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We can get more from spatial, GIS and public domain datasets!

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We can get more from spatial, GIS and public domain datasets!
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CC Attribution - NonCommercial - ShareAlike 4.0 International:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
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- Are prices of short-term rental apartments in your region similar? How similar are they, and at which distance do they tend to be correlated? - Do you have access to a few air pollution measurements but must provide a smooth map over the whole area? - Is your machine learning model based on remote sensing data from Earth Observation satellites, and do you want to include data sampled on Earth? - Do you work with county-level socio-economic factors, but you want to get insights at a finer scale? `if any(answer)`, then come and see what we can do with the `pyinterpolate` package designed exactly for spatial interpolation!