01 — Work
Borkum Analytics Sustainable Tourism
Project Overview
A comprehensive data analysis project examining visitor behaviors, demographics, and sustainability attitudes on Borkum Island. Using advanced analytics and machine learning techniques, I processed survey data to extract actionable insights about tourism patterns and environmental perceptions, contributing to sustainable tourism development strategies for this North Sea destination.
Project Details
- Client
- Prof.Dr. Iris Lorscheid - University of Europe for Applied Sciences
- Timeline
- Jan 2024 — Jun 2024
- Role
- Data Analyst / Visualization Specialist
- Team
- 2 members + collaborative
Challenge
Transforming raw survey data from diverse visitors into meaningful insights about tourism behaviors, sustainability attitudes, and environmental concerns on Borkum Island. The challenge involved cleaning inconsistent survey data, developing appropriate analytical models, and creating visualizations that clearly communicated complex patterns to stakeholders without technical backgrounds.
Approach
I employed a systematic data science methodology beginning with thorough data cleaning and preprocessing of survey responses. Using R and data analysis libraries (dplyr), I applied statistical techniques to identify visitor segments and analyze their behaviors. I created insightful visualizations using ggplot2, and other visualization tools to effectively communicate findings.
Key Results
- —Identified 5 distinct visitor clusters with unique demographic and behavioral patterns
- —75% visitor satisfaction demonstrated
- —Climate and price most influential factors
- —Comprehensive report with actionable recommendations
Technologies Used
- R
- Python
- ggplot2
- dplyr
- Data Visualization
View & Demo
Key Features
Survey data cleaning and preprocessing
Visitor segmentation via clustering analysis
Sustainability attitude analysis
Statistical visualization with ggplot2
Stakeholder-friendly reporting
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