Welcome to the Location Data Blog!

 

Learn how companies all over the globe are utilising location data to enhance business operations and improve profitability. Keep up with industry updates, best practices, and key learnings from location intelligence projects we have executed.

Tackling last mile delivery challenges with POI and mobile location data


Last-mile, food delivery, e-commerce, and postal services companies constantly strive to improve their services and operational efficiency to remain competitive. In this highly saturated space, the quality of location data powering delivery apps and platforms can be detrimental to the efficiency of operations. In this post, we will discuss how location data (mobile location and POI data) enables businesses to locate customers accurately, optimize mapping and routing systems, and minimize the waste of time and resources.

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Geolancer database doubles to 1 million POI in just 7 weeks!

We recently announced that Quadrant had gathered 500,000 Points-of-Interest (POI) via Geolancer, our industry-leading Data-as-a-Service platform. Previously, it took us 10 weeks to double the size of our database from 250,000 POI. This time around, we are delighted to share that we have achieved the same feat even quicker (a mere month and a half) while crossing the 1 million mark!  

Having consistently delivered on our promise of a high-quality, scalable, affordable, and customizable POI solution, we have generated surging demand across a host of sectors and have rapidly expanded our coverage to fulfil it. 

Here, we recap some of our successful projects from the last quarter to highlight the diversity of the use cases that we are enabling.  

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Using geospatial data to advance sustainability in government and business

Big data is a powerful tool for tackling climate change – humanity’s greatest challenge in the 21st century. Geospatial data is a form of Big Data that is drastically improving mitigation/adaptation strategies for climate change and increasing the likelihood that we collectively accomplish the Sustainable Development Goals. By giving businesses and governments a firm understanding of evolving mobility patterns and spatial relationships, geospatial intelligence can decrease resource consumption, lower pollution and carbon emissions, enhance our resilience to climate-induced natural disasters, and more.  

Researchers, businesses, and government agencies leverage mobile location and Point-of-Interest (POI) data to advance sustainability. Let's look at some of these use cases. 

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In-field POI data collection at scale - a revolution for the digital mapping industry

Location-based technology and services, such as ride-hailing, online marketplaces, and food delivery have become a vital part of daily life. The effectiveness of these services depends on accurate, recent, and comprehensive point-of-interest (POI) data. However, most public maps and widely used POI data sources are outdated.

Experts predict that the need for location-based services will continue to increase, with a projected growth rate of 24.3% and to an industry market cap of over $300 billion by 2030. As the demand for these services grows, the need for reliable mapping solutions becomes increasingly important.

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Creating attribute-rich contextual maps for Gojek with Geolancer (Case Study Preview)

The ride-hailing industry is characterized by stiff competition and new businesses are entering the fray every year. By 2026, the market size for ridesharing services is expected to more than double compared to 2021. For companies to thrive in this fiercely competitive landscape, they must optimize their routing systems to expedite pick-ups, match demand, and deliver more rides per driver for maximum profitability.  

Ridesharing services need accurate and up-to-date POI data to function. However, the underlying maps that enable their operations are outdated, and changes in the physical world are not reflected nearly as quickly as needed. A lack of current and contextual information can cost rideshare companies in terms of lost driver productivity, rider churn, and overall operational inefficiency. 

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