URGENT Python Task - Computational Modelling in the Humanities and Social Sciences
Budget: $10 – $50 USD
Computational Modelling in the Humanities and Social Sciences
The House of Commons SPARQL endpoint (https://api.parliament.uk/sparql/) provides access to
structured data about Members of Parliament (MPs) and questions that have been asked in the House
of Commons. Use this data together with data from Wikidata (where necessary) to answer the following
questions, presenting your findings in the form of a report of up to 3000 words, based on data about
questions asked by MPs between 1 January 2023 and 30 September 2023 inclusive:
Q1.To what extent do Members of Parliament (MPs) tend to ask questions that directly reference
their own constituency or a location in it? You should answer this question by identifying named
entities that refer to places or identifiable geographical features (e.g. “Dartford Crossing”,
“Reading Gaol”, etc.) in asked questions, and determining whether or not these are located in
the MP’s constituency using data from Wikidata. You are free to choose any reasonable method
in doing so – even if doing so will result in some false negatives – (e.g. relying on Wikidata
property P131 “located in the administrative territorial entity”), however you should estimate
how reliable you believe your chosen approach is. (N.B. it is not expected or required that your
approach will result in 100% accuracy.)
Q2.By applying LDA topic modeling and analyzing the results, what (if any) identifiable regional
differences are there in the types of questions asked – e.g. do MPs representing, say,
constituencies located in the North of England tend to ask more questions about certain topics
than those in Southeast England? In answering this question, you should start by aggregating
data into regions larger than an electoral district, such as those denoted by the property “region
of England” https://www.wikidata.org/wiki/Q48091 (For simplicity, you may treat Scotland and
Northern Ireland as two separate regions without further subdivisions, or alternatively use any
reasonable administrative subdivisions for these regions as you see fit). Discuss the assumptions
and limitations of your approach and analysis.
The following SPARQL query can be used as a starting point:
SELECT *
WHERE {
?question <https://id.parliament.uk/schema/writtenQuestionIndexingAndSearchUin> ?qnum .
?person <https://id.parliament.uk/schema/askingPersonHasQuestion> ?question .
?question <https://id.parliament.uk/schema/questionText> ?text .
?question <https://id.parliament.uk/schema/questionAskedAt> ?date .
FILTER (?date >= "2023-01-01+00:00"^^xsd:dateTime && ?date < "2023-10-01+00:00"^^xsd:dateTime)
}
This query returns the following data:
?question Entity representing a question
?person Entity representing the person who asked the question
?qnum Numerical identifier for this question
?text The text of the question
?date The date the question was askedYour report must:
1. Document how data was collected, and briefly state any relevant modules or toolkits that were
used. If using SPARQL queries not contained in your code (other than the one provided), you
must include these as separate files within your code submission.
2. Describe your implementation and the models constructed.
3. Summarize your results in appropriate ways, including use of suitable visualizations.
4. Critically evaluate the models. This must involve critical analysis of the adequacy of the modelling
(e.g. what are the assumptions the models rely on, and do they all hold; are there factors or
biases that might invalidate conclusions drawn?), but should also involve some comparison with
external data and/or published social/political science research on relevant subjects.
5. Discuss what conclusions can be drawn from the model.
In this assignment, please note that:
• You are not expected to resolve issues of lack of recorded data (or incorrectly recorded data) in
additional data sources such as Wikidata. However, you are expected to account for the
potential effects of these in your analysis, and should attempt to quantify these where possible.
• You are allowed to reuse existing code – this includes open source modules and toolkits, Stack
Overflow responses, code described in online tutorials, etc., provided that whenever you do this,
you clearly indicate what you have reused and exactly where it came from (including a URL for an
online source). You should document smaller instances of reuse (e.g. short code examples from
Stack Overflow) as comments in your code, and longer ones both in your code and as citations to
the source in your report. Marks will be awarded for the original parts of your work (i.e. your
extensions to and adaptations or developments of any reused material).
