CASA0006: Data Science for Spatial Systems
Assessment Guidelines
Deadline Wednesday 29th April 2020 @ 5pm
Word Count Minimum 2000 words (not including Python scripts)
The coursework for this module will consist of an individual assignment that tests your ability to conduct in-
depth data analysis. Each student is required to submit a single Jupyter Python Notebook which contains
both the code required to conduct the data analysis and accompanying text which provides context
interpretation.
This coursework represents 100% of the overall module assessment.
Task
Select any open dataset relating to an urban or spatial system of your choice and conduct an advanced
analysis of the dataset. A complete data analysis process should be undertaken – this will include data
validation and cleaning, a data pre-processing phase (e.g. text, image, clustering analysis), and a
comprehensive analysis (including relevant visualisations) of the data, identifying important trends and
insights contained within the dataset. Each stage of the data treatment and analysis process should be well
documented, and keeping with the exploratory, narrative theme described during the course. Marks will be
awarded for both the technical analysis process and the interpretation and choice of analysis methods. The
dataset (or datasets) you choose to analyse is left completely open, although should relate to an urban or
spatial process.
The data analysis process should be captured within a single Jupyter Python notebook. This notebook
should contain all of the code used to complete each of the three stages of the work, in addition to the full
documentation of the analysis process and interpretation of results. The documentation must be a minimum
of 2000 words, note that the provided Python scripting is not included in this word limit.
A breakdown of how the notebook will be marked is as follows:
• Analysis and Interpretation of data – 70%
- Analysis context and aims (incl. reference to relevant literature and projects)
- Data collection, handling, cleaning and management
- Depth and scope of data analysis
- Appropriateness of data visualisation
- Interpretation and reporting of analysis and major findings
- Clarity of presentation of results
• Demonstration of technical skills – 20%
- Choice and rationale of data analysis methods used
• Creativity of analytical work – 10%
At submission, the notebook should be able to be fully executed quickly, therefore all libraries (and their
version numbers) used in analysis must be clearly stated. If the data cleaning and pre-processing stages
require considerable time for execution it is satisfactory that the processed data is provided, alongside a
detailed description of the processing phase. The assessors will return work that has not been provided in an
easily executed format, which will in turn suffer late penalty deductions.
Example Workbooks
Listed below are a number of example data analysis projects using Python and various libraries,
combining code and narrative (to varying extents) within a notebook format. In general, we expect a more
systematic and complete analysis than that offered here – following the steps outlines above.
• Using Python to see how the Times writes about men and women -
http://nbviewer.jupyter.org/gist/nealcaren/5105037
• How Clean are San Francisco’s Restaurants? - http://nbviewer.jupyter.org/github/Jay-Oh-eN/happy-
healthy-hungry/blob/master/h3.ipynb
• Predicting use on NYC Metro -
http://nbviewer.jupyter.org/url/www.asimihsan.com/articles/Intro%20to%20Data%20Science%20-
%20Final%20Project.ipynb
• San Francisco Drug Geography -
http://nbviewer.jupyter.org/github/lmart999/GIS/blob/master/SF_GIS_Crime.ipynb
• New York Taxi Analysis - https://anaconda.org/jbednar/nyc_taxi/notebook - Excellent visualisations
• Buzzfeed analysis of Segregation in St Louis - http://nbviewer.jupyter.org/github/buzzfeednews/2014-
08-st-louis-county-segregation/blob/master/notebooks/segregation-analysis.ipynb - needs better
documentation!
• Graph Properties of the Twitter Stream -
http://nbviewer.jupyter.org/gist/fperez/5681541/TwitterGraphs.ipynb
• Logistic models of well switching in Bangladesh -
http://nbviewer.jupyter.org/github/carljv/Will_it_Python/blob/master/ARM/ch5/arsenic_wells_switching
.ipynb - lacks descriptions of the data
• Clustering Samsung smartphone accelerometer data -
http://nbviewer.jupyter.org/github/herrfz/dataanalysis/blob/master/week4/clustering_example.ipynb
• Exploratory Analysis of the 2014 World Cup Final -
http://nbviewer.jupyter.org/github/rjtavares/football-
crunching/blob/master/notebooks/an%20exploratory%20data%20analysis%20of%20the%20world%
20cup%20final.ipynb
• Data mining Twitter using tweepy -
http://nbviewer.jupyter.org/github/hugadams/twitter_play/blob/master/tweepy_tutorial.ipynb?utm_con
tent=14023248utm_medium=socialutm_source=twitter - very informative!
• Flight Arrivals - http://nbviewer.jupyter.org/github/ResearchComputing/Meetup-Fall-
2013/blob/master/python/lecture_27_arrival.ipynb - lacks full documentation!
• Very nice analysis of how the Circle Line rogue train was caught with data -
https://blog.data.gov.sg/how-we-caught-the-circle-line-rogue-train-with-data-
79405c86ab6a#.oabdxcg86 - GitHub notebook, rather than Jupyter
Once marked, we would encourage you to submit your completed workbooks to nbviewer.jupyter.org or
anaconda.org for wider sharing.
Examples Datasets
We’d encourage you to find an interesting dataset that you all want to work on. Here are a few examples in
case you are struggling to find one.
• NYC GPS taxi data - http://chriswhong.com/open-data/foil_nyc_taxi
• Yelp dataset - https://www.yelp.com/dataset
• UK Land Registry house sales data - http://landregistry.data.gov.uk
• Stop and Search Data by US State - https://openpolicing.stanford.edu/data/
• Traffic Accident and Traffic Flow data for 16 years - https://www.kaggle.com/daveianhickey/2000-16-
traffic-flow-england-scotland-wales/settings
• Real-time crime data in Seattle - https://data.seattle.gov/Public-Safety/Seattle-Police-Department-
911-Incident-Response/3k2p-39jp
• Various FOI data releases can be found on WhatDoTheyKnow -
https://www.whatdotheyknow.com/list/successful
• Crime Data in Buenos Aires - https://github.com/ramadis/delitos-caba
• Lots of open data for Bahrain - https://datasource.kapsarc.org/pages/home/
• City Cellular Traffic Map - https://github.com/caesar0301/city-cellular-traffic-map
• Flight data (requires Google account) - https://bigquery.cloud.google.com/table/bigquery-
samples:airline_ontime_data.flights
• Beijing GPS taxi data - http://research.microsoft.com/apps/pubs/?id=152883
• International Migration data - http://www.global-migration.info/
• Plant Diversity in American National Parks Biodiversity -
https://www.kaggle.com/nationalparkservice/park-biodiversity/data
• Wildlife Trade Database - https://www.kaggle.com/residentmario/cites-wildlife-trade-database/data
• H1-B Visa Petitions - https://www.kaggle.com/nsharan/h-1b-visa/data
• Baltimore Crime Data - https://www.kaggle.com/sohier/crime-in-baltimore
• Chicago Crime Data - https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2
• AWS Honeypot Cyber Attack Data (with originating lat/lngs) -
https://www.kaggle.com/casimian2000/aws-honeypot-attack-data/data
• Vancouver Crime Data - http://data.vancouver.ca/datacatalogue/crime-data.htm