Getting started with infosigasp

Overview

INFOSIGA-SP is the São Paulo State Traffic Accident Information and Management System, maintained by DETRAN-SP. It publishes, as open data, detailed records of every traffic crash in the state of São Paulo from 2015 onward.

The infosigasp package wraps the download and import of those records. It takes care of the things that make the raw files awkward to read directly:

  • the files are encoded in Latin-1 (ISO-8859-1), not UTF-8;
  • fields are separated by semicolons (;);
  • decimal numbers (such as coordinates) use a comma decimal mark;
  • dates are formatted DD/MM/YYYY;
  • each dataset is split across two files (2015–2021 and 2022 onward).
library(infosigasp)

The three datasets

INFOSIGA-SP organises its data into three linked tables:

infosiga_datasets()
#> # A tibble: 3 × 4
#>   dataset   description                                              grain keys 
#>   <chr>     <chr>                                                    <chr> <chr>
#> 1 sinistros Traffic crash events recorded in the state of Sao Paulo. one … id_s…
#> 2 pessoas   People (victims) involved in traffic crashes.            one … id_p…
#> 3 veiculos  Vehicles involved in traffic crashes.                    one … id_v…
  • sinistros — crash events. One row per recorded event, with the date, time, location (including latitude/longitude), road attributes and a breakdown of how many vehicles and victims were involved, by type and severity.
  • pessoas — people (victims). One row per person involved, with demographic attributes, injury severity and, for fatalities, the date and place of death.
  • veiculos — vehicles. One row per vehicle involved, with make/model, manufacturing and model years, colour and type.

All three share the id_sinistro key, so they can be joined together; pessoas and veiculos additionally share id_veiculo.

Reading data

The main entry point is read_infosiga(). The first call downloads the source archive (about 120 MB) into a per-user cache; subsequent calls read from that cache, so you only pay the download cost once.

sinistros <- read_infosiga("sinistros")
sinistros

You can restrict the import to specific years with the year argument, which filters on the year of the crash (ano_sinistro):

recent <- read_infosiga("sinistros", year = 2022:2025)

Processed vs. raw data

By default read_infosiga() returns a processed dataset (clean = TRUE). The processing never renames columns, recodes category labels or drops rows – it only fixes types and source artefacts so the data is ready for analysis. The full, ordered list of steps lives in ?clean_infosiga; in brief:

  • Dates and times. Full dates are parsed to Date and times to hms (in both modes), and the ano_mes_* year-month columns (published as "YYYY/MM") become first-of-month Date values.
  • Whitespace. Leading/trailing whitespace is trimmed from text columns. The source pads some fields to a fixed width (nacionalidade ships as "BRASILEIRA "); untrimmed, those values break grouping and joins.
  • Missing values. The "NAO DISPONIVEL" (“not available”) marker is replaced by NA (trimming runs first, so space-padded markers are caught).
  • Ordered factors. Ordinal columns sort and plot in their natural order instead of alphabetically:
    • dia_da_semana: Domingo < … < Sábado (the Brazilian week starts on Sunday);
    • turno: MADRUGADA < MANHA < TARDE < NOITE;
    • gravidade_lesao (victims): LEVE < GRAVE < FATAL;
    • faixa_etaria_demografica / faixa_etaria_legal: age bands in order.
  • Crash-type flags. The binary tp_sinistro_* columns ("S" / empty) become logical, so you can sum() or filter them directly. The categorical tp_sinistro_primario is left as text.
  • Numeric strings. tempo_sinistro_obito (days from crash to death) becomes integer, and the spurious trailing ".0" the export appends to numero_logradouro ("193.0") is stripped.
  • Coordinates. latitude/longitude outside the São Paulo state bounding box – mis-encoded values and (0, 0) placeholders, about 7% of records – are set to NA as a pair. No rows are dropped; pass clean = FALSE for the raw coordinates.
sinistros <- read_infosiga("sinistros")
levels(sinistros$dia_da_semana)

Because dia_da_semana is an ordered factor, a weekday tabulation comes out in calendar order rather than alphabetically:

table(sinistros$dia_da_semana)

If you would rather have the data exactly as published — every text column as a character vector, with "NAO DISPONIVEL" and the source’s whitespace padding preserved verbatim — pass clean = FALSE:

raw <- read_infosiga("sinistros", clean = FALSE)

