🗺️ Geostatistics in action: the expected number of passenger cars per inhabitant in Sweden.

🍏 Raw data sources are the population grid, the number of registered passenger cars in each Swedish municipality, and the number of inhabitants in each municipality.

🔪 Data preprocessing was done in #QGIS and #geopandas

🥗 Area-to-point support transformation was done in #pyinterpolate

🍻 Visualization in #pygmt

Overall #Python score: 10/10 🐍

The unexpected (stable) release of #pyinterpolate - 0.5.0, named after the Ukrainian Mykolaiv city

The problems with dependencies and Python==3.7 EOL forced this release. It is slightly polished. I've removed annoying warnings and changed the tutorials' structure. I spent a lot of time with grammar checks, and I hope that the sentences in tutorials are closer to the valid English language 😅

Changelog: https://github.com/DataverseLabs/pyinterpolate/blob/main/changelog.rst

Pypi: https://pypi.org/project/pyinterpolate/

The newest stable release of #pyinterpolate
is out! Version 0.4 introduces new concepts:

- Indicator Kriging 📊 ,
- Spatial Dependence Index 📈 ,
- Gridding 🌐 ,
- Point clusters detection and aggregation 🐾 .

This release is named after the city of Kharkiv in Ukraine.

I will post all upcoming updates within this thread. Stay tuned!

#spatial #gis #python #datascience #geospatial

Public data can be irritating. Let's consider a scenario when we build a #machinelearning model that uses #remotesensing data, and we need to mix it with #social, #publichealth, or #economic indicators aggregated over counties. It is not possible.

But, in some cases, we can transform aggregates into high-resolution input. When and how?

The steps are presented in the article here: https://ml-gis-service.com/index.php/2022/12/09/get-more-from-crime-rate-data-and-other-socio-economic-indicators-with-pyinterpolate/

We transform county aggregates of crime rates.

#pyinterpolate #Python #gis #spatial

New release of #pyinterpolate! Version 0.3.5, and:

- the package is computationally stable (no more `LinAlgErrors` in #Kriging system, or even if they are raised, the users will know what went wrong),
- a lot of optimization,
- emphasis put on directional variograms.

#spatial #gis #python #opensource #openscience #datascience

The following steps: directional Poisson Kriging and more tutorials in the documentation.

#introduction

Hi, I'm Simon! I'm Data Scientist and #DataEngineer specializing in #remotesensing, #GIS, #spatial, #ecommerce, #timeseries, and duo of #Python + #opensource :)

Living in Europe 🇪🇺 You can catch me in Helsinki / Vantaa 🇫🇮 Polish nationality 🇵🇱

Creator of #pyinterpolate package: https://pypi.org/project/pyinterpolate/ for Kriging and spatial interpolation 🗺️

#wsknn package for session-based recommendations: https://pypi.org/project/wsknn/ 🛒