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UID:pretalx-foss4g-asia-2023-JP3UYD@talks.osgeo.org
DTSTART;TZID=KST:20231129T162000
DTEND;TZID=KST:20231129T164000
DESCRIPTION:Individual mobility is a crucial indicator of personal health a
 nd behavior. To assess individual mobility and activity\, mobile sensing u
 sing global positioning systems (GPS) and surveys on smartphones are incre
 asingly being employed\, providing high spatial and temporal resolution da
 ta. This advancement has facilitated in-depth studies into the interaction
 s between individuals' real-life behaviors\, health\, personal characteris
 tics\, and environments at a micro spatiotemporal scale. For such studies 
 related to personal health and mobility\, several open-source software pac
 kages (e.g.\, R/Python libraries) and open trajectory data (e.g.\, GPS dat
 a) have been developed.\n\nIn this presentation\, we will introduce our R 
 package development project for individual mobility analytics\, focusing o
 n GPS-based trajectory data processing and analysis at an individual level
 . The open-source package includes: (1) GPS data preprocessing\, (2) const
 ruction of semantically enriched trajectory data from GPS data using autom
 ated methods for home detection\, stop-move detection\, and transport mode
  detection\, and (3) computation of mobility indicators at two different a
 ggregation levels (daily vs. by-person). We will also showcase case studie
 s using open GPS data (such as Geolife) and datasets from other research p
 rojects. Furthermore\, we will discuss the challenges faced during this pr
 oject and outline its future directions.
DTSTAMP:20260517T145341Z
LOCATION:Circle Room
SUMMARY:Individual Mobility Analytics with Open Data and Software - Eun-Kye
 ong Kim
URL:https://talks.osgeo.org/foss4g-asia-2023/talk/JP3UYD/
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