diff --git a/02_activities/assignments/assignment-3-visualization1.ipynb b/02_activities/assignments/assignment-3-visualization1.ipynb new file mode 100644 index 000000000..ff1d022d4 --- /dev/null +++ b/02_activities/assignments/assignment-3-visualization1.ipynb @@ -0,0 +1,226 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4fe199f9", + "metadata": {}, + "source": [ + "Assignment 3: Python visual " + ] + }, + { + "cell_type": "markdown", + "id": "b6231796", + "metadata": {}, + "source": [ + "Data: Death registry statistics in Toronto and the GTA.\n", + "Link: https://open.toronto.ca/dataset/death-registry-statistics/" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "25a964c6", + "metadata": {}, + "outputs": [], + "source": [ + "# loading packages \n", + "import seaborn as sns\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import scipy\n", + "import PIL\n", + "import requests\n", + "from PIL import Image\n", + "import requests\n", + "from io import BytesIO\n", + "import plotly.graph_objects as go" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "44495003", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# loading data\n", + "death = pd.read_csv(\"~/Desktop/DSI/visualization/02_activities/assignments/Death-Registry-Statistics-Data.csv\")\n", + "\n", + "\n", + "# creating a moasic of graphs\n", + "fig, someaxes = plt.subplot_mosaic([['ax1', 'ax3'],\n", + " ['ax2', 'ax3']],\n", + " figsize=(8,8\n", + " ))\n", + "\n", + "\n", + "# plot 1 -ax1\n", + "\n", + "# extracting just the year from time period\n", + "death[\"YEAR\"] = death[\"TIME_PERIOD\"].astype(str).str[:4].astype(int)\n", + "\n", + "\n", + "# sorting the year values by just the year \n", + "death = death.sort_values(\"YEAR\")\n", + "\n", + "# grouping the number of deaths per year \n", + "death_by_year = (\n", + " death.groupby(\"YEAR\", as_index=False)[\"DEATH_LICENSES\"]\n", + " .sum())\n", + "\n", + "# Overall deaths per year\n", + "overall = (death.groupby(\"YEAR\", as_index=False)[\"DEATH_LICENSES\"]\n", + " .sum()\n", + " .sort_values(\"YEAR\"))\n", + "\n", + "# group the total for each year by civic centre\n", + "by_centre = (death.groupby([\"YEAR\", \"CIVIC_CENTRE\"], as_index=False)[\"DEATH_LICENSES\"]\n", + " .sum()\n", + " .sort_values(\"YEAR\"))\n", + "\n", + "for centre, df in by_centre.groupby(\"CIVIC_CENTRE\"):\n", + " someaxes['ax1'].plot(df[\"YEAR\"], df[\"DEATH_LICENSES\"], marker=\"*\", linewidth=1, alpha=0.6, label=str(centre))\n", + "someaxes['ax1'].plot(overall[\"YEAR\"], overall[\"DEATH_LICENSES\"], linewidth=3, marker=\"*\", label=\"Overall\", color='black')\n", + "\n", + "\n", + "someaxes['ax1'].set_xlabel(\"Year\", fontsize=8)\n", + "someaxes['ax1'].set_xticks(np.arange(df[\"YEAR\"].min(), df[\"YEAR\"].max() + 1, 5))\n", + "someaxes['ax1'].set_ylabel(\"Number of deaths\", fontsize=8)\n", + "someaxes['ax1'].set_title(\"Deaths Over Time: Overall vs All Civic Centres\", color='blue', fontsize=10)\n", + "someaxes['ax1'].legend( loc=\"center left\", ncol=1, fontsize=8)\n", + "someaxes['ax1'].tick_params(axis=\"both\", labelsize=8)\n", + "someaxes['ax1'].grid(True) \n", + "\n", + "\n", + "#plot 2 - ax2\n", + "\n", + "# creating totals of each \n", + "place_totals = (\n", + " death.groupby(\"PLACE_OF_DEATH\", as_index=False)[\"DEATH_LICENSES\"]\n", + " .sum()\n", + " .sort_values(\"DEATH_LICENSES\", ascending=False)\n", + ")\n", + "\n", + "\n", + "someaxes['ax2'].bar(place_totals[\"PLACE_OF_DEATH\"], place_totals[\"DEATH_LICENSES\"])\n", + "someaxes['ax2'].set_xlabel(\"Location\", fontsize=8)\n", + "someaxes['ax2'].set_ylabel(\"Number of deaths\", fontsize=8)\n", + "someaxes['ax2'].set_title(\"Location of Deaths\", color='blue', fontsize=10)\n", + "someaxes['ax2'].tick_params(axis=\"both\", labelsize=8)\n", + "\n", + "\n", + "# graph number 3: ax3\n", + "\n", + "# sorting the total number of deaths per year and by location\n", + "by_year_place = (\n", + " death.groupby([\"YEAR\", \"PLACE_OF_DEATH\"], as_index=False)[\"DEATH_LICENSES\"]\n", + " .sum()\n", + ")\n", + "\n", + "# determining the top places without hard coding\n", + "top_places = (\n", + " by_year_place.groupby(\"PLACE_OF_DEATH\")[\"DEATH_LICENSES\"].sum()\n", + " .sort_values(ascending=False)\n", + " .head(2)\n", + " .index\n", + " .tolist()\n", + ")\n", + "\n", + "place_left, place_right = top_places[0], top_places[1]\n", + "\n", + "p = by_year_place.pivot(index=\"YEAR\", columns=\"PLACE_OF_DEATH\", values=\"DEATH_LICENSES\").fillna(0)\n", + "\n", + "# Keep only the two places we want\n", + "p = p[[place_left, place_right]].sort_index()\n", + "\n", + "years = p.index.astype(int)\n", + "left_vals = -p[place_left].values # negative for left side\n", + "right_vals = p[place_right].values # positive for right side\n", + "\n", + "someaxes['ax3'].barh(years, left_vals, label=place_left)\n", + "someaxes['ax3'].barh(years, right_vals, label=place_right)\n", + "\n", + "\n", + "# Make the x-axis show positive numbers on both sides\n", + "max_val = max(p[place_left].max(), p[place_right].max())\n", + "someaxes['ax3'].set_xlim(-max_val * 1.1, max_val * 1.1)\n", + "someaxes['ax3'].xaxis.set_major_formatter(lambda x, pos: f\"{abs(int(x)):,}\")\n", + "\n", + "someaxes['ax3'].set_xlabel(\"Total deaths (per year)\", fontsize=8)\n", + "someaxes['ax3'].set_ylabel(\"Year\", fontsize=8)\n", + "someaxes['ax3'].set_yticks(p.index)\n", + "someaxes['ax3'].set_yticklabels(p.index.astype(int))\n", + "someaxes['ax3'].tick_params(axis=\"both\", labelsize=8)\n", + "someaxes['ax3'].set_title(\"Deaths per Year by Place of Death\", color='blue', fontsize=10)\n", + "someaxes['ax3'].axvline(0, linewidth=1) # center line\n", + "someaxes['ax3'].legend(loc='upper right', ncol=1, fontsize=7.5)" + ] + }, + { + "cell_type": "markdown", + "id": "3e3b5930", + "metadata": {}, + "source": [ + "Description of visualization:\n", + "\n", + "This visualization was created using Python in a Jupyter Notebook environment, primarily with pandas for data cleaning/aggregation and Matplotlib (subplot mosaic) for plotting. My intended audience is city planners, public health officials, and policy analysts who need a quick, evidence-based view of mortality patterns across Toronto and surrounding areas.\n", + "\n", + "The message I’m trying to convey is how recorded deaths change over time, and how location (Toronto vs outside city limits), and civic centres relate to those changes. The line plot compares the overall yearly trend to trends by individual civic centres, while the bar chart summarizes deaths by place of death. The mirrored horizontal bars make it easy to compare Toronto vs outside city limits year-by-year.\n", + "\n", + "Design choices focused on clarity and comparability. I used consistent axes and labeling (Year on the x-axis, deaths on the y-axis), a prominent overall line (black, thicker) to anchor interpretation, and lighter civic-centre lines so the overall trend remains readable. I also set ticks to show every 5 years to reduce clutter while keeping star markers for each year. \n", + "\n", + "To ensure reproducibility, I used scripted data transformation steps (extract year, groupby sums, pivot) rather than using hard code, so this we can use the same script to recreate the same data using the original CSV or using a new CSV (updated or unrelated having the same varaiables). Anyone with the same file and the notebook can re-run the analysis end-to-end and obtain the same outputs. If the tool were not reproducible, results would be harder to verify, update, or audit.\n", + "\n", + "For accessibility, I included clear titles, axis labels, and legible font sizes, and used gridlines to support visual estimation. To further improve accessibility, I could add a colour-blind–safe palette and/or direct labels for key lines instead of a large legend, but in this case, I chose to use very minimal colours in my design. \n", + "\n", + "Individuals and communities impacted include Toronto residents, healthcare providers, and communities experiencing disproportionate mortality outcomes. This kind of visualization can inform resource allocation, emergency planning, and local health interventions.\n", + "\n", + "The “Underwater labour” included modifying columns and extracting data by year and location to improve the comparability of these variables." