Lower middle income,Southern Europe,0000000000000000007d,Albania
+9351.329411764706,18490.0,XK,12,-99,Europe,1895250,4. Lower middle income,Southern Europe,000000000000000000ac,Kosovo
+51473.592156862615,94240.0,HR,12,HRV,Europe,4292095,2. High income: nonOECD,Southern Europe,0000000000000000007e,Croatia
+199275.9843137256,174800.0,NZ,12,NZL,Oceania,4510327,1. High income: OECD,Australia and New Zealand,00000000000000000088,New Zealand
+884440.8941176472,1189000.0,AU,15,AUS,Oceania,23232413,1. High income: OECD,Australia and New Zealand,00000000000000000089,Australia
+15812.69019607843,8374.0,FJ,11,FJI,Oceania,920938,4. Lower middle income,Melanesia,00000000000000000000,Fiji
+442325.7686274506,28020.0,PG,13,PNG,Oceania,6909701,4. Lower middle income,Melanesia,00000000000000000007,Papua New Guinea
+5893.168627450981,723.0,VU,10,VUT,Oceania,282814,4. Lower middle income,Melanesia,00000000000000000059,Vanuatu
+18345.180392156875,1198.0,SB,11,SLB,Oceania,647581,4. Lower middle income,Melanesia,00000000000000000087,Solomon Is.
+16652.53725490195,10770.0,NC,10,NCL,Oceania,279070,2. High income: nonOECD,Melanesia,00000000000000000086,New Caledonia
+17910.117647058833,19340.0,HT,14,HTI,North America,10646714,5. Low income,Caribbean,00000000000000000010,Haiti
+9859578.486274512,1674000.0,CA,15,CAN,North America,35623680,1. High income: OECD,Northern America,00000000000000000003,Canada
+6054365.50980392,1.856E7,US,17,USA,North America,326625791,1. High income: OECD,Northern America,00000000000000000004,United States of America
+39681.9725490196,161900.0,DO,14,DOM,North America,10734247,3. Upper middle income,Caribbean,00000000000000000011,Dominican Rep.
+10517.745098039219,25390.0,JM,12,JAM,North America,2990561,3. Upper middle income,Caribbean,0000000000000000002e,Jamaica
+64306.721568627465,132900.0,CU,14,CUB,North America,11147407,3. Upper middle income,Caribbean,0000000000000000002f,Cuba
+885007.7254901947,2307000.0,MX,17,MEX,North America,124574795,3. Upper middle income,Central America,0000000000000000001b,Mexico
+68785.53725490192,93120.0,PA,12,PAN,North America,3753142,3. Upper middle income,Central America,00000000000000000021,Panama
+50955.345098039244,79260.0,CR,12,CRI,North America,4930258,3. Upper middle income,Central America,00000000000000000022,Costa Rica
+112443.23529411771,33550.0,NI,13,NIC,North America,6025951,4. Lower middle income,Central America,00000000000000000023,Nicaragua
+106765.73333333331,43190.0,HN,13,HND,North America,9038741,4. Lower middle income,Central America,00000000000000000024,Honduras
+18072.84705882353,54790.0,SV,13,SLV,North America,6172011,4. Lower middle income,Central America,00000000000000000025,El Salvador
+104155.3411764706,131800.0,GT,14,GTM,North America,15460732,4. Lower middle income,Central America,00000000000000000026,Guatemala
+21308.643137254887,3088.0,BZ,10,BLZ,North America,360346,4. Lower middle income,Central America,00000000000000000027,Belize
+2889.4980392156854,9066.0,BS,10,BHS,North America,329988,2. High income: nonOECD,Caribbean,00000000000000000013,Bahamas
+7933.235294117641,131000.0,PR,12,PRI,North America,3351827,2. High income: nonOECD,Caribbean,0000000000000000002d,Puerto Rico
+4435.568627450981,43570.0,TT,12,TTO,North America,1218208,2. High income: nonOECD,Caribbean,000000000000000000ad,Trinidad and Tobago
+0.0,2173.0,GL,8,GRL,North America,57713,2. High income: nonOECD,Northern America,00000000000000000016,Greenland
+13.686274509803923,281.8,FK,4,FLK,South America,2931,1. High income: OECD,South America,00000000000000000014,Falkland Is.
+627136.5803921572,879400.0,AR,15,ARG,South America,44293293,3. Upper middle income,South America,00000000000000000009,Argentina
+405493.81960784283,436100.0,CL,14,CHL,South America,17789267,3. Upper middle income,South America,0000000000000000000a,Chile
+40625.01176470591,73250.0,UY,12,URY,South America,3360148,3. Upper middle income,South America,0000000000000000001c,Uruguay
+6814060.937254902,3081000.0,BR,17,BRA,South America,207353391,3. Upper middle income,South America,0000000000000000001d,Brazil
+835082.3372549014,410400.0,PE,15,PER,South America,31036656,3. Upper middle income,South America,0000000000000000001f,Peru
+995206.6784313729,688000.0,CO,15,COL,South America,47698524,3. Upper middle income,South America,00000000000000000020,Colombia
+695175.7019607839,468600.0,VE,15,VEN,South America,31304016,3. Upper middle income,South America,00000000000000000028,Venezuela
+142621.38039215692,8547.0,SR,11,SUR,South America,591919,3. Upper middle income,South America,0000000000000000002a,Suriname
+223966.34509803937,182400.0,EC,14,ECU,South America,16290913,3. Upper middle income,South America,0000000000000000002c,Ecuador
+764771.3098039209,78350.0,BO,14,BOL,South America,11138234,4. Lower middle income,South America,0000000000000000001e,Bolivia
+200122.55294117643,6093.0,GY,11,GUY,South America,737718,4. Lower middle income,South America,00000000000000000029,Guyana
+363318.41568627453,64670.0,PY,13,PRY,South America,6943739,4. Lower middle income,South America,0000000000000000009b,Paraguay
+0.0,16.0,TF,1,ATF,Seven seas (open ocean),140,2. High income: nonOECD,Seven seas (open ocean),00000000000000000017,Fr. S. Antarctic Lands

