Movement Data in GIS - Geobeer Lightning Talk, 2021-03-08
0 likes263 views
The document discusses the challenges and methodologies for utilizing movement data in geographic information systems (GIS), emphasizing the importance of understanding and addressing data messiness and bias. It highlights various data sources such as social media posts and mobile app data while advocating for innovative approaches to data visualization and exploratory analysis. The text also outlines existing research and tools that facilitate the exploration and interpretation of complex spatiotemporal phenomena in this field.
Movement Data in GIS - Geobeer Lightning Talk, 2021-03-08
- 1. MOVEMENT DATA IN GIS Geobeer Switzerland 2021-03-18 Anita Graser
- 2. Opportunistic reuse of data Black box / undocumented data collection Usually biased & messy data MOVEMENT DATA SCIENCE Geotagged social media posts Cell phone network data Mobile app data Vehicle tracking systems Check-in data WiFi tracking “All metadata records are incomplete as it is impossible to foresee future uses” Janowicz et al. (2020) GeoAI Payment data https://www.youtube.com/watch?v=mIktJKbj8Ms
- 3. Gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others Dealing with data that incorporates spatial and often temporal elements Turning Big Spatiotemporal Data into insight and understanding GEOGRAPHIC DATA SCIENCE Big Data & Data Science Geography Geographic / Spatial Data Science Quant. Geogr. & Spatial Statistics & GIScience & Geomatics CS & Math & Statistics AI / ML Humanities STEM Critical Data Studies ICT & Engineering Andrienko et al. (2017) Geographic Data Science Singleton & Arribas-Bel (2021) Geographic Data Science
- 4. Accept messiness in data Need to understand Causes of bias & messiness Consequences of using such data in analyses Data visualization & exploratory approaches CHALLENGES Brunsdon & Comber (2020) Big issues for big data Graser & Dragaschnig (2020) Open Geospatial Tools for Movement Data Exploration
- 5. Complex spatiotemporal phenomena Context & scale dependent Spatial, temporal & attribute uncertainty EXPLORING MOVEMENT DATA Demšar & Virrantaus (2010) Space-time density of trajectories Andrienko & Andrienko (2011) Spatial generalization and aggregation of massive movement data Andrienko et al. (2017) Visual exploration of movement and event data with interactive time masks + Lack of established tools & practices
- 6. AGGREGATING MOVEMENT DATA https://www.youtube.com/watch?v=dRE9Zl7jpUA Graser et al. (2020) The M³ massive movement model Graser et al. (2020) Exploratory Trajectory Analysis for Massive Historical AIS Datasets Graser et al. (2020) Extracting Patterns from Large Movement Datasets
- 7. 7 18/03/2021 https://fosdem.org/2021/schedule/event/geopandasholoviews/ Graser (2021) An exploratory data analysis protocol for identifying problems in continuous movement data.