The focus of the project has shifted towards the seasonality of tourism (changes between the months of the year). Specfically, we will focus on answering these questions:
- What seasonal changes exist in tourism
- What are the general trends throughout the years. Have they changed?
- How predictable are the seasonal trends. What techniques can be used to predict them?
- What factors contribute to a countries popularity in tourism
That last question requires a large number of data sources. We mostly used EuroStat datasets, but also the World Banks' climate API to obtain temperature and precipitation data
Kiarash:
- Viz relating to seasonality and trends and sesonal decomposition
- SARIMA Model cross validation and forecasting
- Map visualisation
- sociodemographic data exploration
- determin how to put the visualization in the poster.
Zhiqing:
- Exploration of map plots
- Report abstract and introduction
- General data exploration
Chengzhong:
- Factors contributing to tourism
- Exploration of regression techinques
- Feature selection and feature weight analysis (permuatation importance and recursive)
- Analysis of features
- Regression model evaluation
- Finding datasets that may potentially contribute to tourism
- Collection, joining, parsing, filtering, and cleaning of multiple data sources
- Early ARIMA exploration, and autocorrelation plots
- Early seasonal trends exploration and plotting
- Will focus on displaying their section during presentation
Michael:
- Exploration in correlation between tourism activity and various aspects of the source/destination (PPP, GDP, Average Income, etc.)
- Exploration and quality control on many data sources.
- Early supervised clustering exploration in global tourism expenditure, receipt, inbound, and outbound.
- Final PDF creation and formatting.
- Gathering country features.