POI data is becoming increasingly important as the number of 'at your door' services grows.
Consumers’ interactions with businesses and places around them have shifted dramatically. Ride-sharing services like Uber and Grab have mostly replaced ordering a taxi on the street. People get their food delivered to their doorstep instead of queuing or setting appointments at their restaurants. From buying clothes to carrying groceries, from locating housekeeping or laundry services, and so on, there is an app for everything. It’s hard to believe that we ever lived in a world without all of these services.
POI data is becoming increasingly important as the number of ‘at your door’ services grows. It would be difficult to provide these services without a precise location reference. To pick up the next passenger, Uber drivers need to know the exact location of the fast food chains & restaurant, and it makes sense for a fast-food company to open a new site in a booming residential area where customers are underserved.
A POI (Point-of-Interest) is a map record of a location that someone finds useful or interesting. A POI is usually specified by its geographic coordinates as well as a few other variables such as name and category: The Fullerton, a hotel located at 1.286546 latitude and 103.853721 longitude, and the Empire State Building, located at 40.748817 latitude and -73.985428 longitude, are both good examples of POIs.
Automatic harvesting of POIs from web sources such as Google Maps, OpenStreetMap, and others is the most basic method of obtaining data. Some businesses employ web scraping software to save POI data directly to a file or database. While some websites, such as OpenStreetMap, allow for the extraction of geospatial data points, others actively restrict bulk scraping by employing temporary methods such as IP blocking.
It is not unlawful to get around these, although it is strongly discouraged. Overall, this method is labor-intensive and time-consuming, making it unsuitable for large-scale POI projects that require a huge volume of data. The data obtained from these sources also necessitates extensive data preparation before it can be used for anything.
Google Maps and other location algorithms depend significantly on companies and places providing their own data. If people do not refresh these data points freely, information could become outdated, resulting in inconsistency and inaccuracies in datasets.
The use of social media, and hence user-generated material, such as location data, has increased dramatically over the last decade. Many firms depend on user-generated location information or buy it from companies that run program that gather it.
As part of the business registration procedure, most government agencies ask enterprises to submit their commercial address. These postal addresses can be combined to create a POI dataset. Many countries make this information available to the public. The Accounting and Corporate Authority (ACRA) of Singapore makes historical and current data on 1.5 million corporations available for public usage, study, and application development as part of its open data strategy.
Businesses, on the other hand, close, grow, and relocate offices, or function from a site other than their official registered address. Because they may not update this information with the government body on a regular basis, the data may become obsolete and erroneous over time.
Some POI internet companies pay or contract employees to manually maintain their database, which includes travelling around the city with a smartphone running a purpose-built app, adding new locations and confirming existing ones. In comparison to other ways, this strategy ensures accuracy and a steady stream of POI data.
This data collection strategy also prevents invading users’ security and selling their location information. Other than the physical location, these purpose-built apps do not save, gather, or exchange any data.
Consumers and businesses may engage more easily in the physical world with the help of POI data. As a result, data collectors strive to create comprehensive datasets of selected places. Companies and governments can use POI data to detect regional trends and patterns and gain meaningful knowledge into:
Retailers can utilize POI data in the correct context to track traffic to one of their sites or one of their competitors. With accurate address data, logistics companies may save money and improve customer service. POI data is used by real estate companies to pick sites and design projects based on market potential.
POI data can be used by governments to enforce rules, monitor public health and well-being, and design public infrastructure and services, among other things. The primary purpose of POI data is to identify a point of interest, pinpoint its precise position, and assist businesses in better understanding what’s going on in the area so they can make better, more educated decisions.
To ensure quick delivery and maximize staff productivity, the food and eating business requires reliable POI data. Customer loyalty and operational expenses are influenced by the speed and efficacy of delivery timeframes. Because the food delivery industry is so competitive, it only takes a few clicks for clients to switch to another app. Food delivery apps can establish dependable fulfilment systems and gain a competitive advantage by using trustworthy residential and business POI data.
As more people shop online, the cost of last-mile delivery has risen dramatically. As a result, in order to remain successful, businesses must optimize their logistics. Postal services, transportation and freight enterprises, on-demand transportation companies, and marketplaces may all enhance their routes, reduce errors, plan placement and extension of pickup and delivery sites, and more by using accurate and trustworthy POI data. They can also employ POI data to create dependable apps that are both efficient and time-saving. Accurate POI data may be used by cab hailing firms to improve their apps, optimize their routes, and respond to consumer requests more quickly.
