New Taxi Company Hired Advertising Agency To Advertise ✓ Solved

A New Taxi Company Hired An Advertising Agency To Advertise Their Serv

Familiarize yourself with both datasets. Note, that the second dataset files are very large (up to 1 GB). The ridership data is also given in separate files grouped by taxi companies (e.g., Yellow). Pick a dataset related to any company that services the Times Square area. With the dataset structures (field names or dictionaries) in mind, use Word to design a flow chart of the algorithm to describe the process of identifying the top five screens that would be seen most often by the taxi riders. Note that you don’t need to provide code, and you don’t need to calculate top screens, just provide a pseudo code for the algorithm that would perform that task. Pseudocode is a somewhat structured description of the steps of an algorithm written in plain English. You may also use variable names to refer to the same data multiple times if needed.

Sample Paper For Above instruction

The rapid growth of digital advertising combined with the expansive taxi ridership in New York City presents an innovative avenue for targeted marketing strategies. For a new taxi company aiming to optimize their advertising impact in Times Square, identifying the most frequently encountered screens by taxi passengers becomes critical. An effective approach involves analyzing large datasets encompassing taxi ridership and screen locations to develop a comprehensive algorithm for selecting the top five screens with the highest passenger exposure. This paper elucidates a pseudocode algorithm, structured in plain English, designed to accomplish this task by integrating datasets and deriving meaningful insights for targeted advertising placement.

The primary step involves familiarizing with both datasets, which include the list of advertising screens at Times Square and extensive taxi ridership data. The screen dataset contains fields such as screen ID, geographic coordinates (latitude and longitude), and screen capacity or visibility duration. The large ridership dataset, often in CSV, XML, or JSON formats, includes trip details like pick-up and drop-off locations, timestamps, taxi IDs, and possibly the taxi company. Given the considerable size of ridership files (up to 1 GB), efficient data handling techniques such as streaming or partial loading may be necessary.

The subsequent step involves analyzing the structure of both datasets to identify relevant variables. For the screens, the geographic locations are critical, while for the ridership data, pick-up locations are essential indicators of where taxi passengers are likely to see screens. The goal is to estimate the frequency with which riders are near specific screens.

The algorithm proceeds as follows:

1. Input the dataset containing the list of Times Square screens, including their geographic coordinates.
2. Input the large taxi ridership dataset, which may be divided into multiple files grouped by taxi companies. For simplicity, select a relevant file for processing.
3. For each trip record in the ridership dataset:
   • Extract the pick-up location coordinates (latitude and longitude).
   • Determine proximity to each advertisement screen by calculating the distance between the pick-up location and each screen location using the Haversine formula or a similar geographic distance metric.
   • If the pick-up location falls within a predefined radius (e.g., 100 meters) of a screen location, increment a counter associated with that screen.
4. Aggregate the counts for each screen, which represent the estimated number of taxi riders who could observe that screen based on pick-up proximity.
5. Sort the screens in descending order based on their aggregated counts to identify which screens have the highest passenger proximity.
6. Select the top five screens from this sorted list as the best options for advertising placement, ensuring maximum passenger exposure.

This pseudocode captures the essential steps to analyze large ridership datasets and screen locations for targeted advertising. It emphasizes proximity calculations, data aggregation, and sorting, enabling the taxi company to focus their advertising on screens with the highest passenger footfall. Implementing this algorithm with efficient data processing techniques will ensure scalability given the dataset sizes and geographic considerations.

References

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