osmnx
opentopodata
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osmnx | opentopodata | |
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14 | 3 | |
4,663 | 295 | |
- | - | |
9.6 | 6.9 | |
2 days ago | 2 months ago | |
Python | Python | |
MIT License | MIT License |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
osmnx
- I played with a python module called OSMnx to create the roadmaps of some cities. These include major highways,motorways,roads and streets that carry most of the traffic.
- Planning a straight line across Norwich, the best route I could find just scraped a silver.
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Anyone here familiar with GeoPandas in Python? Can GeoPandas calculate driving distances and drive time?
You want osmnx
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[OC] Neighborhood walkability in Delhi
OSM road network data was analyzed using osmnx (https://github.com/gboeing/osmnx). Plotted using QGIS.
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Rate Limit Causing Pause
I think you’re hitting this issue right now: https://github.com/gboeing/osmnx/issues/832
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Video: Why Open Data Matters for Cycling: Visualizing a Cycling City
RDS-TMC transmits traffic data over FM radio. https://en.wikipedia.org/wiki/Traffic_message_channel Some is encrypted, some not.
It might be possible to model traffic with OSMnx to assign weights to roads by expected traffic levels https://github.com/gboeing/osmnx
Depending on where you live you might be able to get traffic data and maybe traffic models from public authorities using Freedom of Information requests.
- I just want things to work
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Number of Public Transport Stations & Doctors in each city in Germany
Osmnx might also be a way to download data for the whole country. It breaks the area up in smaller parts automatically and also downloads the parts from Overpass. https://github.com/gboeing/osmnx
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Tacoma!
You can make your own with a variety of tools, but I like the results from OSMnx offhand.
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Need to get a national (US) file of cycleways.
I have no idea if this works for such large regions, but https://geoffboeing.com/publications/osmnx-complex-street-networks/ is an awesome tool to visualize street (and maybe bike) networks.
opentopodata
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I built an alternative to the Google Elevation API using open data
You can self-host an API serving open data (I also develop https://github.com/ajnisbet/opentopodata/ which helps you do this) but elevation data comes in different formats and quality levels which are non-trivial to merge without artefacts, and the datasets are too large for a typical cloud workflow.
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Show HN: An alternative to the Google Elevation API using open data
Hi HN!
GPXZ ([https://www.gpxz.io/](https://www.gpxz.io/)) is an API for elevation point queries. These APIs are used for things like flight planning, flood analysis, elevation profile visualisation, and weather forecast localisation.
Google Maps is the name brand elevation API but it's expensive, you are restricted in how you use the data, they decrease accuracy for batch queries, and their dataset is being left behind by all the amazing open data sources released in the last few years.
You can self-host an API serving open data (I also develop [https://github.com/ajnisbet/opentopodata/](https://github.co... which helps you do this) but elevation data comes in different formats and quality levels which are non-trivial to merge without artefacts, and the datasets are too large for a typical cloud workflow.
So I built the 12 TB GPXZ dataset, which carefully merges open lidar and satellite sources covering the whole globe. The idea is that with a single dataset you can get the best-possible data in your area of interest.
The [gpxz.io](http://gpxz.io) API runs on a boring stack: nginx, uwsgi and python on dedicated servers. Everything is in a django monolith, except the API which I recently liberated from the monolith into a standalone flask service. I have a few freelance customers who are paying me for data processing and managed opentopodata hosting that have been extremely helpful in validating this idea.
I know this is a niche service compared to most Show HNs, happy to answer any questions about elevation data or geospatial APIs!
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Ask HN: Do you use an elevation API?
Cool, I learned about abstreet the other day from HN! Thanks for the feedback.
These days I run everything geospatial in docker containers, the dependencies in geo are tricky.
Interesting about the memory/caching issues. I was going to suggest rasterio which I use for batch queries in https://github.com/ajnisbet/opentopodata and comes bundled with its own gdal binary, but looks like you're already using that.
I've also used zarr+tifffile for geotiffs in particular, it's faster and avoids a lot of gdal's warts, but you still need something like rasterio to read the geospatial metadata and handle projections.
What are some alternatives?
BlenderGIS - Blender addons to make the bridge between Blender and geographic data
geemap - A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
pyosmium - Python bindings for libosmium
whitebox-python - WhiteboxTools Python Frontend
overpass-wizard - :dizzy: Human friendly way to generate Overpass API queries
felicette - Satellite imagery for dummies.
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
atlas - OSM in memory
elevation_lookups - Takes an input file of paths described as series of points, outputs a file of data about the elevation changes along those paths.
qgis-outdoor-map - QGIS project for an outdoor map based on OpenStreetMap data.
whitebox - WhiteboxTools Python Frontend [Moved to: https://github.com/giswqs/whitebox-python]