Scalable Geospatial Data Science Github Pages

Leo Migdal

-Dec 4, 2025, 1:58 PM

scalable geospatial data science github pages

The big data landscape is vast and continuously growing and it is also becoming aware of the challenges when dealing with geospatial data. This project will go over and compare the recent open source developments in this space that enable working with geospatial data at scale. Scripts and notebooks for the presentation "Scalable Geospatial Data Science". Slides can be found here: https://njanakiev.github.io/slides/scalable-geospatial-data-science/ This project is licensed under the MIT license. See the LICENSE for details.

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Forked from deepVector/geospatial-machine-learning A curated list of resources focused on Machine Learning in Geospatial Data Science. Forked from satellite-image-deep-learning/techniques Resources for performing deep learning on satellite imagery Forked from chrieke/awesome-satellite-imagery-datasets These course materials cover the lectures for the course held for the first time in spring 2022 at IT University of Copenhagen.

Public course page: https://learnit.itu.dk/local/coursebase/view.php?ciid=940 Materials were slightly improved and reordered after the course. Prerequisites: Basics in data science (including statistics, Python and pandas) Ideal level/program: 1st year Master in Data Science · 1. Geometric objects · 2. Geospatial data in Python · 3. Choropleth mapping · 4.

Spatial weights · 5. Spatial autocorrelation · 6. Spatial clustering · 7. Point pattern analysis · 8. OpenStreetMap and OSMnx · 9. Spatial networks · 10.

Bicycle networks · 11. Individual mobility · 12. Mobility patterns · 13. Aggregate mobility and urban scaling · 14. Sustainable mobility and geospatial epidemiology · See: https://github.com/anerv/GDS2022_exercises

The course materials were adapted/inspired from a number of sources, standing on the shoulders of giants, ordered by appearance in the course: The Geospatial Neighborhood Analysis Package Automated Valuation Machine Learning Model for Lima House Pricing Repository for the website of the book (github hosting support) Spatial Data Science Complementary Features Spatially-Encouraged Spectral Clustering, a method of discovering clusters/deriving labels for spatially-referenced data with attribute/labels attached.

Geospatial Data Science with Julia presents a fresh approach to data science with geospatial data and the programming language. It contains best practices for writing clean, readable and performant code in geoscientific applications involving sophisticated representations of the (sub)surface of the Earth such as unstructured meshes made of 2D and 3D geometries. Most importantly, you will learn a set of geospatial features that is much richer than the simple features implemented in traditional geographic information systems (GIS). This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. First off, thank you for considering contributing to this book. It’s people like you that make this project so much fun.

Below are a few suggestions to facilitate the review process: This book is open source and fully reproducible thanks to the amazing Quarto project. You can edit the pages directly on GitHub and submit a pull request for review. If you are not familiar with this process, consider reading the first contributions guide. Data science is concerned with finding answers to questions on the basis of available data, and communicating that effort. Besides showing the results, this communication involves sharing the data used, but also exposing the path that led to the answers in a comprehensive and reproducible way.

It also acknowledges the fact that available data may not be sufficient to answer questions, and that any answers are conditional on the data collection or sampling protocols employed. This book introduces and explains the concepts underlying spatial data: points, lines, polygons, rasters, coverages, geometry attributes, data cubes, reference systems, as well as higher-level concepts including how attributes relate to geometries and how... The relationship of attributes to geometries is known as support, and changing support also changes the characteristics of attributes. Some data generation processes are continuous in space, and may be observed everywhere. Others are discrete, observed in tesselated containers. In modern spatial data analysis, tesellated methods are often used for all data, extending across the legacy partition into point process, geostatistical and lattice models.

It is support (and the understanding of support) that underlies the importance of spatial representation. The book aims at data scientists who want to get a grip on using spatial data in their analysis. To exemplify how to do things, it uses R. In future editions we hope to extend this with examples using Python (see, e.g., Bivand 2022a) and Julia. It is often thought that spatial data boils down to having observations’ longitude and latitude in a dataset, and treating these just like any other variable. This carries the risk of missed opportunities and meaningless analyses.

For instance, We introduce the concepts behind spatial data, coordinate reference systems, spatial analysis, and introduce a number of packages, including sf (Pebesma 2018, 2022a), stars (Pebesma 2022b), s2 (Dunnington, Pebesma, and Rubak 2023) and lwgeom... 2019; Wickham 2022) extensions, and a number of spatial analysis and visualisation packages that can be used with these packages, including gstat (Pebesma 2004; Pebesma and Graeler 2022), spdep (Bivand 2022b), spatialreg (Bivand and... 2022). Like data science, spatial data science seems to be a field that arises bottom-up in and from many existing scientific disciplines and industrial activities concerned with application of spatial data, rather than being a... Although there are various activities trying to scope it through focused conferences, symposia, chairs and study programs, we believe that the versatility of spatial data applications and questions will render such activity hard.

Giving this book the title “spatial data science” is not another attempt to define the bounds of this field but rather an attempt to contribute to it from our 3-4 decades of experience working... As a consequence, the selection of topics found in this book has a certain bias towards our own areas of research interest and experience. Platforms that have helped create an open research community include the ai-geostats and r-sig-geo mailing lists, sourceforge, r-forge, GitHub, and the OpenGeoHub summer schools organized yearly since 2007. The current possibility and willingness to cross data science language barriers opens a new and very exciting perspective. Our motivation to contribute to this field is a belief that open science leads to better science, and that better science might contribute to a more sustainable world. The following open source GitHub repositories have been developed as a part of the project:

Twitter Sentiment Geographical Index (TSGI) ArcGIS Enterprise for Geospatial Big Data You can access this notebook (in a Docker image) on this GitHub repo. In this lecture, we are going to use Dask-GeoPandas package to read a large vector dataset from Source Cooperative. Then use Dask parrallel computation to apply a spatial join operation to two geospatial DataFrames. Our target dataset is the Google-Microsoft Open Buildings - combined by VIDA dataset hosted on Source Cooperative.

This is a combined version of the Google and Microsoft Open Buildings datasets and it has files in GeoParquet format hosted on AWS S3 bucket. Read the dataset description to familiarize yourself with the dataset and its structure. GeoParquet is a relatively new and open data format for column-oriented geospatial data. This format is build on the existing Apache Parquet format which is a very powerful format replacing CSV. You can check the specification here, and read more about the format on this website. In short, this format is interoperable, compressed and designed to work with large scale datasets.

Source Cooperative is a neutral, non-profit data-sharing utility that allows trusted organizations to share data without purchasing a data portal SaaS subscription or managing infrastructure. Source Cooperative is managed by Radiant Earth, and hosts 10s of datasets on its repository.

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The Big Data Landscape Is Vast And Continuously Growing And

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Forked From DeepVector/geospatial-machine-learning A Curated List Of Resources Focused On

Public Course Page: Https://learnit.itu.dk/local/coursebase/view.php?ciid=940 Materials Were Slightly Improved And Reordered

Spatial Weights · 5. Spatial Autocorrelation · 6. Spatial Clustering

Spatial weights · 5. Spatial autocorrelation · 6. Spatial clustering · 7. Point pattern analysis · 8. OpenStreetMap and OSMnx · 9. Spatial networks · 10.