Spatial Data Science Github Pages

Leo Migdal

-Dec 4, 2025, 1:58 PM

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. 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.

This organization hosts repositories created as part of the lab's research activities. The Lab is based on the Department of Human Geography and Spatial Planning, Utrecht University, led by Dr SM Labib. Details of the lab activities will follow...! Luc Anselin, Grant Morrison, Angela Li, Karina Acosta This book contains the R version of the GeoDa Workbook developed by Luc Anselin. It accompanies the Introduction to Spatial Data Science course taught at the University of Chicago.

Each chapter was originally developed as a standalone lab tutorial for one week of the class. As a result, it is possible to work through a single chapter on its own, though we recommend going from the beginning to the end. This book is still actively under development and may not work for you when you access it. For versions of the lab notebooks that have been tested and are not undergoing changes, please see the Tutorials page on our Spatial Analysis with R website. We have developed an R data package (geodaData) to use along with this book, so you can work through through the exercises immediately. To install it, run the following in your R console:

We assume that workshop attendees have used RStudio and are familiar with the basics of R. If you need a refresher, this R for Social Scientists tutorial developed by Data Carpentry is a good place to start. Additionally, Luc Anselin’s introductory R lab notes can be found on the CSDS Tutorials page. 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 Spatial Data Science across Languages Community brings together developers and users from the common and emerging programming languages used for spatial data science.

Spatial data science (SDS) concerns the analysis of spatial data in various contexts. We focus broadly on geospatial and geographic space, with some applications to general image spaces, local reference frames - everything from microscopical to astronomical space. Open source programming languages commonly used in spatial data science for analysis include Python, R and Julia. Our community is also interested in JavaScript and TypeScript, C++ and Rust. These languages are used by millions of users on a daily basis to solve spatial data problems, visualise and analyse spatial data. A number of the challenges that we face transcend the particular programming languages.

Such challenges range from: the interpretation of the underlying data; the way the data are represented in computers; visualisation; scalability and efficiency of implementations; the use of upstream libraries like GDAL, GEOS and PROJ,... The Spatial Data Science Across Languages Community aims to bring developers and users together to help build understanding and solve common problems, as well as discussing problems specific to particular language communities. Land Cover Classification with Python and Spectral Indices This blocks explore spatial data, old and new. We start with an overview of traditional datasets, discussing their benefits and challenges for social scientists; then we move on to new forms of data, and how they pose different challenges, but also exciting... These two areas are covered with clips and slides that can be complemented with readings.

Once conceptual areas are covered, we jump into working with spatial data in R or Python, which will prepare you for your own adventure in exploring spatial data. All data is spatial - data comes from observation, and observation needs to happen somewhere and at some time. This makes all data spatial. For a lot of data, the location expressed in spatial, earth-bound coordinates of observation is not of prime importance: These core spatial geometries are all supported in R package sf and Python library geopandas. The basis of every type of geometry is the point.

A point is simply a coordinate in 2D, 3D or 4D space such as: A line string is a sequence of points with a straight line connecting the points, for example: Nature-Based Solutions are able to provide natural, multifaceted solutions to problems many cities around the world face. Since urban space is scarce, potential vertical spaces (facades and roofs) have to be considered for greening. This project aims to find potential vertical spaces in dense urban areas that are suitable to be greened, using deep learning and geospatial analysis. The project contains two Jupyter Notebooks that can be run seperately.

Facades.ipynb estimates the greening potential on building facades using image segmentation. The output is a geopackage of sample points containing a facade Greening Potential Score (GPS). Roofs.ipynb estimates the greening potential on rooftops using geospatial analysis. The output is a geopackage of all suitable buildings (filtered) and all buildings (unfiltered) containing the roof GPS. Note: as of this moment, the study area is limited to Amsterdam only. To run the project in Google Colab, clone the complete project to your Google Drive folder (e.g.

/My Drive/MyFolderName). Then, open one of the Jupyter Notebooks in Google Colab. Written instructions are also included within each notebook that explain each step of the process. It is recommended that for hardware acceleration a GPU is set and high RAM usage is turned on. To change this in Google Colab, go to Runtime > Change runtime type > Hardware accelerator to select a GPU (V100 GPU recommended) and turn on high RAM usage. Book found at: https://r-spatial.org/book/

Spatial Data Science Github Pages

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