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1. Introduction
This part of the assessment for Introduction to Data Science comprises a piece of individual coursework to assess your
ability to analyse data using R/RStudio and to then communicate your findings. Given a specific topic and dataset (see Section 2),
you should identify a specific problem or topic you would like to investigate (e.g., where or when particular types of crime occur, or
co-occur). You will then need to pre-process and analyse the dataset to identify patterns and relationships that address your selected
problem/topic. This should involve using techniques learned throughout the practical sessions that will help you to demonstrate your
R skills, such as summarising datasets, statistical modelling or data visualisation, to highlight and illustrate particular aspects of the
data you want to communicate (e.g., particular patterns or trends).
This coursework aims to follow the stages involved in a ‘typical’ data science process: (i) define the question(s) to address (note,
sometimes this does not come at the start of the process, but after initial exploration of the data); (ii) gather data; (iii) transform, clean
and structure the data; (iv) explore and analyse the data; and (v) communicate the findings of the data analysis. This often occurs in
an iterative manner and centred on one or multiple questions you are seeking to address. For example, the data discovery process
in Figure 1 presents an example of the stages involved in data discovery as an iterative process1 and you can find more details in
Section 3. This is also similar to the data science process we have been using in class from the “Doing Data Science” book (O’Neil &
Schutt, 2013).
Fig. 1 Example data discovery process (Jones, 2014: p.2)
You should write a 3,000 word structured report (see Section 4) that describes the approach you have taken to explore and
analyse the data for the selected problem/topic. You report should clearly communicate the results of your data analysis and be
written in a way that helps the reader interpret your findings. Note: charts, tables, and appendices are not included in the word count.
This assessment is worth 100% of the overall module mark for INF6027. A pass mark of 50 is required to pass the module as a
whole. Submission deadline: 10am Monday 18th January 2021 via Turnitin. See Section 5 for more general information about
Coursework Submission Requirements within the Information School.
2. The UK Police Dataset
The dataset to be used in this assessment is the UK Police Dataset, which has made public crime data since 2011 (this is an
example of Open Data). There has been a lot of recent interest in analysing publicly available datasets to identify patterns of crime
and gain insights into criminal activity, see for example the crime activity browser by IBM2
. If interested in the topic you can also find
further crime-related datasets produced by the UK Data Service (https://www.ukdataservice.ac.uk/get-data/themes/crime). There is
also an increasing use of crime Open Data used in the media to highlight aspects of policing and criminal activities (see, e.g.,
https://www.bbc.co.uk/news/uk-44044537).
1
You can find out more about this process in (Jones, 2014: p.2): https://tanthiamhuat.files.wordpress.com/2015/07/communicating-data-with-tableau.pdf
2 Open Crime Data, Free for All: https://developer.ibm.com/clouddataservices/2016/11/03/open-crime-data/
Analysis of the UK Police Dataset
A description of the data is available here: http://data.police.uk/about/ also including an explanation on how to download the data3
.
The data are provided as CSV files (note that there is also an API available if you prefer) and provide street-level crime, outcome
and stop and search information broken down by police force4
(in the UK there are 45 territorial police forces and 3 Special Forces)
and 2011 Lower Layer Super Output Areas (LSOA).
The dataset describes crimes reported to UK police during each month in different areas of the UK. Information in the dataset
includes the following: geographical location (longitude and latitude), date (month, year), LSOA code (i.e., the census area), and type
of crime (e.g., vehicle crime, burglary, robbery, etc.). You can select any data from the UK Police Dataset. (This may require multiple
downloads.) You can also aggregate the dataset with other data sources if you want (e.g., census data), which would demonstrate
your ability to join datasets (although you don’t have to do this to pass the coursework as the emphasis of the coursework is on how
you carry out your analysis in R/RStudio and communicate your findings on the UK Police Dataset).
3. What you need to do
The following sections describe what you need to do in order to carry out the coursework. This roughly follows the steps shown in
Fig. 1, but you don’t have to be constrained by this or follow them in this particular order; it is just a suggestion. Also, all the R we
have done in the practical sessions (and the final sessions) should be enough to conduct the coursework, although you may need to
investigate certain areas further that relate specifically to the problem you tackle in your investigation.
3.1. Review the literature and identify research question(s)
As mentioned previously, you should select a specific problem/topic related to the data (the ‘question’ stage in Fig. 1). To decide
what area to focus on you could start by undertaking a brief review of the relevant literature around areas, such as analysis of crime
data, geographical analysis of crime, predictive policing, crime sensing, analysis of crime statistics, etc. For example, these articles
may be a useful starting point:
Vandeviver, C., and Bernasco, W. (2017) The geography of crime and crime control, Applied Geography, Volume 86, pp.
