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@@ -14,6 +14,12 @@ We're still trying to process the predicted arrival times we get from the API in
 ## Documentation
 Have a look in the [`doc/`](doc/) folder for Jupyter Notebooks explaining how we explored developing this project and understanding the data.
 
+### Data Flow and Data Structure
+The scraper runs every minute when the TTC is in service. Each of these runs is logged in `polls`, with a unique `pollid` and a start and end time. 
+During one run of the scraper, each station gets its predicted arrivals requested. This is logged in `requests`, with a unique `requestid` and noting which station it is using `stationid` and `lineid`. 
+For each request, 3 predicted arrivals are recorded for each line and direction at that station. This is stored in `ntas_data` (Next Train Arrival System). This table notes the train's `traindirection` and its unique id `trainid`, the time until the train's arrival `timint` and a `train_message`, whether the train is arriving, at station, or is delayed.
+**For more info:** have a look at the API exploration notebook under [`doc/API_exploration.ipynb`](https://github.com/CivicTechTO/ttc_subway_times/blob/master/doc/API_exploration.ipynb)
+
 ## Analysing the Data
 
 If you don't want to set up the scraper yourself, and you want to look at historical data: read on! The data is currently stored in a PostgreSQL database on Amazon Relational Database Service (RDS). Have a look at [**How to Get Involved**](#how-to-get-involved) to find out how to get access to the data. 
@@ -120,3 +126,4 @@ Otherwise have a look at [open issues](https://github.com/CivicTechTO/ttc_subway
 
 ## Sources of Inspiration
 Boldly following in [others' footsteps](https://blog.sammdot.ca/pockettrack-tracking-subway-trains-is-hard-9c8fdfb7fd3c?source=collection_home---4------0----------)
+See more on the [Resources page](https://github.com/CivicTechTO/ttc_subway_times/wiki/Resources)