Polygon Data: What is it? How can it be used?

How polygon data provides you with valuable insights

Imagine you walk out of the office to get some lunch and right when you are crossing that SUBWAY at the corner of the street, you receive a notification on your phone. It says, “Hey Pierre, ready to satisfy your hunger pangs? SUBWAY is offering 15% off. Join us and use the code MYSUB to avail your offer.” Convenient, isn’t it? You are hungry, the restaurant is close to where you are and they have an offer. It all fits like a puzzle.

This is the appeal of location-based marketing, and Polygon Data drives it. It is a dynamic parameter that leverages the user’s location for various purposes.

What is Polygon Data?

Polygons are virtual boundaries or parameters drawn around a specific Point-of-Interest (POI); it can be a building, a store, a lake, a restaurant, cities, or anything with an actual shape. When a device enters that boundary it allows triggering alerts about your business or any promotional activity. They provide real-world insights about customers or competitors to target audiences based on behaviour, interest, and traffic. 

Polygons can also be drawn for stores that are located within a venue such as airports, malls, and parking lots. These polygons of interior spaces include geocoding details such as latitude and longitude.

It is important to note that polygons need to be accurate, they cannot be “nearly there” or “close enough” otherwise they will provide wrong insights that can lead to undetermined results. 

For example in the image below, the polygon to the left is a perfect example of an accurate boundary. It shows exactly where Lidl is located. But the one to the right shows the location of Lidl a few feet away from the actual location, somewhere in the parking space. 

How is polygon data used?

  • Contextual location information

Companies often use mobility datasets to understand their potential customers’ behaviour. For example, through mobility data, you can get information about how many people are entering your store, where they are coming from if they are going to your competitors’ stores, and what other kinds of stores they are visiting. However, all this information becomes vague if they are not put into a locational context. The mobility data insight can be regarding any location, it can be miles away from your store or very close to your store. Polygons help minimise that range. 

When you draw a polygon around your store and combine it with mobility data, you get detailed insights about the customers who are within that parameter. It can help retailers gain a competitive advantage by understanding which businesses in the specified POIs are proving to be a challenge and how they can create better strategies to overcome those. They also help observe consumer patterns to plan business promotions and inventory.

  • Relation between properties

Polygons create an accurate visualization of buildings which means they also provide information about what is not included within the building’s outline. However, excluding exterior factors of the building makes it easier to understand what is the building’s relationship to its exterior. For example, in the image that we show above, the polygon only surrounds the outline of Lidl but if we only look at POI data it will include the open space outside Lidl which is the parking lot. 

The advantage of polygons drawn out of POI data is that they map out the building where Lidl is located but also provide a view of the parking lot. Polygons show the relationship that buildings share with their external surroundings. It provides excellent scope for investment research by analyzing the surroundings of a property to understand what kind of business within that property would be worth investing in.

  • Co – dependency between locations

Polygons can be fundamental to risk assessment as they can help analyze the co-dependency between two locations. It helps measure and understand what is going on in a particular location by accessing the location of two buildings. A common example is the relationship between an industrial park and a residential area. If an industrial park is close to a residential area the large amount of smoke emitted from the park can affect the area’s residents. There might also be higher instances of garbage disposal as well as hazardous risks caused by chemicals and heavy machinery. 

Insurance companies can specifically benefit from polygon analysis as it helps them estimate how vulnerable a building might be based on its size, shape, location, purpose, and occupancy. It can also prevent them from making uninformed decisions and losing out on investments.

Echo’s Polygon Datasets

Our POI datasets are scaled up to ensure quality and we utilize that information to provide our clients with accurate polygons for the locations of their interest. We train our algorithms to go through even the most minute details in an image to analyse them and associate them with our POI dataset to be able to create clear-cut polygons so that our clients do not have to spend time or money doing it themselves. 

Our polygon maps contribute to a better understanding of ground activities and can also improve transparency on the consumer habits of that area. 

Would you like to check out our datasets?

Fill out the form to request a sample.

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