pages/00_home.py

@@ -1,59 +1,137 @@
+import  ee
+import geemap
 import solara
 
-
+class Map(geemap.Map):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.dataset = ee.Image("UMD/hansen/global_forest_change_2023_v1_11")
+        self.add_forest_loss_gain_data()
+        self.add_plot_gui()
+        # self.download_data()
+
+    def add_forest_loss_gain_data(self):
+        self.add_basemap("Esri.WorldImagery")
+        treeloss = self.dataset.select(["loss"]).selfMask()
+        treegain = self.dataset.select(["gain"]).selfMask()
+        self.add_layer(treeloss, {"palette": "red"}, "Tree loss")
+        self.add_layer(treegain, {"palette": "yellow"}, "Tree gain")
+        self.add("layer_manager")
+
+    def download_data(self):
+        treecover = self.dataset.select(["treecover2000"])
+        threshold = 10
+        treecover_bin = treecover.gte(threshold).selfMask()
+        countries = ee.FeatureCollection(geemap.examples.get_ee_path("countries"))
+        style = {"color": "#000000ff", "fillColor": "#00000000"}
+        self.add_layer(countries.style(**style), {}, "Countries")
+        geemap.zonal_stats(
+            treecover_bin,
+            countries,
+            "forest_cover.csv",
+            stat_type="SUM",
+            denominator=1e6,
+            scale=1000,
+        )
+
+class PieChartPlotter:
+    def __init__(self):
+        self.geemap = geemap
+        self.dataset = self.geemap.ee.Image("UMD/hansen/global_forest_change_2023_v1_11")
+        self.zonal_forest_area_by_country()
+    
+    def zonal_forest_area_by_country(self):
+        self.geemap.pie_chart(
+        "data/forest_cover.csv", names="NAME", values="sum", max_rows=20, height=400
+        ).show()
+
+class BarChartPlotter:
+    def __init__(self):
+        self.geemap = geemap
+        self.dataset = self.geemap.ee.Image("UMD/hansen/global_forest_change_2023_v1_11")
+        self.zonal_forest_area_by_country()
+    
+    def zonal_forest_area_by_country(self):
+        self.geemap.bar_chart(
+        "data/forest_cover.csv", x="NAME", y="sum", max_rows=20, height=400,
+        x_label="Country", y_label="Forest area (km2)",
+        ).show()
+    
 @solara.component
 def Page():
-    with solara.Column(align="center"):
-        markdown = """
-        ## Earth Engine Web Apps
-        
-        ### Introduction
-
-        **A collection of Earth Engine web apps developed using [Solara](https://github.com/widgetti/solara) and geemap**
-
-        - Web App: <https://giswqs-solara-geemap.hf.space>
-        - GitHub: <https://github.com/opengeos/solara-geemap>
-        - Hugging Face: <https://huggingface.co/spaces/giswqs/solara-geemap>
-
-        
-        ### How to deploy this app on Hugging Face Spaces
-
-        1. Go to <https://huggingface.co/spaces/giswqs/solara-geemap/tree/main> and duplicate the space to your own space.
-
-            ![](https://i.imgur.com/gTg4V2x.png)
-
-        2. You need to set `EARTHENGINE_TOKEN` in order to use Earth Engine. The token value should be copied from the following file depending on your operating system:
-
-            ```text
-            Windows: C:\\Users\\USERNAME\\.config\\earthengine\\credentials
-            Linux: /home/USERNAME/.config/earthengine/credentials
-            MacOS: /Users/USERNAME/.config/earthengine/credentials
-            ```
-
-            Simply open the file and copy **ALL** the content to the `EARTHENGINE_TOKEN` environment variable.
-
-            ![](https://i.imgur.com/i04gzyH.png)
-
-            ![](https://i.imgur.com/Ex37Ut7.png)
-
+    ee.Initialize()            
+    with solara.Column(style={"min-width": "500px"}):
+        Map.element(
+            center=[40, -100],
+            zoom=4,
+            height="600px",
+        )
+
+        with solara.Column(align="center"):
+            markdown = """
+            ## Forest cover gain and loss mapping
+            
+            ### Forest cover mapping
+
+            **For this analysis we will be using [Hansen Global Forest Change v1.11 (2000-2023) dataset](https://developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2023_v1_11) and geemap. First, we will compute the zonal statistics to identify the countries with the largest forest area, and then plot them. Here the base tree cover imagery is taken from 2000**
 
