📝 பயிற்சி

Geography data takes many forms — each best visualized by a specific graph type. Choosing the right graph is the first + most crucial step.

**1. Line Graph (கோட்டு வரைபு)**
- **Use:** Show trends + changes over time. Continuous data.
- **X-axis:** time (years, months); **Y-axis:** value.
- **Examples:** Population growth 1871-2012; GDP trend; temperature time-series.
- **Variations:** Multiple line (compare countries); compound line (stacked area); cumulative line.
- **Strength:** Reveals trends + acceleration + inflection points at a glance.

**2. Bar Chart (வரிக்கட்டை வரைபு)**
- **Use:** Compare values across discrete categories.
- **Construction:** Equal-width bars; spaced; height = value.
- **Examples:** Population by district; export value by commodity; rainfall by month.
- **Variations:** Simple, multiple/grouped, stacked, 100% stacked, horizontal, population pyramid.
- **Strength:** Easy comparison; ranking via descending order.

**3. Pie Chart (வட்ட வரைபு)**
- **Use:** Show proportional composition (parts of whole). Categories sum to 100%.
- **Angle formula:** (value/total) × 360°.
- **Examples:** SL ethnic composition; export commodity share; land use breakdown.
- **Limits:** Max ~7 categories (slices too thin); not for time-series; not for negative values.
- **Strength:** Visual proportions; majority/minority obvious.

**4. Climograph (காலநிலை வரைபு)**
- **Use:** Show a location's climate — monthly temperature + rainfall together.
- **X-axis:** 12 months; **Left Y-axis:** temperature (°C, as line); **Right Y-axis:** rainfall (mm, as bars).
- **Examples:** Nuwara Eliya, Anuradhapura, Colombo climographs.
- **Strength:** Dual data series; reveals climate zone at a glance (equatorial, tropical wet/dry, Mediterranean, temperate, polar).

**5. Scatter Plot (சிதறிய புள்ளி வரைபு)**
- **Use:** Show correlation between two continuous variables.
- **Construction:** Each (x, y) data point plotted; trend line optional.
- **Examples:** Income vs life expectancy across countries; rainfall vs crop yield.
- **Reveals:** Positive correlation (both rise together), negative (opposite), no correlation, outliers.
- **Strength:** Identifies relationships between variables.

**6. Histogram (வரிசை வரைபு)**
- **Use:** Show frequency distribution of continuous data binned into intervals.
- **Construction:** X-axis = data ranges (bins); Y-axis = frequency count. Bars touching (no gaps).
- **Examples:** Rainfall distribution; income distribution; age distribution.
- **Strength:** Reveals data shape — normal, skewed, bimodal.

**7. Choropleth Map (படிமப் பகுதி வரைபு)**
- **Use:** Show spatial distribution of data by regional units.
- **Construction:** Map of regions; each shaded by data value (darker = higher).
- **Examples:** Population density by district; literacy rate by country; deforestation by province.
- **Strength:** Spatial patterns + geography intuition.
- **Limits:** Hides intra-region variation; size doesn't reflect importance.

**8. Cartogram**
- **Use:** Show data by distorting geographic size proportional to a variable.
- **Examples:** World cartogram by population (Africa enlarged, Russia shrunk); GDP cartogram (USA enlarged).
- **Strength:** Reveals data magnitude vs geographic size.

**9. Pictograph (படிம வரைபு)**
- **Use:** Show data using repeated icons proportional to value.
- **Construction:** Choose icon (person, factory, tree); decide unit value (1 icon = 1M); repeat icons.
- **Examples:** Population by country with person icons.
- **Strength:** Visually appealing; intuitive for general audience.
- **Limits:** Imprecise for exact comparison.

**10. Population Pyramid (சனத்தொகை கோபுரம்)**
- **Use:** Show age-sex structure of a population.
- **Construction:** Horizontal back-to-back bar chart. Left side = males; right side = females. Each row = age cohort (0-4, 5-9, ...).
- **Examples:** SL pyramid 2012 (narrowing top showing aging); Niger pyramid (broad base showing youthful population).
- **Reveals:** Demographic stage (broad base = stage 2-3; rectangular = stage 4; inverted = stage 5).
- **Strength:** Compact demographic snapshot.

