Predictive modeling:
Task: Ambulance Demand
Data Generating Process:
Dates of Coverage {Please identify the specific dates that will be used for this dataset in your modeling effort}
Frequency of data collection {how often is the data collected? After every incident? Daily? Yearly?}
Agency / Organization collecting the data {who specifically is collecting the data? Please avoid using general references like “government” or “police}
Original Unit of Analysis {What is the original unit of analysis for the data as provided? Calls for service? Census tracts? Cities?}
Transformed Unit of Analysis{i.e. are you modifying the call data to support your model? Hint: if you are doing “demand” model you will be aggregating the data.}
Data Generation Description{here, I want you in your own words to describe how you think the data was generated. Think 2-3 sentences.}
Data Collector{Who collects the data? A dispatcher? A Census taker?}
Triggering Process{What triggers the data to be collected? A call for service? A yearly survey process?}
Process Alignment{What system captures the data? Is it hand entered? What existing business process does the data align with [i.e. data on ATM transactions come from someone using an ATM; data from calls for service come from dispatch records for calls coming in to 911, etc.]?}

Predictive modeling is a statistical technique used to forecast future events based on historical data. In the context of ambulance demand, predictive modeling can be invaluable for healthcare and emergency services to anticipate and prepare for periods of high demand. To build an effective model, understanding the data-generation process is crucial. This involves knowing when and how the data is collected, who collects it, and the systems in place for its recording.
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