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More detail on use case
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explore-multiple-datasets-together/index.md

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# Explore multiple datasets together
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## Current situation
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The user experience for exploring datasets currently drives many Jupyter users to open
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QGIS or ArcGIS to explore their data, and they report friction switching between
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applications.
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Some current tools aren't quite meeting user needs, e.g.:
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* `my_gdf.explore()`: Only shows one dataset at a time
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* `my_dataarray.plot()`: Shows one dataset, non-interactive
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* JupyterGIS: Symbology configuration is manual and low-level
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* `ipyleaflet`: Low-level. Can't render Xarray DataArrays dynamically (see possible
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solution: <https://github.com/davidbrochart/xarray_leaflet>)
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## Proposed improvement
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Prototype: <https://github.com/geojupyter/jupyter-microgis>
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* Add layers to a map for exploration from a Jupyter Notebook:
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Add layers to a map for exploration within a Jupyter Notebook:
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```python
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from ... import explore
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```python
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from ... import explore
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explore(
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explore(
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my_xarray_dataarray,
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{'data': my_geodataframe, 'symbology': 'choropleth'},
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)
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```
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* Support rioxarray DataArrays
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* Support geopandas GeoDataFrames
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* Maybe: Support WMTS?
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)
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```
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* Support rioxarray DataArrays
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* Support geopandas GeoDataFrames
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* Maybe: Support WMTS?
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* Support some curated default symbology options
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* Choropleth: number of steps, classification mode, ?
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* Symbol map: shape, min/max size, ...
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* Plain language search
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* Produce Python one-liners to bring that dataset into their notebook, e.g. `geopandas.read_file(...)` and `xarray.open_mfdataset(...)`
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* Display the data on slippy map widget (e.g. DeckGL)
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## Questions
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### Is this `leafmap`?
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Is this idea basically [`leafmap`](https://leafmap.org/)?
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If we add support to directly visualize xarray datasets with TiTiler in leafmap, does
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that meet the goals of this use case?

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