f6067f58f3f54fe227482529ab3ac6d3
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workbench.ipynb
|
Geospatial workbench
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1be34f836d2b513931123ad833ff446d
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aligner.ipynb
|
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95b6c39ac95b62048485d8d5a28687c8
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py_r_parquet.ipynb
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Back and forth between geo-R/Python with Parquet
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693117fc8f754d01f61cad53025436c9
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contextily_labels.ipynb
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Contextily labels examples
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61fd268c6b728e205ef5fe1a99631763
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raster_choropleths.ipynb
|
Choropleths for raster files in PySAL
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6edf32b749cc5d987e0e07117a99c8a4
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fuas.ipynb
|
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c909209bc58fc0deddf46d8ec8fce6d0
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h3_pysal.ipynb
|
H3 + PySAL
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0eebcffe8447036f426163dd7b2e73ad
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ons_api.ipynb
|
Access ONS geographies through their API
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df4ec263c9b4aec08017c1d1eb9258f0
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gridded_buildings.ipynb
|
Extracting the most building-dense grid cells in Spain
|
17b28779848970ff1b88d9930525ad2e
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houston_in_15m.ipynb
|
Houston 15 minute walks
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a2d08e1ca6369a95a1271fcc201f83c5
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disparities.ipynb
|
Deprivation disparities between contiguous English areas
|
8d8f931a4414a52d82480db4f37c183e
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networkW.ipynb
|
Spatial Weights Matrices as bundled networks
|
2117d718afd16157f44fa8258a0ad57d
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serialise_geo_feather.ipynb
|
(De-)serialise geo objects in feather
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e9cee717a64125a39db269b77598d998
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strava_boroughs.ipynb
|
Strava Boroughs
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0695d8eb6764dc6d31c7a2de6bc7a121
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pt2xy2pt.ipynb
|
Conversion on `geopandas` points to XYs to points
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1be17cfb4f06d2ec8bab2f230b84137a
|
palindromes.ipynb
|
Finding the longest palindrome in the UK
|
326c212df73efccad5ec1ff43cd0a369
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legendgrams.ipynb
|
Histograms as choropleth legends
|
ba9103ee8d6f1645ab845344215f786b
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geoplot_viridis.ipynb
|
`geoplot` Viridis
|
691ad184280590d1219ffcf9a1678030
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brexit_lisa.ipynb
|
|
8b5a7b93d4085223f1c5
|
maup.ipynb
|
A computational exploration of the Modifiable Areal Unit Problem
|
41940dfe7bf4f987eeaa
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pandas_dask_test.ipynb
|
Quick comparison between `pandas` and `dask` groupby functionality.
|
0495ddec8f6ab6e242c9
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main_effect_plots.ipynb
|
Main Effect Plots
|
657e0568df7a63362762
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pysal_lisa_maps.ipynb
|
LISA cluster maps with `PySAL`
|
af4efcfe5302f860d365
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pysal_r_bench.ipynb
|
PySAL-R benchmarking
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next
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