Data Visualization (AD-703 (A)) Unit 3 Notes

How unit 3 is examined

This unit covers the chart types and the tools used to draw them; the marks sit in geospatial maps, network diagrams and a Matplotlib line-chart program, while charts, interactivity, Tableau and R are unasked.

Basic and advanced charts and graphs

<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>

Definition. <mark>A chart encodes data values as visual marks such as bars, lines, points or colour cells, so that comparisons, trends and patterns can be seen at a glance.</mark>

Key points.

A bar chart compares a numeric value across categories using bar length, and its baseline must start at zero.
A line chart joins points in time order, so it shows trends over time such as runs per over.
A scatter plot places each record as a point on two numeric axes, so it shows correlation, clusters and outliers.
A histogram splits a numeric range into bins and shows the frequency in each, so it shows the distribution of one variable.
A heat map colours the cells of a matrix by value, so it shows intensity, for example a correlation matrix.

Pitfall: A bar chart is for categories and a histogram for continuous bins, so do not confuse them.

Geospatial visualization: maps, choropleth maps, geospatial heat maps

Definition. <mark>Geospatial visualization displays data that has a geographic location on a map, so that spatial patterns, such as where values are high or clustered, become visible.</mark>

Key points.

Need: tables of place names hide location, whereas a map shows distance, neighbourhood and regional patterns instantly.
A choropleth map shades predefined regions (states, districts) by a value, for example population density by state.
A geospatial heat map colours point density over a map, so hotspots such as accident spots appear without region boundaries.
A symbol or point map places a marker at each coordinate (latitude, longitude).
Applications with examples: (i) weather forecasting, rainfall maps; (ii) disease spread, health cases by district; (iii) transport and logistics, delivery routes; (iv) crime analysis, crime hotspots; (v) urban planning, land use and population density.

Answer frame. Open with the definition; state the need (location and spatial patterns); name the three map types; list the five applications each with one example; close with a line on better decisions.

Asked: [7 marks] (Dec 2024) Explain the need for Geospatial visualization. List five application where Geospatial visualization is required.

Network visualization: node-link diagrams, force-directed graphs

Definition. <mark>A node-link diagram draws entities as nodes and their relationships as links (edges); a force-directed graph is a node-link layout that positions nodes by simulating physical forces.</mark>

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Key points.

Nodes represent entities (people, computers) and links represent relations (friendship, connection), and links may be directed or weighted.
Force-directed layout treats links as springs that pull connected nodes together and nodes as charged particles that repel each other.
Working: start with random positions, compute attraction and repulsion forces, move nodes, and repeat until the layout settles at minimum energy.
Example: in a social network, friend groups appear as tight clusters and influential people sit in the centre.
Advantages: it needs no manual placement, shows clusters and structure, and looks balanced with few crossings.
Limitations: it becomes cluttered ("hairball") for large networks, is slow, and the layout differs on each run.

Answer frame. Open with the two definitions; draw the node-link diagram; explain the spring and repulsion working with the social-network example; close with advantages and limitations.

Asked: [7 marks] (Dec 2024) Explain node-link diagram and force-directed graphs for network visualization.

Interactive visualization: interactivity and user engagement techniques

Definition. <mark>Interactive visualization lets the user manipulate the display, by filtering, zooming or hovering, to explore the data instead of only viewing a fixed picture.</mark>

Key points.

Tooltips and hover show exact values on demand without cluttering the chart.
Zoom, pan and filtering let the user drill from an overview to detail.
Linked views and brushing highlight the same records across several charts.
These techniques raise user engagement and support exploration; Plotly and D3.js are common tools.

Introduction to programming libraries for data visualization

Definition. <mark>Matplotlib is the basic Python plotting library, Seaborn is built on it for statistical plots, and Plotly makes interactive web charts.</mark>

Key points.

