IS415 Take-home Exercise 4
Wong Qi Ying
25/5/2020
Determining factors affecting the unequal development of Brazil at the municipality level.
1 Importing Required Packages, Creating commonly used function
packages = c('olsrr', 'corrplot', 'ggpubr', 'sf', 'spdep', 'GWmodel', 'tmap', 'geobr', 'dataMeta', 'dplyr', 'tidyr', 'tidyverse')
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}`%notin%` <- Negate(`%in%`)2 Importing, Preparing and Wrangling Data
Geospatial: * 2016 Municipality Boundary from geobr
Aspatial: * BRAZIL_CITIES.csv * Data_Dictionary.csv.
2.1 Geospatial Data
2.1.1 Importing the 2016 Municipality data as sf objects
# Municipalities_sf <- read_municipality(year=2016)Saving Municipalities_sf locally
# saveRDS(Municipalities_sf, file = "Municipalities_sf.rds")For assignment submission and markdown file knitting, this file will be used.
Retrieving locally saved Municipalities_sf.rds
Municipalities_sf <- readRDS(file = "Municipalities_sf.rds")Checking the imported 2016 Municipality data by plotting a map
ggplot() +
geom_sf(data=Municipalities_sf, fill="#2D3E50", color="#FEBF57", size=.15, show.legend = FALSE) +
labs(subtitle="Brazil Municipalities, 2016", size=8) +
theme_minimal()
Taking a look at the imported 2016 Municipality data
Municipalities_sf## Simple feature collection with 5572 features and 4 fields
## geometry type: GEOMETRY
## dimension: XY
## bbox: xmin: -73.99045 ymin: -33.75118 xmax: -28.83594 ymax: 5.271841
## CRS: 4674
## First 10 features:
## code_muni name_muni code_state abbrev_state
## 1 1100015 Alta Floresta D'oeste 11 RO
## 2 1100023 Ariquemes 11 RO
## 3 1100031 Cabixi 11 RO
## 4 1100049 Cacoal 11 RO
## 5 1100056 Cerejeiras 11 RO
## 6 1100064 Colorado Do Oeste 11 RO
## 7 1100072 Corumbiara 11 RO
## 8 1100080 Costa Marques 11 RO
## 9 1100098 Espigão D'oeste 11 RO
## 10 1100106 Guajará-Mirim 11 RO
## geom
## 1 POLYGON ((-62.19465 -11.827...
## 2 POLYGON ((-62.53648 -9.7322...
## 3 POLYGON ((-60.37075 -13.363...
## 4 POLYGON ((-61.0008 -11.2973...
## 5 POLYGON ((-61.49976 -13.005...
## 6 POLYGON ((-60.50475 -12.966...
## 7 POLYGON ((-61.34273 -12.666...
## 8 POLYGON ((-63.71199 -11.650...
## 9 POLYGON ((-60.94827 -10.988...
## 10 POLYGON ((-65.37724 -10.431...2.1.3 Working with Coordinate Reference System
Checking the Coordinate Reference System
st_crs(Municipalities_sf)## Coordinate Reference System:
## User input: 4674
## wkt:
## GEOGCS["SIRGAS 2000",
## DATUM["Sistema_de_Referencia_Geocentrico_para_las_AmericaS_2000",
## SPHEROID["GRS 1980",6378137,298.257222101,
## AUTHORITY["EPSG","7019"]],
## TOWGS84[0,0,0,0,0,0,0],
## AUTHORITY["EPSG","6674"]],
## PRIMEM["Greenwich",0,
## AUTHORITY["EPSG","8901"]],
## UNIT["degree",0.0174532925199433,
## AUTHORITY["EPSG","9122"]],
## AUTHORITY["EPSG","4674"]]- Projection: EPSG:4674
- Unit: degree
- Datum: Sistema de Referencia Geocentrico para las AmericaS 2000
- Ellipsoid: GRS 1980
2.2 Aspatial data
A quick look at the csv files reveals that they are delimited by semicolons instead of commas.
2.2.1 Importing csv files as tibble data frames
Cities <- read_delim("data/aspatial/BRAZIL_CITIES.csv", delim = ";",)
Datadict <- read_delim("data/aspatial/Data_Dictionary.csv", delim = ";" )Checking the imported tibble data frames
Cities## # A tibble: 5,573 x 81
## CITY STATE CAPITAL IBGE_RES_POP IBGE_RES_POP_BR~ IBGE_RES_POP_ES~ IBGE_DU
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Abad~ GO 0 6876 6876 0 2137
## 2 Abad~ MG 0 6704 6704 0 2328
## 3 Abad~ GO 0 15757 15609 148 4655
## 4 Abae~ MG 0 22690 22690 0 7694
## 5 Abae~ PA 0 141100 141040 60 31061
## 6 Abai~ CE 0 10496 10496 0 2791
## 7 Abaí~ BA 0 8316 8316 0 2572
## 8 Abaré BA 0 17064 17064 0 4332
## 9 Abat~ PR 0 7764 7764 0 2499
## 10 Abdo~ SC 0 2653 2653 0 848
## # ... with 5,563 more rows, and 74 more variables: IBGE_DU_URBAN <dbl>,
## # IBGE_DU_RURAL <dbl>, IBGE_POP <dbl>, IBGE_1 <dbl>, `IBGE_1-4` <dbl>,
## # `IBGE_5-9` <dbl>, `IBGE_10-14` <dbl>, `IBGE_15-59` <dbl>, `IBGE_60+` <dbl>,
## # IBGE_PLANTED_AREA <dbl>, `IBGE_CROP_PRODUCTION_$` <dbl>, `IDHM Ranking
## # 2010` <dbl>, IDHM <dbl>, IDHM_Renda <dbl>, IDHM_Longevidade <dbl>,
## # IDHM_Educacao <dbl>, LONG <dbl>, LAT <dbl>, ALT <dbl>, PAY_TV <dbl>,
## # FIXED_PHONES <dbl>, AREA <dbl>, REGIAO_TUR <chr>, CATEGORIA_TUR <chr>,
## # ESTIMATED_POP <dbl>, RURAL_URBAN <chr>, GVA_AGROPEC <dbl>,
## # GVA_INDUSTRY <dbl>, GVA_SERVICES <dbl>, GVA_PUBLIC <dbl>, ` GVA_TOTAL
## # ` <dbl>, TAXES <dbl>, GDP <dbl>, POP_GDP <dbl>, GDP_CAPITA <dbl>,
## # GVA_MAIN <chr>, MUN_EXPENDIT <dbl>, COMP_TOT <dbl>, COMP_A <dbl>,
## # COMP_B <dbl>, COMP_C <dbl>, COMP_D <dbl>, COMP_E <dbl>, COMP_F <dbl>,
## # COMP_G <dbl>, COMP_H <dbl>, COMP_I <dbl>, COMP_J <dbl>, COMP_K <dbl>,
## # COMP_L <dbl>, COMP_M <dbl>, COMP_N <dbl>, COMP_O <dbl>, COMP_P <dbl>,
## # COMP_Q <dbl>, COMP_R <dbl>, COMP_S <dbl>, COMP_T <dbl>, COMP_U <dbl>,
## # HOTELS <dbl>, BEDS <dbl>, Pr_Agencies <dbl>, Pu_Agencies <dbl>,
## # Pr_Bank <dbl>, Pu_Bank <dbl>, Pr_Assets <dbl>, Pu_Assets <dbl>, Cars <dbl>,
## # Motorcycles <dbl>, Wheeled_tractor <dbl>, UBER <dbl>, MAC <dbl>,
## # `WAL-MART` <dbl>, POST_OFFICES <dbl>Datadict## # A tibble: 96 x 6
## FIELD DESCRIPTION REFERENCE UNIT SOURCE X6
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 CITY "Name of the City" <NA> <NA> - <NA>
## 2 STATE "Name of the State" <NA> <NA> - <NA>
## 3 CAPITAL "1 if Capital of State" <NA> <NA> - <NA>
## 4 IBGE_RES_~ "Resident Population " 2010 - https://sidra.ibg~ <NA>
## 5 IBGE_RES_~ "Resident Population Bra~ 2010 - https://sidra.ibg~ <NA>
## 6 IBGE_RES_~ "Redident Population For~ 2010 - https://sidra.ibg~ <NA>
## 7 IBGE_DU "Domestic Units Total " 2010 - https://sidra.ibg~ <NA>
## 8 IBGE_DU_U~ "Domestic Units Urban " 2010 - https://sidra.ibg~ <NA>
## 9 IBGE_DU_R~ "Domestic Units Rural" 2010 - https://sidra.ibg~ <NA>
## 10 IBGE_POP "Resident Population Reg~ 2010 - https://sidra.ibg~ <NA>
## # ... with 86 more rows- From the above descriptions in DataDict and Cities data, the fields that contain categorical data:
- CAPITAL
- CATEGORIA_TUR
- Many fields are from other years besides 2016. For this study, the fields will be taken to be from the same year.
2.2.2 Checking for NA Fields in Datadict
Checking for NA Fields in Datadict
sum(is.na(Datadict$FIELD))## [1] 15Removing NA Fields in Datadict
Datadict <- Datadict[!(is.na(Datadict$FIELD)),]Checking the number of rows remaining
nrow(Datadict)## [1] 81Originally, there were 96 rows. The 15 NA rows have been successfully removed.
2.2.3 Checking info in Datadict
Checking that the number of Fields in Datadict corresponds to the number of columns in Cities
nrow(Datadict)## [1] 81ncol(Cities)## [1] 81The number of fields and columns tally.
Checking thast each Datadict Field corresponds to a Cities column
notacitiescol <- Datadict[!(Datadict$FIELD %in% colnames(Cities)),]
notacitiescol## # A tibble: 3 x 6
## FIELD DESCRIPTION REFERENCE UNIT SOURCE X6
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 IDHM Ra~ HDI Ranking 2010 - http://www.br.undp.org/con~ <NA>
## 2 GVA_TOT~ Total Gross Adde~ 2016 $ 1,00~ https://www.ibge.gov.br/es~ <NA>
## 3 WALLMART Total number of ~ 2018-12 - https://tabloide.walmartbr~ <NA>There are 3 fields that do not correspond to a Cities column. They are: * IDHM Ranking * GVA_TOTAL * WALLMART
Further inspection of the issues
Cities## # A tibble: 5,573 x 81
## CITY STATE CAPITAL IBGE_RES_POP IBGE_RES_POP_BR~ IBGE_RES_POP_ES~ IBGE_DU
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Abad~ GO 0 6876 6876 0 2137
## 2 Abad~ MG 0 6704 6704 0 2328
## 3 Abad~ GO 0 15757 15609 148 4655
## 4 Abae~ MG 0 22690 22690 0 7694
## 5 Abae~ PA 0 141100 141040 60 31061
## 6 Abai~ CE 0 10496 10496 0 2791
## 7 Abaí~ BA 0 8316 8316 0 2572
## 8 Abaré BA 0 17064 17064 0 4332
## 9 Abat~ PR 0 7764 7764 0 2499
## 10 Abdo~ SC 0 2653 2653 0 848
## # ... with 5,563 more rows, and 74 more variables: IBGE_DU_URBAN <dbl>,
## # IBGE_DU_RURAL <dbl>, IBGE_POP <dbl>, IBGE_1 <dbl>, `IBGE_1-4` <dbl>,
## # `IBGE_5-9` <dbl>, `IBGE_10-14` <dbl>, `IBGE_15-59` <dbl>, `IBGE_60+` <dbl>,
## # IBGE_PLANTED_AREA <dbl>, `IBGE_CROP_PRODUCTION_$` <dbl>, `IDHM Ranking
## # 2010` <dbl>, IDHM <dbl>, IDHM_Renda <dbl>, IDHM_Longevidade <dbl>,
## # IDHM_Educacao <dbl>, LONG <dbl>, LAT <dbl>, ALT <dbl>, PAY_TV <dbl>,
## # FIXED_PHONES <dbl>, AREA <dbl>, REGIAO_TUR <chr>, CATEGORIA_TUR <chr>,
## # ESTIMATED_POP <dbl>, RURAL_URBAN <chr>, GVA_AGROPEC <dbl>,
## # GVA_INDUSTRY <dbl>, GVA_SERVICES <dbl>, GVA_PUBLIC <dbl>, ` GVA_TOTAL
## # ` <dbl>, TAXES <dbl>, GDP <dbl>, POP_GDP <dbl>, GDP_CAPITA <dbl>,
## # GVA_MAIN <chr>, MUN_EXPENDIT <dbl>, COMP_TOT <dbl>, COMP_A <dbl>,
## # COMP_B <dbl>, COMP_C <dbl>, COMP_D <dbl>, COMP_E <dbl>, COMP_F <dbl>,
## # COMP_G <dbl>, COMP_H <dbl>, COMP_I <dbl>, COMP_J <dbl>, COMP_K <dbl>,
## # COMP_L <dbl>, COMP_M <dbl>, COMP_N <dbl>, COMP_O <dbl>, COMP_P <dbl>,
## # COMP_Q <dbl>, COMP_R <dbl>, COMP_S <dbl>, COMP_T <dbl>, COMP_U <dbl>,
## # HOTELS <dbl>, BEDS <dbl>, Pr_Agencies <dbl>, Pu_Agencies <dbl>,
## # Pr_Bank <dbl>, Pu_Bank <dbl>, Pr_Assets <dbl>, Pu_Assets <dbl>, Cars <dbl>,
## # Motorcycles <dbl>, Wheeled_tractor <dbl>, UBER <dbl>, MAC <dbl>,
## # `WAL-MART` <dbl>, POST_OFFICES <dbl>- IDHM Ranking is present in Cities as IDHM Ranking 2010
- As for GVA_Total, it seems to be present as a column in Cities
- WALLMART is misspelled, it should be WAL-MART as seen in Cities
However, when trying to access the GVA_Total column in Cities, it does not work:
head(Cities$GVA_TOTAL,5)## NULLThe GVA_TOTAL column is only accessible via the column number
head(Cities[38],5)## # A tibble: 5 x 1
## ` GVA_TOTAL `
## <dbl>
## 1 145858.
## 2 167215.
## 3 261162.
## 4 403241.
## 5 1154075.Upon inspection of BRAZIL_CITIES.csv, the GVA_TOTAL field has an extra white space. Renaming the column in Cities should resolve the issue.
Renaming the rows and columns
Datadict[19, "FIELD"] <- "IDHM Ranking 2010"
colnames(Cities)[38] <- "GVA_TOTAL"
Datadict[80, "FIELD"] <- "WAL-MART"Checking that the GVA_TOTAL column can be retrieved by name.
head(Cities$GVA_TOTAL,5)## [1] 145857.6 167215.3 261161.9 403241.3 1154074.8The GVA_TOTAL column can now be retrieved via name.
Checking again that each Datadict Field corresponds to a Cities column
notacitiescol <- Datadict[!(Datadict$FIELD %in% colnames(Cities)),]
notacitiescol## # A tibble: 0 x 6
## # ... with 6 variables: FIELD <chr>, DESCRIPTION <chr>, REFERENCE <chr>,
## # UNIT <chr>, SOURCE <chr>, X6 <chr>Now, all fields correspond to a Cities column.
2.2.4 Ensuring that the Cities are Municipalities
Checking for empty rows in Municipalities_sf$name_muni
nrow(is.na(Municipalities_sf$name_muni))## NULLNone of the municipality names in Municipalities_sf are NA.
List of Municipalities in Brazil, Capitalized and without dashes
Municipalities_list <- tolower(Municipalities_sf$name_muni)
str_replace_all(Municipalities_list, "-", " ")Retaining only Municipalities from the Cities data frame
Municipalitiesinfo <- Cities[(tolower(Cities$CITY) %in% Municipalities_list),]Checking whether the number of rows Municipalities_info tallies with the number of Municipalities in Brazil
nrow(Municipalities_sf)## [1] 5572nrow(Municipalitiesinfo)## [1] 5572All 5572 municipalities have information from BRAZIL_CITIES.csv
2.2.5 Checking whether municipal names are unique
sum(duplicated(Municipalitiesinfo$CITY))## [1] 274There are 274 municipals with the same name.
