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UID:pretalx-sotm2026-83LBSJ@pretalx.com
DTSTART;TZID=CET:20260828T150500
DTEND;TZID=CET:20260828T152500
DESCRIPTION:Alongside the enrichment of the OpenStreetMap database over the
  years\, numerous tools have been developed to interact with or to create 
 derivative products: editors\, data extraction tools\, geocoding services\
 , slippy maps\, or routing engines. Most of these tools are accessible vi
 a scripting languages through APIs or dedicated libraries. Among these lan
 guages\, R stands out as a free and open source programming language and s
 oftware for statistical computing and data visualization. Complemented by 
 more than 23\,500 user-contributed packages hosted on the Comprehensive R 
 Archive Network in 2026\, the language’s versatility allows users to bui
 ld unified workflows within an environment conducive to reproducible resea
 rch. R is widely used for research purposes in many fields beyond statisti
 cs\, such as biology\, social sciences\, or geomatics. A subset of its pac
 kages forms a robust and mature spatial ecosystem that makes it easy to ha
 ndle and display spatial data. Consequently\, R provides seamless access t
 o various APIs and tools for downloading and filtering OpenStreetMap data\
 , querying shortest routes and itineraries\, or generating both static and
  interactive maps. \n\nAs part of our research activities\, we have develo
 ped some of the key OpenStreetMap-related R packages. For example\, the _o
 srm_ package has enabled the integration of time-distance calculations in 
 many academic studies by providing a straightforward access to the OSRM ro
 uting engine. The package has been widely used in numerous published paper
 s across various disciplines (health\, transport\, environmental science\,
  or education). Thus\, we argue that R enables researchers to efficiently 
 use OpenStreetMap thanks to a unified and reproducible environment and spe
 cialized packages.\n\nIn this presentation\, we first explore a curated se
 lection of R packages designed to interact with the OpenStreetMap database
 . We begin with packages that download and extract data\, such as _osmextr
 act_ and _osmdata_. Then\, we explore solutions that interface third party
  softwares for geocoding (Nominatim with _tidygeocoder_) and routing (OSRM
  with _osrm_\, Valhalla with _valh_). Finally\, we address dynamic cartogr
 aphy (using _leaflet_) and creation of static maps from raster tiles (usin
 g _maptiles_) or from vector objects (using _maposm_). \n\nTo illustrate t
 he practical application of these tools\, we conclude with a short\, seaml
 ess and fully reproducible example. From the geocoding of an address\, to 
 the extraction of amenities or the use of routing engines and till the cre
 ation of a publication-ready map\, we propose a complete workflow that har
 ness the power of R ecosystem to create insightful analysis from OpenStree
 tMap data.
DTSTAMP:20260609T184716Z
LOCATION:La Réunion
SUMMARY:From OpenStreetMap APIs to Insightful Data Analysis: Extraction\, A
 nalysis\, and Mapping with R - Louis Laurian
URL:https://pretalx.com/sotm2026/talk/83LBSJ/
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