Spatial vs Temporal Datasets
Understanding the difference between where and when
Spatial Dataset
A spatial dataset contains information about locations or positions in space. The main focus is “Where?” — coordinates, regions, or pixels.
Land Cover Classification (10 points)
| # | X | Y | Class |
|---|---|---|---|
| 1 | 12 | 18 | Forest |
| 2 | 28 | 42 | Water |
| 3 | 35 | 29 | Urban |
| 4 | 48 | 55 | Water |
| 5 | 55 | 20 | Forest |
| 6 | 62 | 48 | Urban |
| 7 | 78 | 35 | Forest |
| 8 | 85 | 60 | Water |
| 9 | 92 | 12 | Urban |
| 10 | 105 | 40 | Forest |
Examples: Land Cover Detection, Medical Image Classification, Satellite Image Analysis
Basic Calculations
- Centroid (mean coordinates): X̄ = (12+28+35+48+55+62+78+85+92+105) / 10 = 60.0, Ȳ = (18+42+29+55+20+48+35+60+12+40) / 10 = 35.9
- Class distribution: Forest: 4, Water: 3, Urban: 3 → most common class is Forest
- Bounding box: X ∈ [12, 105], Y ∈ [12, 60] → width = 93, height = 48
- Approximate distance between farthest points (using Euclidean): between (12,18) and (105,40) → √((93)² + (22)²) ≈ 95.6 units
Common models: CNN, Vision Transformers, U‑Net
Temporal Dataset
A temporal dataset contains information collected over time. The main focus is “When?” — sequences, trends, or events in chronological order.
Daily Temperature Monitoring (10 days)
| Day | Temp (°C) | Weather Class |
|---|---|---|
| 1 | 28 | Normal |
| 2 | 31 | Normal |
| 3 | 34 | Hot |
| 4 | 37 | Very Hot |
| 5 | 39 | Very Hot |
| 6 | 36 | Hot |
| 7 | 32 | Normal |
| 8 | 29 | Normal |
| 9 | 26 | Cool |
| 10 | 23 | Cool |
Examples: ECG Classification, EEG Signal Analysis, Human Activity Recognition
Basic Calculations
- Mean temperature: (28+31+34+37+39+36+32+29+26+23) / 10 = 31.5 °C
- Maximum: 39 °C (Day 5), Minimum: 23 °C (Day 10)
- Range: 39 − 23 = 16 °C
- Simple moving average (last 3 days): (29+26+23) / 3 = 26.0 °C (cooling trend)
- Overall trend: increasing until day 5, then decreasing → peak at day 5
Common models: RNN, LSTM, GRU, Temporal CNN, Transformers
Key Differences
| Feature | Spatial Dataset | Temporal Dataset |
|---|---|---|
| Main Question | 📍 Where? | ⏰ When? |
| Data Structure | Location‑Based (coordinates, pixels) | Time‑Based (sequences, ordered) |
| Example (10 points) | Land cover: (X,Y,Class) | Daily temp: (Day, Temp, Class) |
| Typical Visualization | Map / 2D scatter | Timeline / line plot |
| ML Category | Spatial Classification / Segmentation | Time‑Series Classification / Forecasting |
| Common Models | CNN, Vision Transformers | RNN, LSTM, GRU, Temporal CNN |
| Example Calculation | Centroid: (60.0, 35.9) | Mean: 31.5 °C, Peak: 39 °C |
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