
Echo Analytics partners with CARTO to make geospatial data available to everyone
Echo Analytics partners with CARTO to make geospatial data available to everyone Echo Analytics and CARTO announce their partnership to ensure their clients can get
June 28, 2021
Point of Interest data plays a vital role in enabling businesses like yours to make data-driven decisions; be it when analysing competitors, selecting a new retail site, or while observing new market trends. In other words, POI data can be an invaluable resource to solve the use case of your company, no matter the industry, with the information provided as such:
While POI data gives you basic information about a particular location, POI attributes can provide a higher level of analysis. These could include opening and closing hours of stores, stock tickers, precise building footprint, popularity index, etc.
Let’s say you own a burger chain that leverages POI data to conduct neighbourhood profiling. Such analysis will give your business a better understanding of relevant popular hotspots and new restaurants opening throughout your Area of Interest (AOI) thus identifying where to open new locations.
While looking for Points-of-Interest data, it is important to consider the attributes included in the vendor’s data schema. Below are the most common attributes tied to Points of Interest:
Attribute | Description |
ID | Unique identifier |
Name | Business/POI name |
Alternate Name | An alternate name of the POI |
Lat/Long | Location latitude refers to a geographic coordinate specifying the north-south & east-west position of a POI |
Address | Address number and street name |
Locality | City, town |
Region | State, province, or region |
Postcode | Postcode or Zip code |
Country | Country code |
While these attributes are all important, not all of them are of equal value while conducting a geospatial analysis. In short, you should prioritise data providers who offer attributes that best fit your use case. With this being said, here are some additional unique attributes that have proven to add major value to a POI dataset:
Attribute | Description |
Telephone | Telephone number with a local formatting |
Fax | Fax number with a local formatting |
The email address of the organization | |
Website | Office website of the business |
Neighborhood | The neighborhood(s) in which the POI is found |
Lat/Long | Location latitude refers to a geographic coordinate specifying the north-south & east-west position of a PO! |
Chain Name | Chain entity associated with the organization (Zara-Inditex) |
Parent Organization | Legal entity that runs the stated brand/franchise. This is the indicator of the business group that the company is listed under. |
Stock Tickers | Abbreviations to identify publicly traded companies worldwide |
Category Labels | Category labels defining the category branch (E.g., Automotive, Food and Dining, Retail, etc.) |
Radius Size | POI boundaries defined by a radius or a centroid that takes in the geographical boundaries of a POI in a circular format Shapefile Polygon shapefiles that geofence the exact geographical |
Popularity Index | Indicates the level of popularity for a specific PO! |
Hours of operations | Opening and Closing Hours |
Website URL | URL of the official website of the PO! |
Mobility Index | How much people living in the area move within a day |
Population Index | Population living in Area |
Age Distribution | Represents the age distribution within an area |
Employment Index | Indicates employment rate |
Evaluated appropriately, additional attributes like the ones mentioned lead to intelligent business strategies by providing a context to a determined location. A great mix of attributes will help you draw the right conclusions from the data and gain a competitive edge in their market.
If you are a Hedge Fund Portfolio Manager, you are probably looking to conduct a trend analysis for a list of brands tied to a publicly listed company. When deciding on a data vendor, you and the research analyst should seek providers that will be able to offer a dataset with relevant attributes such as stock ticker association, precise building footprint, mobility and transaction patterns on a POI level and frequency of data refresh.
These attributes will help you analyse trends and make better decisions with regard to your stock positions by anticipating quarterly results of publicly listed companies before their statements go public.
Alternatively, an Urban Planner looking to enhance metropolitan traffic flows will find this attribute to be less relevant. This is why for his/her use case, it would be best to tap into attributes like the lat/longs, addresses, real estate prices, postcodes, POI categories and so forth. This mix of attributes will help you understand the mix of POIs in any given area in your city and better plan new city investments.
A Marketer, on the other hand, can use POI attributes such as building shapefiles to create virtual parameters using geofencing techniques. This allows him/her to improve their conversion rates by targeting the right customers at the right place and time.
The right combination of POI attributes, when analyzed, can provide valuable insights for better planning and forecasting, effective targeting, operational optimization, and much more. While evaluating POI data providers make sure to always check:
If the data is recent enough for your use case?
What level of accuracy does your use case require?
How is the data sourced?
Do you need to source any additional datasets to complement that dataset?
Are you evaluating different providers to see what attributes are essential to your business needs? Or do you need help with how to make the best use of POI data? Read our blog post on getting started with POI data to set yourself on the right track.
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