link to list of municipalities in the semiarid region

The user could select a given biome (Amazonia, Caatinga, Cerrado, Mata Atlantica, Pampa, Pantanal) and get all municipalities that belong to it, in a way similar to #38 and #39.

data: ftp://geoftp.ibge.gov.br/recortes_para_fins_estatisticos/malha_de_setores_censitarios/censo_2010/base_de_faces_de_logradouros/

info:
ftp://geoftp.ibge.gov.br/recortes_para_fins_estatisticos/malha_de_setores_censitarios/censo_2010/base_de_faces_de_logradouros/1_Leia_me/Base%20de%20Faces%20de%20Logradouros%20do%20CD%202010.pdf

Source: Atlas do Desenvolvimento, do Ipea

@rafapereirabr , I imagine that you created ipeaGIT because the ipea (https://github.com/ipea) group name was taken.
But it is actually ours, and I just added you as owner.
Consider migrating the project there if you want.

for future consideration: add a read_terras_indigenas function that grabs polygons from FUNAI

Igor, eu sugiro mover a vignette 2 de Georeferencing-gain para outro branch do repositório, e trazer ela para o main branch quando tivermos artigo em estágio mais avançado. Assim ela nao entra na submissão versão 1.0 do pacote para o CRAN. O que acha?

personal opinion here: I don't like to have to tell the function which subset of the data I want. The natural expectation is that you will get all the data. Making code_muni="all" the default would avoid this.

I think having this default at least for the small datasets (mun, uf, micro, macro) makes sense.

Far large dataset such as setor censitario, faces, etc. than you could force user to define *_muni="all"

1. Create script prep_urban_areas do download and clan IBGE data on urbanized areas (years 2005 and 2015)

2. Create geobr function read_urban_area() to download the data

obs. data available at ftp://geoftp.ibge.gov.br/organizacao_do_territorio/tipologias_do_territorio/areas_urbanizadas_do_brasil

Oi Guilherme, use aqueles shape files de áreas de risco do IBGE que usamos no outro trabalho do ODS. Ok? abs

Passar para pasta ./geobr/prep_data os scripts em R que foram usados para tratamento dos dados brutos. Isso serve para fins de documentação

e ai @rafapereirabr ! Ta ficando legal!!

Minor suggestion: add UF name to municipalities dataset.

Sometimes the person will only have UF names and Municipality names (as in my case now with the Brasil Mais Produtivo data). And municipal names are not unique, only within State.

Brazilian Central bank updates on a monthly basis a database with addresses of all bank branches in Brazil.

There is a database for branches, PAE, PAB (these two are smaller branches, usually inside large companies) and consórcio administrator.

Link is below

https://www.bcb.gov.br/estabilidadefinanceira/agenciasconsorcio

Paulo, alterar o nome das colunas para seguir padrão do pacote. Isso precisa ser alterado tanto no script da função quanto na base de dados

nome das cols atualmente: cod_areapond, cod_mum, cod_uf

como deve ficar: code_weighting_area, code_muni, code_state

Function calls such as:

states <- read_state(year=2010, code_state = "all")

create one progress bar each time a state will be donloaded, summing up 27 progress bars in this case. Possibly creating one single progress bar that grows as each state is downloaded would be more interesting for the user.

Colocar todas bases de área de ponderação com mesma estrutura de arquivo.

colunas cod_areapond, cod_mun e geom

There's a typo in the README.md file. Coming soon , not Comming soon. :)

incluir opção de ler a sigla do estado, exemplo m <- read_uf(cod_uf="SP", ano=2010)

when I choose a single UF, like cod_micro = 24 , the read_micro_region function returns the right geometries, but it does not retain the full microregion codes. Instead they are all "24".

That will be a serious problem if a user later wants to merge microregion data (employment rates, % rural population, ...) from some other source onto this dataframe.

Adicionar função read_country. Que envolve

# 1 carregar dados dos estados
read_state(cod_state="all")
# Dar merge nos poligonos. Um das funções abaixo:
st_union 

 st_combine

O manual.pdf diz read_uf, mas no pacote a função que faz tal operação parece ser a read_state.

It would be interesting if the function (or functions) implemented to solve #38, #39, and #45 allows the user to choose what to do with municipalities that are not fully within the region. Some possibilities:

1. Include all municipalities as long as they have some overlap with the region (maybe the default)
2. Remove the municipalities in the border
3. Cut the polygos of municipalities in the border in order to guarantee that the returned area is the same of the region

Do you really need the files at: /geobr/data/* ?

