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  • Gspatial.ai – one of its kind platform for bulk historical air quality and weather data launched

    Published on January 17, 2022

    • Ambee’s Gspatial.ai will offer street-by-street, hourly historical air quality, and weather data for over 70+ countries covering the last 20+ years
    • Businesses, researchers, and government bodies can utilize Gspatial.ai’s historical air quality and weather data to mitigate climate change loss through deep insights and strategic decision-making

    Bangalore: Ambee, a climate intelligence company that supplies hyperlocal environmental data in real-time, has launched Gspatial.ai, a new offering that provides historical air quality and weather data to businesses and governments across 70+ countries all over the world. The service includes street-by-street hourly air quality and weather data spanning the last 20+ years.

    Ambee’s Gspatial.ai will benefit healthcare, pharma, insurance, logistics, retail, and even government bodies. Gspatial.ai helps mitigate the risks of climate change through deep and actionable insights on environmental factors, helping companies assess risks and make sound business decisions. Businesses can easily shop for historical air quality and weather data through Gspatial.ai’s website or a purpose-built API dashboard.

    Commenting on the launch of Gspatial.ai, Madhusudan Anand, CTO and Co-Founder, of Ambee, said, “Our mission is to improve the health of millions of people but also the planet itself by developing products that can impact those affected by climate change.” He added that Gspatial.ai is a step in that direction, with easy access to reliable and accurate historical information on hyperlocal environmental factors. “This will help businesses understand how environmental events can have an impact on revenue. Industries such as oil & gas, energy, automobiles, insurance, healthcare, agriculture, and forestry are amongst the most vulnerable to climate risks. Businesses can use historical environmental data when looking at capital allocation, development of products or services, and supply-chain management. Through this product, we hope businesses can stay resilient to climate disasters, profitable and sustainable while helping their customers stay healthy,” explained Mr. Anand.

    Then and Now

    The Swiss Re Institute’s recent Climate Economics Index stress test reported that the global economy could lose up to 18% GDP from climate change by 2050. Further, the Environmental Research Letters journal at the University College of London, reports that by the year 2100, climate change will cost the world six times more than originally thought. The United in Science 2021 report says that the average global temperature was the highest on record for the last five years. Carbon dioxide levels are at their highest in 650,000 years. This past year has also recorded extreme weather and climate calamities, an effect of human-induced climate change.

    It is clear that climate change is a huge risk to all businesses. Access to accurate and historical environmental data can help reduce loss and insulate against further risk through adaptability and climate resilience.

    More on Gspatial Platform

    One of the most important benefits of historical environmental data is risk assessment and insurance. Historical environmental data services like Gspatial.ai help in creating risk models that predict weather patterns; protect solvency by better investment decisions; aid resource optimization, and encourage policymakers to take action. For instance, environmental data oversees precipitation events (floods and mudslides); wildfire frequency and severity; temperature spikes; crop growth; soil erosion; and more. In the same vein, when it comes to logistics, for example, environmental intelligence will accurately identify a delivery window or adjust ETAs according to the weather; optimize inventory before inclement weather; and establish clear and cost-effective transportation, among other advantages.

    Upcoming features in the Gspatial.ai platform:

    1. Hyperlocal emissions with CO2 in PPM and CO2e in Gigatons,
    2. Pollen, vegetation, satellite images, historical fires data,
    3. Historical severe weather events data,
    4. Bulk historical climate data,
    5. Environmental forecast data for more than 80 years and lots more.


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