1. Intro to Geospatial Data Science
Keely Roth, PhD
Sacramento Women in Data Science
August 23, 2016

2. We’ll cover… Spatial data basics Cartographic principles
Ways to get started
Resources

3. Why geospatial data science?
Maps are powerful tools for visualizing and analyzing.

4. What makes data spatial?
attributes associated with a location
arise from a spatial process
can be an object or a field

5. Spatial Data Basics
More than just where it is…

6. Key Concepts For working with spatial data
Spatial Reference Systems: Geographic Coordinate Systems
Key Terms:
Geoid
Ellipsoid
Datum

7. Key Concepts For working with spatial data
Spatial Reference Systems: Projected Coordinate Systems

8. Key Concepts For working with spatial data

9. Key Concepts For working with spatial data

10. Types of Spatial Data Analysis
What can we do with spatial data?
ESRI: Spatial Analysis

11. Cartographic Principles
Just because you can map it, doesn’t mean you should…

12. Good Cartography: Map Elements
What does my map need on it?
Slocum’s Map Elements:
frame and neat line
mapped area
inset
title & subtitle
legend
data source
scale
orientation

13. Good Cartography: Design principles
How do I design something people want to look at?
Visual Contrast
Legibility
Figure-Ground
Organization
Hierarchical
Organization
Balance
ESRI MAP DESIGN

14. Good Cartography: Guiding principles
What to keep in mind when making maps
BEFORE
DURING
AFTER
Should I map this?
Who is going to look at this map?
What is the key message (yes, just one) this map will carry?
What spatial representation of my data will best highlight the information I want to present?
What colors & symbology emphasize my map’s information?
What is the appropriate spatial scale for my map?
Am I showing the data honestly?
Is my map easy to understand?
Is everything I’ve mapped essential?

15. Getting Started
How can I make a map and analyze my spatial data?

16. Many ways to make a map (& analyze it)
Choices, choices, choices..
Platform: Desktop, Web, Mobile
Software: Commercial v. Open Source
Map Interface: Static v. Dynamic

17. Typical Data science Tools: R & Python
Packages: sp
rgdal
maps
maptools
rgeos
raster
ggmap
Libraries:
shapely
GDAL/OGR
pyQGIS
pyshp
pyproj
GeoPandas
PySAL
Rasterio
Fiona
cartopy
matplotlib
R map top:
R map bottom:
Python map top:
Python map bottom:

18. WEB-BASEd TOOLS: Google Earth Engine
development in JavaScript or Python
many data layers available

19. WEB-BASEd TOOLS: Openstreetmap (& friends)

21. Resources GENERAL KNOWLEDGE SOFTWARE/TOOLS Geospatial Analysis
(e-textbook by de Smith, Goodchild, Longley)
13 Free GIS Software Options
Open Source Geospatial Foundation
A gentle introduction to GIS
R packages for spatial data analysis
Coordinate systems, map projections, and geographic transformations (ESRI)
Spatial.ly: Visualisation, Analysis and Resources in R
Intro to Spatial Data Visualization in R
Geospatial Python
GIS in Python
Intro to Spatial Data Analysis with Python (YouTube)
How Mapbox Works
CartoDB Workshop by UCLA Mapshare
Intro to Google Earth Engine API