Google Maps Platform
Beyond the Map: How we optimize maps data for our customers
Editor’s Note: This post comes to you from Eli Danziger, as part of the Beyond the Map Series which gives you a behind the scenes look at how we map the world to help our customers build businesses and experiences for their end users.
Over the past few months we’ve highlighted a variety of ways that we map the world to help people explore and get things done–and how that same data enables customers to build location-based experiences and businesses. As we hope you’ve noticed, we’re constantly working on new ways of mapping the world for our users. But at the same time we’re also working directly with our customers to identify areas where we can improve our maps data to better power their businesses. In this Beyond the Map installment, we’re taking a closer look at the work we’ve done in Southeast Asia to ensure our rides and delivery customers are able to meet their business objectives and exceed their users’ expectations.
First, data quality is important to all our customers–but particularly important to customers that power on-demand services. Whether it’s a ride or food delivery, end users expect accurate ETAs, to be picked up where they actually are, and have their dinner delivered without a hassle. An inaccurate ETA that makes someone late for work or a delivery snafu that delivers cold dinner to someone’s table can be the difference between whether a user returns to the service or opts for an alternative. That’s why we worked with rides and deliveries customers in Southeast Asia to understand their unique needs and have applied many of the techniques described in Beyond the Map to improve our regional maps data and help our partners drive immediate business results.
Adding more roads to power more rides
The most obvious and impactful way to help our customers power more rides is to add more roads to our data. We’ve doubled down on our efforts in road creation in Southeast Asia to ensure not only more roads, but also that we’re adding roads that are accurate, connected and highly local so that we can make sure trips can be completed down to the last mile and to the many new points of interest and addresses we’ve added to the region. So far we’ve added more than 80,000 kilometers of these local roads, with much of that coming from 2-wheeler routes. Previously, we tailored our road network for cars. But when we saw the need to support 2-wheeler networks, we made sure our maps cover these roads that are so important for getting around locally and completing the last mile of local trips. As we shared in our last Beyond the Map post, we’re committed to continuing to add 2-wheeler roads throughout the region.
Improving geocodes for more efficient pickups
Through customer feedback, we came to understand that our reverse geocoding results did not always return all of the small businesses and points of interest that were located in certain regions. Using a variety of the technologies and tactics we’ve explained so far in Beyond the Map, we’ve added millions of addresses and points of interest in Southeast Asian countries. By incorporating those into our reverse geocoding database, we were able to significantly increase coverage in areas where addresses are sparse. This coverage improvement has helped us ensure that the vast majority–more than 95 percent of reverse geocoding calls–return a result that's close to the request. With improved reverse geocoding results, users can better find the places they're looking for, making it easy for them to get picked up, dropped off, or have food delivered to the right address.
In the on demand rides and deliveries space and beyond, we’re always working to understand our customers’ unique needs and challenges, so we can capture new maps data, improve existing data, and build the right products to help them succeed. From mapping narrow roads with Street View 3-wheelers to extracting information from imagery using machine learning, we’re committed to mapping the world and reflecting real-world changes with the shortest possible latency.
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