Geocomputation module used in Geography BA/BSc at King's College London
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README.md

Practical responsibilities!

  • Add 30 minutes to lectures for 'talking through the practical'

  • Add questions into the practical to test higher-level knowledge/understanding

  • 1: Code Camp Recap [JM]

    • Scripts vs Notebooks
    • Errors
    • Data Types & Input
    • Operators
    • Comments
    • Conditionals & Logic
    • Some basic loops
  • 2: Functions & Packages [JR]

    • Lists
    • Dictionaries
    • More loops [Lists of Lists; Dictionaries of Lists]
    • Use a lib to read a remote file
    • Use string.split to parse CSV data
    • Use a lib to parse CSV data
    • Create a function to read a remote file
    • Create a function to parse CSV data
    • Calculating values derived from CSV data (using function on LoL)
    • Calculating values derived from CSV data (using function on DoL)
  • 3: Working with Data [JM]

    • Methods (for Pandas)
    • Link back to Week 2 and DoL
    • Pandas syntax for columns (both '.' and foo['bar'] and why they exist)
    • Describe, summary, etc.
    • Introduce string.format()
    • Max/Min/Range and other stats functions (e.g. IQR)
    • Also an opportunity to introduce reading .gz/.zip files directly in pandas
    • Look at basic pandas plotting functionality (lead into Seaborn in Week 4)
  • 4: Visualising Data [JR? Complete]

    • Using pandas with header data
    • Statistics as judgement, not truth -- plotting as first step on this path
    • Seaborn
    • 3D plots
    • [Interpreting plots and other summary metrics]
    • Saving a plot
    • Automating analysis (loops but over data series now)
  • 5: Assessed Notebook [JR]

    • Assign reading for following week: Openshaw 1998 and Wyly 2014
  • 6: Working with Subsets of Data [JM]

    • Non-spatial Joins - join air quality data to existing LSOA data
    • Using the index [from Week 4]
    • Renaming columns (show how this was done in Data Loader nb)
    • Finding values, grouping by values (for Boroughs) [from Week 4]
    • Sampling data [from Week 4] -- will just highlight quickly
    • Matching on parts of a word, extracting parts of a word [from Week 4]
    • Exercises: Initial exploratory analysis of pollution data
  • 7: Transformation and Standardisation [JR]

    • Thinking in 'data space' [From Week 4]
    • What counts as extreme?
    • Finding outliers
    • Residuals (first exposure to this)
    • Simple Transformations??? [from Week 4]
    • Simple Standardisations??? [from Week 4]
  • 8: Making a Map [JR]

    • Objects and methods [copy from Spatial Analysis Week 2? 3?]
      • (use geopandas to anchor this)
    • PySAL and loading shapefiles
    • Look at impact of transforms on understanding of map
    • Joins? (non-spatial already done in week 6)
      • Illustrate using Airbnb raw data (also gives geopandas whirl)!
  • 9: Correlation and Residuals [JM]

    • Builds on standardisation and normalisation from Week 7
    • Possibly use scipy here since it has rank and Pearson correlation, and is a very well-used lib in the real world
    • Plot residuals on map and in graph!
    • Seaborn (can include r^2 and rank correlation)
  • 10: Aggregation and group-by [JM]

    • Brings in issues of scale and, implicitly, MAUP