FOSS4G-Asia 2023 Seoul

This workshop will provide an introduction to performing common GIS/geospatial tasks using Python geospatial tools such as OWSLib, Shapely, Fiona/Rasterio, and common geospatial libraries like GDAL, PROJ, pycsw, as well as other tools from the geopython toolchain.

Working with data has many challenges on it’s own and when you add a geospatial factor into makes it even harder. In my belief data never lives in isolation, it always exists in relation to something (sorry NoSQL) and PostgreSQL provides a great starting point as our not just geospatial database but database for everything.

In this talk we’ll talk briefly about long history of postgres and how it’s ecosystem can help you build a database for all your needs. In particular we’ll look at extensions like:

• PostGIS (obviously)
• pg_raster (for handling rasters)
• timescaleDB (for handling time series data)
• uber h3 (h3 indexing for your geospatial data)
• ogr_fdw (OGR FDW allows you to connect to any OGR supported data source.)
• pg_eventserv, pg_cron, pg_ivm and many more will be referenced

Moreover we’ll look at how you can combine some extensions like (postgis[spatial] + timescaleDB[temporal]) to create something that fits your use case.

Now we have designed our database but it’s time to deploy it on the cloud, let’s look at available solutions which includes from self-managed to service based and everything in between.

• Managed services like - AWS RDS. Aurora, CrunchyData, EDB, Timescale
• Deploying your own using Linux Instances
• Deploying on K8s clusters using postgres operators

We’ll use OSM data as our sample datasets plus more as needed.

The Uber H3 library is a powerful geospatial indexing system that offers a versatile and efficient way to index and query geospatial data. It provides static indexing scheme that allows for fast and accurate calculations of geospatial distances, as well as easy partitioning of data into regions. In this proposal, we suggest using the Uber H3 indexing library in Postgres for geospatial data analytics.

Postgres is an open-source relational database management system that provides robust support for geospatial data processing through the PostGIS extension. PostGIS enables the storage, indexing, and querying of geospatial data in Postgres, and it offers a range of geospatial functions to manipulate and analyze geospatial data.

However, the performance of PostGIS can be limited in some cases when dealing with large datasets or complex queries. This is where the Uber H3 library can be of great use. By integrating Uber H3 indexing with Postgres, we can improve the performance of PostGIS, especially for operations that involve partitioning of data, distance calculations and zonal statistics

This talk will demonstrate the use of Uber H3 indexing library in Postgres for geospatial data processing through a series of examples and benchmarks. The proposed presentation will showcase the benefits of using Uber H3 indexing for geospatial data processing in Postgres, such as improved query performance and better partitioning of data.

The proposed presentation will be of interest to developers, data scientists, and geospatial analysts who work with geospatial data in Postgres. It will provide a practical guide to integrating Uber H3 indexing with Postgres, and offer insights into the performance gains and applications of this integration.

The MapLibre Organization is an umbrella for open-source mapping libraries with the two central rendering projects being MapLibre GL JS and MapLibre Native which are surrounded by an ecosystem of plugins and tools to generate maps.

MapLibre started as a fork of Mapbox GL JS 1.13 in December 2020, after Mapbox announced a second version of the library that was no longer open-sourced.

In this talk we'll showcase Maplibre with a few examples, state of libraries in the ecosystem including native sdks, current development process and more.