The DW Destination Index™ integrates Passport Index data, Cost of Living data, Ease of Doing Business data, and Quality of Life data. Below is a step-by-step guide portraying the philosophical thought process behind the its creation.

​

Step 1: Identifying Data Sources

1. Passport Index Data: Data from the world's leading global passport-ranking platform, Henley & Partner's Henley Passport Index.

2. Cost of Living Data: Data was gathered from the world's leading Cost of Living & Quality of Life database, numbeo.com courtesy of former Google software engineer, Mladen Adamovic.

3. Ease of Doing Business Data: Data was obtained from the World Bank’s Ease of Doing Business rankings, which are currently the most reliable and accurate on Earth.

4. Quality of Life Data: Data was gathered once again from the world's leading Cost of Living & Quality of Life database, numbeo.com, courtesy of former Google software engineer, Mladen Adamovic.

​

​

Step 2: Defining Key Metrics

1. Passport Strength (P): Metrics such as visa-free destinations or global mobility scores have been included.

2. Cost of Living (C): Standardised costs such Average Monthly Net Salary (After Tax), apartment rent food, transportation, etc. have been used.

3. Ease of Doing Business (E): Metrics such as startup costs, tax rates, and ease of registering a business have been factored in.

4. Quality of Life (Q): Lifestyle aspects such as healthcare, education, safety, infrastructure, and environmental quality have been leveraged into the DW Destination Index™.

 

​

Step 3: Standardising Data

Normalization: Raw data from the aformentioned sources has been converted into a consistent scale (e.g., 0 to 100 or z-scores) to allow comparison across categories.

 

The various data sources have been normalised by calculating the z-score:
(z=x−μσz = \frac{x - \mu}{\sigma}z=σx−μ​),
where: xxx is the value,

            μ\muμ is the mean,

            and σ\sigmaσ is the standard deviation. 

 

Weighting: The weights have been assigned to each metric based on their relative importance as established from survey-based weighting results:

​​

ADJUSTED WEIGHTS (Justified by Research)

​​

​

​

​

​

​

​

​​​

 

 

​

Step 4: Combining Metrics into a Composite Score

1. Each normalised metric has been multiplied by its assigned weight.

2. Sum the weighted scores to calculate the final composite index score for each country: Composite Score=(P×wP)+(C×wC)+(B×wB)+(Q×wQ)\text{Composite Score} = (P \times w_P) + (C \times w_C) + (B \times w_B) + (Q \times w_Q)Composite Score=(P×wP​)+(C×wC​)+(B×wB​)+(Q×wQ​)

 

 

Step 5: Developing the DW Destination Index™ Framework

1. Categorisation: Countries have been divided into categories (i.e., high, medium, or low Destination Usefulness) based on their scores.

2. Ranking: Countries have been ranked from highest to lowest based on their composite Destinational Usefulness scores.

 

 

Step 6: Visualising and Presenting the Data

1. Visual tools have been used present the findings:

   • Maps: Heatmaps have been used to display scores geographically.

   • Graphs: Bar charts, scatter plots, and line charts have been included.

   • Tables: A ranked list of countries with scores for each category has been provided.

2. Data visualization tools like Tableau, Power BI, and Python libraries, namely Matplotlib and Seaborn, have been utilised.

 

​

Step 7: Validating the Index

1. Internal Validation: the  DW Destination Index™ is constantly fact-checked for consistency in data integration and scoring by Claude Machiha, a certified Business Intelligence and Data Analysis (BIDA®) professional.

2. External Validation: the DW Destination Index™ is quarterly checked for economic and statistical accuracy by Diversitas Wealth's outsourced Analytics and Consulting Firm, PricewaterhouseCoopers (PwC).

 

​

Step 8: Publishing and Maintainence

1. Publishing: the DW Destination Index™ has been published on the Diversitas Wealth website, and will soon have a mobile app by 31 December 2028.

2. Regular Updates: the data is updated quarterly and on an ad-hoc basis, i.e. as and when changes occur, in order to maintain relevance.

3. Feedback Mechanism: Viewers and users are free to provide feedback for further refinement.

 

 

Tools & Technologies Used

• Data Processing: Python (Pandas, NumPy), Excel.

• Visualisation: Tableau, Power BI, and Python (Matplotlib & Seaborn libraries).

• Database Management: SQL programming & Microsoft SQL Server.