Solving Sustainability Problems with Aralia

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Aralia is an open data analytics and sharing platform that uses the "Data Planet" concept to securely manage, explore, and analyze data from different domains in a cross-planet comparative manner.

This tutorial will not only introduce you to Aralia's interface, but also show you how Aralia can help you answer real sustainability questions through a Malaysian case study on "inclusive growth".

🎯 Question Scenario: What is Inclusive Growth?

The core commitment of the UN SDGs is to 'Leave No One Behind (LNOB)'.

But economic growth does not necessarily equate to social justice. We want to answer these questions:

• Which regions have experienced GDP growth?
• Has growth been accompanied by a decline in poverty?
• Has economic growth improved income inequality?
• Ultimately, has growth translated into better daily living conditions?

👉 We will use the Aralia SDGs Planet to explore the data for Malaysia from 2015-2019.

🧭 Step 1 - Exploring GDP growth

Let's start with economic performance.

1. Select a dataset
   • Go to Planet SDG
   • Select Malaysia Annual Real GDP data set
   • Focus on 2015-2019 (most recent and complete annual data)
2. Check the Data Profile
   • Preview the dataset and get a quick overview of the variables: state, year, GDP at purchasers' prices and other fields.
   • Verify where data came from and when it was updated
3. Data Exploration
   • X-axis → state
   • Y-axis → GDP growth
   • Filter → 2015-2019 and purchasers' prices

📊 Results:

• All states show positive growth in 2015-2019
• Kuala Lumpur is the fastest growing state.
• Sabah is lagging behind.

👉 Insight: development is uneven. gdp growth ≠ inclusive progress.

The next question to ask is: has this growth reduced poverty?

🧭 Step 2 - Explore poverty trends (Transplore)

Next, we look at the relationship between GDP growth and poverty reduction.

1. Transplore
   • In Transplore, link the GDP dataset to the Malaysia Poverty Rate dataset
   • Select relative poverty rate as the variable to analyze
2. Setting up the analysis
   • Take the values for 2016 and 2019
   • Calculate the change in poverty rate by state in Analytic Panel

📊 Results:

• Kuala Lumpur, Labuan, Kedah, Perlis → GDP growth & poverty decline
• Selangor, Johor → GDP growth, but poverty increases

👉 Insight: the benefits of economic growth are unevenly distributed, with some regions being left behind despite rising GDP.

🧭 Step 3 - Exploring Income Inequality

Next, we ask: Has economic growth reduced income inequality?

1. Choose a data set
   • Use the Gini Coefficient data set
   • Indicator range: 0 ~ 1
     • 0 = perfect equality
     • 1 = extreme inequality
2. Setting up the analysis
   • Capture Gini Coefficients for 2016 and 2019 in Transplore
   • Calculate changes in Analytic Panel

📊 Results:

• Kuala Lumpur and Kedah → GDP growth with more balanced incomes
• Selangor and Melaka → GDP growth, but with increased inequality

👉 Insight: The core of inclusive growth is not 'how much' but 'who really benefits'.

🧭 Step 4 - Connecting to wider living conditions

Inclusive growth is not just about GDP or income, but about whether people's lives actually improve.

With Aralia Transplore, we can link together data from different areas, such as:

• Health
• Education
• Public safety
• Agriculture

and ask questions like:

• Do areas with high GDP growth also have better access to water and electricity?
• Are school graduation rates rising in areas with higher labor force participation rates?
• Do states with higher health care investments have improved public safety?

👉 In Aralia, these data can be automatically concatenated through common fields such as state, year, and so on, eliminating the need for manual cleanup or mapping.

🧭 Step 5 - Save and Share (Data Landmark)

When you are done analyzing, you can save the results to Data Landmark:

1. Click Save Landmark
2. Name the Landmark: "Malaysia Inclusive Growth 2015-2019
3. Add a comment:
4. Set permission → Private / Group / Public

👉 This allows you to review, share with your team, or show results publicly at any time.

💡 Why use Aralia?

• Secure: Data can be analyzed across domains without downloading or copying.
• Efficient: Automatic linking of GDP, poverty, education and health data.
• Trustworthy: All figures come from official datasets and are traceable, no Wikipedia guesswork.
• Decision-oriented: Helps answer the question "who really benefits and who is left behind".

✨ Analytical Insight

This case demonstrates the power of Aralia:

• Starting with a single indicator (GDP) and expanding to multiple dimensions (poverty, inequality, living conditions).
• Helps policy makers, researchers, and businesses to quickly grasp "appearance vs. truth".
• Cross-planet data streaming and analytics without an engineering background