BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.osgeo.org//foss4g-2026//talk//DBAG8B 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-DBAG8B@talks.osgeo.org DTSTART;TZID=JST:20260901T170000 DTEND;TZID=JST:20260901T173000 DESCRIPTION:Background\n\nUrban parks provide essential ecosystem services and recreational opportunities that contribute to physical health\, mental well-being\, and social interaction in dense metropolitan areas. Evidence -based park planning requires accurate and fine-grained measurements of vi sitor numbers\, spatial distribution patterns\, and movement trajectories. Conventional data collection approaches—such as manual observation\, su rveys\, and GPS tracking experiments—are labor-intensive\, costly\, and difficult to implement continuously at scale. Although commercial mobility datasets derived from smartphones provide large-scale behavioral informat ion\, they are typically proprietary and financially restrictive. Meanwhil e\, geotagged social media data have been widely used to estimate recreati onal visitation\; however\, these data are often sparse and insufficient f or capturing microscale spatio-temporal and trajectory-level behavior patt erns.\n\nWith the proliferation of Web 2.0 and volunteered geographic info rmation (VGI)\, volunteered street view imagery (VSVI) platforms such as M apillary offer a novel open-data source with global coverage. Unlike tradi tional social media posts that consist of isolated image points\, VSVI dat a are organized into sequential\, geotagged photo trajectories\, inherentl y embedding movement information. This structural advantage suggests poten tial for behavioral analysis at fine spatial and temporal resolutions. Nev ertheless\, VSVI contributions are generated by self-selected individuals and often dominated by a small number of highly active users. Consequently \, it remains unclear whether VSVI contribution behavior reflects general park-use behavior or primarily captures the habits of specific contributor s. Furthermore\, given the spatial heterogeneity of VSVI contributions and the potential role of data volume in improving data quality\, we hypothes ize that the statistical validity of such data depends on contribution vol ume.\n\nMethods\n\nThis study empirically evaluates whether VSVI can serve as a reliable proxy for park-use behavior and investigates whether its ef fectiveness increases with higher levels of contribution. The analysis was conducted in 49 metropolitan parks located in Tokyo’s 23 wards\, one of Japan’s major hotspots of Mapillary activity. Three dimensions of park- use behavior were examined: (1) estimation of park visitor counts\, (2) sp atio-temporal distribution patterns\, and (3) travel-path characteristics. Two reference datasets were employed. First\, official annual park visito r counts (2019) published by the Tokyo Metropolitan Government were used t o validate visitation estimation. Second\, commercial Real People Flow Dat a (May 2023) provided anonymized GPS trajectories from approximately 869\, 840 users (about 6% of Tokyo’s population)\, generating around 65 millio n records per day. After preprocessing—restricting to walking trips with in park boundaries and correcting for temporal gaps and cross-park traject ories—523\,057 PeopleFlow trajectories were retained for analysis. VSVI metadata were collected from Mapillary (2014–March 2024)\, yielding 242\ ,534 cleaned image points organized into 2\,007 sequences contributed by 4 5 users within park areas.\n\nResults\n\nTo assess visitation estimation c apacity\, Spearman’s rank correlation analyses were conducted between of ficial visitor counts and four VSVI contribution indicators: number of ima ge points\, number of sequences\, number of photo-user-days (PUD)\, and nu mber of creators. When considering all parks collectively\, none of the in dicators showed statistically significant correlations with official visit or counts. However\, when parks were grouped based on mean PUD into low- a nd high-contribution categories\, moderate and statistically significant c orrelations emerged in the high-PUD group for PUD and number of creators ( p < 0.05)\, whereas low-PUD parks showed no significant relationships. The se results indicate that VSVI data may not universally estimate visitation levels but can approximate visitor counts in parks with sufficiently dens e contribution activity.\n\nSpatio-temporal distribution patterns were com pared at a 20 m × 20 m grid resolution using three indicators derived fro m both datasets: point counts\, dwell time per trip\, and average speed. W hen pooling all parks\, statistically significant but weak positive correl ations were observed between VSVI and PeopleFlow data for all indicators ( p < 0.001). However\, park-specific analyses revealed substantial heteroge neity. The proportion of parks exhibiting significant positive correlation s was markedly higher in the high-PUD group\, particularly for spatial ind icators such as point density and dwell time. Improvements for average spe ed were comparatively moderate. These findings suggest that increased cont ribution volume mitigates individual behavioral bias and enhances represen tativeness at the aggregated spatial level.\n\nTravel-path characteristics were further compared at the park level\, including temporal features (st art time\, end time\, duration)\, standardized movement range features (tr avel distance\, standard deviation ellipse (SDE) area\, and SDE perimeter normalized by park area)\, and behavioral features (average speed and devi ation rate). Strong and highly significant correlations were identified fo r standardized travel distance\, SDE perimeter\, and SDE area\, indicating a high degree of consistency between VSVI-derived trajectories and refere nce mobility data in terms of movement range characteristics. Average spee d showed moderate positive correlations. In contrast\, temporal features e xhibited no significant relationships\, suggesting fundamental differences between photo-sharing behavior and general recreational timing patterns. Group-specific analyses indicated that correlations were generally stronge r in parks with a higher number of VSVI trips\, particularly for movement range indicators\, although variability remained across parks and metrics. \n\nConclusions and implications\n\nOverall\, the results demonstrate that VSVI can serve as a meaningful proxy for selected aspects of park-use beh avior\, especially spatial distribution and movement range characteristics . However\, its reliability is strongly conditioned by contribution volume . Parks with higher contribution density—often scenic or tourism-oriente d sites—show substantially stronger alignment with commercial mobility d ata. Low-contribution parks exhibit weaker or inconsistent relationships\, underscoring the importance of sufficient data accumulation in VGI-based behavioral inference. For the FOSS4G community\, this study demonstrates h ow crowdsourced open street-level imagery can complement proprietary mobil ity datasets in large-scale urban behavioral analysis. By empirically exam ining the relationship between contribution volume and analytical validity \, the study contributes to ongoing discussions on data-quality growth\, r epresentativeness\, and the practical integration of open geospatial data into reproducible urban analytics workflows. While limitations related to GPS accuracy\, timestamp uncertainty\, and contextual heterogeneity remain \, the results highlight the potential of open\, crowd-sourced street-leve l imagery as a scalable complement to traditional data sources in spatial planning and geospatial research. DTSTAMP:20260717T234906Z LOCATION:Cosmos1 SUMMARY:Capturing park-use behavior patterns using volunteered street view imagery: Does the likelihood increase with the volume of contributions? - Xinrui Zheng URL:https://talks.osgeo.org/foss4g-2026/talk/DBAG8B/ END:VEVENT END:VCALENDAR