Banks and financial organizations that wish to keep their competitive edge in the face of growing regulation and disruption from modern technologies should use POI and mobility information. In the insurance industry, the POI information in a certain region can be critical in risk assessment and policy formulation for that region. POI information can also be used to establish a network of bank counters or ATMs across a city in retail banking. Retail banks may use mobility data to find new ways to engage clients, improve touch points, and increase operational efficiency.
POI data, when combined with mobile location data, can help retailers enhance profitability and improve customer service. Knowing where their customers are can aid in the optimization of out-of-home advertising, resulting in increased foot traffic in their stores. POI data can also assist merchants in determining the footfall of their competitors. Retailers can also do performance and comparison analytics for all locations in a region using POI data. Customers can be directed from internet venues to actual retail establishments using POI data.
Customers always want to know about the facilities and attractions in an area when considering to buy or rent a house. Before investing or building in a new neighborhood, real estate developers can use POI data to acquire information. Providing detailed information about their project’s surrounds might also help to boost sales. Customers can use POI data in online property directories to get precise information about a neighborhood, facilities in their regions of interest, directions, and more.
The availability of a network and high-speed internet is critical for mobile usage, television viewing, and other activities. As a result, telecom providers must prepare for network capacity in a region now more than ever. POI data can assist them in determining coverage requirements, conducting competitive analysis, and determining where new infrastructure should be built. POI data can be combined with other data points like as area density and competition to help telecom companies plan shop expansions and offer attractive program to their clients.
Out-of-home marketing strategies can be planned and optimized using POI data. Businesses can send targeted, relevant messages and drive customers to their places of sale by using location-based marketing. Inaccurate POI data may quickly turn a location-based campaign into a barrage of useless communications, wasting money and eroding brand equity.
POI data can be used in a variety of ways by governments, local governments, and emergency and public service agencies. They can use POI data to correctly map regions and optimize services such as public transportation, emergency healthcare, and law enforcement, among others. POI can help speed up tasks like contact tracking, criminal alerts, and more when combined with mobility data.
An optimal POI database updates in response to changes in the physical environment. New businesses start, existing businesses relocate or close, neighborhoods grow, new parking facilities open in high-density locations and so on. Furthermore, metropolitan environments are ever-changing. The COVID-19 epidemic has had a significant impact on our planet, as many enterprises went out of business and closed. To keep our complicated digital economy running, all of these changes in the physical world must be logged, and POI databases accomplish just that.
POIs can be plotted on a map in a variety of media, each with its own granularity and data representation. POI attributes can be used to specify a location on a map and define its spatial relationship with nearby locations using these formats.
The geographic positions of a location are known as coordinates, and they are commonly given in latitude and longitude (Lat/Long). A POI can be recognized using only the latitude and longitude as data points. This information can be recorded through GPS-enabled devices or from satellite-based mapping providers (such as smartphones, fitness trackers, tablets, etc.). It is possible to prove a bigger virtual border for a real-world geographical location using several coordinates, known as a Lat/Long Boundary or Geofence.
A POI’s physical address, which commonly includes a government-assigned Pin, Postal, or Zip Code, is another frequent way to describe it. The postal code system, which was designed to aid mail carriers in sorting and delivering mail effectively, has now become extensively used to locate the location of a site of interest. Postal codes, on the other hand, do not have a universal standard. A postal code can identify a particular housing block in some countries, such as Singapore. Hong Kong, on the other hand, despite its size and population, does not use the postal code system at all. Most areas are in the middle, where a postal code isn’t enough to define a specific point of interest but can be useful.
Geohash is a geocoding technique invented by Gustavo Niemeyer that permits the presentation of a position everywhere in the world to use an alphanumeric string. Geohash is a one-of-a-kind string created by converting two-dimensional geographic coordinates (latitude and longitude) into a series of digits and letters. Based on the duration of the string, a Geohash might be as ambiguous or as precise as desired. A Geohash is a spatial indexing system that employs grids to divide the world into small grids recursively, with each additional grid introducing an extra level of precision.
One of the main advantages of a Geohash is that it is very good at locating a POI. You can exclude most undesired areas upfront by dividing a wider area into grids and focusing
just on the square where your possible targets are located. It also enables for speedier geo-fencing than the traditional lat/long method, which saves both time and money.
H3, like Geohash, is a hierarchical geospatial indexing system. It does, however, use hexagonal grids rather than rectangular grids. Uber’s popular ride-sharing app H3 was created to improve ride pricing and dispatch by visualizing and studying spatial data. Early in 2020, the H3 grid system was made open-source, and it is steadily gaining popularity. For efficient radial lookups, the H3 system is the best option. As a result, H3 is the greatest option for computing distance between two points and for creating circular geo fences.
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