Geographic information system (GIS) data comes from interfaces that capture location or spatial data. It helps develop applications that use artificial intelligence (AI). Here are some compelling examples.
Providing Tailored Travel Content Based on a Person’s Location
Some companies are well-aware that knowing a person’s location through GIS data allows them to give maximally relevant material. For example, Booking.com increasingly uses personalization to cater to users. Its technology can detect a person’s location, then provide booking recommendations. It wants to go even further and use AI to offer options that could streamline their trips.
Some of the suggestions might be based on a person’s habits. If AI learns that a person often books a taxi to leave the airport after a flight lands, it may offer ordering the cab in advance to avail of a better rate.
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Many people also provide location cues on social media, such as to “check into” different locations away from home. Those could assist AI apps with providing relevant information, too.
Implementing Location-Based Information for Food Delivery Apps
Many of the most popular food delivery apps have AI components that don’t necessarily need location information to function. For example, SmartBite is an app marketed to Malaysian customers. It reportedly uses AI to forecast demand and share information with restaurant partners. Then, those providers can get prepared for upcoming busy periods. SmartBite also shows people menu options according to past orders.
Location-based data comes into the picture because most food delivery apps give users an approximate time of when to expect their food. If the estimates are incorrect most of the time, people will lose trust in that delivery provider and possibly get angry at the restaurant.
Once an AI app collects information about a person’s location, it can then take other factors into account. They include the average cooking time for a menu item or how busy the restaurant when someone ordered.
Using GIS and AI for Infrastructure Maintenance
The combination of AI and GIS data is also spurring progress for infrastructure maintenance. For example, utility providers can dig into mapping data and find out which areas of a community are most likely to experience issues from downed power lines or need service calls from technicians due to faulty equipment. Then, predictive analytics driven by AI can help companies know which places need the most attention to avoid problems.
Another application that blends GIS data and AI enables automated street sign monitoring. Without that technology, municipal crews must monitor for the presence and condition of road signs manually, a method that exposes them to traffic risks. By using images from Google Street View, researchers developed a tool that’s nearly 96% accurate in detecting signs and boasts almost 98% accuracy in identifying the sign type.
It’s a free and open-source method that the creators say is easy to scale. Before long, small towns, large ones and the ones in between may use it to keep tabs on road signs. Another example used in the United Kingdom involves depending on mapping technology and AI to pinpoint route restrictions.
Applying AI for Better Location Inference
Location data on social media is that it’s not always trustworthy. Instead of inputting location information such as “Dallas, Texas,” a person might instead write “Lost in my own mind,” “Enjoying a little slice of paradise” or something else that does not reveal their correct location.
However, one company built an improved geo-inference model by using artificial intelligence components. It takes various factors into account, including what a social media post says, the first and last name of the user, plus time zone and time stamp information to make better guesses about where a person is when they publish on social media.
Consider the common scenario where an individual might live in the United States but frequently posts on social media during a month-long trip to Ireland. If an AI algorithm determines that the individual is not still in the United States when using social media, that information could help brands give enhanced content to people based on their real-time locations.
On a related note, an international team of researchers developed an algorithmic tool to predict where people live with 90% accuracy and do so in a matter of minutes. It does so by looking at Twitter posts and related data. But, this option raises some privacy concerns since it can reveal other things that a person might prefer to keep private, such as if they’ve recently visited a rehab facility.
Abundant Potential to Explore
This overview shows that GIS data and AI can both make apps content more appropriate for users. But, developers must implement these things in ways that remain mindful of user privacy.
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