Here’s an opportunity to get-together (virtually) with others interested in GIS, to talk-shop and keep track of what's happening. Topics will include what we're doing with GIS-applications, training courses worth doing, references worth the study-time, and more (mostly in Python).
These will be Café-style meetings (https://hub.iridescent.nz/s/oeZTyEyrByrwERt - hosted by NZOSS. Thanks!). The format could be similar to a stand-up meeting where each participant briefly introduces themselves and mentions how they are working on or need help with GIS. Once everyone has had a chance to do that, we’ll discuss whichever of those topics that grab folks’ interest.
To inspire your thinking, at the foot of this message there's a long and exhaustive list of directions in which the SIG might choose to go. Although originating from the Auckland Branch, the SIG is expected to include folk from all around the country - and overseas practitioners also welcome.
We’ll be governed by the NZPUG Code of Conduct (https://python.nz/about/code-of-conduct/). Meetings will not (usually) be recorded.
Don’t just RSVP for the meeting-URL, rustle-up a bunch of interested friends and colleagues from amongst your personal contacts!
Advanced notice: continuing the theme, a heads-up for our May PUG-meeting, when our own Kim Ollivier will be talking about how he's using GIS to inform a local conservation project's efforts.
Looking forward to seeing you there!
William Hamilton, Chief of all he surveys,
and dn, General Factotum
Suggestions, inspirations, and GIS ideas:
1. **Introduction to Python for GIS**: Start with the basics of Python programming language, emphasizing its relevance and applications in GIS.
2. **Geospatial Libraries**: Introduce popular Python libraries like GeoPandas, Shapely, Fiona, Pyproj, GDAL/OGR, and others used for handling geospatial data.
3. **Data Visualization**: Explore techniques for visualizing geospatial data using libraries like Matplotlib, Seaborn, Plotly, and Folium.
4. **Spatial Analysis**: Cover spatial analysis techniques such as buffering, overlay operations, proximity analysis, and spatial statistics using libraries like PySAL (Python Spatial Analysis Library).
5. **Web Mapping**: Dive into web mapping frameworks like Leaflet.js and Folium for creating interactive maps and integrating them into web applications.
6. **Remote Sensing**: Introduce basics of remote sensing and how to work with satellite imagery and raster data using libraries like rasterio and GDAL.
7. **Geocoding and Reverse Geocoding**: Explore techniques for converting addresses to geographic coordinates and vice versa using APIs like Google Maps API or OpenStreetMap Nominatim.
8. **Spatial Databases**: Introduce spatial databases like PostGIS and how to interact with them using Python.
9. **Geospatial Data Formats**: Discuss common geospatial data formats such as Shapefile, GeoJSON, KML, and how to read, write, and manipulate them in Python.
10. **GIS Application Development**: Explore the development of GIS applications using frameworks like Flask or Django, integrating geospatial functionality into web applications.
11. **Automation and Scripting**: Discuss techniques for automating GIS tasks and scripting repetitive workflows using Python.
12. **Case Studies and Projects**: Showcase real-world examples and projects where Python GIS techniques have been applied, allowing members to learn from practical examples.