n_occur <- data.frame(table(Municipalitiesinfo$CITY))
n_occur[n_occur$Freq > 1,]## Var1 Freq
## 47 Água Boa 2
## 48 Água Branca 3
## 87 Alagoinha 2
## 137 Alto Alegre 3
## 152 Alto Paraíso 2
## 171 Alvorada 2
## 198 Amparo 2
## 216 Anchieta 2
## 250 Antônio Carlos 2
## 258 Aparecida 2
## 305 Araguanã 2
## 338 Araruna 2
## 355 Areia Branca 2
## 402 Atalaia 2
## 417 Aurora 2
## 466 Bandeirantes 2
## 478 Baraúna 2
## 488 Barra Bonita 2
## 496 Barra De São Miguel 2
## 517 Barracão 2
## 528 Barro Alto 2
## 539 Batalha 2
## 557 Belém 3
## 564 Belmonte 2
## 606 Boa Esperança 3
## 614 Boa Vista 2
## 626 Bocaina 2
## 638 Bom Jardim 3
## 642 Bom Jesus 5
## 655 Bom Jesus Do Tocantins 2
## 664 Bom Sucesso 3
## 668 Bonfim 2
## 673 Bonito 4
## 685 Borborema 2
## 720 Brejinho 2
## 766 Buritis 2
## 803 Cachoeira Dourada 2
## 807 Cachoeirinha 3
## 826 Cafelândia 2
## 833 Caiçara 2
## 886 Campestre 2
## 903 Campo Alegre 2
## 918 Campo Grande 2
## 951 Canápolis 2
## 952 Canarana 2
## 957 Candeias 2
## 982 Cantagalo 3
## 988 Capanema 2
## 996 Capela 2
## 1026 Caracol 2
## 1038 Caraúbas 2
## 1097 Cascavel 2
## 1118 Catanduvas 2
## 1143 Cedral 2
## 1144 Cedro 2
## 1148 Centenário 2
## 1236 Colinas 2
## 1246 Colorado 2
## 1279 Condado 2
## 1280 Conde 2
## 1411 Cruzeiro Do Sul 3
## 1459 Davinópolis 2
## 1543 Douradina 2
## 1568 Eldorado 2
## 1595 Entre Rios 2
## 1619 Esperantina 2
## 1642 Estrela Do Norte 2
## 1666 Fátima 2
## 1679 Feira Nova 2
## 1706 Filadélfia 2
## 1720 Floresta 2
## 1741 Formoso 2
## 1814 General Carneiro 2
## 1894 Guaíra 2
## 1915 Guaraci 2
## 1916 Guaraciaba 2
## 1971 Hidrolândia 2
## 1980 Humaitá 2
## 2070 Iguatu 2
## 2095 Inajá 2
## 2100 Independência 2
## 2102 Indianópolis 2
## 2156 Ipueiras 2
## 2160 Iracema 2
## 2174 Irati 2
## 2185 Itabaiana 2
## 2226 Itajá 2
## 2241 Itambé 3
## 2271 Itapeva 2
## 2276 Itapiranga 2
## 2287 Itaporanga 2
## 2358 Jaborandi 2
## 2381 Jacutinga 2
## 2405 Jandaíra 2
## 2421 Japurá 2
## 2428 Jardim 2
## 2435 Jardinópolis 2
## 2444 Jatobá 2
## 2526 Jundiá 2
## 2537 Jurema 2
## 2545 Jussara 3
## 2584 Lagoa Dos Patos 2
## 2588 Lagoa Grande 2
## 2593 Lagoa Santa 2
## 2603 Lajeado 2
## 2622 Laranjal 2
## 2812 Maravilha 2
## 2868 Massaranduba 2
## 2928 Mesquita 2
## 2936 Milagres 2
## 2955 Mirador 2
## 3007 Monte Alegre 2
## 3022 Monte Castelo 2
## 3052 Morrinhos 2
## 3082 Mulungu 2
## 3084 Mundo Novo 3
## 3111 Natividade 2
## 3116 Nazaré 2
## 3162 Nova Aurora 2
## 3187 Nova Fátima 2
## 3216 Nova Olímpia 2
## 3217 Nova Olinda 3
## 3237 Nova Santa Rita 2
## 3245 Nova União 2
## 3247 Nova Veneza 2
## 3261 Novo Horizonte 3
## 3276 Novo Santo Antônio 2
## 3336 Ouro Branco 3
## 3341 Ouro Verde 2
## 3352 Pacatuba 2
## 3370 Palestina 2
## 3381 Palmas 2
## 3383 Palmeira 2
## 3398 Palmital 2
## 3425 Paraíso 2
## 3469 Parnamirim 2
## 3478 Passagem 2
## 3501 Pau D'Arco 2
## 3511 Paulista 2
## 3533 Pedra Branca 2
## 3541 Pedra Preta 2
## 3602 Petrolândia 2
## 3622 Pilar 2
## 3625 Pilões 2
## 3646 Pinhalzinho 2
## 3647 Pinhão 2
## 3678 Piranhas 2
## 3700 Pitangueiras 2
## 3711 Planalto 4
## 3831 Praia Grande 2
## 3835 Prata 2
## 3841 Presidente Bernardes 2
## 3844 Presidente Dutra 2
## 3849 Presidente Juscelino 2
## 3850 Presidente Kennedy 2
## 3853 Presidente Médici 2
## 3861 Primavera 2
## 3893 Queimadas 2
## 3911 Quixaba 2
## 3934 Redenção 2
## 3958 Riachão 2
## 3964 Riachinho 2
## 3967 Riacho De Santana 2
## 3972 Riachuelo 2
## 4008 Rio Branco 2
## 4013 Rio Claro 2
## 4043 Rio Negro 2
## 4106 Ruy Barbosa 2
## 4121 Salgadinho 2
## 4132 Saltinho 2
## 4158 Santa Bárbara 2
## 4169 Santa Cecília 2
## 4174 Santa Cruz 3
## 4200 Santa Filomena 2
## 4203 Santa Helena 4
## 4206 Santa Inês 4
## 4207 Santa Isabel 2
## 4214 Santa Lúcia 2
## 4216 Santa Luzia 4
## 4224 Santa Maria 2
## 4244 Santa Rita 2
## 4262 Santa Rosa De Lima 2
## 4270 Santa Teresinha 2
## 4275 Santa Terezinha 4
## 4283 Santana 2
## 4321 Santo André 2
## 4372 São Bento 2
## 4391 São Carlos 2
## 4396 São Domingos 5
## 4418 São Francisco 4
## 4423 São Francisco De Paula 2
## 4434 São Gabriel 2
## 4444 São Gonçalo Do Amarante 2
## 4455 São João 2
## 4456 São João Batista 2
## 4491 São João Do Paraíso 2
## 4542 São José Do Divino 2
## 4594 São Martinho 2
## 4626 São Pedro 2
## 4654 São Sebastião 2
## 4674 São Simão 2
## 4678 São Tomé 2
## 4684 São Vicente 2
## 4696 Sapucaia 2
## 4699 Sarandi 2
## 4784 Serrinha 2
## 4794 Sertãozinho 2
## 4829 Sítio Novo 2
## 4831 Sobradinho 2
## 4838 Soledade 2
## 4867 Tabatinga 2
## 4896 Tangará 2
## 4906 Tapejara 2
## 4908 Taperoá 2
## 4910 Tapira 2
## 4911 Tapiraí 2
## 4937 Tavares 2
## 4952 Teodoro Sampaio 2
## 4966 Terra Nova 2
## 4969 Terra Roxa 2
## 5000 Toledo 2
## 5043 Trindade 2
## 5045 Triunfo 3
## 5078 Turmalina 2
## 5083 Turvo 2
## 5161 Valença 2
## 5170 Vargem 2
## 5173 Vargem Bonita 2
## 5183 Várzea 2
## 5189 Várzea Grande 2
## 5202 Vera Cruz 4
## 5219 Viana 2
## 5225 Viçosa 3
## 5281 Wenceslau Braz 2Municipalitiesinfo[Municipalitiesinfo$CITY %in% n_occur$Var1[n_occur$Freq > 1],]## # A tibble: 508 x 81
## CITY STATE CAPITAL IBGE_RES_POP IBGE_RES_POP_BR~ IBGE_RES_POP_ES~ IBGE_DU
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Água~ MT 0 20856 20844 12 6437
## 2 Água~ MG 0 15195 15195 0 4170
## 3 Água~ PI 0 16451 16451 0 4733
## 4 Água~ AL 0 19377 19377 0 4734
## 5 Água~ PB 0 9449 9449 0 2562
## 6 Alag~ PE 0 13759 13753 6 4108
## 7 Alag~ PB 0 13576 13576 0 3703
## 8 Alto~ SP 0 4102 4102 0 1375
## 9 Alto~ RR 0 16448 16422 26 2169
## 10 Alto~ RS 0 1848 1848 0 624
## # ... with 498 more rows, and 74 more variables: IBGE_DU_URBAN <dbl>,
## # IBGE_DU_RURAL <dbl>, IBGE_POP <dbl>, IBGE_1 <dbl>, `IBGE_1-4` <dbl>,
## # `IBGE_5-9` <dbl>, `IBGE_10-14` <dbl>, `IBGE_15-59` <dbl>, `IBGE_60+` <dbl>,
## # IBGE_PLANTED_AREA <dbl>, `IBGE_CROP_PRODUCTION_$` <dbl>, `IDHM Ranking
## # 2010` <dbl>, IDHM <dbl>, IDHM_Renda <dbl>, IDHM_Longevidade <dbl>,
## # IDHM_Educacao <dbl>, LONG <dbl>, LAT <dbl>, ALT <dbl>, PAY_TV <dbl>,
## # FIXED_PHONES <dbl>, AREA <dbl>, REGIAO_TUR <chr>, CATEGORIA_TUR <chr>,
## # ESTIMATED_POP <dbl>, RURAL_URBAN <chr>, GVA_AGROPEC <dbl>,
## # GVA_INDUSTRY <dbl>, GVA_SERVICES <dbl>, GVA_PUBLIC <dbl>, GVA_TOTAL <dbl>,
## # TAXES <dbl>, GDP <dbl>, POP_GDP <dbl>, GDP_CAPITA <dbl>, GVA_MAIN <chr>,
## # MUN_EXPENDIT <dbl>, COMP_TOT <dbl>, COMP_A <dbl>, COMP_B <dbl>,
## # COMP_C <dbl>, COMP_D <dbl>, COMP_E <dbl>, COMP_F <dbl>, COMP_G <dbl>,
## # COMP_H <dbl>, COMP_I <dbl>, COMP_J <dbl>, COMP_K <dbl>, COMP_L <dbl>,
## # COMP_M <dbl>, COMP_N <dbl>, COMP_O <dbl>, COMP_P <dbl>, COMP_Q <dbl>,
## # COMP_R <dbl>, COMP_S <dbl>, COMP_T <dbl>, COMP_U <dbl>, HOTELS <dbl>,
## # BEDS <dbl>, Pr_Agencies <dbl>, Pu_Agencies <dbl>, Pr_Bank <dbl>,
## # Pu_Bank <dbl>, Pr_Assets <dbl>, Pu_Assets <dbl>, Cars <dbl>,
## # Motorcycles <dbl>, Wheeled_tractor <dbl>, UBER <dbl>, MAC <dbl>,
## # `WAL-MART` <dbl>, POST_OFFICES <dbl>As seen from the above, the municipalities with the same names are not from the same state. To uniquely identify the municipalities in this study: * Use both name and state * Use geographical coordinates
2.2.6 Retaining Selected Columns/Fields
Taking a look at the Fields again
Datadict## # A tibble: 81 x 6
## FIELD DESCRIPTION REFERENCE UNIT SOURCE X6
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 CITY "Name of the City" <NA> <NA> - <NA>
## 2 STATE "Name of the State" <NA> <NA> - <NA>
## 3 CAPITAL "1 if Capital of State" <NA> <NA> - <NA>
## 4 IBGE_RES_~ "Resident Population " 2010 - https://sidra.ibg~ <NA>
## 5 IBGE_RES_~ "Resident Population Bra~ 2010 - https://sidra.ibg~ <NA>
## 6 IBGE_RES_~ "Redident Population For~ 2010 - https://sidra.ibg~ <NA>
## 7 IBGE_DU "Domestic Units Total " 2010 - https://sidra.ibg~ <NA>
## 8 IBGE_DU_U~ "Domestic Units Urban " 2010 - https://sidra.ibg~ <NA>
## 9 IBGE_DU_R~ "Domestic Units Rural" 2010 - https://sidra.ibg~ <NA>
## 10 IBGE_POP "Resident Population Reg~ 2010 - https://sidra.ibg~ <NA>
## # ... with 71 more rows81 fields are present in Datadict.
Fields that do not affect the unequal development of Brazil at the municipality level/Removing non-explanatory variables/Fields not of interest:
fieldstoremove <- list(
"HOTELS",
"BEDS",
"Cars",
"Motorcycles",
"Wheeled_tractor",
"UBER",
"MAC",
"WAL-MART",
"POST_OFFICES",
"PAY_TV",
"FIXED_PHONES",
"IBGE_1",
"IBGE_1-4",
"IBGE_5-9",
"IBGE_10-14",
"IBGE_60+",
"GDP",
"POP_GDP",
"ESTIMATED_POP",
"IBGE_CROP_PRODUCTION_$",
"GVA_AGROPEC",
"GVA_INDUSTRY",
"GVA_SERVICES",
"GVA_PUBLIC",
"GVA_TOTAL",
"GVA_MAIN",
"IDHM_Renda",
"IDHM Ranking 2010",
"IDHM",
"COMP_TOT",
"COMP_A",
"COMP_B",
"COMP_C",
"COMP_D",
"COMP_E",
"COMP_F",
"COMP_G",
"COMP_H",
"COMP_I",
"COMP_J",
"COMP_K",
"COMP_L",
"COMP_M",
"COMP_N",
"COMP_O",
"COMP_P",
"COMP_Q",
"COMP_R",
"COMP_S",
"COMP_T",
"COMP_U")
length(fieldstoremove)## [1] 51XX fields are being removed:
HOTELS, BEDS, Cars, Motorcycles, Wheeled_tractor, UBER, WAL-MART, MAC, POST_OFFICES, PAY_TV, FIXED_PHONES and STATE do not affect unequal economic development.
The age groups besides IBGE_15-59 (Resident Population Regular Urban Planning - from 15 to 59 y.o) have been removed since they are not part of the economically active group. They do not contribute to the economic development. This study will focus on the economically active group.
GDP and POP_GDP are removed they make up GDP_CAPITA - GDP_CAPITA is GDP/POP_GDP, and hence they should not be used as explanatory variables.
Remove ESTIMATED_POP - same reason for removing POP_GDP.
IBGE_CROP_PRODUCTION_$: Crop Production in 1,000 reais is part of GDP or GDP_CAPITA and should not be used as an explanatory variable.
GVA_AGROPEC, GVA_INDUSTRY, GVA_SERVICES, GVA_PUBLIC, GVA_MAIN: “Gross value added is the value of output less the value of intermediate consumption; it is a measure of the contribution to GDP made by an individual producer, industry or sector”. Since it is an indicator related to GDP, all GVA related fields should be removed. https://stats.oecd.org/glossary/detail.asp?ID=1184
IDHM_Renda: Human Development Index Gross National Income Index. It is defined as “gross domestic product, plus net receipts from abroad of compensation of employees, property income and net taxes less subsidies on production”. Since it is an extended version of GDP, it will be removed as well. https://data.oecd.org/natincome/gross-national-income.htm
IDHM Ranking 2010, IDHM: Human Development Index Ranking 2010 and Human Development Index are inidicators of the inequality. It is a “summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living.” Since they are indicators themselves and also incorporate the GNI, which contains the GDP, they should be removed. http://hdr.undp.org/en/content/human-development-index-hdi
All COMP fields are dropped since the number of companies is not a good indication of unequal economic development.
STATE is retained to identify municipalities with the same name
LONG AND LAT are not explanatory variables but will be needed to perform a st_join later on.
Removing the fields from Datadict
Datadict_cleaned <- Datadict[!(Datadict$FIELD %in% fieldstoremove),]
Datadict_cleaned## # A tibble: 30 x 6
## FIELD DESCRIPTION REFERENCE UNIT SOURCE X6
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 CITY "Name of the City" <NA> <NA> - <NA>
## 2 STATE "Name of the State" <NA> <NA> - <NA>
## 3 CAPITAL "1 if Capital of State" <NA> <NA> - <NA>
## 4 IBGE_RES_~ "Resident Population " 2010 - https://sidra.ibg~ <NA>
## 5 IBGE_RES_~ "Resident Population Bra~ 2010 - https://sidra.ibg~ <NA>
## 6 IBGE_RES_~ "Redident Population For~ 2010 - https://sidra.ibg~ <NA>
## 7 IBGE_DU "Domestic Units Total " 2010 - https://sidra.ibg~ <NA>
## 8 IBGE_DU_U~ "Domestic Units Urban " 2010 - https://sidra.ibg~ <NA>
## 9 IBGE_DU_R~ "Domestic Units Rural" 2010 - https://sidra.ibg~ <NA>
## 10 IBGE_POP "Resident Population Reg~ 2010 - https://sidra.ibg~ <NA>
## # ... with 20 more rowsThe 50 fields have been removed. 31 rows remain in the cleaned Datadict data frame.
Retaining the explanatory variables in Municipalitiesinfo (columns corresponding to the remaining Datadict fields)
MunicipalitiesSC <- subset(Municipalitiesinfo, select = c(names(Municipalitiesinfo) %in% Datadict_cleaned$FIELD))
ncol(MunicipalitiesSC)## [1] 30The number of columns in the MunicipalitiesSC data frame tallies with the number of fields in the Datadict_cleaned data frame.
2.2.7 Performing NA checks on MunicipalitiesSC
Checking for rows with NA values in the MunicipalitiesSC data frame
MunicipalitiesSC[rowSums(is.na(MunicipalitiesSC))!=0,]## # A tibble: 3,811 x 30
## CITY STATE CAPITAL IBGE_RES_POP IBGE_RES_POP_BR~ IBGE_RES_POP_ES~ IBGE_DU
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Abad~ GO 0 6876 6876 0 2137
## 2 Abad~ MG 0 6704 6704 0 2328
## 3 Abae~ MG 0 22690 22690 0 7694
## 4 Abae~ PA 0 141100 141040 60 31061
## 5 Abai~ CE 0 10496 10496 0 2791
## 6 Abaí~ BA 0 8316 8316 0 2572
## 7 Abaré BA 0 17064 17064 0 4332
## 8 Abat~ PR 0 7764 7764 0 2499
## 9 Abel~ PA 0 6780 6780 0 1880
## 10 Abel~ SC 0 17100 17084 16 4739
## # ... with 3,801 more rows, and 23 more variables: IBGE_DU_URBAN <dbl>,
## # IBGE_DU_RURAL <dbl>, IBGE_POP <dbl>, `IBGE_15-59` <dbl>,
## # IBGE_PLANTED_AREA <dbl>, IDHM_Longevidade <dbl>, IDHM_Educacao <dbl>,
## # LONG <dbl>, LAT <dbl>, ALT <dbl>, AREA <dbl>, REGIAO_TUR <chr>,
## # CATEGORIA_TUR <chr>, RURAL_URBAN <chr>, TAXES <dbl>, GDP_CAPITA <dbl>,
## # MUN_EXPENDIT <dbl>, Pr_Agencies <dbl>, Pu_Agencies <dbl>, Pr_Bank <dbl>,
## # Pu_Bank <dbl>, Pr_Assets <dbl>, Pu_Assets <dbl>3811 rows have NA values.
Checking for columns with NA values in the MunicipalitiesSC data frame
colSums(is.na(MunicipalitiesSC))## CITY STATE CAPITAL IBGE_RES_POP
## 0 0 0 7
## IBGE_RES_POP_BRAS IBGE_RES_POP_ESTR IBGE_DU IBGE_DU_URBAN
## 7 7 9 9
## IBGE_DU_RURAL IBGE_POP IBGE_15-59 IBGE_PLANTED_AREA
## 80 7 7 2
## IDHM_Longevidade IDHM_Educacao LONG LAT
## 7 7 8 8
## ALT AREA REGIAO_TUR CATEGORIA_TUR
## 8 2 2287 2287
## RURAL_URBAN TAXES GDP_CAPITA MUN_EXPENDIT
## 2 2 2 1492
## Pr_Agencies Pu_Agencies Pr_Bank Pu_Bank
## 2230 2230 2230 2230
## Pr_Assets Pu_Assets
## 2230 2230There are no issues with CITY, STATE and CAPITAL.
Out of the 5572 rows, it seems that the same:
- 2 rows are having NA values for almost all their columns
- 7 rows are having NA values for their IBGE and IDHM related columns: IBGE_RES_POP, IBGE_RES_POP_BRAS,IBGE_RES_POP_ESTR, IBGE_DU, IBGE_DU_URBAN, IBGE_POP, IBGE_15-59, IDHM_Longevidade, IDHM_Educacao
- 8 rows are having NA values for their LONG, LAT and ALT columns
- 2287 rows are having NA values for their REGIAO_TUR and CATEGORIA_TUR columns
- 2230 rows are having NA values for their Pr_Agencies, Pu_Agencies, Pr_Bank, Pu_Bank, Pr_Assets, Pu_Assets columns
80 rows are having NA values for their IBGE_DU_RURAL column
1492 are having NA values for their MUN_EXPENDIT: Municipal expenditures - in reais column.
The columns with the NA values will be dropped since an unbiased analysis cannot be performed. This is except for the LONG, LAT and ALT values which can be found.
