Assignment 3

.gitignore

@@ -2,4 +2,5 @@
 .vscode/
 uv.lock
 visualization-env
-.venv
+.venv
+.venv_visualization

02_activities/assignments/assignment_1.md

@@ -5,12 +5,30 @@
 ### Requirements:
 
 - During every class, follow along with sample code from the slides. All code that you should be running in Python is formatted as follows:
+`
+For day 15 01 2026 check: "02_activities\assignments\participation\15_01_2026.ipynb"
+`
 
   > If code in a slide looks like this, you should be running it to generate results.
 
 - When there are individual or group activities in submodules, make notes of answers and key points from discussions
+  20 - 01 - 2026 
+A good visualization should contain the three following design considerations together: Aesthetic, Accurate and Honest, and easy to perceive
+The purpose of the visualization should be clear before the design process. 
+One has to be mindful about the audience before preparing the visual. 
+The medium where the visualization is going to be presented should be considered and the visualization should be designed accordingly. Grey scale for printing etc. 
+Different datasets can be visualized by using different plotting schema selections. Color bars can be selected according to different datasets and how these data relate with each other. 
+Gestalt principles can be used to visualize data. These principles are proximity, similarity, continuity, closure, enclosure and connection. 
+Don't make 3D plots without a cause. 
 - Following each lesson with code, submit a document (either .py or a Jupyter notebook) containing the functioning code from that day's lesson, along with any written notes or comments.
 
+  22 - 01 - 2026 
+Visuals should be reproducible. 
+
+  27 - 01 - 2026 
+Visuals should be accesible to color blind people. 
+Avoiding overwhelming users. Select the font and do the figure while being mindful for people suffering from dylexia. 
+
 ### Why am I doing this assignment?:
 
 - This ongoing assignment ensures active participation in the course, and assesses the learning outcomes:

02_activities/assignments/assignment_2.md

@@ -7,10 +7,11 @@
 - Data visualizations are important tools for communication and convincing; we need to be able to evaluate the ways that data are presented in visual form to be critical consumers of information 
 - To test your evaluation skills, locate two public data visualizations online, one good and one bad  
     - You can find data visualizations at https://public.tableau.com/app/discover or https://datavizproject.com/, or anywhere else you like! 
+    bad example: https://public.tableau.com/app/profile/arjen.groeneveld/viz/24healthstatsfeb25/Dashboard1
 - For each visualization (good and bad):  
     - Explain (with reference to material covered up to date, along with readings and other scholarly sources, as needed) why you classified that visualization the way you did.
       ```
-      Your answer...
+      This visualization is bad because it is not following some of the gestalt principles.
 
 
 

02_activities/assignments/assignment_3/1314-009crime_severity_index_csv_2008-12.csv

