Thanks to @geopandas team for providing a great example setup.
The last thing missing are some rewrites of paths to allow seamless switching to previous versions. If anyone understands the sphinx magic involved, any help would be highly appreciated.
GeoPandas has released an alpha of v1.0.0 - time to check it works with your code.
Details at https://martinfleischmann.net/geopandas-1.0-is-coming.-what-will-change/, changes include switching default loader from fiona to pyogrio (nice! I often forget to use pyogrio and it'll save me time), requiring Shapely 2.0, removing built-in datasets, and deprecating some stuff.
I've been playing with #OpenStreetMaps via #osmnx, which is awesome , but I struggle with simple stuff like adding a bunch of places as markers. Everything looks a bit like the owl drawing meme, either showing something too easy and useless, or something too advanced and also useless or beyond my comprehension. Maybe some other Python tools?
(I know about Marcelo's fabulous PrettyMaps but it is not exactly a viz tool)
I did not know edge bundling of trajectory data was such a niche use case, that it isn't widely adapted in Python.
I am trying to visualize some EU-wide mobility and edge bundling would be great. There are some cool non-geographic implementations around (Datashader, Holoviews), but they do not work well with geographic data and #GeoPandas dataframes.
Luckily there are these obscure repositories with some #geospatial implementations, but dunno whether I need to debug bunch of code.
Mapping relationships between #Neo4j spatial nodes with #GeoPandas
Previously, we mapped neo4j spatial nodes. This time, we want to take it one step further and map relationships. A prime example, are the relationships between #GTFS StopTime and Trip nodes.
For example, this is the Cypher query to get all StopTime nodes of Trip 17:
In the recent post "Setting up a graph db using GTFS data & Neo4J", we noted that -- unfortunately -- Neomap is not an option to visualize spatial nodes anymore.
Observation: #OpenSoftware spatial analytics tools (#QGIS, #GDAL, #GeoPandas)—despite heroic efforts from their developers—are frequently broken and/or unusable because of fragile and complex dependencies.
Research Questions:
Why has the open source spatial analytics ecosystem resisted greater centralization or coordination?
What social and material factors shape(d) these relationships?
How does popular proprietary software like ArcGIS distort open source alternatives?
Update: Some progress — I converted the network to a geodataframe and filtered it by the "name" column, it feels very awkward yet and I'm not sure what I'm doing.
I'm so happy that we're finally in a place where we can whip up a quick trajectory data exploration app with maps and graphs from rather arbitrary inputs, be it plain old csv with x/y in any crs known to proj, or fancy gis formats in just a few minutes.
It's minimal extra effort and seems to impress most people so much more than comparable plots in a notebook 🤷♀️😀
@andrewfrench in #geopandas, you probably need to split the line into short segments, calculate the distances between segments and points, and then plot the segments with color defined by the computed distances ... it's similar to what #movingpandas does to visualize speed