Creating a list of columns to remove
colstoremove <- list(
"REGIAO_TUE",
"CATEGORIA_TUR",
"MUN_EXPENDIT",
"IBGE_RES_POP",
"IBGE_RES_POP_BRAS",
"IBGE_RES_POP_ESTR",
"IBGE_DU",
"IBGE_DU_URBAN",
"IBGE_DU_RURAL",
"IBGE_POP",
"IBGE_15-59",
"IDHM_Longevidade",
"IDHM_Educacao",
"REGIAO_TUR",
"CATEGORIA_TUR",
"Pr_Agencies",
"Pu_Agencies",
"Pr_Bank",
"Pu_Bank",
"Pr_Assets",
"Pu_Assets")
length(colstoremove)## [1] 21Removing the fields in Datadict_cleaned
Datadict_cleaned <- Datadict_cleaned[!(Datadict_cleaned$FIELD %in% colstoremove),]Removing the columns with NA values in MunicipalitiesSC
MunicipalitiesSC <- subset(MunicipalitiesSC, select = c(names(MunicipalitiesSC) %in% Datadict_cleaned$FIELD))Checking the 2 rows with the NA values
MunicipalitiesSC_NArows_RURALURBAN <- MunicipalitiesSC[is.na(MunicipalitiesSC$RURAL_URBAN),]
MunicipalitiesSC_NArows_RURALURBAN## # A tibble: 2 x 11
## CITY STATE CAPITAL IBGE_PLANTED_AR~ LONG LAT ALT AREA RURAL_URBAN
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Lago~ RS 0 NA NA NA NA 10159. <NA>
## 2 Sant~ BA 0 NA -39.5 -12.8 223. NA <NA>
## # ... with 2 more variables: TAXES <dbl>, GDP_CAPITA <dbl>From the above checks, Lagoa Dos Patos (RS) and Santa Teresinha (BA) are the rows with the NA data in most columns. From online research, * Lagoa Dos Patos is a lagoon in Brazil. https://www.tapes.rs.gov.br/index.php/2019/09/10/lagoa-dos-patos/ * Santa Teresinha is a municipality. https://www.santaterezinha.mt.gov.br/Municipio/Prefeito/
Based on research on dealing with missing data: Missing Data Techniques with SAS,University of California, Los Angeles https://stats.idre.ucla.edu/wp-content/uploads/2017/01/Missing-Data-Techniques_UCLA.pdf
The common techniques for dealing with missing data are:
- Complete case analysis (listwise deletion)
- Mean imputation
- Single Imputation
- Stochastic Imputation
For this study, Complete Case Analysis will be employed. The cases with missing data on any variable of interest will be dropped. It is recognized that there will be drawbacks:
- Loss of cases/data
- Biased estimates
Removing the rows
MunicipalitiesSC <- MunicipalitiesSC[!( (MunicipalitiesSC$CITY == "Lagoa Dos Patos" & MunicipalitiesSC$STATE == "RS") |
(MunicipalitiesSC$CITY == "Santa Teresinha" & MunicipalitiesSC$STATE == "BA") ),]Checking that the 2 rows with the NA values have been removed
MunicipalitiesSC_NArows_RURALURBAN <- MunicipalitiesSC[is.na(MunicipalitiesSC$RURAL_URBAN),]
MunicipalitiesSC_NArows_RURALURBAN## # A tibble: 0 x 11
## # ... with 11 variables: CITY <chr>, STATE <chr>, CAPITAL <dbl>,
## # IBGE_PLANTED_AREA <dbl>, LONG <dbl>, LAT <dbl>, ALT <dbl>, AREA <dbl>,
## # RURAL_URBAN <chr>, TAXES <dbl>, GDP_CAPITA <dbl>The Municipalities with missing LONG, LAT and ALT data
MunicipalitiesSC_NArows_LONG <- MunicipalitiesSC[is.na(MunicipalitiesSC$LONG),]
MunicipalitiesSC_NArows_LONG## # A tibble: 7 x 11
## CITY STATE CAPITAL IBGE_PLANTED_AR~ LONG LAT ALT AREA RURAL_URBAN
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Baln~ SC 0 93 NA NA NA 63.4 Sem classi~
## 2 Moju~ PA 0 36466 NA NA NA 4988. Sem classi~
## 3 Para~ MS 0 84067 NA NA NA 5061. Sem classi~
## 4 Pesc~ SC 0 215 NA NA NA 107. Sem classi~
## 5 Pinh~ RS 0 15509 NA NA NA 438. Rural Adja~
## 6 Pint~ RS 0 2977 NA NA NA 105. Sem classi~
## 7 Sant~ BA 0 358 NA NA NA 719. Rural Adja~
## # ... with 2 more variables: TAXES <dbl>, GDP_CAPITA <dbl>MunicipalitiesSC_NArows_LAT <- MunicipalitiesSC[is.na(MunicipalitiesSC$LAT),]
MunicipalitiesSC_NArows_LAT## # A tibble: 7 x 11
## CITY STATE CAPITAL IBGE_PLANTED_AR~ LONG LAT ALT AREA RURAL_URBAN
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Baln~ SC 0 93 NA NA NA 63.4 Sem classi~
## 2 Moju~ PA 0 36466 NA NA NA 4988. Sem classi~
## 3 Para~ MS 0 84067 NA NA NA 5061. Sem classi~
## 4 Pesc~ SC 0 215 NA NA NA 107. Sem classi~
## 5 Pinh~ RS 0 15509 NA NA NA 438. Rural Adja~
## 6 Pint~ RS 0 2977 NA NA NA 105. Sem classi~
## 7 Sant~ BA 0 358 NA NA NA 719. Rural Adja~
## # ... with 2 more variables: TAXES <dbl>, GDP_CAPITA <dbl>MunicipalitiesSC_NArows_ALT <- MunicipalitiesSC[is.na(MunicipalitiesSC$ALT),]
MunicipalitiesSC_NArows_ALT## # A tibble: 7 x 11
## CITY STATE CAPITAL IBGE_PLANTED_AR~ LONG LAT ALT AREA RURAL_URBAN
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Baln~ SC 0 93 NA NA NA 63.4 Sem classi~
## 2 Moju~ PA 0 36466 NA NA NA 4988. Sem classi~
## 3 Para~ MS 0 84067 NA NA NA 5061. Sem classi~
## 4 Pesc~ SC 0 215 NA NA NA 107. Sem classi~
## 5 Pinh~ RS 0 15509 NA NA NA 438. Rural Adja~
## 6 Pint~ RS 0 2977 NA NA NA 105. Sem classi~
## 7 Sant~ BA 0 358 NA NA NA 719. Rural Adja~
## # ... with 2 more variables: TAXES <dbl>, GDP_CAPITA <dbl>The municipalities with the missing data are the same 7:
- Balneário Rincãob(SC)
- Mojuí Dos Campos (PA)
- Paraíso Das Águas (MS)
- Pescaria Brava (SC)
- Pinhal Da Serra (RS)
- Pinto Bandeira (RS)
- Santa Terezinha (BA)
Getting addresses from the Municipality Office websites: * https://www.balneariorincao.sc.gov.br/ * http://www.mojuidoscampos.pa.gov.br/
Using Google Earth to search for the Municipality Offices (Prefeitura Municipal): * Balneário Rincão + Long: -49.231260 + Lat: -28.823339 + Alt: 7m * Lagoa Dos Patos
+ Long: + Lat: + Alt: * Mojuí Dos Campos + Long: -54.643362 + Lat: -2.679589 + Alt: 96m * Paraíso Das Águas + Long: -53.011786 + Lat: -19.021352 + Alt: 606m * Pescaria Brava
+ Long: -48.867814 + Lat: -28.393653 + Alt: 10m * Pinhal Da Serra
+ Long: -51.175047 + Lat: -27.870884 + Alt: 912m * Pinto Bandeira
+ Long: -51.454123 + Lat: -29.093820 + Alt: 638m * Santa Terezinha (BA)
+ Long: -39.558080 + Lat: -12.728505 + Alt: 254m
“Google Earth Map for Prefeitura de Balneário Rincão”
For the Paraíso Das Águas office, it could not be found using Google Earth’s search function. The address used was from these municipal website: http://www.paraisodasaguas.ms.gov.br/
Manually adding the missing LONG, LAT and ALT values to the MunicipalitiesSC data frame
MunicipalitiesSC$LONG[MunicipalitiesSC$CITY == "Balneário Rincão" & MunicipalitiesSC$STATE == "SC"] <- (-49.231260)
MunicipalitiesSC$LAT[MunicipalitiesSC$CITY == "Balneário Rincão" & MunicipalitiesSC$STATE == "SC"] <- (-28.823339)
MunicipalitiesSC$ALT[MunicipalitiesSC$CITY == "Balneário Rincão" & MunicipalitiesSC$STATE == "SC"] <- 7.00
MunicipalitiesSC$LONG[MunicipalitiesSC$CITY == "Mojuí Dos Campos" & MunicipalitiesSC$STATE == "PA"] <- (-54.643362)
MunicipalitiesSC$LAT[MunicipalitiesSC$CITY == "Mojuí Dos Campos" & MunicipalitiesSC$STATE == "PA"] <- (-2.679589)
MunicipalitiesSC$ALT[MunicipalitiesSC$CITY == "Mojuí Dos Campos" & MunicipalitiesSC$STATE == "PA"] <- 96.00
MunicipalitiesSC$LONG[MunicipalitiesSC$CITY == "Paraíso Das Águas" & MunicipalitiesSC$STATE == "MS"] <- (-53.011786)
MunicipalitiesSC$LAT[MunicipalitiesSC$CITY == "Paraíso Das Águas" & MunicipalitiesSC$STATE == "MS"] <- -53.011786
MunicipalitiesSC$ALT[MunicipalitiesSC$CITY == "Paraíso Das Águas" & MunicipalitiesSC$STATE == "MS"] <- 606.00
MunicipalitiesSC$LONG[MunicipalitiesSC$CITY == "Pescaria Brava" & MunicipalitiesSC$STATE == "SC"] <- (-48.867814)
MunicipalitiesSC$LAT[MunicipalitiesSC$CITY == "Pescaria Brava" & MunicipalitiesSC$STATE == "SC"] <- (-28.393653)
MunicipalitiesSC$ALT[MunicipalitiesSC$CITY == "Pescaria Brava" & MunicipalitiesSC$STATE == "SC"] <- 10.00
MunicipalitiesSC$LONG[MunicipalitiesSC$CITY == "Pinhal Da Serra" & MunicipalitiesSC$STATE == "RS"] <- (-51.175047)
MunicipalitiesSC$LAT[MunicipalitiesSC$CITY == "Pinhal Da Serra" & MunicipalitiesSC$STATE == "RS"] <- (-27.870884)
MunicipalitiesSC$ALT[MunicipalitiesSC$CITY == "Pinhal Da Serra" & MunicipalitiesSC$STATE == "RS"] <- 912.00
MunicipalitiesSC$LONG[MunicipalitiesSC$CITY == "Pinto Bandeira" & MunicipalitiesSC$STATE == "RS"] <- (-51.454123)
MunicipalitiesSC$LAT[MunicipalitiesSC$CITY == "Pinto Bandeira" & MunicipalitiesSC$STATE == "RS"] <- (-29.093820)
MunicipalitiesSC$ALT[MunicipalitiesSC$CITY == "Pinto Bandeira" & MunicipalitiesSC$STATE == "RS"] <- 638.00
MunicipalitiesSC$LONG[MunicipalitiesSC$CITY == "Santa Terezinha" & MunicipalitiesSC$STATE == "BA"] <- -39.558080
MunicipalitiesSC$LAT[MunicipalitiesSC$CITY == "Santa Terezinha" & MunicipalitiesSC$STATE == "BA"] <- -12.728505
MunicipalitiesSC$ALT[MunicipalitiesSC$CITY == "Santa Terezinha" & MunicipalitiesSC$STATE == "BA"] <- 254.00Checking that the LONG, LAT and ALT values are now all filled
MunicipalitiesSC_NArows_LONG <- MunicipalitiesSC[is.na(MunicipalitiesSC$LONG),]
MunicipalitiesSC_NArows_LONG## # A tibble: 0 x 11
## # ... with 11 variables: CITY <chr>, STATE <chr>, CAPITAL <dbl>,
## # IBGE_PLANTED_AREA <dbl>, LONG <dbl>, LAT <dbl>, ALT <dbl>, AREA <dbl>,
## # RURAL_URBAN <chr>, TAXES <dbl>, GDP_CAPITA <dbl>MunicipalitiesSC_NArows_LAT <- MunicipalitiesSC[is.na(MunicipalitiesSC$LAT),]
MunicipalitiesSC_NArows_LAT## # A tibble: 0 x 11
## # ... with 11 variables: CITY <chr>, STATE <chr>, CAPITAL <dbl>,
## # IBGE_PLANTED_AREA <dbl>, LONG <dbl>, LAT <dbl>, ALT <dbl>, AREA <dbl>,
## # RURAL_URBAN <chr>, TAXES <dbl>, GDP_CAPITA <dbl>MunicipalitiesSC_NArows_ALT <- MunicipalitiesSC[is.na(MunicipalitiesSC$ALT),]
MunicipalitiesSC_NArows_ALT## # A tibble: 0 x 11
## # ... with 11 variables: CITY <chr>, STATE <chr>, CAPITAL <dbl>,
## # IBGE_PLANTED_AREA <dbl>, LONG <dbl>, LAT <dbl>, ALT <dbl>, AREA <dbl>,
## # RURAL_URBAN <chr>, TAXES <dbl>, GDP_CAPITA <dbl>Performing NA checks again
MunicipalitiesSC[rowSums(is.na(MunicipalitiesSC))!=0,]## # A tibble: 1 x 11
## CITY STATE CAPITAL IBGE_PLANTED_AR~ LONG LAT ALT AREA RURAL_URBAN TAXES
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 Japu~ AM 0 420 -67.0 -1.88 69.8 NA Rural Remo~ 1490.
## # ... with 1 more variable: GDP_CAPITA <dbl>There is 1 row with missing area data.
Retrieving Japurá (AM) area data from source stated in Datadict: 55.827,203 km2 https://www.ibge.gov.br/geociencias/organizacao-do-territorio/estrutura-territorial/15761-areas-dos-municipios.html?t=acesso-ao-produto&c=1
MunicipalitiesSC$AREA[MunicipalitiesSC$CITY == "Japurá" & MunicipalitiesSC$STATE == "AM"] <- 55.827203Performing NA checks again
MunicipalitiesSC[rowSums(is.na(MunicipalitiesSC))!=0,]## # A tibble: 0 x 11
## # ... with 11 variables: CITY <chr>, STATE <chr>, CAPITAL <dbl>,
## # IBGE_PLANTED_AREA <dbl>, LONG <dbl>, LAT <dbl>, ALT <dbl>, AREA <dbl>,
## # RURAL_URBAN <chr>, TAXES <dbl>, GDP_CAPITA <dbl>There are no more NA data.
2.2.8 Editing Columns in MunicipalitiesSC
Multiple linear regression can only be performed on explanatory variables that are continuous or categorical.
Taking a look at the current columns/fields:
colnames(MunicipalitiesSC)## [1] "CITY" "STATE" "CAPITAL"
## [4] "IBGE_PLANTED_AREA" "LONG" "LAT"
## [7] "ALT" "AREA" "RURAL_URBAN"
## [10] "TAXES" "GDP_CAPITA"Datadict_cleaned## # A tibble: 11 x 6
## FIELD DESCRIPTION REFERENCE UNIT SOURCE X6
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 CITY "Name of the Ci~ <NA> <NA> - <NA>
## 2 STATE "Name of the St~ <NA> <NA> - <NA>
## 3 CAPITAL "1 if Capital o~ <NA> <NA> - <NA>
## 4 IBGE_PL~ "Planted Area (~ 2017 1 hectare (1 h~ https://sidra.ibge~ <NA>
## 5 LONG "City Latitude " 2010 - ftp://geoftp.ibge.~ <NA>
## 6 LAT "City Longitude~ 2010 - ftp://geoftp.ibge.~ <NA>
## 7 ALT "City Elevation~ 2010 1 meter ftp://geoftp.ibge.~ <NA>
## 8 AREA "City area (squ~ 2018 1 squared Kilo~ https://www.ibge.g~ <NA>
## 9 RURAL_U~ "Rural or Urban~ 2016 - https://www.ibge.g~ <NA>
## 10 TAXES "Taxes" 2016 $ 1,000 reais https://www.ibge.g~ <NA>
## 11 GDP_CAP~ "Gross Domestic~ 2016 - https://www.ibge.g~ <NA>- RURAL_Urban is categorical, but has different categories.
Editing the columns:
- Turning RURAL_URBAN into IS_URBAN, IS_RURAL and IS_INTERMEDIARY: Municipality takes on 1 if true and 0 if false
Municipalities_derived <- MunicipalitiesSC %>%
group_by(CITY) %>%
mutate(`IS_URBAN` = case_when(`RURAL_URBAN` == "Urbano" ~ 1, `RURAL_URBAN` != "Urbano" ~ 0) ) %>%
mutate(`IS_RURAL` = case_when(`RURAL_URBAN` == "Rural Remoto" | `RURAL_URBAN` == "Rural Adjacente" ~ 1,
`RURAL_URBAN` != "Rural Remoto" | `RURAL_URBAN` != "Rural Adjacente" ~ 0) ) %>%
mutate(`IS_INTERMEDIARY` = case_when(`RURAL_URBAN` == "Intermediário Adjacente" ~ 1, `RURAL_URBAN` != "Intermediário Adjacente" ~ 0) )
Municipalities_derived## # A tibble: 5,570 x 14
## # Groups: CITY [5,298]
## CITY STATE CAPITAL IBGE_PLANTED_AR~ LONG LAT ALT AREA RURAL_URBAN
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Abad~ GO 0 319 -49.4 -16.8 894. 147. Urbano
## 2 Abad~ MG 0 4479 -47.4 -18.5 753. 881. Rural Adja~
## 3 Abad~ GO 0 10307 -48.7 -16.2 1018. 1045. Rural Adja~
## 4 Abae~ MG 0 1862 -45.4 -19.2 645. 1817. Urbano
## 5 Abae~ PA 0 25200 -48.9 -1.72 10.1 1611. Urbano
## 6 Abai~ CE 0 2598 -39.0 -7.36 403. 180. Rural Adja~
## 7 Abaí~ BA 0 895 -41.7 -13.3 674. 539. Rural Remo~
## 8 Abaré BA 0 2058 -39.1 -8.72 316. 1605. Rural Remo~
## 9 Abat~ PR 0 1197 -50.3 -23.3 579. 229. Rural Adja~
## 10 Abdo~ SC 0 5502 -51.0 -27.6 721. 237. Rural Adja~
## # ... with 5,560 more rows, and 5 more variables: TAXES <dbl>,
## # GDP_CAPITA <dbl>, IS_URBAN <dbl>, IS_RURAL <dbl>, IS_INTERMEDIARY <dbl>Removing unnecessary columns
colstoremove <- list(
"RURAL_URBAN"
)
Municipalities_derived <- subset(Municipalities_derived, select = c(names(Municipalities_derived) %notin% colstoremove))
Municipalities_derived## # A tibble: 5,570 x 13
## # Groups: CITY [5,298]
## CITY STATE CAPITAL IBGE_PLANTED_AR~ LONG LAT ALT AREA TAXES
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Abad~ GO 0 319 -49.4 -16.8 894. 147. 2.06e4
## 2 Abad~ MG 0 4479 -47.4 -18.5 753. 881. 1.29e4
## 3 Abad~ GO 0 10307 -48.7 -16.2 1018. 1045. 2.68e4
## 4 Abae~ MG 0 1862 -45.4 -19.2 645. 1817. 2.70e4
## 5 Abae~ PA 0 25200 -48.9 -1.72 10.1 1611. 9.52e4
## 6 Abai~ CE 0 2598 -39.0 -7.36 403. 180. 4.04e3
## 7 Abaí~ BA 0 895 -41.7 -13.3 674. 539. 2.02e3
## 8 Abaré BA 0 2058 -39.1 -8.72 316. 1605. 6.21e0
## 9 Abat~ PR 0 1197 -50.3 -23.3 579. 229. 5.77e0
## 10 Abdo~ SC 0 5502 -51.0 -27.6 721. 237. 2.31e3
## # ... with 5,560 more rows, and 4 more variables: GDP_CAPITA <dbl>,
## # IS_URBAN <dbl>, IS_RURAL <dbl>, IS_INTERMEDIARY <dbl>2.2.9 Spatial join - Joining Municipalities5641_sf and MunicipalitiesSC_md
Convert Municipalities to sf object
Municipalities_tojoinwith <- st_as_sf(Municipalities_derived, coords = c("LONG","LAT"), crs=4674)Removing unnecessary columns
colstoremove <- list(
"CITY",
"STATE"
)
Municipalities_tojoinwith <- subset(Municipalities_tojoinwith, select = c(names(Municipalities_tojoinwith) %notin% colstoremove))
Municipalities_tojoinwith## Simple feature collection with 5570 features and 9 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: -72.9165 ymin: -53.01179 xmax: -32.43519 ymax: 4.58544
## CRS: EPSG:4674
## # A tibble: 5,570 x 10
## CAPITAL IBGE_PLANTED_AR~ ALT AREA TAXES GDP_CAPITA IS_URBAN IS_RURAL
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 319 894. 147. 2.06e4 20665. 1 0
## 2 0 4479 753. 881. 1.29e4 25592. 0 1
## 3 0 10307 1018. 1045. 2.68e4 15628. 0 1
## 4 0 1862 645. 1817. 2.70e4 18250. 1 0
## 5 0 25200 10.1 1611. 9.52e4 8222. 1 0
## 6 0 2598 403. 180. 4.04e3 6370. 0 1
## 7 0 895 674. 539. 2.02e3 6983. 0 1
## 8 0 2058 316. 1605. 6.21e0 6257. 0 1
## 9 0 1197 579. 229. 5.77e0 21174. 0 1
## 10 0 5502 721. 237. 2.31e3 24739. 0 1
## # ... with 5,560 more rows, and 2 more variables: IS_INTERMEDIARY <dbl>,
## # geometry <POINT [°]>Municipalities <- st_join(Municipalities_sf, Municipalities_tojoinwith)
Municipalities## Simple feature collection with 5572 features and 13 fields
## geometry type: GEOMETRY
## dimension: XY
## bbox: xmin: -73.99045 ymin: -33.75118 xmax: -28.83594 ymax: 5.271841
## CRS: 4674
## First 10 features:
## code_muni name_muni code_state abbrev_state CAPITAL
## 1 1100015 Alta Floresta D'oeste 11 RO 0
## 2 1100023 Ariquemes 11 RO 0
## 3 1100031 Cabixi 11 RO 0
## 4 1100049 Cacoal 11 RO 0
## 5 1100056 Cerejeiras 11 RO 0
## 6 1100064 Colorado Do Oeste 11 RO 0
## 7 1100072 Corumbiara 11 RO 0
## 8 1100080 Costa Marques 11 RO 0
## 9 1100098 Espigão D'oeste 11 RO 0
## 10 1100106 Guajará-Mirim 11 RO 0
## IBGE_PLANTED_AREA ALT AREA TAXES GDP_CAPITA IS_URBAN IS_RURAL
## 1 18288 337.74 7067.03 23186.17 18732.17 0 0
## 2 7115 138.69 4426.57 216095.93 20618.18 1 0
## 3 41086 236.06 1314.35 5.51 21202.96 0 1
## 4 18180 177.45 3792.89 194940.22 22130.78 1 0
## 5 52326 262.81 2783.30 58.16 22721.08 1 0
## 6 6268 419.09 1451.06 18466.33 16410.55 0 0
## 7 71742 269.17 3060.32 8.74 27040.60 0 1
## 8 1395 145.21 4987.18 7.06 12102.82 0 1
## 9 5588 262.53 4518.03 42149.00 16650.27 0 0
## 10 787 133.41 24855.72 97.10 15732.01 1 0
## IS_INTERMEDIARY geom
## 1 1 POLYGON ((-62.19465 -11.827...
## 2 0 POLYGON ((-62.53648 -9.7322...
## 3 0 POLYGON ((-60.37075 -13.363...
## 4 0 POLYGON ((-61.0008 -11.2973...
## 5 0 POLYGON ((-61.49976 -13.005...
## 6 1 POLYGON ((-60.50475 -12.966...
## 7 0 POLYGON ((-61.34273 -12.666...
## 8 0 POLYGON ((-63.71199 -11.650...
## 9 1 POLYGON ((-60.94827 -10.988...
## 10 0 POLYGON ((-65.37724 -10.431...Checking the values in Municipalities
summary(Municipalities)## code_muni name_muni code_state abbrev_state
## Min. :1100015 Bom Jesus : 5 31 : 853 MG : 853
## 1st Qu.:2512175 São Domingos: 5 35 : 645 SP : 645
## Median :3146354 Bonito : 4 43 : 499 RS : 499
## Mean :3253966 Santa Helena: 4 29 : 417 BA : 417
## 3rd Qu.:4119264 Santa Inês : 4 41 : 399 PR : 399
## Max. :5300108 Santa Luzia : 4 42 : 295 SC : 295
## (Other) :5546 (Other):2464 (Other):2464
## CAPITAL IBGE_PLANTED_AREA ALT AREA
## Min. :0.000000 Min. : 0 Min. : 0.0 Min. : 3.57
## 1st Qu.:0.000000 1st Qu.: 910 1st Qu.: 169.4 1st Qu.: 204.33
## Median :0.000000 Median : 3470 Median : 406.4 Median : 415.81
## Mean :0.004848 Mean : 14167 Mean : 893.4 Mean : 1514.99
## 3rd Qu.:0.000000 3rd Qu.: 11174 3rd Qu.: 629.1 3rd Qu.: 1025.52
## Max. :1.000000 Max. :1205669 Max. :874579.0 Max. :159533.33
## NA's :3 NA's :3 NA's :3 NA's :3
## TAXES GDP_CAPITA IS_URBAN IS_RURAL
## Min. : -14159 Min. : 3191 Min. :0.0000 Min. :0.0000
## 1st Qu.: 1305 1st Qu.: 9056 1st Qu.:0.0000 1st Qu.:0.0000
## Median : 5093 Median : 15866 Median :0.0000 Median :1.0000
## Mean : 118882 Mean : 21113 Mean :0.2614 Mean :0.6039
## 3rd Qu.: 22208 3rd Qu.: 26154 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :117125387 Max. :314638 Max. :1.0000 Max. :1.0000
## NA's :3 NA's :3 NA's :3 NA's :3
## IS_INTERMEDIARY geom
## Min. :0.0000 MULTIPOLYGON :2773
## 1st Qu.:0.0000 POLYGON :2799
## Median :0.0000 epsg:4674 : 0
## Mean :0.1232 +proj=long...: 0
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :3Municipalities_NArows <- Municipalities[is.na(Municipalities$IBGE_PLANTED_AREA),]
Municipalities_NArows## Simple feature collection with 3 features and 13 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: -53.63821 ymin: -33.61653 xmax: -50.53778 ymax: -18.7813
