Seah Boon Keong - Chat with OpenDOSM Datasets

This capability allows users to discover and retrieve datasets from the OpenDOSM collection using a conversational interface. It employs a natural language processing (NLP) engine to interpret user queries and map them to specific datasets, leveraging a curated index of 163 datasets. The integration with the MCP framework enables seamless access to datasets without requiring users to have technical expertise.

This capability provides users with tools to perform correlation analysis between different datasets. It uses statistical methods to identify relationships and dependencies among variables, allowing users to input specific datasets and receive correlation metrics. The implementation leverages built-in statistical libraries to compute correlations efficiently and present results in an understandable format.

This capability enables users to perform ARIMA (AutoRegressive Integrated Moving Average) forecasting on selected datasets. It utilizes time series analysis techniques to predict future values based on historical data trends. The tool is designed to automatically handle data preprocessing and model selection, providing users with a straightforward interface to generate forecasts with minimal input.

This capability allows users to retrieve the most recent updates from the OpenDOSM datasets. It employs a versioning system to track changes in the datasets and provides users with the latest available data upon request. The implementation ensures that users always have access to the most current information without manual checks.

This capability enables users to input natural language queries which are then parsed and transformed into structured queries that can be executed against the dataset. It utilizes NLP techniques to understand user intent and context, ensuring that the queries are accurately interpreted and mapped to the appropriate datasets or functions.