European Temperature Analysis with PCA

Project Summary

This project applies Principal Component Analysis (PCA) to monthly temperature data from European cities to identify climate patterns based on geographical location. The analysis reveals how European cities cluster according to temperature characteristics and geographical factors, reducing 16 dimensions to just 2 principal components that capture 93.9% of the variance.

93.9%

Variance Explained

16 → 2

Dimensionality Reduction

Climate Groups

78.0%

PC1 - North-South Gradient

Dataset Description

Temperature Dataset Characteristics

The dataset contains monthly temperature data from multiple European cities, including 12 monthly temperature measurements plus additional geographical variables.

Features

Monthly Temperatures: January through December
Average Temperature: Annual mean temperature
Thermal Amplitude: Annual temperature range
Geographical Coordinates: Latitude and Longitude

Data Characteristics

Multiple European Cities: Various geographical locations
16 Total Variables: 12 months + average + amplitude + coordinates
Complete Records: No missing values
Standardized Data: Preprocessed for PCA analysis

Key Insight: Strong correlations between monthly temperatures, with latitude emerging as the dominant factor influencing temperature patterns across Europe.

Methodology and Analysis

Exploratory Analysis

Correlation Matrix Analysis
Heatmap Visualization
Scatter Matrix Plots
Data Standardization

PCA Implementation

Principal Component Analysis
Component Selection Criteria
Kaiser Rule Application
Elbow Method Analysis
Variance Thresholds

Visualization

Observation Projection
Correlation Circles
Component Interpretation
Quality Measures (COS²)

Results and Findings

PCA Results

Component	Eigenvalue	Variance Explained	Cumulative Variance	Interpretation
PC1	12.49	78.0%	78.0%	North-South Temperature Gradient
PC2	2.54	15.9%	93.9%	Continental vs Maritime Climate
PC3	0.64	4.0%	97.9%	Secondary Climate Patterns

Climate Classification

European cities naturally cluster into four distinct climate groups based on the two principal components:

Cold + Maritime: Northwestern cities (Dublin, London, Amsterdam)
Cold + Continental: Northeastern cities (Helsinki, Moscow, Stockholm)
Warm + Maritime: Southwestern cities (Lisbon, Barcelona, Bordeaux)
Warm + Continental: Southeastern cities (Athens, Rome, Madrid)

Key Determinants

The analysis revealed that latitude is the dominant factor (78% of variance), while continentality explains an additional 16% of temperature variability.

Longitude showed minimal influence on annual temperature patterns, confirming that north-south position is far more significant than east-west position in Europe.

Key Findings

Dual Climate Classification

European cities can be classified simultaneously using two main axes: north-south (average temperature) and continental-maritime (thermal amplitude), creating a comprehensive climate classification system.

Geographical Patterns

Clear geographical distributions emerged: Northwest (cold+maritime), Northeast (cold+continental), Southwest (warm+maritime), and Southeast (warm+continental), with few exceptions.

Climate Theory Confirmation

The analysis confirmed established climate theories: oceanic influence softens temperatures, continentality extremizes temperatures, and latitude is the dominant factor in average temperatures.

Technologies Used

Languages & Libraries

Python 3.8+
scikit-learn
pandas & numpy
matplotlib & seaborn

Algorithms

Principal Component Analysis
Dimensionality Reduction
Correlation Analysis
Standardization Techniques

Techniques

Component Selection Criteria
Variance Analysis
Exploratory Data Analysis
Multivariate Visualization

Conclusions

This project successfully demonstrated the power of Principal Component Analysis for reducing complex multidimensional climate data into interpretable components. The two-dimensional representation captured 93.9% of the variance while providing clear insights into European climate patterns.

Main Contributions

Successful dimensionality reduction from 16 to 2 components
Clear interpretation of climate patterns across Europe
Confirmation of established geographical climate theories
Development of a dual-axis climate classification system

Key Learnings

PCA is highly effective for geographical and climate data analysis
Latitude dominates European temperature variations
Continentality provides important secondary differentiation
Visualization techniques are crucial for interpreting PCA results