CyberFaCES

Modules

• Introduction to Foundations

  Module 1 • 30 minutes

  The other modules in this curriculum assume a little background: how to run a Python notebook, what a table and a map layer are, and how to load data into QGIS. This module teaches exactly that, from zero. If you have never written code or opened a GIS, start here.

• Python Basics

  Module 2 • 2 hours

  Running cells, variables, text, lists, dictionaries, decisions, loops and functions. Six short notebooks, each covering one idea and ending with an exercise and its solution; the examples use Indiana counties.

• Working with Data

  Module 3 • 1 hour

  Tables with pandas and charts with matplotlib.

• Geospatial Python Basics

  Module 4 • 2 hours

  Map layers with GeoPandas, coordinate reference systems, attribute and spatial joins, and rasters with rasterio.

• Map Your County in Python

  Module 5 • 1 hour

  Capstone: a Census table to a finished, shareable map. A local food bank wants a one-page map of where households without a car live in Tippecanoe County, Indiana, to plan a delivery service. You will deliver it as a PNG image that a reader with no GIS experience can understand.

• Introduction to Data Management

  Module 6 • 40 minutes

  The research data lifecycle, why geospatial data needs extra care, and data management plans

• Geospatial Data Models

  Module 7 • 45 minutes

  Vector and raster data, topology, resolution, and choosing a representation

• Geospatial File Formats

  Module 8 • 45 minutes

  Shapefile, GeoPackage, GeoJSON, GeoTIFF and others, and which to use when

• Coordinate Reference Systems

  Module 9 • 45 minutes

  Datums, geographic and projected systems, EPSG codes, and defining versus reprojecting

• Designing Your Data

  Module 10 • 50 minutes

  Data dictionaries, field types, domains, unique IDs and relationships

• Organizing, Versioning and Backing Up

  Module 11 • 45 minutes

  Folder structures, file names, portable projects, version control and the 3-2-1 rule

• Data Quality

  Module 12 • 45 minutes

  Elements of spatial data quality, geometry errors, and quality checks

• Introduction to Data Collection

  Module 13 • 35 minutes

  When to use existing data or collect your own; vector and raster data; judging data quality; FAIR data

• Data from the U.S. Census Bureau

  Module 14 • 1 hour

  Census geography, the Decennial Census and the ACS, margins of error, and the Census API

• Geocoding and Adding Spatial Context

  Module 15 • 1 hour

  Turning addresses into points, checking match rates, and downloading features from OpenStreetMap

• Collecting Data in the Field

  Module 16 • 40 minutes

  Methods and apps, designing a schema, GPS accuracy, ethics and quality checks

• Introduction to Data Processing

  Module 17 • 35 minutes

  What happens between download and analysis, and how to keep processing repeatable

• Cleaning and Preparing Data in Python

  Module 18 • 1 hour

  Fix identifiers, types, special codes, duplicates and geometry problems with pandas and GeoPandas

• Geoprocessing in Python

  Module 19 • 1 hour

  Buffers, selecting by location, clip, intersect, dissolve, areal weighting and nearest features in GeoPandas

• Raster Processing in Python

  Module 20 • 1 hour

  Map algebra, reclassification and zonal statistics with rasterio and rasterstats

• Automating Repeated Steps in Python

  Module 21 • 1 hour

  Functions and loops that run one workflow for many distances, layers and counties

• Introduction to Data Visualization

  Module 22 • 1 hour

  Why we visualize, how charts mislead, and design principles that apply to every figure

• Cartography Checklists

  Module 23 • 1 hour

  The questions to answer before making any map: audience, message, data, medium

• Fundamentals of Map Design

  Module 24 • 1 hour

  Map elements, color schemes, projections, thematic map types, normalization and classification

• Cartography in Python

  Module 25 • 1 hour

  Finished maps in Python: layer order, labels with halos, basemaps, locator insets, scale bars, small multiples and export

• Accessibility in Map Design

  Module 26 • 1 hour

  Designing maps for people with color vision deficiency, testing palettes and writing alt text

• Park Access in Lafayette (Python)

  Module 27 • 1 hour

  Who can walk to a public park in Lafayette and West Lafayette, and are children and households without a car as well served? In Python

• Network Analysis

  Module 28 • 1 hour

  How far is it by road, and how long does it take? Road networks, shortest paths and travel time with osmnx and networkx

• Spatial Autocorrelation with PySAL

  Module 29 • 1 hour

  Are similar values clustered, and where? Spatial weights, global and local Moran's I with libpysal, esda and splot

• Spatial Clustering

  Module 30 • 1 hour

  Where do points concentrate into hotspots? DBSCAN, HDBSCAN and K-Means on Chicago crime points

• NDVI Analysis

  Module 31 • 1 hour

  How green is the landscape in a satellite image? NDVI from Landsat, classification and machine learning

• Case Study: Food Deserts

  Module 32 • 1 hour

  Where do people have low income and live far from a supermarket? Map the USDA Atlas, then rebuild its analysis from open data

• Introduction to Data Curation

  Module 33 • 40 minutes

  What curation is, the curation lifecycle, public access policies and the FAIR principles

• Metadata

  Module 34 • 50 minutes

  Kinds of metadata, essential geospatial elements, and standards such as ISO 19115, DataCite and DDI

• Documentation

  Module 35 • 45 minutes

  README files, codebooks, provenance, and citing data

• Preservation

  Module 36 • 45 minutes

  Durable formats, checksums, the OAIS model, trustworthy repositories and very large data

• Sharing and Publishing Data

  Module 37 • 50 minutes

  Choosing a repository, DOIs, licenses, access levels, and the FAIR and CARE principles

• Ethics and Sensitive Location Data

  Module 38 • 50 minutes

  Re-identification, aggregation and geomasking, sensitive places, consent and review

Instructor

Aniket Dev Roy

61 modules