I would like to make predictions about how crowded a location (postal code mapped to lat/lng coords) would be. I would like to provide predictions to questions like: what’s the crowd density going to be like for the area surrounding postal code 12345 for 50metre next Monday between 6pm to 8pm?
For a start I’m working with historical transaction data (as a proxy for human activity) which contain time stamps and postal codes. I would also like to incorporate new transaction data into my model as they occur.
Does this make sense?
- Filter the historical transactions based on requested time (between 6pm to 8pm).
- Cluster the transactions based on the desired proximity to get a daily transaction totals for the area.
- Use the daily totals as training data for my regression model.
- Use the model to predict
Would I need to rerun the entire process to account for new data which arrives in real time (e.g every 5mins)? Are there any incremental ML models which could be suitable?