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+,Crime Severity Index � Baseline 2006 = 100,,,,
+Geography (Footnotes in parentheses�see bottom of spreadsheet),2008,2009,2010,2011,2012
+Canada (34),90.39,87.53,82.67,77.43,75
+Newfoundland and Labrador,71.82,72.54,80.22,73.56,69.62
+Prince Edward Island,68.29,65.82,65.72,66.47,72.62
+Nova Scotia,84.02,83.79,83.36,79.14,76.48
+New Brunswick,71.76,70.69,69.18,66.25,68.41
+Quebec (34),83.17,81.67,76.26,74.19,70.71
+Ontario (7),70.68,68.93,65.15,60.7,58.4
+"Ottawa-Gatineau, Ontario/Quebec (6)",70.19,68.75,62.8,59.88,59.24
+"Ottawa-Gatineau, Ontario part (6)",68.48,67.01,60.66,58.28,57.04
+"Kingston, Ontario (8)",68.02,64.78,62.56,59.75,58.29
+"Peterborough, Ontario (8)",66.83,66.54,68.03,63.78,63.4
+"Toronto, Ontario (25)",66.71,63.85,59.7,55.39,52.13
+"Hamilton, Ontario (25)",77.08,73.76,70.82,63.94,60.06
+"St.Catharines-Niagara, Ontario",80.07,75.92,69.95,61.09,63.85
+"Kitchener-Cambridge-Waterloo, Ontario",68.81,73.94,68.01,63.32,58.34
+"Brantford, Ontario (8)",104.34,105.87,99.59,91.62,92.23
+"Guelph, Ontario (8)",57.55,59.77,50.61,47.84,50.52
+"London, Ontario",84.31,87.56,82.61,78.8,74.49
+"Windsor, Ontario",75.38,71.31,65.85,62.31,66.53
+"Barrie, Ontario (8)",63.7,64.42,60,53.81,51.32
+"Sudbury, Ontario",73.45,81.14,84.7,79.46,71.66
+"Thunder Bay, Ontario",106.71,112.53,111.73,105.49,87.95
+Manitoba,129.04,136.4,125.8,114.34,112.41
+Saskatchewan (11),153.11,150.29,149.17,144.76,139
+Alberta,112.13,105.8,98.43,87.79,85.59
+British Columbia (32),120.86,110.63,102.68,95.15,93.35
+Yukon,182.76,180.81,170.85,154.78,156.69
+Northwest Territories (15),340.27,322.56,343.47,336.61,340.98
+Nunavut (15),329.44,337.15,348.68,321.18,325.57
+Footnotes:,,,,,
+1,"The crime severity index is calculated using Incident-based Uniform Crime Reporting Survey (UCR2) data. For the period from 1998 to 2008 Incident-based UCR2 data are not available for all respondents. In order to report this level of detail for police services still reporting to the Aggregate Uniform Crime Reporting Survey (UCR) over this time, a process of imputation was applied to derive counts for violations that do not exist on their own in the aggregate survey. For approximately 80% of the aggregate offence codes, there is a 1:1 mapping with a new incident-based violation code. For violations where this was not the case, such as the aggregate other Criminal Code category, it was necessary to estimate (impute) this figure using the distribution of other Criminal Code offences from existing Incident-based UCR2 respondents.",,,,
+2,"During the production of each year's crime statistics, data from the previous year are revised to reflect any updates or changes that have been received from the police services.",,,,
+3,"The methodology for calculating census metropolitan area (CMA) populations was modified in 2003. Starting in 1996, the populations for CMAs have been adjusted to reflect the actual policing boundaries within the CMA and do not reflect the official Statistics Canada population for these CMAs. CMA data are included within province-level data.",,,,
+35,"Police reported statistics may be affected by differences in the way police services deal with minor offences. In some instances, police or municipalities might chose to deal with some minor offences using municipal by-laws or provincial provisions rather than Criminal Code provisions. Counts are based on the most serious violation in the incident.",,,,
+34,"In 2012, it was discovered that the Montreal Police Service had been incorrectly applying the agreed upon definition for reporting child pornography incidents to the Uniform Crime Reporting Survey (UCR2). As such, the number of violations has been revised for the years 2008 to 2011.",,,,
+16,"The crime severity index includes all Criminal Code violations including traffic, as well as drug violations and all Federal Statutes.",,,,
+20,"The Police Reported Crime Severity Index (PRCSI) measures changes in the level of severity of crime in Canada from year to year. In the index, all crimes are assigned a weight based on their seriousness. The level of seriousness is based on actual sentences handed down by the courts in all provinces and territories. More serious crimes are assigned higher weights, less serious offences lower weights. As a result, more serious offences have a greater impact on changes in the index. The PRCSI is standardized to 100 in base year 2006.",,,,
+21,"The Crime Severity Index is not available for police services or detachments with populations less than 1,000. Data for police services or detachments with populations less than 5,000 should be used with caution.",,,,
+7,"During the revision of 2001 data for the province of Ontario, a discrepancy in methodology applied by the forces in the province using the Ontario Municipal and Provincial Police Automated Co-operative (OMPPAC) system was detected. These forces report approximately one-third of the total criminal incidents for the province and include the Ontario Provincial Police and about 60 small and mid-sized municipal forces. This discrepancy resulted in an over-count of less serious criminal incidents. During 2003 and 2004, the Canadian Centre for Justice Statistics (CCJS) consulted with affected police services and analyzed both historical Aggregate Uniform Crime Reporting Survey (UCR) data and more recent Incident-based Uniform Crime Reporting Survey (UCR2) microdata to determine the impact of this over-reporting. The effect at the Canada level was, on average, 1% per year from 1977 to 2000. This over-counting peaked in 1991, where it was estimated that the total crime rate for the country was over-estimated by approximately 1.8%. However, the overall historical trend changed very little. The impact in Ontario is more noticeable, but the overall trend is still very similar. On average, the effect was 2% to 3% per year, with the biggest change occurring in 1991, where the Ontario crime rate is 5% lower as a result of the adjustment. For further information, refer to the report: <A HREF=""http://www.statcan.gc.ca/imdb-bmdi/document/3302_D13_T9_V1-eng.pdf"">Summary of Historical Adjustments to Crime Data for Ontario 1977-2000</A>.",,,,
+6,The census metropolitan area (CMA) of Ottawa-Gatineau was formerly known as Ottawa-Hull.,,,,
+8,"Abbotsford-Mission, British Columbia and Kingston, Ontario became new census metropolitan areas (CMAs) as of the 2001 Census. Moncton, New Brunswick, Peterborough, Ontario, Brantford, Ontario, Guelph, Ontario, Barrie, Ontario, and Kelowna, British Columbia became new CMAs as of the 2006 Census",,,,
+25,"In 2009, slight changes were made to the respondent-to-CMA mapping for this census metropolitan area (CMA) which have resulted in minor revisions to previously published data.",,,,
+11,"Prior to 1999, a number of Royal Canadian Mounted Police (RCMP) detachments in Saskatchewan were double counting the number of actual offences of impaired driving. This over-counting was corrected in 1999, therefore, comparisons with previous years should be made with caution. It is recommended that the analysis of impaired driving be based on persons charged data rather than actual offences.",,,,
+32,"Youth crime in British Columbia may be somewhat undercounted due to the Crown charge-approval system in place whereby, even for minor offences, there must be approval by the Crown before charges are laid after police have made an arrest. This approval is based on the substantial likelihood of conviction.",,,,
+15,"1998 data in this table for Northwest Territories also includes Nunavut. In 1999, Nunavut, which comprises the eastern part of the old Northwest Territories, officially became a Canadian territory. Starting in 1999, data are displayed separately for the Northwest Territories and Nunavut.",,,,

