Overview: This project revolves around time series forecasting for mobility data in King County, US. Utilizing Python and employing the ARIMA (AutoRegressive Integrated Moving Average) model, the goal is to predict mobility trends. The insights derived from this analysis can provide valuable information for public health, urban planning, and policy-making.
Tool: Python, a versatile and powerful programming language, is employed for its rich libraries and tools, making it ideal for data analysis and modeling tasks.
Methods: The ARIMA (AutoRegressive Integrated Moving Average) model is utilized for time series forecasting. ARIMA combines autoregressive (AR) and moving average (MA) components, making it effective for capturing trends and seasonality in time series data. The integrated (I) component helps stabilize the series through differencing.
Data Source: The primary data source is Google's COVID-19 Mobility Reports, offering valuable insights into mobility trends. The dataset can be accessed at Google COVID-19 Mobility.
Data Description: Comprehensive information about the mobility data, including its nuances and variables, is available in the official data description provided by Google. This documentation ensures a clear understanding of the dataset's structure and context. Access the description here.
Key Steps:
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Data Retrieval:
- Extract mobility data from the provided Google COVID-19 Mobility Reports, ensuring accurate and up-to-date information.
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Data Preprocessing:
- Cleanse and preprocess the data, addressing missing values and outliers to ensure the accuracy of the analysis.
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ARIMA Modeling:
- Implement the ARIMA model to capture underlying trends and patterns in the mobility data, providing a foundation for forecasting.
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Forecasting:
- Utilize the trained ARIMA model to make predictions about future mobility trends in King County, US.
Project Impact: This analysis holds the potential to assist public health officials, urban planners, and policymakers. By predicting mobility trends, informed decisions can be made regarding social distancing measures, traffic management, and resource allocation, contributing to a more efficient and responsive public infrastructure.
Note: This project's accuracy and relevance heavily depend on the quality and recency of the data obtained from the Google COVID-19 Mobility Reports. Continuous monitoring and validation of the model's predictions against real-world data are crucial for ensuring its effectiveness in guiding decision-making processes.