## CRS: 4674
## code_muni name_muni code_state abbrev_state CAPITAL
## 4607 4300001 Lagoa Mirim 43 RS NA
## 4608 4300002 Lagoa Dos Patos 43 RS NA
## 5163 5006275 Paraíso Das Águas 50 MS NA
## IBGE_PLANTED_AREA ALT AREA TAXES GDP_CAPITA IS_URBAN IS_RURAL
## 4607 NA NA NA NA NA NA NA
## 4608 NA NA NA NA NA NA NA
## 5163 NA NA NA NA NA NA NA
## IS_INTERMEDIARY geom
## 4607 NA POLYGON ((-52.62241 -32.146...
## 4608 NA POLYGON ((-51.2719 -30.0389...
## 5163 NA POLYGON ((-52.90027 -18.782...Removing these rows
toremove <- list(4300001, 4300002, 5006275)
Municipalities_woNA <- Municipalities[!(Municipalities$code_muni %in% toremove),]
summary(Municipalities_woNA)## code_muni name_muni code_state abbrev_state
## Min. :1100015 Bom Jesus : 5 31 : 853 MG : 853
## 1st Qu.:2512101 São Domingos: 5 35 : 645 SP : 645
## Median :3146255 Bonito : 4 43 : 497 RS : 497
## Mean :3253276 Santa Helena: 4 29 : 417 BA : 417
## 3rd Qu.:4119152 Santa Inês : 4 41 : 399 PR : 399
## Max. :5300108 Santa Luzia : 4 42 : 295 SC : 295
## (Other) :5543 (Other):2463 (Other):2463
## CAPITAL IBGE_PLANTED_AREA ALT AREA
## Min. :0.000000 Min. : 0 Min. : 0.0 Min. : 3.57
## 1st Qu.:0.000000 1st Qu.: 910 1st Qu.: 169.4 1st Qu.: 204.33
## Median :0.000000 Median : 3470 Median : 406.4 Median : 415.81
## Mean :0.004848 Mean : 14167 Mean : 893.4 Mean : 1514.99
## 3rd Qu.:0.000000 3rd Qu.: 11174 3rd Qu.: 629.1 3rd Qu.: 1025.52
## Max. :1.000000 Max. :1205669 Max. :874579.0 Max. :159533.33
##
## TAXES GDP_CAPITA IS_URBAN IS_RURAL
## Min. : -14159 Min. : 3191 Min. :0.0000 Min. :0.0000
## 1st Qu.: 1305 1st Qu.: 9056 1st Qu.:0.0000 1st Qu.:0.0000
## Median : 5093 Median : 15866 Median :0.0000 Median :1.0000
## Mean : 118882 Mean : 21113 Mean :0.2614 Mean :0.6039
## 3rd Qu.: 22208 3rd Qu.: 26154 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :117125387 Max. :314638 Max. :1.0000 Max. :1.0000
##
## IS_INTERMEDIARY geom
## Min. :0.0000 MULTIPOLYGON :2773
## 1st Qu.:0.0000 POLYGON :2796
## Median :0.0000 epsg:4674 : 0
## Mean :0.1232 +proj=long...: 0
## 3rd Qu.:0.0000
## Max. :1.0000
## The rows have been removed.
3 Choropleth Map - GDP Per Capita 2016 at the Municipality Level
Distribution of GDP per capita, 2016 at the Municipality Level
qtm(Municipalities, fill = "GDP_CAPITA")
Plotting Choropleth Maps by abbrev_state for zoomed in views
tmap_mode("plot")
tm_shape(Municipalities)+
tm_polygons("GDP_CAPITA") +
tm_facets(by = "abbrev_state") +
tm_layout(title.size = 20)
4 Exploratory Data Analysis
4.1 EDA using statistical graphics - Histograms
Plot the distribution of GDP_CAPITA
ggplot(data=Municipalities_woNA, aes(x=`GDP_CAPITA`)) +
geom_histogram(bins=20, color="black", fill="light blue")
Right-skewed distribution can be seen above.
4.2 Plotting Multiple Histograms - distribution of variables
CAPITAL <- ggplot(data=Municipalities_woNA, aes(x= `CAPITAL`)) +
geom_histogram(bins=20, color="black", fill="light blue")
IBGE_PLANTED_AREA <- ggplot(data=Municipalities_woNA, aes(x= `IBGE_PLANTED_AREA`)) +
geom_histogram(bins=20, color="black", fill="light blue")
ALT <- ggplot(data=Municipalities_woNA, aes(x= `ALT`)) +
geom_histogram(bins=20, color="black", fill="light blue")
AREA <- ggplot(data=Municipalities_woNA, aes(x= `AREA`)) +
geom_histogram(bins=20, color="black", fill="light blue")
TAXES <- ggplot(data=Municipalities_woNA, aes(x= `TAXES`)) +
geom_histogram(bins=20, color="black", fill="light blue")
IS_URBAN <- ggplot(data=Municipalities_woNA, aes(x= `IS_URBAN`)) +
geom_histogram(bins=20, color="black", fill="light blue")
IS_RURAL <- ggplot(data=Municipalities_woNA, aes(x= `IS_RURAL`)) +
geom_histogram(bins=20, color="black", fill="light blue")
IS_INTERMEDIARY <- ggplot(data=Municipalities_woNA, aes(x= `IS_INTERMEDIARY`)) +
geom_histogram(bins=20, color="black", fill="light blue")
ggarrange(CAPITAL,IBGE_PLANTED_AREA, ALT, AREA, TAXES, IS_URBAN, IS_RURAL, IS_INTERMEDIARY, ncol = 3, nrow = 4)
Most variables are right skewed and most have huge ranges.
5 Multiple Regression Model
5.1 Plotting a scatterplot matrix
There is a need to ensure that the independent variables used to build multiple regression models are not highly correlated.
Removing geometries, creating Municipalities_df
Municipalities_woNA_df <- as.data.frame(Municipalities_woNA)
Municipalities_woNA_df## code_muni name_muni code_state abbrev_state CAPITAL
## 1 1100015 Alta Floresta D'oeste 11 RO 0
## 2 1100023 Ariquemes 11 RO 0
## 3 1100031 Cabixi 11 RO 0
## 4 1100049 Cacoal 11 RO 0
## 5 1100056 Cerejeiras 11 RO 0
## 6 1100064 Colorado Do Oeste 11 RO 0
## 7 1100072 Corumbiara 11 RO 0
## 8 1100080 Costa Marques 11 RO 0
## 9 1100098 Espigão D'oeste 11 RO 0
## 10 1100106 Guajará-Mirim 11 RO 0
## 11 1100114 Jaru 11 RO 0
## 12 1100122 Ji-Paraná 11 RO 0
## 13 1100130 Machadinho D'oeste 11 RO 0
## 14 1100148 Nova Brasilândia D'oeste 11 RO 0
## 15 1100155 Ouro Preto Do Oeste 11 RO 0
## 16 1100189 Pimenta Bueno 11 RO 0
## 17 1100205 Porto Velho 11 RO 1
## 18 1100254 Presidente Médici 11 RO 0
## 19 1100262 Rio Crespo 11 RO 0
## 20 1100288 Rolim De Moura 11 RO 0
## 21 1100296 Santa Luzia D'oeste 11 RO 0
## 22 1100304 Vilhena 11 RO 0
## 23 1100320 São Miguel Do Guaporé 11 RO 0
## 24 1100338 Nova Mamoré 11 RO 0
## 25 1100346 Alvorada D'oeste 11 RO 0
## 26 1100379 Alto Alegre Dos Parecis 11 RO 0
## 27 1100403 Alto Paraíso 11 RO 0
## 28 1100452 Buritis 11 RO 0
## 29 1100502 Novo Horizonte Do Oeste 11 RO 0
## 30 1100601 Cacaulândia 11 RO 0
## 31 1100700 Campo Novo De Rondônia 11 RO 0
## 32 1100809 Candeias Do Jamari 11 RO 0
## 33 1100908 Castanheiras 11 RO 0
## 34 1100924 Chupinguaia 11 RO 0
## 35 1100940 Cujubim 11 RO 0
## 36 1101005 Governador Jorge Teixeira 11 RO 0
## 37 1101104 Itapuã Do Oeste 11 RO 0
## 38 1101203 Ministro Andreazza 11 RO 0
## 39 1101302 Mirante Da Serra 11 RO 0
## 40 1101401 Monte Negro 11 RO 0
## 41 1101435 Nova União 11 RO 0
## 42 1101450 Parecis 11 RO 0
## 43 1101468 Pimenteiras Do Oeste 11 RO 0
## 44 1101476 Primavera De Rondônia 11 RO 0
## 45 1101484 São Felipe D'oeste 11 RO 0
## 46 1101492 São Francisco Do Guaporé 11 RO 0
## 47 1101500 Seringueiras 11 RO 0
## 48 1101559 Teixeirópolis 11 RO 0
## 49 1101609 Theobroma 11 RO 0
## 50 1101708 Urupá 11 RO 0
## 51 1101757 Vale Do Anari 11 RO 0
## 52 1101807 Vale Do Paraíso 11 RO 0
## 53 1200013 Acrelândia 12 AC 0
## 54 1200054 Assis Brasil 12 AC 0
## 55 1200104 Brasiléia 12 AC 0
## 56 1200138 Bujari 12 AC 0
## 57 1200179 Capixaba 12 AC 0
## 58 1200203 Cruzeiro Do Sul 12 AC 0
## 59 1200252 Epitaciolândia 12 AC 0
## 60 1200302 Feijó 12 AC 0
## 61 1200328 Jordão 12 AC 0
## 62 1200336 Mâncio Lima 12 AC 0
## 63 1200344 Manoel Urbano 12 AC 0
## 64 1200351 Marechal Thaumaturgo 12 AC 0
## 65 1200385 Plácido De Castro 12 AC 0
## 66 1200393 Porto Walter 12 AC 0
## 67 1200401 Rio Branco 12 AC 1
## 68 1200427 Rodrigues Alves 12 AC 0
## 69 1200435 Santa Rosa Do Purus 12 AC 0
## 70 1200450 Senador Guiomard 12 AC 0
## 71 1200500 Sena Madureira 12 AC 0
## 72 1200609 Tarauacá 12 AC 0
## 73 1200708 Xapuri 12 AC 0
## 74 1200807 Porto Acre 12 AC 0
## 75 1300029 Alvarães 13 AM 0
## 76 1300060 Amaturá 13 AM 0
## 77 1300086 Anamã 13 AM 0
## 78 1300102 Anori 13 AM 0
## 79 1300144 Apuí 13 AM 0
## 80 1300201 Atalaia Do Norte 13 AM 0
## 81 1300300 Autazes 13 AM 0
## 82 1300409 Barcelos 13 AM 0
## 83 1300508 Barreirinha 13 AM 0
## 84 1300607 Benjamin Constant 13 AM 0
## 85 1300631 Beruri 13 AM 0
## 86 1300680 Boa Vista Do Ramos 13 AM 0
## 87 1300706 Boca Do Acre 13 AM 0
## 88 1300805 Borba 13 AM 0
## 89 1300839 Caapiranga 13 AM 0
## 90 1300904 Canutama 13 AM 0
## 91 1301001 Carauari 13 AM 0
## 92 1301100 Careiro 13 AM 0
## 93 1301159 Careiro Da Várzea 13 AM 0
## 94 1301209 Coari 13 AM 0
## 95 1301308 Codajás 13 AM 0
## 96 1301407 Eirunepé 13 AM 0
## 97 1301506 Envira 13 AM 0
## 98 1301605 Fonte Boa 13 AM 0
## 99 1301654 Guajará 13 AM 0
## 100 1301704 Humaitá 13 AM 0
## 101 1301803 Ipixuna 13 AM 0
## 102 1301852 Iranduba 13 AM 0
## 103 1301902 Itacoatiara 13 AM 0
## 104 1301951 Itamarati 13 AM 0
## 105 1302009 Itapiranga 13 AM 0
## 106 1302108 Japurá 13 AM 0
## 107 1302207 Juruá 13 AM 0
## 108 1302306 Jutaí 13 AM 0
## 109 1302405 Lábrea 13 AM 0
## 110 1302504 Manacapuru 13 AM 0
## 111 1302553 Manaquiri 13 AM 0
## 112 1302603 Manaus 13 AM 1
## 113 1302702 Manicoré 13 AM 0
## 114 1302801 Maraã 13 AM 0
## 115 1302900 Maués 13 AM 0
## 116 1303007 Nhamundá 13 AM 0
## 117 1303106 Nova Olinda Do Norte 13 AM 0
## 118 1303205 Novo Airão 13 AM 0
## 119 1303304 Novo Aripuanã 13 AM 0
## 120 1303403 Parintins 13 AM 0
## 121 1303502 Pauini 13 AM 0
## 122 1303536 Presidente Figueiredo 13 AM 0
## 123 1303569 Rio Preto Da Eva 13 AM 0
## 124 1303601 Santa Isabel Do Rio Negro 13 AM 0
## 125 1303700 Santo Antônio Do Içá 13 AM 0
## 126 1303809 São Gabriel Da Cachoeira 13 AM 0
## 127 1303908 São Paulo De Olivença 13 AM 0
## 128 1303957 São Sebastião Do Uatumã 13 AM 0
## 129 1304005 Silves 13 AM 0
## 130 1304062 Tabatinga 13 AM 0
## 131 1304104 Tapauá 13 AM 0
## 132 1304203 Tefé 13 AM 0
## 133 1304237 Tonantins 13 AM 0
## 134 1304260 Uarini 13 AM 0
## 135 1304302 Urucará 13 AM 0
## 136 1304401 Urucurituba 13 AM 0
## 137 1400027 Amajari 14 RR 0
## 138 1400050 Alto Alegre 14 RR 0
## 139 1400100 Boa Vista 14 RR 1
## 140 1400159 Bonfim 14 RR 0
## 141 1400175 Cantá 14 RR 0
## 142 1400209 Caracaraí 14 RR 0
## 143 1400233 Caroebe 14 RR 0
## 144 1400282 Iracema 14 RR 0
## 145 1400308 Mucajaí 14 RR 0
## 146 1400407 Normandia 14 RR 0
## 147 1400456 Pacaraima 14 RR 0
## 148 1400472 Rorainópolis 14 RR 0
## 149 1400506 São João Da Baliza 14 RR 0
## 150 1400605 São Luiz 14 RR 0
## 151 1400704 Uiramutã 14 RR 0
## 152 1500107 Abaetetuba 15 PA 0
## 153 1500131 Abel Figueiredo 15 PA 0
## 154 1500206 Acará 15 PA 0
## 155 1500305 Afuá 15 PA 0
## 156 1500347 Água Azul Do Norte 15 PA 0
## 157 1500404 Alenquer 15 PA 0
## 158 1500503 Almeirim 15 PA 0
## 159 1500602 Altamira 15 PA 0
## 160 1500701 Anajás 15 PA 0
## 161 1500800 Ananindeua 15 PA 0
## 162 1500859 Anapu 15 PA 0
## 163 1500909 Augusto Corrêa 15 PA 0
## 164 1500958 Aurora Do Pará 15 PA 0
## 165 1501006 Aveiro 15 PA 0
## 166 1501105 Bagre 15 PA 0
## 167 1501204 Baião 15 PA 0
## 168 1501253 Bannach 15 PA 0
## 169 1501303 Barcarena 15 PA 0
## 170 1501402 Belém 15 PA 1
## 171 1501451 Belterra 15 PA 0
## 172 1501501 Benevides 15 PA 0
## 173 1501576 Bom Jesus Do Tocantins 15 PA 0
## 174 1501600 Bonito 15 PA 0
## 175 1501709 Bragança 15 PA 0
## 176 1501725 Brasil Novo 15 PA 0
## 177 1501758 Brejo Grande Do Araguaia 15 PA 0
## 178 1501782 Breu Branco 15 PA 0
## 179 1501808 Breves 15 PA 0
## 180 1501907 Bujaru 15 PA 0
## 181 1501956 Cachoeira Do Piriá 15 PA 0
## 182 1502004 Cachoeira Do Arari 15 PA 0
## 183 1502103 Cametá 15 PA 0
## 184 1502152 Canaã Dos Carajás 15 PA 0
## 185 1502202 Capanema 15 PA 0
## 186 1502301 Capitão Poço 15 PA 0
## 187 1502400 Castanhal 15 PA 0
## 188 1502509 Chaves 15 PA 0
## 189 1502608 Colares 15 PA 0
## 190 1502707 Conceição Do Araguaia 15 PA 0
## 191 1502756 Concórdia Do Pará 15 PA 0
## 192 1502764 Cumaru Do Norte 15 PA 0
## 193 1502772 Curionópolis 15 PA 0
## 194 1502806 Curralinho 15 PA 0
## 195 1502855 Curuá 15 PA 0
## 196 1502905 Curuçá 15 PA 0
## 197 1502939 Dom Eliseu 15 PA 0
## 198 1502954 Eldorado Do Carajás 15 PA 0
## 199 1503002 Faro 15 PA 0
## 200 1503044 Floresta Do Araguaia 15 PA 0
## 201 1503077 Garrafão Do Norte 15 PA 0
## 202 1503093 Goianésia Do Pará 15 PA 0
## 203 1503101 Gurupá 15 PA 0
## 204 1503200 Igarapé-Açu 15 PA 0
## 205 1503309 Igarapé-Miri 15 PA 0
## 206 1503408 Inhangapi 15 PA 0
## 207 1503457 Ipixuna Do Pará 15 PA 0
## 208 1503507 Irituia 15 PA 0
## 209 1503606 Itaituba 15 PA 0
## 210 1503705 Itupiranga 15 PA 0
## 211 1503754 Jacareacanga 15 PA 0
## 212 1503804 Jacundá 15 PA 0
## 213 1503903 Juruti 15 PA 0
## 214 1504000 Limoeiro Do Ajuru 15 PA 0
## 215 1504059 Mãe Do Rio 15 PA 0
## 216 1504109 Magalhães Barata 15 PA 0
## 217 1504208 Marabá 15 PA 0
## 218 1504307 Maracanã 15 PA 0
## 219 1504406 Marapanim 15 PA 0
## 220 1504422 Marituba 15 PA 0
## 221 1504455 Medicilândia 15 PA 0
## 222 1504505 Melgaço 15 PA 0
## 223 1504604 Mocajuba 15 PA 0
## 224 1504703 Moju 15 PA 0
## 225 1504752 Mojuí Dos Campos 15 PA 0
## 226 1504802 Monte Alegre 15 PA 0
## 227 1504901 Muaná 15 PA 0
## 228 1504950 Nova Esperança Do Piriá 15 PA 0
## 229 1504976 Nova Ipixuna 15 PA 0
## 230 1505007 Nova Timboteua 15 PA 0
## 231 1505031 Novo Progresso 15 PA 0
## 232 1505064 Novo Repartimento 15 PA 0
## 233 1505106 Óbidos 15 PA 0
## 234 1505205 Oeiras Do Pará 15 PA 0
## 235 1505304 Oriximiná 15 PA 0
## 236 1505403 Ourém 15 PA 0
## 237 1505437 Ourilândia Do Norte 15 PA 0
## 238 1505486 Pacajá 15 PA 0
## 239 1505494 Palestina Do Pará 15 PA 0
## 240 1505502 Paragominas 15 PA 0
## 241 1505536 Parauapebas 15 PA 0
## 242 1505551 Pau D'arco 15 PA 0
## 243 1505601 Peixe-Boi 15 PA 0
## 244 1505635 Piçarra 15 PA 0
## 245 1505650 Placas 15 PA 0
## 246 1505700 Ponta De Pedras 15 PA 0
## 247 1505809 Portel 15 PA 0
## 248 1505908 Porto De Moz 15 PA 0
## 249 1506005 Prainha 15 PA 0
## 250 1506104 Primavera 15 PA 0
## 251 1506112 Quatipuru 15 PA 0
## 252 1506138 Redenção 15 PA 0
## 253 1506161 Rio Maria 15 PA 0
## 254 1506187 Rondon Do Pará 15 PA 0
## 255 1506195 Rurópolis 15 PA 0
## 256 1506203 Salinópolis 15 PA 0
## 257 1506302 Salvaterra 15 PA 0
## 258 1506351 Santa Bárbara Do Pará 15 PA 0
## 259 1506401 Santa Cruz Do Arari 15 PA 0
## 260 1506500 Santa Izabel Do Pará 15 PA 0
## 261 1506559 Santa Luzia Do Pará 15 PA 0
## 262 1506583 Santa Maria Das Barreiras 15 PA 0
## 263 1506609 Santa Maria Do Pará 15 PA 0
## 264 1506708 Santana Do Araguaia 15 PA 0
## 265 1506807 Santarém 15 PA 0
## 266 1506906 Santarém Novo 15 PA 0
## 267 1507003 Santo Antônio Do Tauá 15 PA 0
## 268 1507102 São Caetano De Odivelas 15 PA 0
## 269 1507151 São Domingos Do Araguaia 15 PA 0
## 270 1507201 São Domingos Do Capim 15 PA 0
## 271 1507300 São Félix Do Xingu 15 PA 0
## 272 1507409 São Francisco Do Pará 15 PA 0
## 273 1507458 São Geraldo Do Araguaia 15 PA 0
## 274 1507466 São João Da Ponta 15 PA 0
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## 2424 3116308 Cipotânea 31 MG 0
## 2425 3116407 Claraval 31 MG 0
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## 2427 3116605 Cláudio 31 MG 0
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## 2429 3116803 Coluna 31 MG 0
## 2430 3116902 Comendador Gomes 31 MG 0
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## 2433 3117207 Conceição Das Pedras 31 MG 0
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## 2437 3117603 Conceição Do Pará 31 MG 0
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## 2443 3118007 Congonhas 31 MG 0
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## 2485 3121704 Diogo De Vasconcelos 31 MG 0
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## 2705 3140001 Mariana 31 MG 0
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## 2710 3140407 Marmelópolis 31 MG 0
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## 2721 3141207 Matutina 31 MG 0
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## 2724 3141504 Mendes Pimentel 31 MG 0
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## 2806 3148103 Patrocínio 31 MG 0
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## 2808 3148301 Paula Cândido 31 MG 0
## 2809 3148400 Paulistas 31 MG 0
## 2810 3148509 Pavão 31 MG 0
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## 2814 3148806 Pedra Do Anta 31 MG 0
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## 2816 3149002 Pedra Dourada 31 MG 0
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## 2818 3149150 Pedras De Maria Da Cruz 31 MG 0
## 2819 3149200 Pedrinópolis 31 MG 0
## 2820 3149309 Pedro Leopoldo 31 MG 0
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## 2828 3150000 Pescador 31 MG 0
