Ibamam

DOI

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This package aims to provide the basis for environmental fines and notices from the Brazilian Institute for the Environment and Renewable Natural Resources (IBAMA).

You can visit the Brazilian Open Data Portal in the IBAMA repository at Portal Brasileiro de Dados Abertos no repositóio do IBAMA.

If you want to use the most current database without having to install the package, I recommend that you use the following code:

How to install?

This package can be installed via GitHub (https://github.com/) using the following code in R:

# install.packages("devtools")
devtools::install_github("cccneto/Ibamam")
library(Ibamam)

How to use Ibamam?

The Ibamam package has seven functions. Below we describe the tasks and how you can use them.

Function to {obter_arquivos_multas_distribuidas}

This function allows you to scrape the data related to Quantity of Environmental Fines Distributed by Protected Property, for example, Flora, Fauna, among others. The function makes available all AI drawn up with or without judgment. Its update is daily, with a daily temporal scope and having a National geopolitical content.

Examples

# Downloading data
# env_fines_distributed <- obter_arquivos_multas_distribuidas()

# To view only the first 100 lines
# env_fines_distributed %>% head(100)

What data can I access with the function {obter_arquivos_multas_distribuidas}?

  • dataAuto : date of issue of the tax assessment notice.
  • dataPagamento : date of payment of the infraction.
  • municipio : municipality / city.
  • numAI : identification number of the infraction notice.
  • tipoInfracao: type of infraction committed.
  • ultimaAtualizacaoRelatorio : Informs when the data was last updated.
  • uf : state of the federation
  • tipoAuto: auto type defines the category of the occurrence - examples: fine, daily fine, warning.
  • moeda: informs which currency.
  • situacaoDebito : monetary status of the infraction.
  • enquadramentoLegal: informs which legal device framing the infraction notice.
  • nomeRazaoSocial: identifies the name of the person or company assessed.
  • cpfCnpj : informs the CPF or CNPJ of the assessed taxpayer.
  • valorAuto : informs the monetary amount of the tax assessment notice.

Function to {obter_arquivos_arrecadacao}

This function allows you to scrape data related to Collection of Environmental Fines Distributed by Protected Property, for example, Flora, Fauna, among others. The function makes available all AI drawn up with or without judgment. The database includes information on volumes of paid environmental fines, consolidated by legally protected assets. Its update is daily, with a daily temporal scope and having a National geopolitical content.

What data can I access with the function {obter_arquivos_arrecadacao}?

  • dataAuto : date of issue of the tax assessment notice.

  • dataPagamento : date of payment of the infraction.

  • municipio : municipality / city.

  • numAI: identification number of the infraction notice.

  • valorPago: amount paid up to the date of the last update of the report.

  • tipoInfracao: type of infraction committed.

    • ultimaAtualizacaoRelatorio : Informs when the data was last updated.

  • parcela: installment number

  • quantidadeParcela: number of payments in installments.

  • uf: state of the federation

  • tipoAuto: auto type defines the category of the occurrence - examples: fine, daily fine, warning.

  • moeda: informs which currency.

  • situacaoDebito : monetary status of the infraction.

  • enquadramentoLegal: informs which legal device framing the infraction notice.

  • nomeRazaoSocial: identifies the name of the person or company assessed.

  • cpfCnpj: informs the CPF or CNPJ of the assessed taxpayer.

  • valorAuto: informs the monetary amount of the tax assessment notice.

  • valorbaseParcela: informs the value of the installments for payment of the auto value.

After downloading the two databases with the functions {obt_arquivos_multas_distribuidas} and {obt_arquivos_arrecadacao}, now you need to clean the data. To do so, activate the function as follows:

Example

# Downloading data
# fines_collected <- obter_arquivos_arrecadacao()

# To view only the first 100 lines
# fines_collected %>% head(100)

Function to{limpar_dados} - wrangling data!

This function allows you to wrangling the data that you downloaded with the functions {obtain_arquivos_arrecadacao} scrape the data referring to the volume of the collection of Ibama with environmental fines distributed by well guarded, example: Flora, Fauna, among others. The penalties were fixed by law and applied according to the classification of the infraction committed, among others. The function makes available all AI drawn up with or without judgment.

Examplo

# wrangling database "fines_collected"
# fines_collected_clear <- limpar_dados(env_fines_distributed)

# limpando base "multas_distribuidas"
# env_fines_distributed_clear <- limpar_dados(env_fines_distributed)

Function to{baixar_id_municipios}

With the data from fines distributed and fines collected, you may wish to carry out some analyses requiring spatial data. For that, we need to configure the data first. The first step is to use the function {baix_id_municipios} to get the codes from the IBGEMunicipalityCodeTable (https://www.ibge.gov.br/explica/codigos-dos-municipios.php ). The function will create a new data frame containing the municipal id codes, id state (uf), and Brazilian municipalities’ names.

Example

# get municipalities id codes 
# id_codes <- baixar_id_municipios()

Função para{juntar_bases}

Agora precisamos unir as bases do Ibama (multas_arrecadadas_limpo e multas_distribuidas_limpo) com os codigos municipais do IBGE (id_codes). Para isso aplique a função conforme descrito abaixo:

Exemplo

# Join  *fines_colecteds* com os codigos municipais
# fines <- juntar_bases(base_ibama = fines_collected_clear, base_ibgecode = id_codes)

# Join  *multas_distribuidas* com os códigos municipais
# fines_autos <- juntar_bases(base_ibama = env_fines_distributed_clear, base_ibgecode = id_codes)

If you want to see the ready bases, enter the codes below:

# fines  %>% head()

# ou 

# fines_autos %>% head()

How to quote the package

  • DOI

  • “CRUZ NETO, Claudiano C., MILZ, Beatriz., MACEDO, Samuel (2021). Ibamam: Pacote para tratamento da Base de dados para multas e autuações ambientais aplicadas pelo Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis - Brasil. https://doi.org/10.5281/zenodo.4509344”.