Matplotlib gives full control over line, bar, scatter and histogram plots through matplotlib.pyplot.
Seaborn gives attractive default themes and statistical plots such as heat maps, box plots and pair plots, working directly on pandas data.
Plotly produces interactive charts with hover, zoom and export, viewable in a browser.
Program steps: import pyplot, define the data lists, call plt.plot, label the axes, add a title and grid, then plt.show().

import matplotlib.pyplot as plt

overs = [5, 10, 15, 20]
runs = [50, 90, 130, 180]
plt.plot(overs, runs, marker='o')
plt.xlabel('Overs')
plt.ylabel('Runs')
plt.title('Run rate of a cricket match')
plt.grid(True)
plt.show()   # line rising from 50 at over 5 to 180 at over 20

Answer frame. Import first, then the lists, plt.plot, labels, title, grid and plt.show(); add a note that the average run rate is 9 runs per over at over 20.

Asked: [7 marks] (Dec 2024) Write code to plot a line chart to depict the run rate of a cricket match from given data using Matplotlib.

Introduction to data visualization tools: Tableau

Definition. <mark>Tableau is a business intelligence tool that builds interactive charts and dashboards by drag and drop, with no coding needed.</mark>

Key points.

It connects to Excel, CSV, SQL databases and cloud sources.
Fields are dragged to rows, columns and the Marks card to build a view.
Several sheets combine into a dashboard with filters and actions.
Dashboards can be published for sharing on Tableau Server or Public.

Visualization using R

Definition. <mark>R is a statistical programming language whose ggplot2 package draws charts by layering data, aesthetics and geometric objects (a grammar of graphics).</mark>

Key points.

Base R functions such as plot(), hist() and barplot() give quick charts.
ggplot2 builds a chart as ggplot(data, aes(x, y)) + geom_point(), adding layers with +.
Themes, facets and labels refine the chart.
Shiny turns R charts into interactive web apps.

Last-minute revision

A chart maps data values to visual marks: bar for categories, line for trends, scatter for correlation, histogram for distribution, heat map for intensity.
A choropleth map shades regions by value; a geospatial heat map shows point density.
Five geospatial applications: weather, disease spread, transport, crime, urban planning.
A node-link diagram shows nodes (entities) and links (relations).
Force-directed layout: links act as springs, nodes repel, and iteration settles at minimum energy.
Force-directed limitation: hairball clutter on large networks.
Matplotlib is the base library, Seaborn is statistical, Plotly is interactive.
Matplotlib line chart: import, lists, plt.plot, xlabel, ylabel, title, grid, show.
Cricket data: overs 5, 10, 15, 20 with runs 50, 90, 130, 180.
Tableau is drag-and-drop BI; R uses ggplot2 layers and Shiny for interactivity.

Memory hooks

Choropleth = "choose regions, paint by value".
Force-directed = springs pull, charges push.
Matplotlib order: I-D-P-L-T-G-S (import, data, plot, label, title, grid, show).
Bar = categories, histogram = bins.

Coverage checklist

Basic and advanced charts and graphs: bar charts, line charts, scatter plots, histograms, and heat maps: no past questions.
Geospatial visualization: maps, choropleth maps, geospatial heat maps: Dec 2024 7 marks, need and five applications.
Network visualization: node-link diagrams, force-directed graphs: Dec 2024 7 marks.
Interactive visualization: interactivity and user engagement techniques: no past questions.
Introduction to programming libraries for data visualization: Matplotlib, Seaborn, Plotly: Dec 2024 7 marks, Matplotlib line chart.
Introduction to data visualization tools- Tableau: no past questions.
Visualization using R: no past questions.

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Data Visualization (AD-703 (A)) - Unit 3 Short Notes

How unit 3 is examined

Basic and advanced charts and graphs

Geospatial visualization: maps, choropleth maps, geospatial heat maps

Network visualization: node-link diagrams, force-directed graphs

Interactive visualization: interactivity and user engagement techniques

Introduction to programming libraries for data visualization

Introduction to data visualization tools: Tableau

Visualization using R

Last-minute revision

Memory hooks

Coverage checklist