Geospatial Data Analysis with Python
Geospatial Data Analysis with Python
Resources
Syllabus
Resources for Students
Student Initial Setup
Student weekly workflow
Resources for Instructors
Initial Setup for Instructor
Weekly Procedures for Instructor
Instructor thoughts and lessons learned
Technical Resources
Github and Github Classroom Notes
Jupyter Notes
Shell Notes
Conda and Jupyter Setup Instructions
Core Package Notes
QGIS Notes
Migrating from the UW Course Jupyterhub
Final Project
Open Data for Projects
Code of Conduct
Modules
01: Shell and git/Github
Introduction
Demo
Lab01 Exercises
02: Python and iPython/Jupyter
Introduction
A quick, practical intro to the Jupyter Notebook
IPython: beyond plain Python
Python Demo
Lab02 Exercises
03: Numpy, Pandas and Matplotlib
Introduction
Demo: NumPy, Pandas
Demo: Matplotlib, Backends
Lab03 Excercises
04: Vector1, Geopandas, CRS, Projections
Introduction
Demo: GeoPandas and CRS
Demo: Projection Tradeoffs and Distortion - Tissot
Lab04 Exercises
05: Raster Fundamentals: GDAL, rasterio, Landsat-8
Introduction
Demo: Landsat and Dynamic Data Access
Demo: Raster fundamentals, Rasterio, Band Math with Arrays
Demo: NumPy array masking, indexing, selection
Lab05 Exercises #1
Lab05 Exercises #2
06: Geometries, Spatial Operations, Visualization
Introduction
Vector 2: Geometries, Spatial Operations and Visualization Demo
Vector 2: Geometries, Spatial Operations and Visualization Exercises
07: Reprojection, Clipping, Sampling, Zonal Stats
Introduction
07 Raster2 Demo
Strategies for Dynamic DEM Data Download and Use
07 Raster2 Exercises
08: Vector time series: SNOTEL data for the Western U.S.
08_Vector_TimeSeries_SNOTEL_prep
08 Demo: Python Time
08 Demo: Trajectory Analysis
08 Demo: SNOTEL Query and Download
08 Excercises: Vector time series, SNOTEL
09: Multidimensional (N-d) Arrays: xarray, ERA5 Climate reanalysis
09 Introduction: Multidimensional (N-d) arrays, xarray, ERA5 climate reanalysis data
09 Exercises 0: ERA5 Data Download
09 Exercises 1: Intro and Global Climatology
09 Exercises 2: WA state hourly data
10: Conda, Dask, Pangeo
Excercises
.md
.pdf
repository
open issue
03: Numpy, Pandas and Matplotlib
03: Numpy, Pandas and Matplotlib
ΒΆ
previous
Lab02 Exercises
next
Introduction