• Copying any material (this includes code and text) in your submission from any source without
clear acknowledgement is likely to constitute plagiarism, which can have serious consequences
as described in the Teaching and Learning Handbook:
https://www.dur.ac.uk/learningandteaching.handbook/6/2/4/
• Marks will not be awarded under the “Technical depth/clarity of implementation” sections for
parts of your code that reimplement code available in widely used libraries or modules.
• You may use any programming language suitable for the task; use of Python 3.x is recommended
where possible.
• In writing your report, you should assume that the reader is familiar with everything covered
during the lectures for this module. Concepts and techniques important to your implementation
which were not introduced during the module should be explained.
• The word limit is exclusive of figures and references. You may include a maximum of 20 figures
(recommended: around 5-10 figures). Please use a 12pt font, single-spacing, and A4 pages. Your
report should begin with an introduction, and does not require a separate abstract.
Submission (2 files)
• A written report as a PDF file.
• An archive (.zip / .tar.gz) containing the code for your implementation, including instructions for
how to run it in a README.TXT file. If any data that your code relies on is over 10 Mb in size,
include instructions in this file for how to obtain it and do not include it in the archive. Your code
is not marked separately, but serves as supporting evidence for the criteria indicated in the
marking scheme.
Marks Assessed by
Q1. Appropriateness, technical depth and clarity of implementation
Results and evaluation
Conclusions and interpretation of results 15 Report
Q2. Appropriateness, technical depth and clarity of implementation
Results and evaluation
Conclusions and interpretation of results
Presentation (including academic writing, structure, and referencing)
https://drive.google.com/file/d/1V7HZHUM50N2ihUiuhoYoWepSejyAXEMn/view?google_abuse=GOOGLE_ABUSE_EXEMPTION%3DID%3Dc4cbacbf643bf982:TM%3D1705497811:C%3Dr:IP%3D223.227.61.41-:S%3DwSHuPGZlcScPSyyd0Xmjv9Q%3B+path%3D/%3B+domain%3Dgoogle.com%3B+expires%3DWed,+17-Jan-2024+16:23:31+GMT
The House of Commons SPARQL endpoint (https://api.parliament.uk/sparql/) provides access to
structured data about Members of Parliament (MPs) and questions that have been asked in the House
of Commons. Use this data together with data from Wikidata (where necessary) to answer the following
questions, presenting your findings in the form of a report of up to 3000 words, based on data about
questions asked by MPs between 1 January 2023 and 30 September 2023 inclusive:
Q1.To what extent do Members of Parliament (MPs) tend to ask questions that directly reference
their own constituency or a location in it? You should answer this question by identifying named
entities that refer to places or identifiable geographical features (e.g. “Dartford Crossing”,
“Reading Gaol”, etc.) in asked questions, and determining whether or not these are located in
the MP’s constituency using data from Wikidata. You are free to choose any reasonable method
in doing so – even if doing so will result in some false negatives – (e.g. relying on Wikidata
property P131 “located in the administrative territorial entity”), however you should estimate
how reliable you believe your chosen approach is. (N.B. it is not expected or required that your
approach will result in 100% accuracy.)
Q2.By applying LDA topic modeling and analyzing the results, what (if any) identifiable regional
differences are there in the types of questions asked – e.g. do MPs representing, say,
constituencies located in the North of England tend to ask more questions about certain topics
than those in Southeast England? In answering this question, you should start by aggregating
data into regions larger than an electoral district, such as those denoted by the property “region
of England” https://www.wikidata.org/wiki/Q48091 (For simplicity, you may treat Scotland and
Northern Ireland as two separate regions without further subdivisions, or alternatively use any
reasonable administrative subdivisions for these regions as you see fit). Discuss the assumptions
and limitations of your approach and analysis.
The following SPARQL query can be used as a starting point:
SELECT *
WHERE {
?question <https://id.parliament.uk/schema/writtenQuestionIndexingAndSearchUin> ?qnum .
?person <https://id.parliament.uk/schema/askingPersonHasQuestion> ?question .
