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UID:pretalx-foss4g-2022-academic-track-MCGBBT@talks.osgeo.org
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DESCRIPTION:# Introduction\n\nOpenStreetMap (OSM) has evolved to one of the
  most used geographic databases. It is a major knowledge source for many g
 eographic topics addressed by researchers\, professionals and the general 
 public. To satisfy these diverse needs and capabilities\, the linked commu
 nities surrounded the project with an ever growing ecosystem of analyses t
 ools (e.g. OSM Contributors\, 2022). The most prominent analysis topic is 
 data quality (Senaratne et al. 2015) where e.g. intrinsic indicators are u
 sed to estimate completeness (Brückner et al. 2021). Furthermore the comm
 unity is also interested in insights such as leader-boards or activity rep
 orts (e.g. Neis\, 2022). In recent years analyses have also more and more 
 shifted towards doing large scale analyses (e.g. Herfort et al. 2021).\n\n
 This diversity of tools can be a challenge for data users who will find th
 emselves in a universe of highly specialised or complex tools using differ
 ent programming languages\, platforms\, interfaces\, output formats etc. W
 hile there have been efforts to provide users with higher level data insig
 ht and analyses platforms\, these still mostly concentrate on or are limit
 ed to certain topics or regions. To our knowledge no tool exists to analys
 e and combine topic independent aspects of the data at the highest possibl
 e resolution: single OSM elements.\n\nThe presented software (available at
  https://gitlab.gistools.geog.uni-heidelberg.de/giscience/ideal-vgi/osm-el
 ement-vectorisation) sets out to bridge this gap by integrating multiple a
 spects of the OSM ecosystem into one workflow that allows the quantitative
  assessment of selected OSM elements or all elements in a defined region. 
 This enables new insights in a formalised and easy to use manner. The resu
 lt is a vectorisation of single OSM elements (sometimes also called embedd
 ing or feature construction). By producing a machine readable result\, the
  tool can be used for manual data investigations as well as for the ever g
 rowing field of machine learning where it can be linked to a range of labe
 ls.\n\n# Software\n\nThe tool is centred around a python package providing
  a command line interface suitable also for novice users. It draws on othe
 r sources where necessary such as POST-requests and Java. Further data pro
 cessing is done using the R scripting language while all data is stored in
  a PostGIS enhanced PostgreSQL database and can be exported automatically 
 to .csv-files. The AGPL v3 license as well as the code structure and docum
 entation enable others to also use it as a framework to implement their ow
 n analyses logic in combination with the current procedure. A default setu
 p using Docker is provided for fast installation including a minimal examp
 le. The tool is fully functional and in use in our current research. Yet\,
  it is under active development towards a web interface and functionality 
 extensions. While the development was made with land-use and land-cover (L
 ULC) information in mind\, the tool can be seamlessly applied to any polyg
 onal OSM data such as buildings and also supports linear and point data. T
 he tool is resilient towards missing data and can recover from many common
  issues like failed connections. The backend remains in a sane state throu
 ghout the workflow and error messages enable the user to adapt to any fail
 ures and simply rerun the tool that will automatically pick up from the la
 st savepoint. Benchmarks have shown that the tool is capable of processing
  around 1k elements per hour making it a suitable tool for larger analyses
  of custom regions or element sets.Out of the endless number of possible d
 ata aspects\, a set of 32 are currently available for the user to choose. 
 These cover aspects concerning the element itself (e.g. object area\, geom
 etric complexity and object age) but also the surrounding data (e.g. the m
 apping saturation and community activeness) and the editors (e.g. their ex
 perience\, localness or editing software used).\n\n# Application\n\nTo pro
 ve its potential\, the tool is applied to a set of 1k randomly selected OS
 M LULC elements. We picked OSM LULC as an example as it has been shown to 
 be valuable for applications such as earth surface monitoring. The results
  provide a status report on the already available data to the OSM communit
 y. It further enables a more informed planning of future activities like o
 rganised mapping or data curation efforts and enables data consumers to ma
 ke informed decisions on data usage by answering the question: What is OSM
  LULC made of? First\, three exemplary hypotheses were tested statisticall
 y on a global as well as a continental scale to analyse the triangular rel
 ation between elements' size\, age and location in terms of population den
 sity. In a second step\,  k-means clustering was used to identify clusters
  based on the properties of the OSM objects. Before clustering\, the data 
 were standardised and stripped of any geographic information as we were hy
 pothesising that the different clusters might be linked to different geogr
 aphic regions.\n\n\nThe results showed that larger objects were more frequ
 ently encountered in regions with a lower population density due to the 'n
 atural' factor of higher fragmentation in these areas. Yet\, the effect wa
 s surprisingly small on a global scale. A general mapping order where area
 s of high population density are mapped before lower population density ar
 eas could not be confirmed globally. This may be caused by a complex inter
 action between several indicators and regional tendencies\, that remains t
 o be fully understood. Regional tendencies were shown e.g. for the age of 
 objects with North America and Europe containing older objects than Africa
  and Asia. The five k-means clusters formed interesting groups worth furth
 er investigation. For example the North American lakes or the complex Euro
 pean elements were each detected as distinct clusters by the algorithm.\n\
 n\n# Outlook\n\nOur current and future work will investigate the causes of
  these insights and link them e.g. to data quality to identify OSM element
 s that need the communities' attention. The presented tool already enables
  other data users to join us on this path.
DTSTAMP:20260907T024705Z
LOCATION:Room Modulo 3
SUMMARY:OpenStreetMap Element Vectorisation - A tool for high resolution da
 ta insights and its usability in the land-use and land-cover domain - Mori
 tz Schott
URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/MCGBBT/
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