BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//talks.osgeo.org//foss4g-europe-2026//speaker//RVDUBZ
BEGIN:VTIMEZONE
TZID:EET
BEGIN:STANDARD
DTSTART:20001029T050000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:EET
TZOFFSETFROM:+0300
TZOFFSETTO:+0200
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T040000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:EEST
TZOFFSETFROM:+0200
TZOFFSETTO:+0300
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-foss4g-europe-2026-JZNM93@talks.osgeo.org
DTSTART;TZID=EET:20260630T113000
DTEND;TZID=EET:20260630T120000
DESCRIPTION:**This introductory talk aims at giving a conceptual overview a
 nd application examples for using Discrete Global Grid Systems in daily GI
 S analysis and visualisation tasks.**\n\nNear-real-time data\, time series
  data and other spatio-temporal event data are often subject to the need f
 or cartographic visualisation sooner or later\, regardless of their intend
 ed purpose of analysis. If a web visualisation is planned\, data preparati
 on is a challenging task\, as a prototypical quick-and-dirty web presentat
 ion in the browser quickly reaches its performance limits with large amoun
 ts of data. Depending on the application\, the raw data volumes can be hug
 e. The provision of raw data via OGC web services also scales poorly with 
 increasing data volumes.\n\nThis is where aggregation or generalisation of
  the data becomes necessary. Self-defined grids\, official or national gri
 d systems and discrete global grid systems (DGGS) are very well suited for
  this. Some variants such as the INSPIRE-based [geographical grid for Germ
 any](https://gdz.bkg.bund.de/index.php/default/inspire/sonstige-inspire-th
 emen/geographische-gitter-fur-deutschland-in-lambert-projektion-geogitter-
 inspire.html)\, [Uber's hexagonal grid system H3](https://h3geo.org/) and 
 [Google's hierarchical grid system S2](http://s2geometry.io/) will be brie
 fly compared in the presentation.\n\nAlthough web mapping frameworks such 
 as OpenLayers already offer practical methods such as [HexBin](https://vig
 lino.github.io/ol-ext/doc/doc-pages/ol.source.HexBin.html) for creating a 
 hexagon grid for a spatial aggregation of source data\, all source data mu
 st first be transferred to the client's browser\, which — experience has
  shown — quickly leads to performance problems. \n\nTwo project examples
  are giving a glimpse on more effective approaches for efficiently aggrega
 ting time series data in the hexagonal grid system H3:\n\n- Via post-proce
 ssing at database level using the PostgreSQL extension [h3-pg](https://git
 hub.com/postgis/h3-pg) and\n- in real time during streaming data processin
 g with Apache Flink by using the [H3 implementation in Java](https://githu
 b.com/uber/h3-java).\n\nThe ultimate goal in both cases is always a high-p
 erformance provision of the aggregated result data via OGC web interfaces 
 such as WMS/WFS or API – Maps/API – Features. In this way\, results ar
 e easily exchangeable and can be used flexibly in analysis tools of variou
 s players.\n\nFinally\, [DGGAL](https://dggal.org/) and [Vgrid](https://vg
 rid.gishub.vn/) with bindings for Python and QGIS will be recommended as h
 andy open-source tools. We may also take a look at the DGGS working group 
 of the OGC and the OGC standard [API – DGGS](https://www.ogc.org/de/stan
 dards/dggs/).\n\nThe talk aims to present an approach to grid aggregation 
 that is as generic as possible\, so that the transferability of this pract
 ical methodology to numerous other statistical use cases is conveyed. In p
 articular\, traffic data\, movement data and sensor data can be elegantly 
 visualised with little effort and prepared for further analysis applicatio
 ns.
DTSTAMP:20260604T233806Z
LOCATION:A02
SUMMARY:Discrete global grid systems for spatio-temporal aggregation and vi
 sualisation - Michael Scholz
URL:https://talks.osgeo.org/foss4g-europe-2026/talk/JZNM93/
END:VEVENT
END:VCALENDAR