## 2829 3150109 Piau 31 MG 0
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## 2836 3150570 Pintópolis 31 MG 0
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## 2850 3151909 Pocrane 31 MG 0
## 2851 3152006 Pompéu 31 MG 0
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## 2853 3152131 Ponto Chique 31 MG 0
## 2854 3152170 Ponto Dos Volantes 31 MG 0
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## 2856 3152303 Porto Firme 31 MG 0
## 2857 3152402 Poté 31 MG 0
## 2858 3152501 Pouso Alegre 31 MG 0
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## 2863 3153004 Pratinha 31 MG 0
## 2864 3153103 Presidente Bernardes 31 MG 0
## 2865 3153202 Presidente Juscelino 31 MG 0
## 2866 3153301 Presidente Kubitschek 31 MG 0
## 2867 3153400 Presidente Olegário 31 MG 0
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## 2869 3153608 Prudente De Morais 31 MG 0
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## 2878 3154408 Ressaquinha 31 MG 0
## 2879 3154457 Riachinho 31 MG 0
## 2880 3154507 Riacho Dos Machados 31 MG 0
## 2881 3154606 Ribeirão Das Neves 31 MG 0
## 2882 3154705 Ribeirão Vermelho 31 MG 0
## 2883 3154804 Rio Acima 31 MG 0
## 2884 3154903 Rio Casca 31 MG 0
## 2885 3155009 Rio Doce 31 MG 0
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## 2887 3155207 Rio Espera 31 MG 0
## 2888 3155306 Rio Manso 31 MG 0
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## 2893 3155801 Rio Pomba 31 MG 0
## 2894 3155900 Rio Preto 31 MG 0
## 2895 3156007 Rio Vermelho 31 MG 0
## 2896 3156106 Ritápolis 31 MG 0
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## 2899 3156403 Romaria 31 MG 0
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## 2904 3156809 Sabinópolis 31 MG 0
## 2905 3156908 Sacramento 31 MG 0
## 2906 3157005 Salinas 31 MG 0
## 2907 3157104 Salto Da Divisa 31 MG 0
## 2908 3157203 Santa Bárbara 31 MG 0
## 2909 3157252 Santa Bárbara Do Leste 31 MG 0
## 2910 3157278 Santa Bárbara Do Monte Verde 31 MG 0
## 2911 3157302 Santa Bárbara Do Tugúrio 31 MG 0
## 2912 3157336 Santa Cruz De Minas 31 MG 0
## 2913 3157377 Santa Cruz De Salinas 31 MG 0
## 2914 3157401 Santa Cruz Do Escalvado 31 MG 0
## 2915 3157500 Santa Efigênia De Minas 31 MG 0
## 2916 3157609 Santa Fé De Minas 31 MG 0
## 2917 3157658 Santa Helena De Minas 31 MG 0
## 2918 3157708 Santa Juliana 31 MG 0
## 2919 3157807 Santa Luzia 31 MG 0
## 2920 3157906 Santa Margarida 31 MG 0
## 2921 3158003 Santa Maria De Itabira 31 MG 0
## 2922 3158102 Santa Maria Do Salto 31 MG 0
## 2923 3158201 Santa Maria Do Suaçuí 31 MG 0
## 2924 3158300 Santana Da Vargem 31 MG 0
## 2925 3158409 Santana De Cataguases 31 MG 0
## 2926 3158508 Santana De Pirapama 31 MG 0
## 2927 3158607 Santana Do Deserto 31 MG 0
## 2928 3158706 Santana Do Garambéu 31 MG 0
## 2929 3158805 Santana Do Jacaré 31 MG 0
## 2930 3158904 Santana Do Manhuaçu 31 MG 0
## 2931 3158953 Santana Do Paraíso 31 MG 0
## 2932 3159001 Santana Do Riacho 31 MG 0
## 2933 3159100 Santana Dos Montes 31 MG 0
## 2934 3159209 Santa Rita De Caldas 31 MG 0
## 2935 3159308 Santa Rita De Jacutinga 31 MG 0
## 2936 3159357 Santa Rita De Minas 31 MG 0
## 2937 3159407 Santa Rita De Ibitipoca 31 MG 0
## 2938 3159506 Santa Rita Do Itueto 31 MG 0
## 2939 3159605 Santa Rita Do Sapucaí 31 MG 0
## 2940 3159704 Santa Rosa Da Serra 31 MG 0
## 2941 3159803 Santa Vitória 31 MG 0
## 2942 3159902 Santo Antônio Do Amparo 31 MG 0
## 2943 3160009 Santo Antônio Do Aventureiro 31 MG 0
## 2944 3160108 Santo Antônio Do Grama 31 MG 0
## 2945 3160207 Santo Antônio Do Itambé 31 MG 0
## 2946 3160306 Santo Antônio Do Jacinto 31 MG 0
## 2947 3160405 Santo Antônio Do Monte 31 MG 0
## 2948 3160454 Santo Antônio Do Retiro 31 MG 0
## 2949 3160504 Santo Antônio Do Rio Abaixo 31 MG 0
## 2950 3160603 Santo Hipólito 31 MG 0
## 2951 3160702 Santos Dumont 31 MG 0
## 2952 3160801 São Bento Abade 31 MG 0
## 2953 3160900 São Brás Do Suaçuí 31 MG 0
## 2954 3160959 São Domingos Das Dores 31 MG 0
## 2955 3161007 São Domingos Do Prata 31 MG 0
## 2956 3161056 São Félix De Minas 31 MG 0
## 2957 3161106 São Francisco 31 MG 0
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## 5103 4323754 Vitória Das Missões 43 RS 0
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## 5105 4323804 Xangri-Lá 43 RS 0
## 5106 5000203 Água Clara 50 MS 0
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## 5125 5002704 Campo Grande 50 MS 1
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## 5129 5003108 Corguinho 50 MS 0
## 5130 5003157 Coronel Sapucaia 50 MS 0
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## 5132 5003256 Costa Rica 50 MS 0
## 5133 5003306 Coxim 50 MS 0
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## 5142 5004106 Guia Lopes Da Laguna 50 MS 0
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## 5185 5100102 Acorizal 51 MT 0
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## 5188 5100300 Alto Araguaia 51 MT 0
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## 5196 5101258 Araputanga 51 MT 0
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## 5199 5101605 Barão De Melgaço 51 MT 0
## 5200 5101704 Barra Do Bugres 51 MT 0
## 5201 5101803 Barra Do Garças 51 MT 0
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## 5205 5102603 Campinápolis 51 MT 0
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## 5208 5102686 Campos De Júlio 51 MT 0
## 5209 5102694 Canabrava Do Norte 51 MT 0
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## 5212 5102850 Castanheira 51 MT 0
## 5213 5103007 Chapada Dos Guimarães 51 MT 0
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## 5222 5103403 Cuiabá 51 MT 1
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## 5227 5103700 Feliz Natal 51 MT 0
## 5228 5103809 Figueirópolis D'oeste 51 MT 0
## 5229 5103858 Gaúcha Do Norte 51 MT 0
## 5230 5103908 General Carneiro 51 MT 0
## 5231 5103957 Glória D'oeste 51 MT 0
## 5232 5104104 Guarantã Do Norte 51 MT 0
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## 5234 5104500 Indiavaí 51 MT 0
## 5235 5104526 Ipiranga Do Norte 51 MT 0
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## 5247 5105259 Lucas Do Rio Verde 51 MT 0
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## 5249 5105507 Vila Bela Da Santíssima Trindade 51 MT 0
## 5250 5105580 Marcelândia 51 MT 0
## 5251 5105606 Matupá 51 MT 0
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## 5254 5106000 Nortelândia 51 MT 0
## 5255 5106109 Nossa Senhora Do Livramento 51 MT 0
## 5256 5106158 Nova Bandeirantes 51 MT 0
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## 5263 5106232 Nova Olímpia 51 MT 0
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## 5266 5106265 Novo Mundo 51 MT 0
## 5267 5106273 Novo Horizonte Do Norte 51 MT 0
## 5268 5106281 Novo São Joaquim 51 MT 0
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## 5270 5106307 Paranatinga 51 MT 0
## 5271 5106315 Novo Santo Antônio 51 MT 0
## 5272 5106372 Pedra Preta 51 MT 0
## 5273 5106422 Peixoto De Azevedo 51 MT 0
## 5274 5106455 Planalto Da Serra 51 MT 0
## 5275 5106505 Poconé 51 MT 0
## 5276 5106653 Pontal Do Araguaia 51 MT 0
## 5277 5106703 Ponte Branca 51 MT 0
## 5278 5106752 Pontes E Lacerda 51 MT 0
## 5279 5106778 Porto Alegre Do Norte 51 MT 0
## 5280 5106802 Porto Dos Gaúchos 51 MT 0
## 5281 5106828 Porto Esperidião 51 MT 0
## 5282 5106851 Porto Estrela 51 MT 0
## 5283 5107008 Poxoréu 51 MT 0
## 5284 5107040 Primavera Do Leste 51 MT 0
## 5285 5107065 Querência 51 MT 0
## 5286 5107107 São José Dos Quatro Marcos 51 MT 0
## 5287 5107156 Reserva Do Cabaçal 51 MT 0
## 5288 5107180 Ribeirão Cascalheira 51 MT 0
## 5289 5107198 Ribeirãozinho 51 MT 0
## 5290 5107206 Rio Branco 51 MT 0
## 5291 5107248 Santa Carmem 51 MT 0
## 5292 5107263 Santo Afonso 51 MT 0
## 5293 5107297 São José Do Povo 51 MT 0
## 5294 5107305 São José Do Rio Claro 51 MT 0
## 5295 5107354 São José Do Xingu 51 MT 0
## 5296 5107404 São Pedro Da Cipa 51 MT 0
## 5297 5107578 Rondolândia 51 MT 0
## 5298 5107602 Rondonópolis 51 MT 0
## 5299 5107701 Rosário Oeste 51 MT 0
## 5300 5107743 Santa Cruz Do Xingu 51 MT 0
## 5301 5107750 Salto Do Céu 51 MT 0
## 5302 5107768 Santa Rita Do Trivelato 51 MT 0
## 5303 5107776 Santa Terezinha 51 MT 0
## 5304 5107792 Santo Antônio Do Leste 51 MT 0
## 5305 5107800 Santo Antônio Do Leverger 51 MT 0
## 5306 5107859 São Félix Do Araguaia 51 MT 0
## 5307 5107875 Sapezal 51 MT 0
## 5308 5107883 Serra Nova Dourada 51 MT 0
## 5309 5107909 Sinop 51 MT 0
## 5310 5107925 Sorriso 51 MT 0
## 5311 5107941 Tabaporã 51 MT 0
## 5312 5107958 Tangará Da Serra 51 MT 0
## 5313 5108006 Tapurah 51 MT 0
## 5314 5108055 Terra Nova Do Norte 51 MT 0
## 5315 5108105 Tesouro 51 MT 0
## 5316 5108204 Torixoréu 51 MT 0
## 5317 5108303 União Do Sul 51 MT 0
## 5318 5108352 Vale De São Domingos 51 MT 0
## 5319 5108402 Várzea Grande 51 MT 0
## 5320 5108501 Vera 51 MT 0
## 5321 5108600 Vila Rica 51 MT 0
## 5322 5108808 Nova Guarita 51 MT 0
## 5323 5108857 Nova Marilândia 51 MT 0
## 5324 5108907 Nova Maringá 51 MT 0
## 5325 5108956 Nova Monte Verde 51 MT 0
## 5326 5200050 Abadia De Goiás 52 GO 0
## 5327 5200100 Abadiânia 52 GO 0
## 5328 5200134 Acreúna 52 GO 0
## 5329 5200159 Adelândia 52 GO 0
## 5330 5200175 Água Fria De Goiás 52 GO 0
## 5331 5200209 Água Limpa 52 GO 0
## 5332 5200258 Águas Lindas De Goiás 52 GO 0
## 5333 5200308 Alexânia 52 GO 0
## 5334 5200506 Aloândia 52 GO 0
## 5335 5200555 Alto Horizonte 52 GO 0
## 5336 5200605 Alto Paraíso De Goiás 52 GO 0
## 5337 5200803 Alvorada Do Norte 52 GO 0
## 5338 5200829 Amaralina 52 GO 0
## 5339 5200852 Americano Do Brasil 52 GO 0
## 5340 5200902 Amorinópolis 52 GO 0
## 5341 5201108 Anápolis 52 GO 0
## 5342 5201207 Anhanguera 52 GO 0
## 5343 5201306 Anicuns 52 GO 0
## 5344 5201405 Aparecida De Goiânia 52 GO 0
## 5345 5201454 Aparecida Do Rio Doce 52 GO 0
## 5346 5201504 Aporé 52 GO 0
## 5347 5201603 Araçu 52 GO 0
## 5348 5201702 Aragarças 52 GO 0
## 5349 5201801 Aragoiânia 52 GO 0
## 5350 5202155 Araguapaz 52 GO 0
## 5351 5202353 Arenópolis 52 GO 0
## 5352 5202502 Aruanã 52 GO 0
## 5353 5202601 Aurilândia 52 GO 0
## 5354 5202809 Avelinópolis 52 GO 0
## 5355 5203104 Baliza 52 GO 0
## 5356 5203203 Barro Alto 52 GO 0
## 5357 5203302 Bela Vista De Goiás 52 GO 0
## 5358 5203401 Bom Jardim De Goiás 52 GO 0
## 5359 5203500 Bom Jesus De Goiás 52 GO 0
## 5360 5203559 Bonfinópolis 52 GO 0
## 5361 5203575 Bonópolis 52 GO 0
## 5362 5203609 Brazabrantes 52 GO 0
## 5363 5203807 Britânia 52 GO 0
## 5364 5203906 Buriti Alegre 52 GO 0
## 5365 5203939 Buriti De Goiás 52 GO 0
## 5366 5203962 Buritinópolis 52 GO 0
## 5367 5204003 Cabeceiras 52 GO 0
## 5368 5204102 Cachoeira Alta 52 GO 0
## 5369 5204201 Cachoeira De Goiás 52 GO 0
## 5370 5204250 Cachoeira Dourada 52 GO 0
## 5371 5204300 Caçu 52 GO 0
## 5372 5204409 Caiapônia 52 GO 0
## 5373 5204508 Caldas Novas 52 GO 0
## 5374 5204557 Caldazinha 52 GO 0
## 5375 5204607 Campestre De Goiás 52 GO 0
## 5376 5204656 Campinaçu 52 GO 0
## 5377 5204706 Campinorte 52 GO 0
## 5378 5204805 Campo Alegre De Goiás 52 GO 0
## 5379 5204854 Campo Limpo De Goiás 52 GO 0
## 5380 5204904 Campos Belos 52 GO 0
## 5381 5204953 Campos Verdes 52 GO 0
## 5382 5205000 Carmo Do Rio Verde 52 GO 0
## 5383 5205059 Castelândia 52 GO 0
## 5384 5205109 Catalão 52 GO 0
## 5385 5205208 Caturaí 52 GO 0
## 5386 5205307 Cavalcante 52 GO 0
## 5387 5205406 Ceres 52 GO 0
## 5388 5205455 Cezarina 52 GO 0
## 5389 5205471 Chapadão Do Céu 52 GO 0
## 5390 5205497 Cidade Ocidental 52 GO 0
## 5391 5205513 Cocalzinho De Goiás 52 GO 0
## 5392 5205521 Colinas Do Sul 52 GO 0
## 5393 5205703 Córrego Do Ouro 52 GO 0
## 5394 5205802 Corumbá De Goiás 52 GO 0
## 5395 5205901 Corumbaíba 52 GO 0
## 5396 5206206 Cristalina 52 GO 0
## 5397 5206305 Cristianópolis 52 GO 0
## 5398 5206404 Crixás 52 GO 0
## 5399 5206503 Cromínia 52 GO 0
## 5400 5206602 Cumari 52 GO 0
## 5401 5206701 Damianópolis 52 GO 0
## 5402 5206800 Damolândia 52 GO 0
## 5403 5206909 Davinópolis 52 GO 0
## 5404 5207105 Diorama 52 GO 0
## 5405 5207253 Doverlândia 52 GO 0
## 5406 5207352 Edealina 52 GO 0
## 5407 5207402 Edéia 52 GO 0
## 5408 5207501 Estrela Do Norte 52 GO 0
## 5409 5207535 Faina 52 GO 0
## 5410 5207600 Fazenda Nova 52 GO 0
## 5411 5207808 Firminópolis 52 GO 0
## 5412 5207907 Flores De Goiás 52 GO 0
## 5413 5208004 Formosa 52 GO 0
## 5414 5208103 Formoso 52 GO 0
## 5415 5208152 Gameleira De Goiás 52 GO 0
## 5416 5208301 Divinópolis De Goiás 52 GO 0
## 5417 5208400 Goianápolis 52 GO 0
## 5418 5208509 Goiandira 52 GO 0
## 5419 5208608 Goianésia 52 GO 0
## 5420 5208707 Goiânia 52 GO 1
## 5421 5208806 Goianira 52 GO 0
## 5422 5208905 Goiás 52 GO 0
## 5423 5209101 Goiatuba 52 GO 0
## 5424 5209150 Gouvelândia 52 GO 0
## 5425 5209200 Guapó 52 GO 0
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## 5430 5209705 Hidrolândia 52 GO 0
## 5431 5209804 Hidrolina 52 GO 0
## 5432 5209903 Iaciara 52 GO 0
## 5433 5209937 Inaciolândia 52 GO 0
## 5434 5209952 Indiara 52 GO 0
## 5435 5210000 Inhumas 52 GO 0
## 5436 5210109 Ipameri 52 GO 0
## 5437 5210158 Ipiranga De Goiás 52 GO 0
## 5438 5210208 Iporá 52 GO 0
## 5439 5210307 Israelândia 52 GO 0
## 5440 5210406 Itaberaí 52 GO 0
## 5441 5210562 Itaguari 52 GO 0
## 5442 5210604 Itaguaru 52 GO 0
## 5443 5210802 Itajá 52 GO 0
## 5444 5210901 Itapaci 52 GO 0
## 5445 5211008 Itapirapuã 52 GO 0
## 5446 5211206 Itapuranga 52 GO 0
## 5447 5211305 Itarumã 52 GO 0
## 5448 5211404 Itauçu 52 GO 0
## 5449 5211503 Itumbiara 52 GO 0
## 5450 5211602 Ivolândia 52 GO 0
## 5451 5211701 Jandaia 52 GO 0
## 5452 5211800 Jaraguá 52 GO 0
## 5453 5211909 Jataí 52 GO 0
## 5454 5212006 Jaupaci 52 GO 0
## 5455 5212055 Jesúpolis 52 GO 0
## 5456 5212105 Joviânia 52 GO 0
## 5457 5212204 Jussara 52 GO 0
## 5458 5212253 Lagoa Santa 52 GO 0
## 5459 5212303 Leopoldo De Bulhões 52 GO 0
## 5460 5212501 Luziânia 52 GO 0
## 5461 5212600 Mairipotaba 52 GO 0
## 5462 5212709 Mambaí 52 GO 0
## 5463 5212808 Mara Rosa 52 GO 0
## 5464 5212907 Marzagão 52 GO 0
## 5465 5212956 Matrinchã 52 GO 0
## 5466 5213004 Maurilândia 52 GO 0
## 5467 5213053 Mimoso De Goiás 52 GO 0
## 5468 5213087 Minaçu 52 GO 0
## 5469 5213103 Mineiros 52 GO 0
## 5470 5213400 Moiporá 52 GO 0
## 5471 5213509 Monte Alegre De Goiás 52 GO 0
## 5472 5213707 Montes Claros De Goiás 52 GO 0
## 5473 5213756 Montividiu 52 GO 0
## 5474 5213772 Montividiu Do Norte 52 GO 0
## 5475 5213806 Morrinhos 52 GO 0
## 5476 5213855 Morro Agudo De Goiás 52 GO 0
## 5477 5213905 Mossâmedes 52 GO 0
## 5478 5214002 Mozarlândia 52 GO 0
## 5479 5214051 Mundo Novo 52 GO 0
## 5480 5214101 Mutunópolis 52 GO 0
## 5481 5214408 Nazário 52 GO 0
## 5482 5214507 Nerópolis 52 GO 0
## 5483 5214606 Niquelândia 52 GO 0
## 5484 5214705 Nova América 52 GO 0
## 5485 5214804 Nova Aurora 52 GO 0
## 5486 5214838 Nova Crixás 52 GO 0
## 5487 5214861 Nova Glória 52 GO 0
## 5488 5214879 Nova Iguaçu De Goiás 52 GO 0
## 5489 5214903 Nova Roma 52 GO 0
## 5490 5215009 Nova Veneza 52 GO 0
## 5491 5215207 Novo Brasil 52 GO 0
## 5492 5215231 Novo Gama 52 GO 0
## 5493 5215256 Novo Planalto 52 GO 0
## 5494 5215306 Orizona 52 GO 0
## 5495 5215405 Ouro Verde De Goiás 52 GO 0
## 5496 5215504 Ouvidor 52 GO 0
## 5497 5215603 Padre Bernardo 52 GO 0
## 5498 5215652 Palestina De Goiás 52 GO 0
## 5499 5215702 Palmeiras De Goiás 52 GO 0
## 5500 5215801 Palmelo 52 GO 0
## 5501 5215900 Palminópolis 52 GO 0
## 5502 5216007 Panamá 52 GO 0
## 5503 5216304 Paranaiguara 52 GO 0
## 5504 5216403 Paraúna 52 GO 0
## 5505 5216452 Perolândia 52 GO 0
## 5506 5216809 Petrolina De Goiás 52 GO 0
## 5507 5216908 Pilar De Goiás 52 GO 0
## 5508 5217104 Piracanjuba 52 GO 0
## 5509 5217203 Piranhas 52 GO 0
## 5510 5217302 Pirenópolis 52 GO 0
## 5511 5217401 Pires Do Rio 52 GO 0
## 5512 5217609 Planaltina 52 GO 0
## 5513 5217708 Pontalina 52 GO 0
## 5514 5218003 Porangatu 52 GO 0
## 5515 