?question <https://id.parliament.uk/schema/questionText> ?text .
?question <https://id.parliament.uk/schema/questionAskedAt> ?date .
FILTER (?date >= "2023-01-01+00:00"^^xsd:dateTime && ?date < "2023-10-01+00:00"^^xsd:dateTime)
}
This query returns the following data:
?question Entity representing a question
?person Entity representing the person who asked the question
?qnum Numerical identifier for this question
?text The text of the question
?date The date the question was askedYour report must:
1. Document how data was collected, and briefly state any relevant modules or toolkits that were
used. If using SPARQL queries not contained in your code (other than the one provided), you
must include these as separate files within your code submission.
2. Describe your implementation and the models constructed.
3. Summarize your results in appropriate ways, including use of suitable visualizations.
4. Critically evaluate the models. This must involve critical analysis of the adequacy of the modelling
(e.g. what are the assumptions the models rely on, and do they all hold; are there factors or
biases that might invalidate conclusions drawn?), but should also involve some comparison with
external data and/or published social/political science research on relevant subjects.
5. Discuss what conclusions can be drawn from the model.
In this assignment, please note that:
• You are not expected to resolve issues of lack of recorded data (or incorrectly recorded data) in
additional data sources such as Wikidata. However, you are expected to account for the
potential effects of these in your analysis, and should attempt to quantify these where possible.
• You are allowed to reuse existing code – this includes open source modules and toolkits, Stack
Overflow responses, code described in online tutorials, etc., provided that whenever you do this,
you clearly indicate what you have reused and exactly where it came from (including a URL for an
online source). You should document smaller instances of reuse (e.g. short code examples from
Stack Overflow) as comments in your code, and longer ones both in your code and as citations to
the source in your report. Marks will be awarded for the original parts of your work (i.e. your
extensions to and adaptations or developments of any reused material).
• Copying any material (this includes code and text) in your submission from any source without
clear acknowledgement is likely to constitute plagiarism, which can have serious consequences
as described in the Teaching and Learning Handbook:
https://www.dur.ac.uk/learningandteaching.handbook/6/2/4/
• Marks will not be awarded under the “Technical depth/clarity of implementation” sections for
parts of your code that reimplement code available in widely used libraries or modules.
• You may use any programming language suitable for the task; use of Python 3.x is recommended
where possible.
• In writing your report, you should assume that the reader is familiar with everything covered
during the lectures for this module. Concepts and techniques important to your implementation
which were not introduced during the module should be explained.
• The word limit is exclusive of figures and references. You may include a maximum of 20 figures
(recommended: around 5-10 figures). Please use a 12pt font, single-spacing, and A4 pages. Your
report should begin with an introduction, and does not require a separate abstract.
Submission (2 files)
• A written report as a PDF file.
• An archive (.zip / .tar.gz) containing the code for your implementation, including instructions for
how to run it in a README.TXT file. If any data that your code relies on is over 10 Mb in size,
include instructions in this file for how to obtain it and do not include it in the archive. Your code
is not marked separately, but serves as supporting evidence for the criteria indicated in the
marking scheme.
Marks Assessed by
Q1. Appropriateness, technical depth and clarity of implementation
Results and evaluation
Conclusions and interpretation of results 15 Report
Q2. Appropriateness, technical depth and clarity of implementation
Results and evaluation
Conclusions and interpretation of results
Presentation (including academic writing, structure, and referencing)
https://drive.google.com/file/d/1V7HZHUM50N2ihUiuhoYoWepSejyAXEMn/view?google_abuse=GOOGLE_ABUSE_EXEMPTION%3DID%3Dc4cbacbf643bf982:TM%3D1705497811:C%3Dr:IP%3D223.227.61.41-:S%3DwSHuPGZlcScPSyyd0Xmjv9Q%3B+path%3D/%3B+domain%3Dgoogle.com%3B+expires%3DWed,+17-Jan-2024+16:23:31+GMT