The objective of this assignment is to assess groups of two students' ability to analyze and solve problems arising in the context of smart cities using tools provided by Data Science. Students are required to write an essay in English consisting of two parts. The first part involves describing the problem, its significance, and presenting indicators to capture related patterns. The second part includes the data analytics part of the problem, which involves dataset selection, data wrangling, spatio-temporal correlation analysis (if required by the problem), visualization, prediction/classification, and possible optimizations to suggest to policy makers.
Part 1: Problem Description and Indicators
- Select a problem related to smart cities, such as traffic management, traffic prediction, energy consumption, waste management and optimization, air pollution, etc.
- Explain the problem, its importance, and its impact on the city and its inhabitants.
- Select or devise one or more indicators to measure the problem and/or the impact of a possible solution. Indicators can include, for example, traffic volume over time, air quality index, energy consumption per capita, waste generation per household, social capital, income.
- Consider the ethical and social implications of the problem and its related indicators.
Part 2: Data Analytics, Optimization and Policy Suggestions
- Identify and select relevant datasets that can be used to analyze the problem. If necessary, implement scraping activities to create the dataset.
- Clean and wrangle the data as necessary to prepare it for analysis.
- Use spatial and temporal analysis tools to identify any correlations or patterns in the data.
- Visualize the data using appropriate tools and techniques.
- If the problem requires it, develop a prediction model to estimate future trends and patterns.
- If the problem requires it, develop a classification/clustering model to assign data points to relevant classes.
- It the problem requires it, develop an optimization model to address the problem, such as an algorithm to assign a population of kids to the most suitable kindergarden considering multiple criteria (distance, school capacity, school facilities…).
- Discuss the main results.
- Suggest possible new policy or optimizations based on the data analysis to address the problem, such as implementing smart traffic management systems, promoting renewable energy sources, or encouraging recycling.
- Consider the ethical and social implications of the policy suggestions.
In this project, we're diving into how people move around in the northern part of Sardinia, starting from the arrivals by airports and ports and focusing mainly on public transport stops to reach key facilities. Transportation is a big deal for making places thrive, and the goal is to understand this and find ways to improve it. To analyze the situation, several indicators are calculated, accompanied by data visualizations. As for airports and ports, the project aims to examine the flows both on a seasonal basis, differentiating by the airport of arrival and departure, and by checking the number of domestic and foreign tourists. The analysis then focuses in particular on the situation of public transportation for tourists, analyzing their current conditions and the possibility of reaching popular destinations such as beaches, as well as for residents and connections to more populated areas. In the end, some suggestions to policy makers are provided.
├── README.md
├── data
├── figures
├── notebooks
├── report
└── slideshow