5218052 Porteirão 52 GO 0
## 5516 5218102 Portelândia 52 GO 0
## 5517 5218300 Posse 52 GO 0
## 5518 5218391 Professor Jamil 52 GO 0
## 5519 5218508 Quirinópolis 52 GO 0
## 5520 5218607 Rialma 52 GO 0
## 5521 5218706 Rianápolis 52 GO 0
## 5522 5218789 Rio Quente 52 GO 0
## 5523 5218805 Rio Verde 52 GO 0
## 5524 5218904 Rubiataba 52 GO 0
## 5525 5219001 Sanclerlândia 52 GO 0
## 5526 5219100 Santa Bárbara De Goiás 52 GO 0
## 5527 5219209 Santa Cruz De Goiás 52 GO 0
## 5528 5219258 Santa Fé De Goiás 52 GO 0
## 5529 5219308 Santa Helena De Goiás 52 GO 0
## 5530 5219357 Santa Isabel 52 GO 0
## 5531 5219407 Santa Rita Do Araguaia 52 GO 0
## 5532 5219456 Santa Rita Do Novo Destino 52 GO 0
## 5533 5219506 Santa Rosa De Goiás 52 GO 0
## 5534 5219605 Santa Tereza De Goiás 52 GO 0
## 5535 5219704 Santa Terezinha De Goiás 52 GO 0
## 5536 5219712 Santo Antônio Da Barra 52 GO 0
## 5537 5219738 Santo Antônio De Goiás 52 GO 0
## 5538 5219753 Santo Antônio Do Descoberto 52 GO 0
## 5539 5219803 São Domingos 52 GO 0
## 5540 5219902 São Francisco De Goiás 52 GO 0
## 5541 5220009 São João D'aliança 52 GO 0
## 5542 5220058 São João Da Paraúna 52 GO 0
## 5543 5220108 São Luís De Montes Belos 52 GO 0
## 5544 5220157 São Luiz Do Norte 52 GO 0
## 5545 5220207 São Miguel Do Araguaia 52 GO 0
## 5546 5220264 São Miguel Do Passa Quatro 52 GO 0
## 5547 5220280 São Patrício 52 GO 0
## 5548 5220405 São Simão 52 GO 0
## 5549 5220454 Senador Canedo 52 GO 0
## 5550 5220504 Serranópolis 52 GO 0
## 5551 5220603 Silvânia 52 GO 0
## 5552 5220686 Simolândia 52 GO 0
## 5553 5220702 Sítio D'abadia 52 GO 0
## 5554 5221007 Taquaral De Goiás 52 GO 0
## 5555 5221080 Teresina De Goiás 52 GO 0
## 5556 5221197 Terezópolis De Goiás 52 GO 0
## 5557 5221304 Três Ranchos 52 GO 0
## 5558 5221403 Trindade 52 GO 0
## 5559 5221452 Trombas 52 GO 0
## 5560 5221502 Turvânia 52 GO 0
## 5561 5221551 Turvelândia 52 GO 0
## 5562 5221577 Uirapuru 52 GO 0
## 5563 5221601 Uruaçu 52 GO 0
## 5564 5221700 Uruana 52 GO 0
## 5565 5221809 Urutaí 52 GO 0
## 5566 5221858 Valparaíso De Goiás 52 GO 0
## 5567 5221908 Varjão 52 GO 0
## 5568 5222005 Vianópolis 52 GO 0
## 5569 5222054 Vicentinópolis 52 GO 0
## 5570 5222203 Vila Boa 52 GO 0
## 5571 5222302 Vila Propício 52 GO 0
## 5572 5300108 Brasília 53 DF 1
## IBGE_PLANTED_AREA ALT AREA TAXES GDP_CAPITA IS_URBAN
## 1 18288 337.74 7067.0300 23186.17 18732.17 0
## 2 7115 138.69 4426.5700 216095.93 20618.18 1
## 3 41086 236.06 1314.3500 5.51 21202.96 0
## 4 18180 177.45 3792.8900 194940.22 22130.78 1
## 5 52326 262.81 2783.3000 58.16 22721.08 1
## 6 6268 419.09 1451.0600 18466.33 16410.55 0
## 7 71742 269.17 3060.3200 8.74 27040.60 0
## 8 1395 145.21 4987.1800 7.06 12102.82 0
## 9 5588 262.53 4518.0300 42149.00 16650.27 0
## 10 787 133.41 24855.7200 97.10 15732.01 1
## 11 4043 162.55 2944.1300 126361.53 23507.78 1
## 12 2588 157.26 6896.6500 397452.14 22739.38 1
## 13 16636 144.20 8509.2700 26232.52 13523.83 0
## 14 5955 259.42 1703.0100 17.33 14199.05 0
## 15 1811 252.17 1969.8500 48.72 17794.04 1
## 16 2601 186.17 6240.9400 144065.66 26493.58 1
## 17 48577 86.90 34090.9500 1902607.30 28836.46 1
## 18 1538 178.22 1758.4700 31620.59 17380.72 0
## 19 19416 124.32 1717.6400 3193.34 23890.32 0
## 20 8441 226.71 1457.8900 98416.85 21204.01 1
## 21 3761 265.23 1197.8000 5806.82 17842.99 0
## 22 111745 595.09 11699.1500 309047.08 25822.66 1
## 23 29184 191.08 7460.2200 31192.35 21385.41 0
## 24 3396 150.93 10071.6400 17910.36 14846.72 0
## 25 2556 182.40 3029.1900 9283.88 14410.14 0
## 26 23049 396.56 3958.2700 9285.64 16438.94 0
## 27 9830 128.00 2651.8200 14828.39 13957.19 0
## 28 5547 153.17 3265.8100 37051.15 15123.80 0
## 29 3242 229.51 843.4500 6195.21 14791.65 0
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## 1734 9522 31.65 128.8100 1235.72 15059.38 0
## 1735 1138 23.33 28.8600 15754.63 15213.66 1
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## 1739 13393 260.71 256.6400 9073.95 10430.90 0
## 1740 4594 260.50 494.5000 7.32 6988.02 0
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## 1742 16305 12.43 360.8800 93217.33 15258.00 1
## 1743 3351 12.58 76.7300 2439.50 12952.90 0
## 1744 5337 220.44 315.1700 15153.71 9874.79 0
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## 1749 16155 159.00 299.2200 15.34 8947.42 1
## 1750 5656 11.41 686.0500 4535.71 6881.21 0
## 1751 14282 154.27 420.7200 40850.64 11990.47 1
## 1752 999 248.61 371.6100 6708.25 9930.24 1
## 1753 194 23.41 35.3300 1309.66 10891.11 0
## 1754 974 220.41 359.5400 12813.83 10880.89 0
## 1755 38 4.29 182.1600 1973534.61 25717.68 1
## 1756 4926 112.32 198.9700 5108.01 10645.71 0
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## 1758 866 2.00 92.2700 42713.14 12571.31 1
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## 1763 1076 154.43 901.0900 19943.85 44297.90 0
## 1764 5076 166.86 442.2100 22139.84 9252.19 0
## 1765 31250 374.12 637.1300 13492.04 9730.56 0
## 1766 1200 34.51 46.4000 41972.67 21836.07 1
## 1767 267 11.21 83.7100 2395.72 8799.86 0
## 1768 5737 153.14 237.7000 14071.96 11855.57 0
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## 1774 3400 17.41 654.1300 4449.18 8566.98 0
## 1775 68 18.26 19.7900 0.89 9610.47 1
## 1776 2990 238.67 242.6800 2865.43 10345.64 0
## 1777 2429 6.00 54.6000 3137.63 8321.89 0
## 1778 6188 7.02 314.7100 6.29 9849.54 0
## 1779 2163 191.89 337.3000 189760.61 17260.51 1
## 1780 7651 211.92 493.4700 24669.16 9803.50 0
## 1781 730 170.96 183.4200 2913.81 10276.19 0
## 1782 6940 11.58 739.7000 112601.04 22348.56 0
## 1783 12513 64.20 365.6200 13716.98 14095.33 0
## 1784 6379 86.58 408.0500 7258.52 11304.93 0
## 1785 15166 172.29 968.9200 160077.49 13548.27 1
## 1786 8187 35.73 162.2700 164188.84 34054.59 1
## 1787 3890 240.79 137.5300 2957.32 9221.13 0
## 1788 138 94.93 63.2000 3.52 13864.16 0
## 1789 578 267.49 101.8900 5125.06 9950.11 0
## 1790 2142 8.95 95.5500 32988.37 16840.43 1
## 1791 1428 221.87 95.4200 8766.58 13019.29 0
## 1792 2355 266.26 407.6600 6850.86 8270.01 0
## 1793 333 140.64 74.3200 4.16 10523.18 0
## 1794 7411 28.57 265.5300 18540.57 12613.48 1
## 1795 6780 216.34 340.8400 12464.23 14789.79 0
## 1796 7480 288.44 757.4500 54.30 14297.99 0
## 1797 3111 203.83 482.4000 20.14 10695.91 0
## 1798 450 221.74 81.2000 3349.95 9384.04 0
## 1799 3016 11.05 155.0200 324609.88 13045.51 1
## 1800 9855 91.53 377.4700 8.53 12630.00 0
## 1801 3020 173.89 82.2000 0.91 9472.98 0
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## 1803 6787 250.18 156.3300 3073.80 9353.89 0
## 1804 1006 3.42 208.7200 3908.18 11239.20 0
## 1805 4566 181.91 1232.6000 12952.95 7723.55 0
## 1806 25685 263.32 439.8300 17230.95 9822.93 0
## 1807 4005 56.91 876.6700 13.52 8818.18 0
## 1808 1833 13.92 92.7200 46909.47 14321.19 1
## 1809 6676 158.98 530.6100 8.95 10476.78 0
## 1810 702 9.55 78.3100 19249.21 15703.90 1
## 1811 1907 298.45 259.0200 29067.85 14805.27 0
## 1812 2291 16.87 102.8300 72351.45 50536.55 1
## 1813 5955 107.12 247.5800 9415.08 9362.65 0
## 1814 5672 22.81 325.2600 8370.34 12374.15 0
## 1815 794 15.39 45.1100 2086.16 8181.64 0
## 1816 1105 70.78 67.6700 1.36 11612.57 0
## 1817 4432 20.49 236.9700 4368.82 8992.64 0
## 1818 2224 6.28 438.0400 68821.97 10172.46 1
## 1819 810 209.44 102.0000 6.15 9067.26 0
## 1820 241 107.01 83.8700 1228.40 9054.03 0
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## 1822 31383 265.83 564.3600 51105.78 11848.25 0
## 1823 1333 86.32 168.3700 4550.32 11753.58 1
## 1824 391 16.47 49.0300 980.02 9866.85 0
## 1825 4355 163.97 1024.1900 42753.86 9688.16 1
## 1826 3413 179.42 306.2600 5.23 8935.41 0
## 1827 4157 146.04 117.5100 27909.05 12389.41 0
## 1828 895 674.22 538.6800 2019.77 6982.70 0
## 1829 2058 316.38 1604.9200 6.21 6256.80 0
## 1830 4570 183.93 181.4800 3269.82 7305.15 0
## 1831 26000 280.71 632.1400 4230.11 5881.41 0
## 1832 6315 299.89 743.0100 5359.91 7955.78 0
## 1833 1767 726.68 735.2500 1626.10 4985.01 0
## 1834 2910 155.10 167.8800 1345.27 10498.68 0
## 1835 2585 141.14 707.5600 605.97 20452.72 1
## 1836 3419 6.40 1482.2900 10219.92 11149.72 0
## 1837 3357 290.73 245.2400 978.85 9144.46 0
## 1838 3046 393.84 431.6700 21554.50 8892.67 0
## 1839 768 203.61 173.4800 24327.00 10611.57 0
## 1840 6982 688.07 822.3700 5202.35 7196.18 0
## 1841 1960 390.05 1336.3600 7495.76 8089.98 0
## 1842 2178 416.65 1590.3200 3.62 6920.19 0
## 1843 836 414.55 1362.3900 5200.03 11074.30 0
## 1844 4864 471.00 1530.0500 3553.36 9772.37 0
## 1845 900 238.08 187.8400 3631.48 5420.50 0
## 1846 4175 432.83 321.6100 4302.43 5809.23 0
## 1847 2832 167.96 293.5300 7584.15 7450.62 0
## 1848 712 500.56 345.2800 2.16 5299.76 0
## 1849 2928 190.11 479.2600 3.56 6150.25 0
## 1850 2686 272.55 150.8300 3687.57 8768.80 0
## 1851 2744 737.28 1489.8000 4073.59 6371.64 0
## 1852 807 70.62 474.6600 6.13 10042.94 0
## 1853 12001 267.94 1495.5500 13925.78 7043.77 0
## 1854 452 216.24 368.9500 3.28 7455.45 0
## 1855 10897 158.92 435.9600 1772.59 8373.25 0
## 1856 943 5.22 174.0100 2204.28 7360.55 0
## 1857 6501 48.17 445.3900 3337.53 8867.20 0
## 1858 19941 672.20 3320.7200 3686.70 8980.47 0
## 1859 2080 365.26 967.5100 5377.19 6418.00 0
## 1860 4490 340.62 409.5100 3019.78 5980.19 0
## 1861 5438 409.89 11422.5400 13852.19 6408.63 0
## 1862 10150 1038.88 1657.4100 11.30 11036.96 0
## 1863 22893 874.57 765.1500 13014.31 14575.54 0
## 1864 2029 722.08 1586.6300 3170.40 6228.75 0
## 1865 5306 121.44 214.4100 1293.89 9759.75 0
## 1866 246696 442.00 7859.7200 312389.83 21596.35 1
## 1867 6440 701.39 414.5100 3.40 5458.28 0
## 1868 2603 379.55 207.3000 15449.88 15517.36 0
## 1869 7889 146.38 201.5900 2191.28 9445.47 0
## 1870 14371 5.31 1939.4500 12.55 11902.74 0
## 1871 3066 846.98 772.7600 5482.72 6466.01 0
## 1872 2940 256.07 553.7600 6112.22 6464.29 0
## 1873 1920 693.01 849.5400 3277.39 6580.50 0
## 1874 1062 319.07 2972.1100 3934.60 5984.36 0
## 1875 17430 436.93 4115.5100 102258.86 11761.80 1
## 1876 378 642.00 467.8100 1699.71 4973.14 0
## 1877 765 943.74 896.8600 3.39 6053.11 0
## 1878 7925 983.31 791.2800 5696.46 9282.54 0
## 1879 5406 639.38 1426.2300 5018.22 6120.15 0
## 1880 2638 645.26 627.6100 3466.68 7822.93 0
## 1881 2150 589.39 518.5700 6553.23 8513.23 0
## 1882 384 537.20 2247.2100 1928.05 7948.60 0
## 1883 580 813.10 2370.4900 2747.85 6197.97 0
## 1884 3834 440.09 2207.6100 87.29 19724.63 1
## 1885 7804 115.23 219.4900 15927.81 9203.10 0
## 1886 1930 487.09 4046.7400 2993.31 5428.47 0
## 1887 360 339.94 512.4400 1430.51 6598.79 0
## 1888 2972 196.03 222.0300 3747.75 6859.34 0
## 1889 1240 54.00 399.9300 33.35 14941.95 1
## 1890 1608 589.35 610.9800 32097.17 10148.80 0
## 1891 900 485.38 540.9100 1822.35 8582.92 0
## 1892 1141 421.70 877.6500 2153.66 4811.03 0
## 1893 3222 811.85 2651.5400 53126.73 12097.20 0
## 1894 7220 764.33 643.6600 15062.33 12819.19 0
## 1895 643 17.52 448.8200 13151.54 25163.04 0
## 1896 1440 465.19 458.3100 3170.95 7512.41 0
## 1897 8754 161.84 584.8500 13692.62 8110.80 0
## 1898 1160 33.54 785.3400 5001296.36 75103.90 1
## 1899 19130 7.00 839.7000 19037.75 9267.06 0
## 1900 6100 475.44 2781.1700 7.96 6437.86 0
## 1901 45780 600.62 7161.8300 42384.80 9941.14 0
## 1902 1212 703.50 460.3900 1798.24 6043.90 0
## 1903 8980 708.19 579.7300 9600.55 6519.35 0
## 1904 7967 7.92 1334.3000 12025.96 9333.93 1
## 1905 530 245.42 447.5800 1448.01 6055.35 0
## 1906 58 110.39 251.6300 508659.92 38581.15 1
## 1907 2747 588.24 433.6400 5.33 6945.91 0
## 1908 2353 668.96 1169.8100 7762.25 6521.19 0
## 1909 6100 397.14 1351.8900 11096.46 6923.63 0
## 1910 1971 373.69 3189.5400 5635.36 7640.73 0
## 1911 622 393.68 629.5900 2604.64 6675.40 0
## 1912 510 418.67 464.7800 34418.31 11347.86 1
## 1913 2201 446.01 805.6300 2815.37 6717.25 0
## 1914 5469 6.00 2377.8700 16860.34 17321.44 0
## 1915 480 62.45 293.4600 3666.21 7597.06 0
## 1916 5186 446.45 2529.4500 7311.03 6466.10 0
## 1917 9390 403.34 9647.0700 39952.56 8027.25 1
## 1918 1537 254.85 713.7900 15480.05 13329.84 0
## 1919 389 606.18 703.3700 9772.47 19612.74 0
## 1920 845 101.34 426.9600 47971.70 9220.60 1
## 1921 1823 589.27 716.2600 1453.69 5744.87 0
## 1922 8426 705.72 566.9700 12.16 7933.89 0
## 1923 284 338.57 3005.3200 2.32 6183.93 0
## 1924 12697 439.96 863.9300 13550.37 7407.38 0
## 1925 1229 131.05 155.7200 5.92 6757.53 0
## 1926 5711 203.23 274.5000 5224.78 8196.42 1
## 1927 38123 629.15 10140.5700 14559.95 11281.10 0
## 1928 533 229.62 164.8000 25089.97 11187.56 0
## 1929 1294 216.77 284.8400 10290.93 8467.14 0
## 1930 15783 421.78 1015.2500 54415.75 10750.02 1
## 1931 1150 205.13 117.5300 433972.32 60168.45 1
## 1932 14122 6.80 931.1100 13283.83 9611.74 0
## 1933 1944 646.55 1348.4400 6442.05 6966.54 0
## 1934 1133 313.05 977.4600 1201.86 8007.66 0
## 1935 2350 236.88 378.4200 12.41 7484.60 0
## 1936 1140 714.73 523.6400 1.96 5956.45 0
## 1937 1160 673.81 2662.8900 4654.60 8220.46 0
## 1938 12600 206.50 883.5200 5.63 7835.18 0
## 1939 251218 574.46 11492.1700 85591.88 33778.30 0
## 1940 4431 475.68 4282.7800 3705.80 8660.05 0
## 1941 218 471.81 182.5900 1407.78 7373.42 0
## 1942 6827 183.22 636.6100 4799.39 6615.40 0
## 1943 3740 684.07 1052.8400 7.53 9146.31 0
## 1944 2505 223.00 139.1200 107196.80 14897.85 1
## 1945 4405 358.23 5935.9400 15997.87 7617.66 0
## 1946 3733 180.84 413.6400 2339.70 7757.99 0
## 1947 88 34.93 184.2300 433842.37 42142.20 1
## 1948 3185 441.05 688.6500 5.46 9833.88 0
## 1949 375 165.85 94.7800 1016.83 7521.17 0
## 1950 1017 376.47 179.3300 1.94 6976.86 0
## 1951 14718 641.36 1890.1300 8879.13 9164.87 0
## 1952 6840 109.33 1187.5100 29850.26 10818.03 0
## 1953 9215 157.06 1299.3600 30780.13 13207.15 0
## 1954 26472 474.02 1992.6400 37953.97 10013.19 0
## 1955 10150 189.79 1425.9700 292153.15 22186.57 1
## 1956 16616 344.57 359.3900 4800.77 12057.66 0
## 1957 1240 481.23 1176.1100 1.58 8178.00 0
## 1958 14210 238.64 1304.4300 1983426.57 21051.29 1
## 1959 2248 421.89 579.6900 5167.49 6827.77 0
## 1960 0 305.27 172.3500 1069.80 7664.18 0
## 1961 2066 191.59 321.0100 2.72 6971.66 0
## 1962 484179 495.55 15901.7500 48434.60 38416.21 0
## 1963 9384 158.66 229.6300 24067.83 9656.97 1
## 1964 430 299.96 384.5900 1894.69 7964.20 0
## 1965 838 1049.36 3817.9500 14209.97 17029.24 0
## 1966 1879 258.50 1566.6100 5.88 10588.92 0
## 1967 1285 92.82 202.1900 1441.49 8085.71 0
## 1968 3182 218.37 106.8500 12022.75 12741.64 1
## 1969 845 622.68 872.8700 1840.27 6630.12 0
## 1970 4770 522.03 1272.3700 112333.01 13886.22 1
## 1971 6331 171.20 2189.4000 4963.13 9874.87 0
## 1972 5361 301.30 338.8000 3593.96 6441.00 0
## 1973 737 241.82 2342.5000 8003.89 7574.04 0
## 1974 1675 632.46 483.2700 3779.87 7837.39 0
## 1975 2774 166.03 230.9500 6.37 7311.82 0
## 1976 7045 955.72 817.3600 15116.91 15888.76 0
## 1977 871 358.99 1139.3800 6.24 9649.66 0
## 1978 6289 699.97 1382.0100 7228.26 6356.68 0
## 1979 703 498.78 954.3700 4.23 6000.32 0
## 1980 418 561.33 698.2500 539.70 6267.37 0
## 1981 18809 98.35 472.6900 5.59 8829.36 0
## 1982 10885 145.18 771.1000 16.22 23738.97 0
## 1983 9243 149.27 318.1300 8092.73 9741.42 0
## 1984 1185 918.59 1834.0000 3443.43 5828.74 0
## 1985 7110 786.10 573.0300 5181.99 5880.13 0
## 1986 2226 427.85 1740.0900 29545.13 12150.11 0
## 1987 358 286.76 138.0200 1291.15 5998.42 0
## 1988 1222 783.34 836.5900 14.79 10274.23 0
## 1989 13256 12.09 591.3100 7.22 12418.30 0
## 1990 2474 442.61 860.2200 6155.76 7028.27 0
## 1991 46359 54.21 1588.5600 563.21 21743.84 1
## 1992 19539 173.20 1082.2600 14453.21 10848.54 0
## 1993 5412 185.80 372.5700 2137.31 5067.74 0
## 1994 5627 144.80 280.4500 29714.68 9022.72 1
## 1995 2250 314.32 3105.2800 41489.06 11054.26 0
## 1996 1236 738.23 1055.7600 3835.26 7333.99 0
## 1997 699 560.92 459.0500 3710.33 7641.78 0
## 1998 1067 593.22 1708.1200 2902.13 7662.72 0
## 1999 5634 722.10 991.8200 