             ```python
-            import geemap
-            geemap.get_ee_token()
-            ```
+                dataset = ee.Image("UMD/hansen/global_forest_change_2023_v1_11")
+                treecover = dataset.select(["treecover2000"])
+                threshold = 10
+                treecover_bin = treecover.gte(threshold).selfMask()
+                countries = ee.FeatureCollection(geemap.examples.get_ee_path("countries"))
+                style = {"color": "#000000ff", "fillColor": "#00000000"}
+                self.add_layer(countries.style(**style), {}, "Countries")
+                geemap.zonal_stats(
+                        treecover_bin,
+                        countries,
+                        "forest_cover.csv",
+                        stat_type="SUM",
+                        denominator=1e6,
+                        scale=1000,
+                )
+
+                self.geemap.pie_chart(
+                    "data/forest_cover.csv", names="NAME", values="sum", max_rows=20, height=400
+                ).show()
 
-            Copy all the content of the printed token and set it as the `EARTHENGINE_TOKEN` environment variable.
+            ```
+            """
 
-        3. After the space is built successfully, click the `Embed this Space` menu and find the `Direct URL` for the app, such as <https://giswqs-solara-geemap.hf.space>.
+            solara.Markdown(markdown)
+        with solara.Column(style={"min-width": "500px"}):
+            pie_chart_plotter = PieChartPlotter()
 
-            ![](https://i.imgur.com/DNM36sk.png)
+        with solara.Column(align="center"):
+            markdown = """
 
-            ![](https://i.imgur.com/KX82lSf.png)
+                **The above give us the percentage overall but not the actual number. Let's plot a bar chart that shows the numbers too**
 
-        4. Add your own apps (*.py) to the `pages` folder.
+                ```python
+                    self.geemap.bar_chart(
+                        "data/forest_cover.csv", x="NAME", y="sum", max_rows=20, height=400,
+                        x_label="Country", y_label="Forest area (km2)",
+                    ).show()
+                ```
+                """
 
-        5. Commit and push your changes to the repository. Wait for the space to be built successfully.
+            solara.Markdown(markdown)
+        with solara.Column(style={"min-width": "500px"}):
+            bar_chart_plotter = BarChartPlotter()
+        
+    with solara.Column(align="center"):
+        markdown = """
 
-        """
+                **Now we can calculate the forest loss area by country and plot it**
 
+                ```python
+                    treeloss_year = dataset.select(["lossyear"])
+            
+                    geemap.zonal_stats( treeloss.gt(0), countries, "treeloss.csv", stat_type="SUM", denominator=1e6, scale=1000,)
+                    
+                    geemap.bar_chart( "treeloss.csv", x="NAME", y="sum", max_rows=20, x_label="Country", y_label="Forest loss area (km2)",).show()
+                ```
+                """
         solara.Markdown(markdown)
+

pages/04_split_map.py

@@ -47,9 +47,18 @@ class Map(geemap.Map):
 
 @solara.component
 def Page():
-    with solara.Column(style={"min-width": "500px"}):
-        Map.element(
-            center=[40, -100],
-            zoom=4,
-            height="600px",
-        )
+        with solara.Column(style={"min-width": "500px"}):
+            Map.element(
+                center=[40, -100],
+                zoom=4,
+                height="600px",
+            )
+
+        with solara.Column(align="center"):
+            markdown = """
+            ## Land cover change mapping using split-panel map
+
+            **For this analysis we will be using [NLCD 2019: USGS National Land Cover Database, 2019 release dataset](https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2019_REL_NLCD) and geemap.**
+            """
+
+            solara.Markdown(markdown)