**Choosing the Right Graph — Decision Tree:**

1. **Showing change over time?** → Line graph.
2. **Comparing discrete categories?** → Bar chart.
3. **Showing proportions/composition?** → Pie chart.
4. **Showing climate?** → Climograph.
5. **Showing correlation?** → Scatter plot.
6. **Showing spatial data?** → Choropleth map.
7. **Showing distribution?** → Histogram.
8. **Showing demography?** → Population pyramid.
9. **Multiple of the above?** → Combined dashboard.

**Best Practices:**
- **Title** (clear + descriptive).
- **Axis labels with units**.
- **Scale appropriate + consistent** (don't truncate axes misleadingly).
- **Legend** for multiple series.
- **Source citation**.
- **Color thoughtful** (avoid red-green for color-blindness).
- **Simplicity** — less is more.
- **Avoid 3D effects** (distort perception).
- **Show data accurately** (no exaggeration).

**Sample Decisions (SL Geography):**
- SL population 1871-2012 → Line graph.
- SL districts by population → Bar chart (descending).
- SL ethnic composition 2012 → Pie chart.
- Climate of Anuradhapura → Climograph.
- Rainfall vs paddy yield → Scatter plot.
- Forest cover by province → Choropleth map.
- Age-sex structure → Population pyramid.
- Tea export destinations → Pie chart or treemap.

Master these graph types + their appropriate use, and you can communicate any geographic data clearly + effectively.

A **climograph (காலநிலை வரைபு)** is a powerful single-image visualization of a location's climate. It combines two key climate variables — temperature + rainfall — on a single chart with 12 months on the X-axis.

**Construction (Step-by-Step):**

1. **Setup axes:**
- **X-axis:** 12 months (Jan, Feb, Mar, ..., Dec).
- **Left Y-axis:** temperature in °C (with scale; usually 0-40°C for tropical, -30 to +30 for temperate).
- **Right Y-axis:** rainfall in mm (with scale; usually 0-600 mm).

2. **Plot temperature:** For each month, plot the **monthly mean temperature** value using the left Y-axis. Connect points with a smooth line.

3. **Plot rainfall:** For each month, draw a **bar** representing monthly total rainfall using the right Y-axis. Bars touch each other (no gaps).

4. **Title:** Location name + period covered (e.g., "Climograph for Colombo, 1991-2020").

5. **Labels:** Both Y-axes with units. X-axis with months.

6. **Legend** (if needed): Temperature line; Rainfall bars.

7. **Source:** Department of Meteorology Sri Lanka or other.

**Reading a Climograph:**

- **Temperature line height** → mean monthly temperature.
- **Temperature line steepness** → seasonal variation.
- **Bar heights** → monthly rainfall.
- **Rain bar pattern** → wet/dry seasons.
- **Annual range** = highest - lowest temperature.
- **Annual total** = sum of all monthly rainfalls.

**Major Climate Zone Patterns:**

**(1) Equatorial Rainforest (Singapore, Colombo wet zone):**
- Temperature: ~25-28°C, very stable (range <3°C).
- Rainfall: heavy (200+ mm) most months, no clear dry season.
- Annual total: 2000+ mm.
- Pattern: "flat-top" temperature, sustained high rainfall.

**(2) Tropical Wet-Dry / Savanna (Hambantota, Jaffna SL dry zone):**
- Temperature: hot 25-32°C all year.
- Rainfall: distinct wet season (1 monsoon) + dry season.
- Annual total: 800-1500 mm.
- Pattern: high temperature, uneven rainfall.

**(3) Tropical Monsoon (most SL, India):**
- Temperature: hot 22-32°C.
- Rainfall: two distinct rainy seasons (SW + NE monsoons).
- Pattern: bimodal rainfall.

**(4) Mediterranean (Athens, Cape Town):**
- Temperature: mild winter (10°C), hot dry summer (28°C).
- Rainfall: winter rain, summer dry.
- Pattern: inverse temp + rain (hot when dry, cool when wet).

