ESE1008 Data Visualisation and Analytics Assessment Brief 2026, Singapore

ESE1008 Assessment Brief

Project Title: Global Environmental Trends
Assessment Type : Report

Background

The Global Environmental Trends dataset provides insights into global climate dynamics, including temperature trends, carbon emissions, sea-level changes, rainfall variability, renewable energy usage, extreme weather events, and forest coverage. This dataset serves as an essential tool for researchers, policymakers, environmental scientists, and educators studying environmental changes and sustainability.

Analysing this dataset can enhance understanding of global warming impacts, help model future environmental scenarios and inform strategies for sustainability and climate change mitigation.

Examples of investigative questions include:

• How does the increase in global average temperatures correlate with rising sea levels?
• Is there a clear relationship between CO2 emissions per capita and renewable energy adoption across regions?

Sources: https://www.kaggle.com/datasets/adilshamim8/temperature

Refer to Table 1 for the explanation of the features/variables.

Note: The data provided has been modified and re-generated for educational purposes to simulate meaningful trends with controlled randomness.

Objectives

The objectives of this project are as follows:

1. Data Cleaning: To perform data cleaning to prepare the dataset for further analysis.

2. Exploratory Data Analysis (EDA): To conduct exploratory data analysis to gain statistical insights into the dataset. Key activities include gathering statistical summaries, plotting box plots and histograms for numerical variables, and creating visual charts for categorical data types. A correlation matrix for all numerical variables should also be included.

3. Formulating Investigative Questions or Hypotheses: To propose preliminary investigative questions or hypotheses based on the dataset. Use data visualization techniques to explore and answer these questions or hypotheses. Go beyond the initial findings to explore specific scenarios in more depth, uncovering additional insights.

4. Data Transformation: To perform data transformations based on insights gained from the EDA. This may include outlier removal and aggregation to improve data quality.

5. Model Selection and Evaluation

a. Employ Linear Regression to predict Average Temperatures

b. Employ Logistic Regression to classify Carbon Emissions. Create a new target variable Carbon_Emission_Level (i.e. for CO2_Emissions_tons_per_capita higher than mean, define it as “high_co2”, else, define it as “low_co2”). Model the probability of the likelihood of a country is likely to be of high carbon emission level based on predictors like average temperature, sea level, renewable energy, rainfall etc. (Hint: You may need to normalize the variables). Evaluate model accuracy and interpret results comprehensively.

Additional Notes

1. Complete Objectives 1, 2, and 3, compiling findings into Report 1, which should be no more than 20 pages.

2. For Objective 2, analyze all variables in the dataset and provide evidence of your work in both Knime and Tableau. In the report, show the statistic table, include any two box plots, two histograms, and two pie charts which are worth mentioning plus the linear co-relation matrix.

3. It is not necessary to answer the preliminary investigative questions in Report 1, answer them in Report 2. You may use AI tools to help generate relevant questions if needed. Propose at least two two-variable and three three-variable (further Insights) questions for Objective 3, ensuring they are unique from those in the introduction. A total of five investigative questions:

• Two questions that examine the relationship between two variables (e.g., “Does a country’s population size correlate with its CO2_Emissions_tons_per_capita over the years?”).
• Three questions that involve three variables (e.g., “How do Sea_Level_Rise_mm, Rainfall_mm, and Extreme_Weather_Events collectively influence Avg_Temperature_degC in different countries?”).

4. For Report 2, perform the necessary data transformations following your EDA and use the data to address the investigative questions. Copy both the investigative questions from the Background section and proposed questions from Report 1 and provide answers for each one. Additionally, include Linear and Logistic Regression model analysis and conclude with a reflection.

5. Reflection: In your reflection, evaluate the dataset’s usefulness, model accuracy, and any feature enhancements (such as additional features) that could improve the model’s predictive accuracy. Keep Report 2 to a maximum of 20 pages.

Data Dictionary (variable descriptions)

Table 1