Predicting Neighbourhood Change with Machine Learning
About this Repository
This repo is intended to support replication and exploration of the analysis undertaken for our Urban Studies article "Understanding urban gentrification through Machine Learning: Predicting neighbourhood change in London". Although we are not in a position to provide individualised support for installation or configuration of the iPython environment, we have attempted to make it as painless as possible for you to get up and running without hosing your existing Python environment. Please note that final visualisation of the results was undertaken in QGIS and R/RStudio, available for free for Mac, PC, and Linux (non-commercial use only for RStudio).
Re-Use & Citation
All code is available under the MIT License so you are free to modify it as you like; however, we would ask that, if you go on to use this work in a substantive way as a basis for further publications, you also cite the Urban Studies article and acknowledge our contribution in an Acknowledgements section.
Installation & Start-Up
You will need to install the Anaconda Python environment in order to run the setup script -- it should not matter if you install the full version or mini-conda so long as you have the conda toolset available to you. For some people the changes to the
.bash_profile file made by Anaconda will cause problems elsewhere, so you are advised to check what effect (if any) the addition of the follow line has on any other tools upon which you rely:
I, personally, use the following alias so that Anaconda is only available when I ask for it by typing
alias conda-start='export PATH="/anaconda3/bin:$PATH"'
In particular I find this relevant for running QGIS 2.x on a Mac using the resources made available by KyngChaos. There seem to be more substantive 'issues' with QGIS 3 that mean you need to specify an environment variable in QGIS directly to get the right Python distribution, so this may no longer be a problem.
Installation instructions are also contained in the head of the YAML script, but are reproduced here for clarity:
conda-env create -f setup.yml source activate mlgent python -m ipykernel install --name mlgent --display-name "ML Gentrification" jupyter lab
Obviously, if you see warning or error messages at any point you should stop and attempt to debug rather than mindlessly proceeding.
At this point you should have a new 'kernel' available in Jupyter called 'ML Gentrification' (or just mlgent if you skipped the naming command). The notebooks have all been set up to expect a kernel with this name and will prompt you to select a different kernel if you've opted to skip the environment creation step above.
Running the Notebooks
The notebooks have been named in such a way as to make it easy to work out the sequence of 'scripts' that need to be run: start with 01.. and finish with 08. Notebook 00 is only needed when you first clone the repo to ensure that the Geoconvert class is working. You'll notice that there are two versions of 08 (Neighbourhood Prediction); this is because I had timeout issues running 08 as a notebook and although the analysis would often complete at some point I had no way of knowing when, or if errors had arisen after the timeout occurred. Consequently, you might wish to run the 08 Python script instead as it will provide output directly to the terminal instead of indirectly via the server.
There are also several scripts to support testing of the class created to automate interaction with the UK Data Service's (originally MIMAS) Geo-Convert tool. This is essential for mapping Census data from the 2001 boundaries to the equivalent 2011 boundaries because of changes in Output Areas (OAs), Lower Super Output Areas (LSOAs), and to a lesser extent Middle Super Output Areas (MSOAs) between the two Censuses. If you are not working with UK Census data then this tool is not relevant (though boundary changes may still impact your results... you have been warned!). I should note that although it is possible, in principle, to update this class to perform any and all of the actions associated with the Geo-Convert web service I have only implemented the 2001 -> 2011 conversion of LSOAs as that is all that I needed.
From time to time the UK Data Service may also update Geo-Convert online forms (this has already happened to me once) and break the
geoconvert class; up to a point I will attempt to correct this quickly, but if the changes are substantial enough then this may not be something I'm able to address immediately.
Where possible I have attempted to either automate the download of the required data, or to make it available directly from the repo as downloaded from the NOMIS web site via their 'Query' service. In theory the NOMIS API should enable the automated selection and downloading of the data used by notebooks 2 and 3, but at the time that I was doing my work the API was broken and the documentation rather poor. As a result you will find all of the source data that cannot be downloaded in the
data/src folder. In the interests of enabling a 'clean distribution' this is the only folder stored in GitHub; all others will be re-created as-needed when you run the notebooks.
Many of the algorithms used in this analysis rely on randomness -- I have set the same seed everywhere that randomness might be used by Python and so your results should match mine. Be aware, however, that minor platform differences or other changes to the code could significaintly alter the results (though we obviously hope not!).