Week 00 - Getting Ready for the Course

Introduction to Spatial Data Science

Welcome to Week 00 of the course! This week is all about getting your technical foundation set up properly and understanding the basic skills you'll need throughout the quarter. We'll be working with several foundational platforms that form the core of modern spatial data science workflows.

Foundational Platforms

Throughout this course, we'll be using six main technological platforms that represent the full spectrum of modern spatial data science tools:

Desktop Applications

  • QGIS (Quantum Geographic Information System) - A powerful, open-source desktop GIS application for comprehensive spatial data analysis, cartographic design, and geoprocessing. QGIS serves as our primary tool for detailed spatial analysis, data editing, and professional map production.
  • OpenRefine - A sophisticated data cleaning and transformation tool that excels at working with messy, real-world datasets. Essential for preparing spatial data by cleaning addresses, standardizing place names, and restructuring tabular data before geocoding and spatial analysis.

Cloud Platforms

  • Google Earth Engine - Google's planetary-scale geospatial analysis platform that provides access to petabytes of satellite imagery and geospatial datasets. Enables large-scale environmental analysis, change detection, and time-series analysis that would be impossible on desktop computers.
  • ArcGIS Online - Esri's cloud-based mapping and analysis platform for web mapping, data sharing, and collaborative spatial analysis. Provides enterprise-grade tools for creating interactive web maps, story maps, and sharing spatial data across organizations.

Cloud/API Services

  • Planet.com - Commercial satellite imagery provider offering high-resolution, high-frequency Earth observation data. Provides access to daily satellite imagery and APIs for monitoring environmental change, urban development, and agricultural patterns at unprecedented temporal and spatial resolution.
  • locator.stanford.edu - Stanford's ArcGIS Server-based geocoding service. 5 API-based regional geocoding services: North American, Latin America, Europe, Middle East & Africa, and Asia & Pacific.

Code/Programming Environments

  • Google Colab with Python libraries - Cloud-based Jupyter notebook environment with pre-installed geospatial Python libraries including GDAL, Shapely, GeoPandas, and jq. Enables reproducible spatial data science workflows, advanced analysis, and integration with cloud data sources without local software installation.
  • Github.com -

Week Overview

This week covers the essential technical skills and setup procedures that will enable you to succeed in all subsequent weeks. Topics include computer management best practices, software installation, account setup, document creation, and getting help when you need it.

Lab Documents

Grading Note

These two Week 00 items include material that must be turned in for grading:

Learning Objectives

By the end of this week, you will be able to:

  • Properly manage files and directories on your computer
  • Install and configure the required software tools
  • Create accounts and access all necessary online platforms
  • Create and submit various document types
  • Effectively seek help when encountering technical issues

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