220-225. (Available online: http://www.sciencedirect.com/science/article/pii/S014362281730838X)
Field, S. (1992) The Effect of Temperature on Crime, The British Journal of Criminology, Volume 32, Issue 3, pp. 340–351.
(Available online: https://doi.org/10.1093/oxfordjournals.bjc.a048222)
Reviewing past literature will help you understand what kinds of analyses are typically undertaken using crime data and provide a
possible source of ideas for what you could do with the UK Police Dataset. Examples of possible topics include, but are not restricted
to, the following:
• Evolution of crimes in an area over time;
• Trends and predictions of crimes and crime rates;
• Analysis of certain types of crime (e.g., vehicle crimes);
• Comparisons of crime types in a region;
• Clustering and classification of data, e.g., by type of crime;
• Normalisation and integration with other datasets (e.g., LSOA census statistics);
• Focus on a certain census dimension (e.g., age of residents in the area);
• Visualisation of the data (e.g., on maps).
3.2. Download, pre-process and explore the data
As well as reviewing relevant academic literature you should also download some data from http://data.police.uk/ and perform an
exploratory analysis (i.e. ‘play’ with the data), to better understand the dataset and also help you to identify a particular problem or
topic you might want to focus on. You must include most recent data in your analysis.
This part of your investigation will include steps to pre-process and transform the data, such as cleaning up the data, dealing with
missing values, standardising numeric values, etc. This may also include combining or joining the data with further datasets, e.g.
census or deprivation data. This reflects the ‘gather’ and ‘structure’ stages in Fig. 1. (Note: this part of the analysis could take a lot of
time so don’t underestimate how much time you will need to spend on this part of the coursework.)
3
You can also find an article describing the accuracy of the data here: https://www.tandfonline.com/doi/full/10.1080/15230406.2014.972456
4
https://en.wikipedia.org/wiki/List_of_police_forces_of_the_United_Kingdom
3.3. Analyse and explore the data
As you identify a topic of interest for your analysis then you should identify the most appropriate techniques (using R and associated
packages) for carrying out your analysis and exploring the data, e.g. you might want to predict crime rates using regression or
compare levels of crime types using statistical tests. This might also be an iterative process whereby you perform some analysis and
then gather (or remove) more data. Where possible relate you analysis to the relevant literature. This relates to the ‘exploring data’
stage in Fig. 3.
Note that this is often an iterative process: as you explore the data you may end up re-designing your research questions, having to
gather more data or having to perform further cleaning as more data quality issues arise. Again, this is all a part of the data discovery
process.
3.4. Write up your findings
Once you have performed analysis on the data and have some results then you need to write up your investigation into a report (this
is the ‘communicate’ stage of Fig. 1). The report should be structured as outlined in Section 4. You will be evaluated on your ability to
plan and undertake data analysis and exploration of crime based on the UK Police Dataset, your ability to engage with the relevant
literature, your use of R (and appropriate packages) and RStudio to process and analyse the data, and the way in which you
communicate your findings within the report for your given problem/topic.
You should also provide your R code as an appendix and marks will be awarded for your clarity, consistency and way in which you
comment your R code (see, e.g. http://stat405.had.co.nz/r-style.html). The specific style you use is not as important as how well you
comment your code so that someone else can follow what you have done and being consistent in whichever style you adopt.
The minimum requirement to pass is to perform at least one type of data analysis (e.g., clustering, prediction, time-series analysis,
etc.) and include at least two visualisations (e.g., charts, maps, etc.) in the report. To obtain a higher mark and more effectively
communicate your findings, you may decide to use more than one dataset or present more than one type of data analysis and/or use
multiple visualisations. Again, you should also engage as much as possible with the appropriate literature.
4. Report structure
You are required to produce a structured report that includes the sections detailed in Table 1. You must state the word count on the
first page of the report. As there is a word count limit (3,000 words) you should aim to make your writing as concise and informative
as possible. Also note that your work will be assessed taking into account the word limit; therefore, we are not expecting detailed
multiple analyses in the report; rather the emphasis should be on the clarity, accuracy and quality in communicating your findings.
Note that words within tables and appendices are not included in the word count.
Table 1: Required content of the structured report.
Section Description Examples of what we will be
looking for and mark allocation Maximum allocated

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