02_activities/assignments/assignment_3/assignment_3.md

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+# Data Visualization
+
+## Assignment 3: Final Project
+
+### Requirements:
+- We will finish this class by giving you the chance to use what you have learned in a practical context, by creating data visualizations from raw data. 
+- Choose a dataset of interest from the [City of Toronto’s Open Data Portal](https://www.toronto.ca/city-government/data-research-maps/open-data/) or [Ontario’s Open Data Catalogue](https://data.ontario.ca/). 
+- Using Python and one other data visualization software (Excel or free alternative, Tableau Public, any other tool you prefer), create two distinct visualizations from your dataset of choice.  
+- For each visualization, describe and justify: 
+    > What software did you use to create your data visualization?
+
+    > Who is your intended audience? 
+
+    > What information or message are you trying to convey with your visualization? 
+
+    > What aspects of design did you consider when making your visualization? How did you apply them? With what elements of your plots? 
+
+    > How did you ensure that your data visualizations are reproducible? If the tool you used to make your data visualization is not reproducible, how will this impact your data visualization? 
+
+    > How did you ensure that your data visualization is accessible?  
+
+    > Who are the individuals and communities who might be impacted by your visualization?  
+
+    > How did you choose which features of your chosen dataset to include or exclude from your visualization? 
+
+    > What ‘underwater labour’ contributed to your final data visualization product?
+
+- This assignment is intentionally open-ended - you are free to create static or dynamic data visualizations, maps, or whatever form of data visualization you think best communicates your information to your audience of choice! 
+- Total word count should not exceed **(as a maximum) 1000 words** 
+
+### Why am I doing this assignment?:  
+- This ongoing assignment ensures active participation in the course, and assesses the learning outcomes: 
+* Create and customize data visualizations from start to finish in Python
+* Apply general design principles to create accessible and equitable data visualizations
+* Use data visualization to tell a story  
+- This would be a great project to include in your GitHub Portfolio – put in the effort to make it something worthy of showing prospective employers!
+
+### Rubric:
+
+| Component         | Scoring  | Requirement                                                                 |
+|-------------------|----------|-----------------------------------------------------------------------------|
+| Data Visualizations | Complete/Incomplete | - Data visualizations are distinct from each other<br>- Data visualizations are clearly identified<br>- Different sources/rationales (text with two images of data, if visualizations are labeled)<br>- High-quality visuals (high resolution and clear data)<br>- Data visualizations follow best practices of accessibility |
+| Written Explanations | Complete/Incomplete | - All questions from assignment description are answered for each visualization<br>- Explanations are supported by course content or scholarly sources, where needed |
+| Code              | Complete/Incomplete | - All code is included as an appendix with your final submissions<br>- Code is clearly commented and reproducible |
+
+## Submission Information
+
+🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.
+
+### Submission Parameters:
+* Submission Due Date: `23:59 - 02/02/2026`
+* The branch name for your repo should be: `assignment-3`
+* What to submit for this assignment:
+    * A folder/directory containing:
+        * This file (assignment_3.md)
+        * Two data visualizations 
+        * Two markdown files for each both visualizations with their written descriptions.
+        * Link to your dataset of choice.
+        * Complete and commented code as an appendix (for your visualization made with Python, and for the other, if relevant) 
+* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/visualization/pull/<pr_id>`
+    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.
+
+Checklist:
+- [ ] Create a branch called `assignment-3`.
+- [ ] Ensure that the repository is public.
+- [ ] Review [the PR description guidelines](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md#guidelines-for-pull-request-descriptions) and adhere to them.
+- [ ] Verify that the link is accessible in a private browser window.
+
+If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.