25773.70 13100.34 0
## 2000 10795 245.99 267.8800 15924.42 8083.50 0
## 2001 2095 725.19 319.1700 112.84 13749.35 1
## 2002 7288 156.01 924.9300 18251.25 10596.36 1
## 2003 2334 261.88 2386.3900 73.34 12008.25 1
## 2004 7553 61.77 401.0300 397.69 17514.19 1
## 2005 18600 10.83 726.2700 9240.01 9397.23 0
## 2006 1644 307.21 1331.8200 3454.05 5284.83 0
## 2007 5751 238.43 310.6200 4544.24 8089.38 0
## 2008 3917 175.30 810.9900 6336.86 11946.19 0
## 2009 324 180.78 876.8000 5197.31 14791.37 0
## 2010 1375 439.33 4310.2400 4170.21 5670.33 0
## 2011 1157 162.62 1225.2900 1553.90 8971.84 0
## 2012 15101 99.08 270.7500 14456.74 12317.23 0
## 2013 17769 63.66 2360.2900 55457.49 12458.80 1
## 2014 5419 252.37 143.4800 1864.02 9497.68 0
## 2015 1627 360.38 1536.3000 6410.77 9545.59 1
## 2016 180 42.52 534.0600 1748.78 7557.83 0
## 2017 2711 201.86 1394.1700 8197.35 10668.11 0
## 2018 17 3.67 118.0400 13710.64 9227.69 1
## 2019 836 101.35 453.1400 2908.04 9446.57 0
## 2020 1806 190.90 1013.0700 2605.67 53223.86 0
## 2021 8 284.46 1651.1600 90276.44 13208.05 1
## 2022 18741 156.01 1556.7500 9785.21 10220.81 0
## 2023 1784 137.40 420.6600 1363.88 7373.39 0
## 2024 232 568.42 344.0900 1946.94 8280.01 0
## 2025 26 251.40 1674.0300 10422.74 12076.64 1
## 2026 285 238.57 547.5100 49978.15 20524.38 0
## 2027 684 828.61 322.0200 4161.39 6869.36 0
## 2028 8637 378.88 1650.5900 9227.84 7498.26 0
## 2029 1681 256.38 314.0600 11028.15 10216.62 1
## 2030 5961 539.96 1199.3700 6417.09 9913.21 0
## 2031 12990 11.29 415.4300 20.07 11115.84 0
## 2032 3970 506.29 1522.3500 3911.57 7983.28 0
## 2033 134736 521.78 9988.7200 18397.64 37817.20 0
## 2034 2026 860.18 1332.4200 4233.91 6138.91 0
## 2035 4560 462.05 2192.9000 112419.29 15208.07 1
## 2036 2813 673.78 924.7400 48909.02 9860.74 1
## 2037 5604 655.82 2466.0100 14789.51 8444.36 0
## 2038 3179 8.94 863.1100 9.65 8432.26 0
## 2039 5845 151.19 643.7500 2739.28 17506.04 0
## 2040 10464 214.15 2969.0300 256.84 15062.91 1
## 2041 6798 276.08 4627.2800 15376.26 7611.16 0
## 2042 6188 328.11 238.6000 3563.25 7238.02 0
## 2043 7451 161.93 262.0500 6243.71 9050.26 0
## 2044 4941 811.31 913.2600 14756.09 8508.55 0
## 2045 31714 372.41 6721.2400 397908.74 15599.23 1
## 2046 3993 217.92 1457.8600 2513.69 9622.37 0
## 2047 4352 626.76 1355.1700 2947.69 5875.61 0
## 2048 2352 185.18 329.1900 960.60 8072.99 0
## 2049 1030 519.38 589.7600 2066.98 7823.03 0
## 2050 896 556.18 498.1000 985.12 9245.76 0
## 2051 675 519.16 912.2200 3250.33 5428.39 0
## 2052 8431 243.63 449.8300 17178.82 9471.91 0
## 2053 3731 285.60 624.3500 2309.29 14585.35 0
## 2054 183 646.14 846.7300 6724.08 13317.59 0
## 2055 1763 867.69 382.9400 3000.67 8240.03 0
## 2056 2073 290.42 189.2600 1201.79 5389.03 0
## 2057 3961 774.23 642.8800 9837.71 6889.61 0
## 2058 14 18.15 57.6600 854208.18 31360.72 1
## 2059 1402 439.08 1283.3300 6.55 9872.05 0
## 2060 1848 883.24 856.6300 5900.35 7792.86 0
## 2061 10090 480.55 1953.3800 24484.21 9342.24 0
## 2062 229749 768.53 3940.5400 635335.12 48937.78 1
## 2063 766 380.20 701.1700 2184.88 5992.19 0
## 2064 222 311.47 1210.1100 4499.49 8039.44 1
## 2065 8028 679.23 2459.1000 13163.75 6366.29 0
## 2066 50 371.42 2545.8600 1461.63 4893.61 0
## 2067 0 6.28 32.2000 36689.50 22017.50 1
## 2068 631 592.53 614.8300 2.10 9603.33 0
## 2069 132 323.50 588.3000 4985.02 9173.22 0
## 2070 2370 461.45 906.6800 4909.29 6535.44 0
## 2071 8962 440.89 1971.7200 5258.57 7160.75 0
## 2072 2375 509.02 549.5400 2544.06 6282.33 0
## 2073 684 323.75 2201.7600 4896.65 7603.95 0
## 2074 1359 514.99 3130.1300 2310.29 6411.49 0
## 2075 1145 974.13 2413.2700 20569.52 13216.09 0
## 2076 2260 2.98 438.1800 81.36 8727.02 0
## 2077 14051 13.71 848.8900 5798.97 10918.37 0
## 2078 73 295.83 1099.3800 2428.35 7026.15 0
## 2079 6020 33.75 742.6900 6.22 9141.08 0
## 2080 829 34.00 605.2100 123029.69 22498.15 1
## 2081 1361 515.40 773.2800 2229.29 5416.22 0
## 2082 11831 145.36 1311.7400 14865.42 12864.08 0
## 2083 4185 545.77 1599.6700 9573.34 8003.47 0
## 2084 87 424.02 420.3600 6351.19 7834.83 0
## 2085 3781 808.59 1751.7800 2692.63 6094.36 0
## 2086 731 356.20 1172.8600 1964.38 6135.31 0
## 2087 9423 488.99 3034.2000 13655.58 5975.48 0
## 2088 1831 512.68 2093.8700 1717.99 6759.90 0
## 2089 5855 1014.78 5744.9700 14372.42 8150.34 0
## 2090 1730 846.81 528.2100 4359.43 6941.83 0
## 2091 9533 1001.26 2462.1500 11749.30 38638.48 0
## 2092 11201 5.59 1787.6300 157584.58 47890.59 0
## 2093 8550 842.48 646.6200 4.27 6704.34 0
## 2094 1800 498.44 1491.9900 4812.98 5469.48 0
## 2095 1044 80.08 104.5400 1586.24 6348.61 0
## 2096 12908 432.98 3853.1800 8437.87 10982.38 0
## 2097 1885 210.30 86.3100 26361.74 9419.42 1
## 2098 12197 228.33 275.8300 12840.99 9164.48 0
## 2099 804 11.83 278.6300 13935.55 8377.23 1
## 2100 9996 50.02 390.9600 2.47 9083.55 0
## 2101 4112 327.68 465.4100 5336.34 7541.17 0
## 2102 2184 433.71 804.6200 2554.66 6894.88 0
## 2103 1054 299.06 346.7900 4823.42 8632.01 0
## 2104 5955 247.98 203.2000 1449.12 10793.64 0
## 2105 482 550.01 475.3800 2004.22 5987.79 0
## 2106 3011 401.17 565.3600 1414.16 4981.62 0
## 2107 3898 161.42 921.7100 6.48 6850.97 0
## 2108 3403 6.05 1316.3800 40871.73 10763.95 1
## 2109 1206 852.25 627.5000 3033.38 5681.88 0
## 2110 2631 416.38 251.3200 1550.17 3190.57 0
## 2111 3660 133.81 637.3200 11183.55 7213.56 0
## 2112 2730 622.65 3313.4200 10932.15 6995.88 0
## 2113 373 297.84 156.9800 2130.65 5920.69 0
## 2114 5292 556.01 1544.9900 8.16 8471.87 0
## 2115 4132 544.13 2562.8200 7117.57 6753.27 0
## 2116 453 745.29 737.4500 2703.04 7249.67 0
## 2117 2211 623.39 1087.0600 11868.94 8563.46 0
## 2118 5498 430.98 2624.1200 8024.55 6115.72 0
## 2119 21190 447.00 435.7100 12415.34 9461.18 0
## 2120 4218 192.92 626.3100 2560.38 8281.87 0
## 2121 9114 242.98 1545.1900 123599.80 18910.87 1
## 2122 664 276.50 596.7700 3.58 6721.43 0
## 2123 492 195.34 158.4900 1341.46 5293.77 0
## 2124 11155 371.38 896.0700 2645.52 5856.58 0
## 2125 1914 1264.08 1825.8600 4851.04 6350.03 0
## 2126 3810 407.65 11626.6400 6048.80 5850.33 0
## 2127 5203 605.28 628.4700 10521.22 9914.36 0
## 2128 1397 439.48 495.8500 4171.18 5770.02 0
## 2129 700 287.01 647.1400 3376.27 6980.13 0
## 2130 7511 142.52 193.4300 1954.85 7446.57 0
## 2131 1070 651.46 511.7600 2448.01 5523.93 0
## 2132 2268 538.29 980.3300 6476.92 5785.11 0
## 2133 1247 692.62 955.3600 2357.10 6210.54 0
## 2134 3820 935.62 769.0000 14951.53 8349.80 0
## 2135 3028 765.38 937.2700 36500.28 9602.73 1
## 2136 55 58.35 314.9300 125178.62 21073.90 1
## 2137 2373 364.99 530.1400 5340.95 7000.47 0
## 2138 10288 4.20 2285.7600 195227.28 17114.47 1
## 2139 100 176.98 1111.5700 2448.85 9290.75 0
## 2140 14253 7.88 1687.8300 14745.82 14230.09 0
## 2141 1939 651.27 232.0600 4096.12 6278.02 0
## 2142 1450 697.09 1209.1700 2526.53 5528.25 0
## 2143 11783 202.67 441.8900 11.05 7959.67 0
## 2144 3173 283.30 2011.0600 10521.22 7842.17 0
## 2145 17687 358.66 1380.8000 6747.98 6819.44 0
## 2146 478 433.41 366.3900 1937.31 6303.34 0
## 2147 2300 227.63 1090.5500 20519.95 8923.76 0
## 2148 2216 400.27 4683.4000 12594.98 7496.64 0
## 2149 3600 319.69 242.3300 5.73 7944.04 0
## 2150 162920 517.79 5979.0000 21.16 20266.70 0
## 2151 1083 223.71 1155.4200 20656.69 9361.70 0
## 2152 8102 612.36 3183.9100 9571.56 6675.43 0
## 2153 4336 154.65 667.3400 3609.23 10514.54 0
## 2154 8945 227.58 1177.4500 41893.49 9311.82 1
## 2155 3525 666.16 1363.7000 1.72 10788.01 0
## 2156 3477 997.90 1115.2500 3453.16 8079.28 0
## 2157 1657 526.39 777.9000 4314.57 6215.17 0
## 2158 2143 558.51 656.2200 3875.06 6468.19 0
## 2159 24322 173.64 738.6100 17503.33 20315.66 0
## 2160 2315 321.75 2207.1600 4886.94 10397.23 0
## 2161 586 366.47 1991.7700 30.71 9861.77 0
## 2162 57 6.85 151.5000 5.08 10759.51 0
## 2163 3 5.87 693.8300 7813229.49 20796.62 1
## 2164 4100 264.43 347.0200 11304.98 7325.37 0
## 2165 11210 296.78 900.0600 4829.36 6467.97 0
## 2166 8325 6.72 1462.9400 17156.18 11955.99 0
## 2167 313 238.50 284.0800 1751.55 8154.16 0
## 2168 128 404.95 379.2700 3456.27 5927.57 0
## 2169 14810 356.57 1623.4500 20674.08 8675.16 0
## 2170 8609 113.06 824.4600 3152.37 7933.68 0
## 2171 2740 457.36 1984.9100 26757.40 9826.29 1
## 2172 5796 539.85 1909.3500 11809.27 7811.76 0
## 2173 3083 239.88 222.6900 1768.24 6097.84 0
## 2174 5052 441.09 6030.0900 8016.99 8516.55 0
## 2175 358 254.00 719.2600 3149.33 6796.16 0
## 2176 1203 7.01 489.3200 83269.53 12211.38 1
## 2177 2973 214.86 261.7400 284784.48 20442.10 1
## 2178 6315 240.62 360.3300 68.32 13727.22 0
## 2179 561402 503.59 15116.4000 75461.64 44548.73 0
## 2180 2800 298.66 289.9600 2618.96 6969.45 0
## 2181 616 10.31 103.2300 8.94 11203.09 1
## 2182 2826 443.91 1754.3600 10111.24 10191.36 1
## 2183 2051 205.32 222.4100 7957.73 8318.56 0
## 2184 454 28.76 269.6100 1695416.01 296459.35 1
## 2185 4432 684.74 1146.0500 3380.10 5582.87 0
## 2186 3600 237.17 294.7700 138455.02 20473.30 0
## 2187 3185 151.09 127.9300 2548.25 7984.37 0
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## 2343 1239 596.09 1599.8800 4005.88 10645.56 0
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## 2435 845 273.35 253.9400 1102.23 8915.02 0
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## 2494 1185 928.86 572.9300 1410.73 6293.20 0
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## 2500 3 238.35 70.2300 3558.51 11335.54 0
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## 2536 32643 859.37 3686.0000 10424.16 19361.85 0
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## 2538 57 740.90 198.7100 1310.94 11771.18 0
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## 2559 750 173.15 2342.3300 568661.05 20207.31 1
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## 2663 110 734.24 97.1700 235208.00 42637.37 1
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## 2703 323 511.85 371.6000 10271.82 12030.77 1
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## 2804 699 766.99 444.2000 1302.11 7310.69 0
## 2805 48158 836.07 3190.1900 428.08 27995.83 1
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## 2840 385 885.83 203.6200 2347.85 10019.99 1
## 2841 534 904.07 124.8000 6.14 11429.06 1
## 2842 185 165.88 190.6800 89.74 42537.98 0
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## 2845 1037 716.37 569.6400 31705.59 14907.58 1
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## 2851 20997 670.13 2551.0700 54.03 21402.21 1
## 2852 2095 422.56 470.6400 197965.70 24700.71 1
## 2853 481 479.74 602.8000 1165.69 9031.14 0
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## 2855 3495 566.63 1749.6800 13168.17 7980.99 0
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## 2858 5333 840.13 542.8000 1114089.11 46860.13 1
## 2859 359 897.62 263.0300 14911.83 24586.97 0
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## 2867 47289 948.12 3503.7400 19312.86 23619.61 0
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## 2869 30 740.20 124.1900 13650.29 12649.66 1
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## 2871 213 977.47 153.5600 0.82 14142.05 1
## 2872 3 726.35 72.2300 11274.33 11129.19 1
## 2873 6643 302.00 763.3600 17225.20 12128.59 0
## 2874 59 191.23 234.3000 3780.95 8350.64 0
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## 2876 1614 1045.83 618.3100 8.07 13562.68 0
## 2877 959 92.39 1081.8000 14521.71 13185.65 0
## 2878 1121 1180.87 183.0600 20308.83 37445.23 0
## 2879 7055 546.06 1719.2700 2977.79 12080.44 0
## 2880 1482 822.83 1315.5400 3640.82 16172.50 0
## 2881 7 815.85 155.4500 457054.93 11110.22 1
## 2882 176 782.72 49.2500 10024.26 24768.93 1
## 2883 10 743.22 229.8100 6.46 13571.47 1
## 2884 3592 356.65 384.3800 9833.12 12602.46 0
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## 2886 70 384.36 479.8200 1100.49 7413.71 0
## 2887 1282 767.88 238.6000 1009.19 6672.13 0
## 2888 276 844.01 231.5400 5576.05 14230.24 0
## 2889 18 424.20 209.3100 7486.40 11783.72 0
## 2890 32236 1076.69 1352.3500 21117.86 48113.61 0
## 2891 5073 795.87 3117.6800 9062.96 8178.13 0
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## 2896 852 1011.69 404.8100 2.26 12633.82 0
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## 3042 2240 821.67 1220.0500 28290.72 11799.22 1
## 3043 396 214.53 193.0800 1231.54 10533.41 0
## 3044 8305 1087.10 1179.2500 46854.38 119080.12 0
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## 3058 2456 593.92 2678.2500 265110.86 46355.79 1
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## 3078 8024 907.00 395.4000 727.27 33603.82 1
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## 3082 16119 636.24 1913.4000 58316.30 27494.32 1
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## 3116 8422 44.14 1398.2200 401126.16 26270.72 1
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## 3123 2893 41.00 286.8500 40.56 18058.93 1
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## 3140 48371 29.17 3496.2600 730527.45 31705.48 1
## 3141 4975 421.33 321.4200 5.86 9506.24 0
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## 3176 455 7.64 813.2100 604530.05 47636.40 1
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## 3178 5009 12.10 638.1500 158552.76 19748.08 1
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## 3181 0 8.84 152.1100 33333.00 22531.94 1
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## 3189 477 43.17 558.2800 29365.05 22554.23 0
## 3190 76 15.09 307.3900 11032.45 19832.89 1
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## 3193 68 401.22 746.9300 45013.31 43895.79 1
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## 3195 23 347.67 306.5100 42433.64 25480.86 1
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## 3202 579 38.00 358.4400 60850.02 15912.49 1
## 3203 103 7.03 50.5400 19.63 16153.08 1
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## 3208 221 115.32 1106.1700 192233.09 25987.98 1
## 3209 0 411.03 241.0400 923567.31 118552.79 1
## 3210 866 32.94 81.7000 95810.65 12874.11 1
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## 3212 1036 7.46 1215.4900 2776064.40 73412.55 1
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## 3214 681 7.87 390.7800 207.88 15607.47 1
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## 3218 0 20.71 41.4900 109216.36 13396.88 1
## 3219 31 644.40 287.9300 28.96 27470.61 1
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## 3221 96 183.60 387.0700 12836.44 16767.72 0
## 3222 0 19.13 19.3900 143063.11 16045.55 1
## 3223 0 117.78 133.7600 2292039.48 46202.31 1
## 3224 579 856.35 935.4300 393.56 25979.30 1
## 3225 508 33.70 520.8100 1408580.57 20625.93 1
## 3226 158 55.80 190.9500 82329.84 19189.62 1
## 3227 176 289.10 571.1200 80.38 21056.32 1
## 3228 1391 2.82 924.3000 69199.20 30584.41 0
## 3229 382 593.56 314.3400 39534.64 18309.81 1
## 3230 52 829.43 791.1400 1435652.31 42564.57 1
## 3231 142 373.95 82.2500 18.18 15248.92 1
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## 3233 5070 195.21 291.8500 14437.95 17507.39 0
## 3234 150 387.46 50.8900 755165.34 137592.50 1
## 3235 102 406.14 284.8300 15269.49 18183.44 1
## 3236 184 33.15 75.7000 599495.51 32314.25 1
## 3237 2340 9.03 709.4000 26751.09 43048.94 0
## 3238 11 400.77 1099.3400 1552400.41 50234.49 1
## 3239 2683 70.09 459.4600 123702.52 26627.87 1
## 3240 394 458.13 846.8000 26907.17 18746.42 0
## 3241 28 549.96 478.7800 10103.05 21096.86 0
## 3242 105 8.98 228.0400 357.50 35788.18 1
## 3243 2098 11.80 1200.2600 68106.12 50690.82 1
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## IS_RURAL IS_INTERMEDIARY geom
## 1 0 1 POLYGON ((-62.19465 -11.827...
## 2 0 0 POLYGON ((-62.53648 -9.7322...
## 3 1 0 POLYGON ((-60.37075 -13.363...
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## 8 1 0 POLYGON ((-63.71199 -11.650...