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "visualization1-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/02_activities/assignments/assignment-3-visualization2.ipynb b/02_activities/assignments/assignment-3-visualization2.ipynb new file mode 100644 index 000000000..e82656966 --- /dev/null +++ b/02_activities/assignments/assignment-3-visualization2.ipynb @@ -0,0 +1,48 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cbe30d85", + "metadata": {}, + "source": [ + "Assignment 3: NON - Python visual \n", + "\n", + "Data: Death registry statistics in Toronto and the GTA.\n", + "Link: https://open.toronto.ca/dataset/death-registry-statistics/" + ] + }, + { + "cell_type": "markdown", + "id": "53d8f4a9", + "metadata": {}, + "source": [ + "This visualization was created using Tableau. The intended audience is city planners, public health officials, and policy teams who need to understand how mortality changes across time and geography in Toronto and surrounding areas.\n", + "\n", + "The message is to show seasonality and change over time in deaths, and how those patterns differ by civic centre. The top line chart shows monthly deaths across years (each year as a separate line), making it easier to spot recurring peaks or months in which the death count is particularly high. The stacked bar chart shows yearly totals broken down by civic centre to identify whether a particular civic centre accounts for a greater proportion of death counts over the years, and the summary table highlights total deaths by civic centre.\n", + "\n", + "In terms of design, I focused on comparability, hierarchy, and clarity. I used consistent scales and clear labeling for months and years. Color encodes categorical differences (year in the top chart; civic centre in the bottom chart), while the layout creates a hierarchy: (1) month-by-month pattern first, (2) yearly totals by civic centre second, (3) overall totals as a quick reference on the right. I also kept chart types simple (line + stacked bars + table) so the viewer can quickly connect patterns across views.\n", + "\n", + "For reproducibility, Tableau can be reproducible if the workflow is saved (as a .twb/.twbx) and the data source is stable. I ensured reproducibility by using consistent fields and repeatable transformations (e.g., parsing dates, grouping by month/year, aggregating death counts) within Tableau rather than manual calculations outside the tool. If the tool or workbook is not shared, reproducibility is reduced because others can’t easily verify the exact steps, filters, or calculated fields used to produce the view.\n", + "\n", + "To support accessibility, I used clear titles, readable labels, and multiple ways to read the same information (trend lines, bar totals, and a table). To improve further, I would ensure a color-blind–safe palette, exact values, and exact names of these civic centres (not provided by the datasheet or data dictonary).\n", + "\n", + "Communities impacted include Toronto residents, healthcare systems, and civic-centre catchments that may experience disproportionate outcomes. These visuals can influence resource allocation, preparedness planning, and targeted interventions.\n", + "\n", + "I included dataset features that best answer the question—time (month/year) and location (civic centre)—and excluded variables that might distract from the core comparison. “Underwater labour” included cleaning/standardizing date fields, choosing appropriate aggregations, validating totals across charts, and iterating on the dashboard layout to make the story readable and consistent." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "visualization1-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/02_activities/assignments/assignment_3.md b/02_activities/assignments/assignment_3.md index 99341bc82..16f575829 100644 --- a/02_activities/assignments/assignment_3.md +++ b/02_activities/assignments/assignment_3.md @@ -61,9 +61,9 @@ * 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. +- [ x] Create a branch called `assignment-3`. +- [ x] Ensure that the repository is public. +- [ x] 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. diff --git a/02_activities/assignments/tableauvisual.png b/02_activities/assignments/tableauvisual.png new file mode 100644 index 000000000..eceb60e33 Binary files /dev/null and b/02_activities/assignments/tableauvisual.png differ