**(5) Hot Desert (Sahara, Arabia):**
- Temperature: extreme hot (35-45°C summer, mild winter).
- Rainfall: very low (<250 mm/year).
- Pattern: high T, near-zero rainfall.

**(6) Temperate Maritime (London, Paris, Auckland):**
- Temperature: moderate (3-20°C); cool winter, mild summer.
- Rainfall: moderate (~500-1000 mm) year-round.
- Pattern: temperature gentle curve, rainfall consistent.

**(7) Continental (Moscow, Chicago, central USA):**
- Temperature: wide range — cold winter (-10°C), hot summer (25°C).
- Rainfall: moderate, often peaks in summer.
- Pattern: steep temperature curve.

**(8) Mountain / Highland (Nuwara Eliya, Darjeeling, Tibet):**
- Temperature: cool throughout (~13-16°C SL highlands).
- Rainfall: variable but often high; SW monsoon peaks in SL hills.
- Pattern: relatively flat low temperature + variable rainfall.

**(9) Tundra / Polar (Murmansk, Antarctic stations):**
- Temperature: cold (-30 to +5°C); long winter, short summer.
- Rainfall: low (<300 mm).
- Pattern: deep V or rectangular temperature, low rainfall.

**Sri Lanka Climograph Examples:**

**Colombo (wet zone, lowland):**
- T: stable ~27°C year-round.
- Rain: dual peaks May-Sep (SW monsoon) and Oct-Dec (NE monsoon); somewhat dry Feb.
- Annual: ~2400 mm.

**Nuwara Eliya (wet zone, highland):**
- T: cool 13-16°C; coldest Jan-Feb (~13°C); warmest Apr-May (~16°C).
- Rain: very heavy May-Sep (SW monsoon, 300+ mm/month); moderate Oct-Dec.
- Annual: ~1900 mm.

**Anuradhapura (dry zone, lowland):**
- T: hot 24-31°C; warmest Apr-May (~31°C); coolest Dec-Jan (~25°C).
- Rain: main rain Oct-Dec (NE monsoon, 200-300 mm/month); dry Feb-Sep.
- Annual: ~1300 mm.

**Hambantota (dry zone, southeast):**
- T: hot 26-30°C.
- Rain: distinct dry Mar-Sep, wet Oct-Dec.
- Annual: ~1000 mm.

**Common Errors in Climograph:**
1. **Plotting temperature as bars + rainfall as line** — reverse convention.
2. **Single Y-axis** — values overlap and confuse.
3. **No units on Y-axes** — incomprehensible.
4. **Missing months** or rearranged order (must be Jan-Dec).
5. **Not labeling which Y-axis is which** — ambiguous.
6. **Inconsistent scale across climographs** — hard to compare.
7. **Confusing temperature scale extending into negative when irrelevant** — Sri Lanka never sees -°C.

**Reading Skills (Common Exam Questions):**
1. What is the warmest/coldest month?
2. What is the wettest/driest month?
3. Calculate annual temperature range.
4. Calculate annual rainfall total.
5. Identify climate type/zone.
6. Identify which season is the wet/dry season.
7. Suggest agriculture suitability based on climate.
8. Compare with another climograph.

Mastering climograph reading is essential for understanding world climate patterns + Sri Lanka's diverse climate zones.

**Pie Chart (வட்ட வரைபு)** is a circular graph divided into slices, each representing a proportion of the whole. The whole circle (360°) represents 100% of the data; each slice represents a category's share.

**When to Use:**
- Show **proportional composition** of a whole.
- Sum of all categories = 100% (or close).
- Limited number of categories (typically **4-7 maximum**).
- For single time point/snapshot.
- For nominal/categorical data.

**When NOT to Use:**
- Many categories (>7) — slices too thin to read.
- Categories with nearly equal values — hard to distinguish.
- Showing change over time — use line graph.
- Showing negative values — pie can't represent.
- Showing absolute values for comparison — bar chart better.
- Showing parts of multiple wholes — stacked bar or multiple pies.

**Construction Steps:**

**Step 1: Calculate slice angles.**
For each category:
- **Angle = (Category value / Total) × 360°**
- e.g., if Sinhalese = 74.9% of population: (74.9/100) × 360 = **269.6°** (≈ 270°).