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## 10 0 0 POLYGON ((-65.37724 -10.431...
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## 13 1 0 POLYGON ((-62.22055 -8.5908...
## 14 1 0 POLYGON ((-61.9569 -11.2959...
## 15 0 0 POLYGON ((-61.89078 -10.193...
## 16 0 0 POLYGON ((-60.469 -11.48155...
## 17 0 0 POLYGON ((-62.86662 -7.9758...
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## 32 0 0 POLYGON ((-62.82233 -8.5003...
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## 45 1 0 POLYGON ((-61.53458 -11.743...
## 46 1 0 POLYGON ((-63.37547 -11.850...
## 47 1 0 POLYGON ((-63.17878 -11.472...
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## 50 1 0 POLYGON ((-62.16508 -11.024...
## 51 1 0 POLYGON ((-61.61761 -9.3287...
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## 70 0 1 POLYGON ((-67.1456 -9.68410...
## 71 0 1 POLYGON ((-68.92109 -8.8144...
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## 76 1 0 POLYGON ((-68.17829 -3.0776...
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## 1637 0 0 POLYGON ((-35.34815 -7.4222...
## 1638 0 0 POLYGON ((-36.05569 -7.9795...
## 1639 0 0 POLYGON ((-35.13009 -7.6624...
## 1640 0 0 POLYGON ((-40.345 -7.84873,...
## 1641 1 0 POLYGON ((-38.00545 -7.7777...
## 1642 1 0 POLYGON ((-37.27704 -8.3926...
## 1643 1 0 POLYGON ((-37.30901 -7.6010...
## 1644 1 0 POLYGON ((-36.82627 -8.5037...
## 1645 1 0 POLYGON ((-38.97932 -7.8461...
## 1646 1 0 POLYGON ((-35.78991 -7.7404...
## 1647 1 0 POLYGON ((-35.95903 -7.7872...
## 1648 0 1 POLYGON ((-35.27288 -7.5912...
## 1649 0 0 POLYGON ((-35.22363 -8.0208...
## 1650 1 0 POLYGON ((-35.60347 -8.8298...
## 1651 1 0 POLYGON ((-37.95164 -9.0974...
## 1652 0 1 POLYGON ((-36.34926 -9.5931...
## 1653 0 0 POLYGON ((-36.66005 -9.6420...
## 1654 0 0 POLYGON ((-35.92889 -9.3755...
## 1655 0 0 POLYGON ((-35.50117 -9.3281...
## 1656 1 0 POLYGON ((-35.99293 -9.7806...
## 1657 0 1 POLYGON ((-37.10989 -9.6060...
## 1658 1 0 POLYGON ((-36.50039 -9.4993...
## 1659 1 0 POLYGON ((-37.21945 -9.7098...
## 1660 0 0 POLYGON ((-36.17694 -9.6213...
## 1661 1 0 POLYGON ((-35.95583 -9.2144...
## 1662 1 0 POLYGON ((-36.96929 -9.3690...
## 1663 0 0 POLYGON ((-36.15964 -9.4675...
## 1664 1 0 POLYGON ((-35.53982 -8.8210...
## 1665 0 0 POLYGON ((-36.31411 -9.7232...
## 1666 1 0 POLYGON ((-36.75258 -9.8826...
## 1667 1 0 POLYGON ((-37.50143 -8.9668...
## 1668 0 1 POLYGON ((-36.09351 -9.2648...
## 1669 1 0 POLYGON ((-37.38029 -9.5028...
## 1670 1 0 POLYGON ((-36.30782 -9.1784...
## 1671 1 0 POLYGON ((-36.57019 -9.5653...
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## 1673 0 0 POLYGON ((-35.76941 -9.6389...
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## 1675 1 0 POLYGON ((-36.7752 -9.55689...
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## 1677 1 0 POLYGON ((-37.04553 -9.3158...
## 1678 1 0 POLYGON ((-36.83435 -9.5088...
## 1679 1 0 POLYGON ((-36.62768 -9.8364...
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## 1681 0 0 POLYGON ((-35.72494 -9.1253...
## 1682 1 0 POLYGON ((-36.92658 -9.6271...
## 1683 1 0 POLYGON ((-35.88881 -8.8626...
## 1684 1 0 POLYGON ((-36.56894 -9.4874...
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## 1687 1 0 POLYGON ((-37.20035 -9.5909...
## 1688 1 0 POLYGON ((-35.46753 -8.8174...
## 1689 1 0 POLYGON ((-35.30225 -9.0291...
## 1690 1 0 POLYGON ((-36.9013 -9.62465...
## 1691 1 0 POLYGON ((-36.16658 -9.8432...
## 1692 1 0 POLYGON ((-35.68923 -9.1009...
## 1693 1 0 POLYGON ((-35.55174 -8.9052...
## 1694 1 0 POLYGON ((-36.40479 -9.7815...
## 1695 1 0 POLYGON ((-36.75286 -9.7098...
## 1696 1 0 POLYGON ((-36.37299 -9.6899...
## 1697 0 0 POLYGON ((-35.66421 -9.4306...
## 1698 1 0 POLYGON ((-36.99848 -9.4616...
## 1699 0 0 POLYGON ((-35.29929 -8.8736...
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## 1701 0 0 POLYGON ((-35.86768 -9.6408...
## 1702 1 0 POLYGON ((-36.22672 -9.5152...
## 1703 1 0 POLYGON ((-36.38936 -9.4253...
## 1704 1 0 POLYGON ((-37.75222 -8.8531...
## 1705 0 0 POLYGON ((-35.50175 -9.0853...
## 1706 0 0 POLYGON ((-35.84034 -9.4067...
## 1707 1 0 POLYGON ((-36.86747 -9.2685...
## 1708 1 0 POLYGON ((-37.24429 -9.5988...
## 1709 0 0 POLYGON ((-35.83737 -9.2858...
## 1710 1 0 POLYGON ((-35.58199 -8.8614...
## 1711 0 1 POLYGON ((-37.22792 -9.5194...
## 1712 1 0 POLYGON ((-37.76882 -9.2887...
## 1713 1 0 POLYGON ((-36.81586 -10.005...
## 1714 1 0 POLYGON ((-37.11773 -9.4376...
## 1715 1 0 POLYGON ((-37.39141 -9.0269...
## 1716 1 0 POLYGON ((-37.30306 -9.6360...
## 1717 0 0 POLYGON ((-36.67487 -9.3102...
## 1718 1 0 POLYGON ((-37.6154 -9.49857...
## 1719 1 0 POLYGON ((-38.02078 -9.1552...
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## 1721 1 0 POLYGON ((-35.58006 -9.1578...
## 1722 1 0 POLYGON ((-36.36556 -9.3641...
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## 1726 1 0 POLYGON ((-36.30038 -9.4282...
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## 1729 0 0 POLYGON ((-35.42188 -8.9206...
## 1730 1 0 POLYGON ((-35.43838 -9.0672...
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## 1732 1 0 POLYGON ((-36.41674 -9.2296...
## 1733 0 0 POLYGON ((-35.92889 -9.3755...
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## 1735 0 0 POLYGON ((-35.81826 -9.5801...
## 1736 0 0 POLYGON ((-37.06326 -9.2741...
## 1737 1 0 POLYGON ((-36.12314 -9.0315...
## 1738 1 0 POLYGON ((-36.87475 -10.053...
## 1739 0 1 POLYGON ((-35.98833 -8.9045...
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## 1742 0 0 POLYGON ((-36.02805 -9.8085...
## 1743 1 0 POLYGON ((-35.342 -9.231805...
## 1744 1 0 POLYGON ((-36.55295 -9.8392...
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## 1746 1 0 POLYGON ((-37.56122 -9.3048...
## 1747 1 0 POLYGON ((-36.38705 -9.5098...
## 1748 1 0 POLYGON ((-36.53766 -9.5332...
## 1749 0 0 POLYGON ((-36.28521 -9.9671...
## 1750 1 0 POLYGON ((-36.97436 -9.6938...
## 1751 0 0 POLYGON ((-35.94914 -9.0347...
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## 1753 1 0 MULTIPOLYGON (((-36.92631 -...
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## 1956 1 0 MULTIPOLYGON (((-38.14979 -...
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## 1975 0 1 MULTIPOLYGON (((-39.62937 -...
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## 1977 1 0 MULTIPOLYGON (((-39.74337 -...
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## 1980 1 0 MULTIPOLYGON (((-40.77305 -...
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## 1982 1 0 MULTIPOLYGON (((-40.14313 -...
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## 1985 1 0 MULTIPOLYGON (((-41.77384 -...
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## 1988 1 0 MULTIPOLYGON (((-42.73946 -...
## 1989 1 0 MULTIPOLYGON (((-39.0595 -1...
## 1990 0 1 MULTIPOLYGON (((-40.16041 -...
## 1991 0 0 MULTIPOLYGON (((-39.42647 -...
## 1992 1 0 MULTIPOLYGON (((-38.29603 -...
## 1993 1 0 MULTIPOLYGON (((-39.30599 -...
## 1994 0 0 MULTIPOLYGON (((-39.69031 -...
## 1995 0 1 MULTIPOLYGON (((-39.77432 -...
## 1996 1 0 MULTIPOLYGON (((-42.59375 -...
## 1997 1 0 MULTIPOLYGON (((-40.13085 -...
## 1998 1 0 MULTIPOLYGON (((-40.91063 -...
## 1999 1 0 MULTIPOLYGON (((-41.43784 -...
## 2000 1 0 MULTIPOLYGON (((-38.741 -11...
## 2001 0 0 MULTIPOLYGON (((-41.72969 -...
## 2002 0 0 MULTIPOLYGON (((-39.59306 -...
## 2003 0 0 MULTIPOLYGON (((-40.35287 -...
## 2004 0 0 MULTIPOLYGON (((-39.29187 -...
## 2005 1 0 MULTIPOLYGON (((-38.99356 -...
## 2006 1 0 MULTIPOLYGON (((-41.10433 -...
## 2007 0 1 MULTIPOLYGON (((-40.02054 -...
## 2008 0 1 MULTIPOLYGON (((-39.81127 -...
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## 5420 0 0 MULTIPOLYGON (((-49.17655 -...
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## 5422 0 1 MULTIPOLYGON (((-50.52665 -...
## 5423 0 0 MULTIPOLYGON (((-49.9732 -1...
## 5424 0 1 MULTIPOLYGON (((-50.10407 -...
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## 5426 1 0 MULTIPOLYGON (((-50.03279 -...
## 5427 1 0 MULTIPOLYGON (((-46.29465 -...
## 5428 1 0 MULTIPOLYGON (((-49.70995 -...
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## 5431 1 0 MULTIPOLYGON (((-49.34389 -...
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## 5435 0 0 MULTIPOLYGON (((-49.45842 -...
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## 5569 0 1 MULTIPOLYGON (((-49.84456 -...
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## 5571 1 0 MULTIPOLYGON (((-48.75586 -...
## 5572 0 0 POLYGON ((-48.20054 -15.500...corrplot(cor(Municipalities_woNA_df[, 5:13]), diag = FALSE, order = "alphabet",
tl.pos = "td", tl.cex = 0.5, method = "square", type = "upper")
Variables with signs of multicollinearity:
- IS_URBAN
- IS_RURAL
- IS_INTERMEDIARY
They will be removed.
5.1.1 Building a model using multiple linear regression method
MLR <- lm(formula = GDP_CAPITA ~
CAPITAL +
IBGE_PLANTED_AREA +
ALT +
AREA +
TAXES, data=Municipalities_woNA)
summary(MLR)##
## Call:
## lm(formula = GDP_CAPITA ~ CAPITAL + IBGE_PLANTED_AREA + ALT +
## AREA + TAXES, data = Municipalities_woNA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -114164 -10750 -4506 4340 288881
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.940e+04 2.800e+02 69.305 < 2e-16 ***
## CAPITAL 2.222e+03 3.978e+03 0.559 0.577
## IBGE_PLANTED_AREA 1.317e-01 5.942e-03 22.172 < 2e-16 ***
## ALT -7.377e-03 1.510e-02 -0.489 0.625
## AREA -2.064e-01 4.710e-02 -4.382 1.20e-05 ***
## TAXES 1.280e-03 1.617e-04 7.917 2.93e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19350 on 5563 degrees of freedom
## Multiple R-squared: 0.09333, Adjusted R-squared: 0.09251
## F-statistic: 114.5 on 5 and 5563 DF, p-value: < 2.2e-16Not all the independent variables are statistically significant. Retaining only explanatory variables with p-value < alpha value of 0.05.
- IBGE_PLANTED_AREA
- AREA
- TAXES
Calibrating the revised model
MLR1 <- lm(formula = GDP_CAPITA ~
IBGE_PLANTED_AREA +
AREA +
TAXES, data=Municipalities_woNA)
ols_regress(MLR1)## Model Summary
## ----------------------------------------------------------------------
## R 0.305 RMSE 19347.310
## R-Squared 0.093 Coef. Var 91.635
## Adj. R-Squared 0.093 MSE 374318400.993
## Pred R-Squared -0.139 MAE 11101.637
## ----------------------------------------------------------------------
## RMSE: Root Mean Square Error
## MSE: Mean Square Error
## MAE: Mean Absolute Error
##
## ANOVA
## -----------------------------------------------------------------------------------
## Sum of
## Squares DF Mean Square F Sig.
## -----------------------------------------------------------------------------------
## Regression 214188317383.503 3 71396105794.501 190.736 0.0000
## Residual 2.083082e+12 5565 374318400.993
## Total 2.29727e+12 5568
## -----------------------------------------------------------------------------------
##
## Parameter Estimates
## -------------------------------------------------------------------------------------------------------
## model Beta Std. Error Std. Beta t Sig lower upper
## -------------------------------------------------------------------------------------------------------
## (Intercept) 19402.956 279.369 69.453 0.000 18855.284 19950.629
## IBGE_PLANTED_AREA 0.132 0.006 0.286 22.170 0.000 0.120 0.143
## AREA -0.206 0.047 -0.056 -4.366 0.000 -0.298 -0.113
## TAXES 0.001 0.000 0.110 8.640 0.000 0.001 0.002
## -------------------------------------------------------------------------------------------------------5.1.2 Checking for multicollinearity
Testing for signs of multicollinearity
ols_vif_tol(MLR1)## Variables Tolerance VIF
## 1 IBGE_PLANTED_AREA 0.9815192 1.018829
## 2 AREA 0.9816339 1.018710
## 3 TAXES 0.9998755 1.000125Since the VIF of the independent variables are < 10, it is safely concluded that there are no sign of multicollinearity among the independent variables.
5.1.3 Test for Non-Linearity
ols_plot_resid_fit(MLR1)
The figure above reveals that:
- most of the data points are not close to the 0 line
- there are no clusters
It can be concluded that the relationships between the dependent variable and independent variables are non-linear and there is no clear sign of heteroscedacity.
5.1.4 Test for Normality Assumption
ols_plot_resid_hist(MLR1)
The residual of the multiple linear regression model resembles normal distribution.
6 Distribution of the Residual of GDP per capita
6.1 Getting Residuals
MLR.output <- as.data.frame(MLR1$residuals)
Municipalities_woNA.res.sf <- cbind(Municipalities_woNA, MLR1$residuals) %>%
rename(`MLR_RES` = MLR1.residuals)Municipalities_woNA.sp <- as_Spatial(Municipalities_woNA.res.sf)
Municipalities_woNA.sp## class : SpatialPolygonsDataFrame
## features : 5569
## extent : -73.99045, -28.83594, -33.75118, 5.271841 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs
## variables : 14
## names : code_muni, name_muni, code_state, abbrev_state, CAPITAL, IBGE_PLANTED_AREA, ALT, AREA, TAXES, GDP_CAPITA, IS_URBAN, IS_RURAL, IS_INTERMEDIARY, MLR_RES
## min values : 1100015, Abadia De Goiás, 11, AC, 0, 0, 0, 3.57, -14158.51, 3190.57, 0, 0, 0, -115606.244165304
## max values : 5300108, Zortéa, 53, TO, 1, 1205669, 874579, 159533.33, 117125386.74, 314637.69, 1, 1, 1, 288732.722150846.2 Displaying the distribution of the residuals on a map
tmap_mode("plot")
tm_shape(Municipalities_woNA.res.sf)+
tm_fill('MLR_RES',
style = "fisher",
palette = "RdBu"
)
The figure reveals some signs of spatial autocorrelation. Explanatory characteristics of the points are generally better at the periphery in the East.
6.3 Moran’s I test
Getting the kernel dist
coords <- coordinates(Municipalities_woNA.sp)
k1 <- knn2nb(knearneigh(coords))
k1dists <- unlist(nbdists(k1, coords, longlat = TRUE))
summary(k1dists)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.386 11.734 15.880 21.366 23.961 369.663The code chunks below have been left out as my laptop was hanging on multiple tries, my sincere apologies.
Computing the distance-based weight matrix
nb <- dnearneigh(coordinates(Municipalities_woNA.sp), 0, 369.663, longlat = FALSE)
summary(nb)Converting the output neighbours lists (i.e. nb) into a spatial weights list object
nb_lw <- nb2listw(nb, style = 'W')
summary(nb_lw)Performing Moran’s I test for residual spatial autocorrelation
lm.morantest(MLR1, nb_lw)- If the p-value < the alpha value of 0.05, reject the null hypothesis that the residuals are randomly distributed.
- If the Observed Global Moran I > 0, it can be inferred that the distribution of the residuals resembles cluster distribution.
7 Geographically Weighted Regression Method
7.1 Building Fixed Bandwidth GWR Model
7.1.1 Computing fixed bandwith
bw.fixed <- bw.gwr(formula = GDP_CAPITA ~
IBGE_PLANTED_AREA +
AREA +
TAXES, data=Municipalities_woNA.sp, approach="CV", kernel="gaussian", adaptive=FALSE, longlat=FALSE)7.1.2 GWModel method - fixed bandwith
gwr.fixed <- gwr.basic(formula = GDP_CAPITA ~
IBGE_PLANTED_AREA +
AREA +
TAXES, data=Municipalities_woNA.sp, bw=bw.fixed, kernel = 'gaussian', longlat = FALSE)Displaying the model output
gwr.fixed7.2 Building Adaptive Bandwidth GWR Model
Calibrating the gwr-absed hedonic pricing model by using adaptive bandwidth approach.
7.2.1 Computing the adaptive bandwidth
Determining the recommended data point to use
bw.adaptive <- bw.gwr(formula = GDP_CAPITA ~
IBGE_PLANTED_AREA +
AREA +
TAXES, data=Municipalities_woNA.sp, approach="CV", kernel="gaussian",
adaptive=TRUE, longlat=FALSE)7.2.2 Constructing the adaptive bandwidth gwr model
gwr.adaptive <- gwr.basic(formula = GDP_CAPITA ~
IBGE_PLANTED_AREA +
AREA +
TAXES, data=Municipalities_woNA.sp, bw=bw.adaptive, kernel = 'gaussian', adaptive=TRUE, longlat = FALSE)Displaying the model output
gwr.adaptive8 Visualizing GWR Output
8.1 Converting SDF into sf data.frame
Municipalities_woNA.adaptive <- st_as_sf(gwr.adaptive$SDF) %>%
st_transform(crs=4674)Municipalities_woNA.adaptive.4674 <- st_transform(Municipalities_woNA.adaptive, 4674)
Municipalities_woNA.adaptive.4674 gwr.adaptive.output <- as.data.frame(gwr.adaptive$SDF)
Municipalities_woNA.adaptive <- cbind(Municipalities_woNA.res.sf, as.matrix(gwr.adaptive.output))Municipalities_woNA.adaptivesummary(gwr.adaptive$SDF$yhat)8.2 Visualizing local R2
tmap_mode("view")
tm_shape(Municipalities)+
tm_polygons() +
tm_shape(Municipalities_woNA.adaptive) +
tm_dots(col = "Local_R2",
#size = 0.15,
border.col = "gray60",
border.lwd = 1) +
tm_view(set.zoom.limits = c(11,14))