Quantitative72 / data-science-book

Becoming a Spatial Data Scientist Materials

Example notebooks to accompany Becoming a Spatial Data Scientist.

Installation requirements

The notebooks in this repository use a ready-to-run Docker image containing Jupyter applications and interactive computing tools. To run the notebooks, please follow the instructions below.

1. Clone this repository

$ git clone [e-mail]:CartoDB/data-science-book.git`
$ cd data-science-book

2. Download and install docker. Follow instructions here: https://docs.docker.com/install/
3. Run the image. Open your terminal and run

$ docker run --user root -p 8888:8888 -e JUPYTER_ENABLE_LAB=yes -e GRANT_SUDO=yes -v "$PWD":/home/jovyan/workspace jupyter/datascience-notebook

A local address will be created. Copy and paste the address in your browser, this will launch Jupyter Lab. Note: If you have another Jupyter server running, make sure it's on a different port than 8888. Otherwise change the port number above or close down the other notebook server.

4. Install libraries and packages. Within Jupyter, click New -> Terminal and go to the directory where you cloned the Github repository and run this script to install the required libraries

$ bash notebooks_start.sh

Note: the installation can take a while (10-15 min)

5. Start experimenting with the code in each of the Chapter directories

Table of Contents

Chapter 1

• Visualizing spatial data with CARTOframes (static preview) - a notebook for easily visualizing your data on a map using CARTOframes.

• Computing measures of spatial dependence (static preview) - a notebook for exploring spatial dependence in your data and visualize the results using CARTOframes.

• Discrete spatial models (static preview) - a notebook with examples of spatial models for discrete processes and visualize the results using CARTOframes.

• Continous spatial models (static preview) - a notebook with examples of spatial models for continuous processes and visualize the results using CARTOframes.

Chapter 2

• Agglomerative Clustering (static preview) - a notebook demonstrating how to create spatially constrained clusters using agglomerative clustering
• DBSCAN (static preview) - a notebook demonstrating how to create clusters of points in geographic coordinates
• SKATER (static preview) - a notebook demonstrating how to create spatially constrained clusters that are homogeneous

Chapter 3

• Travelling Salesman Problem (static preview) - a notebook demonstrating how to solve travelling salesman problem.