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Materials for the 2019 GESIS workshop "Data Wrangling & Exploration with the Tidyverse in R"
Pynamical is a Python package for modeling and visualizing discrete nonlinear dynamical systems, chaos, and fractals.
Dissertation for my MSc in Smart Cities and Urban Analytics (UCL CASA)
Download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.
Files for the Python Workout book
A stand alone, extensible WSIWYG editor based on ProseMirror
Materials for the GESIS 2020 Summer School course "Introduction to R for Data Analysis"
A curated list of awesome tools, tutorials, code, projects, links, stuff about Earth Observation, Geospatial Satellite Imagery
QGIS plugin to export your project to an OpenLayers or Leaflet webmap. No server-side software required.
COVID 19 Social Science Research Tracker
Townsend Deprivation Scores 1971 to 2011
Materials and information relating to the New Forms of Data training series
A web-based tool for geographically masking (anonymizing) spatial (point) data
https://geolinx.github.io/ by S.Alvanides & I.Buchstaller
Bayesian multilevel models course for Gesis 48. March 2019
Create beautiful, publication-quality books and documents from computational content.
Some scripts and notes from the European Summer University in Digital Humanities, Leipzig, Germany
Data and scripts from the project "Lingscape - Citizen science meet linguistic landscaping"
An R Package for Processing and Linking of Spatial Data Within the GeorefUm Project at GESIS
Python Data Science Handbook: full text in Jupyter Notebooks
Always know what to expect from your data.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Make social simulations on real maps! Agent-based modeling for the web.
An open source library and framework for deep learning on satellite and aerial imagery.
Cheat sheet for GDAL/OGR command-line tools
Material for 1 day Introductory Course on Using R (& RStudio) for Spatial Analysis