You can also apply the same processing to a raw import after the fact with clean_infosiga():

processed <- clean_infosiga(raw, "sinistros")

A peek at the structure without downloading

The package ships a small sample of each dataset so you can inspect the columns without any network access:

sample_path <- system.file("extdata", "sinistros_sample.csv", package = "infosigasp")
sample <- readr::read_delim(
  sample_path,
  delim = ";",
  show_col_types = FALSE
)
dim(sample)
#> [1] 100  48
names(sample)
#>  [1] "id_sinistro"                     "tipo_registro"                  
#>  [3] "data_sinistro"                   "ano_sinistro"                   
#>  [5] "mes_sinistro"                    "dia_sinistro"                   
#>  [7] "hora_sinistro"                   "ano_mes_sinistro"               
#>  [9] "dia_da_semana"                   "turno"                          
#> [11] "logradouro"                      "numero_logradouro"              
#> [13] "tipo_via"                        "tipo_local"                     
#> [15] "latitude"                        "longitude"                      
#> [17] "cod_ibge"                        "municipio"                      
#> [19] "regiao_administrativa"           "administracao"                  
#> [21] "conservacao"                     "circunscricao"                  
#> [23] "tp_sinistro_primario"            "qtd_pedestre"                   
#> [25] "qtd_bicicleta"                   "qtd_motocicleta"                
#> [27] "qtd_automovel"                   "qtd_onibus"                     
#> [29] "qtd_caminhao"                    "qtd_veic_outros"                
#> [31] "qtd_veic_nao_disponivel"         "qtd_gravidade_fatal"            
#> [33] "qtd_gravidade_grave"             "qtd_gravidade_leve"             
#> [35] "qtd_gravidade_ileso"             "qtd_gravidade_nao_disponivel"   
#> [37] "tp_sinistro_atropelamento"       "tp_sinistro_colisao_frontal"    
#> [39] "tp_sinistro_colisao_traseira"    "tp_sinistro_colisao_lateral"    
#> [41] "tp_sinistro_colisao_transversal" "tp_sinistro_colisao_outros"     
#> [43] "tp_sinistro_choque"              "tp_sinistro_capotamento"        
#> [45] "tp_sinistro_engavetamento"       "tp_sinistro_tombamento"         
#> [47] "tp_sinistro_outros"              "tp_sinistro_nao_disponivel"

A short analysis

Once imported, the data are ordinary tibbles, so any tidyverse (or base R) workflow applies. For example, counting traffic fatalities per year from the victims dataset:

library(dplyr)

deaths_by_year <- read_infosiga("pessoas") |>
  filter(gravidade_lesao == "FATAL") |>
  count(ano_obito, name = "deaths") |>
  arrange(ano_obito)

deaths_by_year

Or fatalities broken down by the type of victim (driver, passenger, pedestrian):

read_infosiga("pessoas") |>
  filter(gravidade_lesao == "FATAL") |>
  count(tipo_de_vitima, sort = TRUE)

Because sinistros carries latitude and longitude as numeric columns, crash locations can be mapped directly or aggregated by municipality (municipio / cod_ibge).

Managing the cache

The download lives in an operating-system specific cache directory:

infosiga_cache_dir()
#> [1] "/github/home/.cache/R/infosigasp"
infosiga_cache_list()
#> character(0)

The archive is refreshed monthly by DETRAN-SP. To pull the latest version, force a re-download:

infosiga_download(overwrite = TRUE)

To reclaim disk space, clear the cache:

infosiga_cache_clear()

You can point the cache somewhere else for a session (or permanently via your .Rprofile) with the infosigasp.cache_dir option:

options(infosigasp.cache_dir = "~/data/infosiga")

The official data dictionary

INFOSIGA-SP distributes a field-by-field data dictionary (PDF, in Portuguese). infosiga_dictionary() downloads it and returns the paths to the extracted files:

pdfs <- infosiga_dictionary()
basename(pdfs)

Citing the data

Data are published by DETRAN-SP under a Creative Commons Attribution 4.0 licence. When you publish results based on these data, please cite INFOSIGA-SP / DETRAN-SP as the source: https://infosiga.detran.sp.gov.br/.