BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//talks.osgeo.org//foss4g-2026//talk//Y8UGZB
BEGIN:VTIMEZONE
TZID:JST
BEGIN:STANDARD
DTSTART:20000101T000000
RRULE:FREQ=YEARLY;BYMONTH=1
TZNAME:JST
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-foss4g-2026-Y8UGZB@talks.osgeo.org
DTSTART;TZID=JST:20260902T140000
DTEND;TZID=JST:20260902T143000
DESCRIPTION:OpenStreetMap (OSM) is a prominent platform for Volunteered Geo
 graphic Information (VGI)\, wherein a diverse array of geographic data\, i
 ncluding road networks\, buildings\, land use\, and points of interest (PO
 I)\, are updated daily by the community. However\, owing to the collaborat
 ive nature of data generation by numerous contributors\, ensuring the qual
 ity of edits and ongoing monitoring has emerged as a significant challenge
  in recent years (Choe et al.\, 2023). The OpenStreetMap Changeset Analyze
 r (OSMCha) serves as a quality assurance tool for monitoring OSM changeset
 s and automatically flags suspicious edits based on a catalogue of detecti
 on rules covering geometric and tag plausibility\, edit scale\, contributo
 r behavioural patterns\, and edits to specific feature types. Nonetheless\
 , few studies have systematically collected OSMCha data via its API for la
 rge-scale\, country-wide spatiotemporal analysis.\n\nIn this study\, we de
 veloped a series of Python scripts to systematically collect changeset dat
 a from the OSMCha API\, amassing 740\,038 changesets spanning four years f
 rom 2022 to 2025 for the Japanese context. Given API constraints\, such as
  limits on the number of records retrievable per request and potential tim
 eouts\, we implemented monthly batch processing and GeoJSON storage\, a ch
 eckpoint function to allow resumption from interruptions\, and a validatio
 n logic that alerts when the acquisition rate falls below 90% of the expec
 ted total\, thereby ensuring the stable collection of large-scale data. Th
 e collected data were consolidated into a FlatGeobuf database in which 60 
 reason_id-to-name mappings—classified into four categories (geometry/tag
  plausibility\, edit scale\, contributor attributes/behaviour\, and featur
 e-specific rules)—were embedded as attributes. By spatially joining the 
 centroid of each changeset with Japanese administrative-area polygons (wit
 h 98.8–99.2% coverage)\, we appended prefecture names and municipal code
 s. The developed scripts and the resulting dataset—comprising 339\,114 d
 etection occurrences across the four years—will be released as open-sour
 ce on GitHub and Zenodo\, supporting reuse for similar analyses in other c
 ountries and regions.\n\nOur analysis corroborated a significant upward tr
 end in OpenStreetMap (OSM) editing activities in Japan. The annual number 
 of changesets increased by 63.3%\, from 144\,625 in 2022 to 236\,203 in 20
 25. Concurrently\, the number of unique contributors expanded by 72.5%\, f
 rom 3\,660 to 6\,315. Conversely\, the rate of edits identified as suspici
 ous (is_suspect rate) decreased by 11.9 percentage points from 40.7% to 28
 .8%\, suggesting an overall enhancement in the community's data quality. A
 n examination of editing software revealed that while iD remained predomin
 ant\, accounting for 63.1% of edits\, mobile-oriented editors such as Stre
 etComplete experienced a 4.5-fold increase over four years\, with changese
 ts rising from 5\,709 to 25\,576 in the same period. EveryDoor exhibited a
  10.6-fold growth in the same period (from 539 to 5\,692)\, indicating inc
 reasing adoption of field-survey-style editing. Regarding data sources\, s
 urvey-based edits consistently demonstrated lower suspect rates (15.4% ove
 r the four years\, declining to 10.6% in 2025) than non-survey-based edits
  (38.2% in aggregate\, 33.4% in 2025)\, demonstrating clear quantitative a
 dvantages in data quality. AI-assisted edits (RapiD/mapwithai) also showed
