Skip to content
Geocomputation module used in Geography BA/BSc at King's College London
Branch: master
Clone or download
Type Name Latest commit message Commit time
Failed to load latest commit information.
assessments Adding Code1 alt assmnt boxplot fig Nov 3, 2017
data Renamed Data to data Oct 31, 2017
img Ooops, didn't plot class values, only raw Std. Nov 14, 2017
.gitignore Supressing working files. Sep 12, 2016
2016-17_Practical-03-Loops.ipynb update week 1, split out loops Sep 23, 2017
CitiesWithWikipediaData-simple.csv Adding two versions of city data set (derived from Wikipedia) and the… Sep 2, 2016
CitiesWithWikipediaData.csv Trying to bring Prac 5 up-to-date Sep 9, 2016
Code1-Assessment-Prep.ipynb Tweaked submission time. Oct 17, 2017
Data Loader.ipynb changing column names in LSOA data Oct 7, 2017
Greenspace.gpkg update week 1, split out loops Sep 23, 2017
Practical-01-Code Camp Recap.ipynb updates for 2018 Oct 2, 2018
Practical-02-Functions and Packages.ipynb updated for python 3 Sep 16, 2018
Practical-03-Introducing Pandas.ipynb splitting cells for exercises Oct 2, 2018
Practical-03b-API Data.ipynb Revamping for the new year. Aug 18, 2017
Practical-04-Visualising Data.ipynb real update for 2018 Oct 12, 2018
Practical-06-Transforms.ipynb major edits Oct 26, 2018
Practical-07-Manipulating Data.ipynb update for 18-19 Oct 31, 2018
Practical-08-Airbnb Listings.ipynb Added to give students more to work with. Nov 14, 2017
Practical-08-Inheritance.ipynb new year package checks Aug 13, 2018
Practical-08-Making Maps.ipynb Fixing minor typos Nov 12, 2018
Practical-09-Correlation.ipynb minor updates for 2018 Nov 15, 2018
Practical-10-Pandas Merge and Join.ipynb Revamping for the new year. Aug 18, 2017
Practical-10a-Weather-AirQuality.ipynb Minor corrections to 10a and 10b Nov 28, 2016
Practical-10b-LSOA-NS-SeC-Values.ipynb Minor corrections to 10a and 10b Nov 28, 2016
Practical-X-Illustrating Classification.ipynb Will be useful for illustrating classification in more detail and als… Feb 2, 2018 Added note re: Airbnb Nov 8, 2017
Scripts in Spyder.ipynb Splitting out scripts into separate nb from CC recap Sep 16, 2017
Test Install.ipynb Minor tweak to support Python3 Apr 25, 2019
setup.yml Adding chartify. Looks like a nice addition (from Spotify, no less) t… Dec 11, 2018

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
You can’t perform that action at this time.