It seems this makes the package much larger than necessary (I assume because brazil_2010 is a geometry right?) in terms of size.

para carregar base, basta rodar data("brazil_2010") e retornar a return(brazil_2010)

Link to the list of municipalities in the Legal Amazon

Pessoal, por favor se certifiquem de que estamos a sintaxe package::function() para todas funções no pacote.

Sugestão do @pedro-andrade-inpe

usar nomes:

[1] "cod_muni"     "name_muni"    "cod_micro"    "name_micro"   "cod_meso"     "name_meso"    "cod_state"   
 [8] "name_state"   "abbrev_state" "cod_region"   "name_region"  "geometry"  
```

Include table with metadata of all geometries ./data. Something like:

• Na coluna geom informar dados referentes aos municipios

Create one or two vignettes demonstrating the package functions

read_country() with no arguments returns the geometry of Brazil in 2010 with several holes. The data for 2014 and 2015 also have some holes. Perhaps removing the holes manually after computing the union solves the problem as shown in the code below.

(I saw in another issue the discussion that st_union is very slow. unionSpatialPolygons from maptools is much faster)

require(geobr)
require(dplyr)
require(sp)
require(sf)
require(maptools)

sp_states <- read_state(year=2010, code_state = "all") %>% as("Spatial")

result <- unionSpatialPolygons(sp_states, rep(TRUE, 27))

outerRings = Filter(function(f){f@ringDir==1},result@polygons[[1]]@Polygons)
outerBounds = SpatialPolygons(list(Polygons(outerRings,ID=1)))
plot(outerBounds)

m <- st_as_sf(outerBounds)

write_sf(m, "brazil.shp")

data available here: https://concla.ibge.gov.br/classificacoes/por-tema/codigo-de-areas/codigo-de-areas

historical data on municipios_1872_1991 can be found at this address ftp://geoftp.ibge.gov.br/organizacao_do_territorio/malhas_territoriais

para carregar base, basta rodar data("brazil_2010").

Depois é necessário fazer um dissolve da base de acordo com a geografia em cada função

Include cod_uf argument in the read_municipio function

Example:
read_municipio(cod_uf="SP", ano=2010) returns all municipalities of UF 35

Colocar todas bases de mesoregiões com mesma estrutura de arquivo. Exemplo:

          nome_meso cod_meso  Geometry
1 Leste Rondoniense     1102  POLYGON ((-62.22055 -8.5908...
2   Madeira-Guaporé     1101  POLYGON ((-63.32721 -7.9767...
...

Is there any work/planning to build a python version?

If not, can I start one? There is no licensing on the project. So I am not sure about if you are ok with other users building on your .rds files.

test <- read_weighting_area(code_weighting = 35, year=2010)
test <- read_weighting_area(code_weighting = "SP", year=2010)

Os códigos acima retornam "Error in parse_url(url) : length(url) == 1 is not TRUE".

Estou entrando algo errado, ou a função está com problemas?

Avaliar se é possível aproveitar o código em Stata do Philipp Ehrl (professor da Univ. Catolica de Brasilia e bolsista na DIRUR). Ele publicou uma nota na Estudos Economicos sobre o código e o código está disponível aqui.

Colocar todas bases de município com mesma estrutura de arquivo. Exemplo:

         nome_mun   cod_mun      geometry
1      Acrelândia   1200013      POLYGON ((-67.13424 -9.6762...
2    Assis Brasil   1200054      POLYGON ((-69.5814 -10.3806...
...

Geographic names that contain accents seem to cause warnings when I use the read_ function in a US English enviroment. See attached pdf for an example.

Adding a line

options(encoding='UTF-8')

did not change anything.
geobr-test.pdf

A call to read_biomes() without any argument does not work as it requires an year. It could work even without any argument by having 2004 as default value. I don't know whether the other read functions have a default year, but I think they could always return the latest data available when the year argument is not used.

Add the followig two columns to UF data sets

"region_name", "region_code"
North, 1
South, 4
..., ...

Criar função que varre todos arquivos na pasta ./geobr/data e atualiza tabela com metadados do pacote. Exemplo abaixo de tabela de metadados

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