Alternative formula in percent:
- **Angle = % × 3.6** (since 1% = 3.6°).
- e.g., 74.9% × 3.6 = 269.6°.

Sum check: All angles should sum to **360°** (or very close, allowing for rounding).

**Step 2: Draw the circle.**
Use a compass to draw a circle on graph paper. Choose appropriate size (5-10 cm radius typical).

**Step 3: Mark starting point.**
Start from **12 o'clock position** (top of circle). This is convention.

**Step 4: Use protractor.**
For each category in order (largest first):
- Place protractor center at circle center, 0° aligned with 12 o'clock.
- Mark the angle from 12 o'clock position **clockwise**.
- Draw a line from center to that point.
- Repeat for next category from previous endpoint.

**Step 5: Color/shade slices.**
Each slice a different color or pattern. Use color blindness-friendly palette (avoid red-green confusion). Or use shading patterns for black-and-white printing.

**Step 6: Label slices.**
- Category name + percentage on each slice.
- For small slices, use external labels with arrows.
- Font readable.

**Step 7: Title + Source.**
Title at top: "Composition of [variable] [location, year]."
Source at bottom: "Source: [agency]. [date]."

**Example: SL Ethnic Composition 2012**

| Category | % | Angle |
|---|---|---|
| Sinhalese | 74.9 | 269.6° |
| SL Tamil | 11.2 | 40.3° |
| SL Moor | 9.2 | 33.1° |
| Indian Tamil | 4.2 | 15.1° |
| Other | 0.5 | 1.8° |
| **Total** | **100.0** | **360°** |

Sinhalese gets a huge ~3/4 slice; SL Tamils get a small slice; etc. The proportions are immediately visible.

**Variations:**

**1. Simple Pie Chart** — standard.

**2. Exploded Pie** — one slice pulled out for emphasis. Highlights a specific category. Use sparingly.

**3. Donut Chart** — pie with hole in middle. Stylistic; same data. Some argue easier to read angles around hole.

**4. Multi-level / Nested Pie** — concentric circles showing hierarchical data. e.g., inner ring = continents, outer ring = countries.

**5. Percentage Stacked Bar** — alternative to multiple pies; show composition for multiple time points/categories.

**Common Errors:**

1. **3D effect** — distorts proportions. Front slices appear larger than back. **Avoid 3D pie charts.**

2. **Too many categories** — slices too thin to label or distinguish. If you have >7 categories, group small ones into "Others."

3. **Slices not summing to 100%** — recalculate. Total must be 100%.

4. **Random slice order** — should be **largest first, clockwise from 12 o'clock**.

5. **Missing labels** — every slice needs label + percentage.

6. **Inconsistent color across charts** — when comparing pies, keep colors consistent for same category.

7. **Using pie for time-series** — wrong choice. Use line graph.

8. **Using pie for absolute values** — pie shows proportions only. Use bar chart for absolute comparison.

9. **Comparing different-sized pies** — sizes should be consistent unless total varies meaningfully.

10. **Calculating angle wrong** — formula is (value/total) × 360°.

**Alternatives:**

- **Bar chart**: better when you want exact comparison or many categories.
- **Stacked bar (100%)**: shows composition + comparison.
- **Treemap**: shows hierarchical composition with area = value.
- **Waffle chart**: 100 small squares colored by category.
- **Donut chart**: aesthetic alternative.

**Best Practices Summary:**
1. Use pie for proportional composition only (limited use cases).
2. Max 7 categories; group rest as "Other."
3. Order slices by size, largest first, clockwise.
4. Label clearly with name + percentage.
5. Avoid 3D effects.
6. Use colorblind-friendly palette.
7. Title + source mandatory.
8. Consider alternatives if not ideal fit.

**SL Geography Applications:**
- Ethnic composition.
- Religious composition.
- Land use breakdown (e.g., forest, paddy, plantation, urban).
- Sector contribution to GDP (agri, industry, services).
- Export commodity share.
- Tourism source countries.
- Age composition (could also be population pyramid).
- Soil type breakdown.
- Energy mix.

A well-constructed pie chart communicates a powerful message: "This is how the pie is divided." Use it wisely + correctly.