  a marked improvement in suspect rates\, declining from 64.0% in 2022 to 1
 6.0% in 2025\, although their annual volume remained modest at approximate
 ly 1\,700–1\,800 changesets—in line with the cautious adoption pattern
  surrounding AI-assisted mapping (Andorful et al.\, 2026). PLATEAU-derived
  edits\, leveraging Japan's national 3D city-model dataset\, also grew 4.3
 -fold over the same period (Seto et al.\, 2023).\n\nA longitudinal analysi
 s spanning four years identified a significant alteration in the detection
  rules of OSMCha around 2024. Detection reasons associated with geometry a
 nd tag plausibility\, such as "Invalid tag modification" (reason_id=42) an
 d "Motorway/trunk geometry modified" (reason_id=91)\, were recorded close 
 to or over 10\,000 times annually in 2022 and 2023\, but these instances n
 early vanished post-2024. Concurrently\, there was a notable increase in d
 etections based on user attributes\, including "User has multiple blocks" 
 (reason_id=83) and "suspect_word" (reason_id=1)\, indicating a shift in th
 e algorithm's emphasis from geometry and tag plausibility assessments to m
 onitoring contributor behavioural patterns. The frequency of review reques
 ts escalated from 3\,151 in 2022 to 8\,600 in 2025\, reflecting an increas
 e in peer-review activities within the community. In terms of user retenti
 on\, 84.7% of the 14\,978 contributors were active for only one year\, wit
 h only 2.9% (433 contributors) maintaining activity throughout the entire 
 four-year period.\n\nSpatial analysis employed kernel density estimation (
 KDE) to visualise the geographical concentration of editing activities\, u
 tilising the centroid coordinates of all changesets. Significant concentra
 tions were identified in three major metropolitan areas—Tokyo\, Osaka\, 
 and Nagoya—replicating\, at the within-country prefecture scale\, the cr
 oss-national spatial bias pattern reported by Quattrone et al. (2015)\, wh
 ereby power users concentrate in urban centres while occasional contributo
 rs distribute their edits more uniformly. The January 2024 Noto Peninsula 
 earthquake further triggered a marked increase in crisis mapping activitie
 s within Ishikawa Prefecture\, resulting in a nationwide doubling of chang
 esets to 30\,228 for that month\, with participation from 1\,536 unique us
 ers in Japan. This phenomenon underscores the spatial and temporal respons
 iveness of VGI during disaster events. Furthermore\, substantial regional 
 disparities were observed in the suspect rate by prefecture: over half of 
 the edits were flagged in Iwate (59.3%) and Kumamoto (55.4%) prefectures\,
  whereas the rates were considerably lower in Kyoto (20.3%) and Oita (20.6
 %) prefectures.\n\nThis study makes a significant contribution by developi
 ng a method for large-scale data extraction from the OSMCha API and presen
 ting a comprehensive spatiotemporal analytical framework encompassing 740\
 ,038 changesets over four years for the entirety of Japan. The scripts and
  nationwide dataset developed in this study will be released as open-sourc
 e on Zenodo\, thereby providing a reproducible workflow that can be utilis
 ed for similar studies in regions beyond Japan. Looking forward\, we inten
 d to pursue qualitative analysis of harmful-flagged cases\, international 
 comparative analyses using OSMCha (with Benelux as an initial target)\, an
 d panel-data analysis tracking the activity trajectories of individual con
 tributors\, with the objective of accumulating knowledge that contributes 
 to the sustainable enhancement of data quality in VGI communities.
DTSTAMP:20260717T225749Z
LOCATION:Cosmos2
SUMMARY:Spatiotemporal Analysis of OpenStreetMap Editing Activities in Japa
 n Using the OSMCha - Toshikazu Seto
URL:https://talks.osgeo.org/foss4g-2026/talk/Y8UGZB/
END:VEVENT
END:VCALENDAR