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.

Saif Kakakhel

Saif Kakakhel

Recent Posts

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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Geolancer database doubles in just 10 weeks - hitting 500,000 POI

A few weeks ago, we announced that Quadrant had collected 250,000 POIs through Geolancer. Today, we are excited to share that we have doubled the size of our database to over half a million POIs!  

The increasing demand for Geolancer's manually collected, recent, and attribute-rich data has encouraged our rapid expansion. Besides hitting the half-million mark, we are happy to report that three of the world's top 5 mapping companies are now leveraging Geolancer's industry-leading POI data.

Besides rapid growth, we also have other exciting news to share.



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Solution Brief: Generate actionable business intelligence with raw location data

As a proxy for human mobility within the physical world, raw location data provides unparalleled insights into people’s behaviors and needs. Businesses harness such intelligence to boost performance while governments and non-profit organizations leverage it to execute people-centric development programs.   

However, the location data industry is teeming with issues (obscure supply chains, poor quality control, inflexible business practices, an abundance of non-compliant data, etc.). These factors make it extremely challenging for data scientists and analysts to generate actionable intelligence.   

We’ve crafted a solution brief that details all these problems and elaborates on how Quadrant is solving them through our industry-leading location data offering. The brief also contains use cases and customer success stories with proven results for various use cases. 

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Automating in-field POI collection to rapidly deliver high-quality datasets

Quadrant’s POI Data-as-a-Service platform, Geolancer, is superior to conventional POI data collection tools in many ways. Geolancer’s ability to quickly provide high-quality, customizable metadata on demand makes it stand out in a sea of POI data providers. Our bespoke POI datasets allow businesses to increase operational efficiency, build better products, and enhance customer experience.  

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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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