How Advancing Earth Analytics Can Mitigate Catastrophic Events

In recent years, extreme weather events and natural disasters have increased in frequency and intensity. As the human population grows and industrial development expands, deforestation, energy use, and emissions are soaring, rising global temperatures and triggering catastrophic events in nature. Advancing earth analytics is key to better understanding the relationships between people and their environment and how we can mitigate devastating events. 

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Understanding Our Impact on the Earth

There is enormous value and information hidden in massive Earth datasets that can help identify which human activities are the most impactful and help inform mitigation strategies for natural disasters. Adopting advanced analytics tools that can handle big Earth datasets is critical to our ability to elevate our observations and understand our planet in a geospatial context.

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Earth analytics, also known as Earth science analytics, is a rapidly growing field that uses data science to study Earth data and solve environmental issues. Earth data is vast and varied - it comes in various formats, such as geoscience, statistics, and social data. Earth data analytics consolidates disparate datasets into a unified space where users can draw correlations and conclusions. 

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A significant component of Earth data analytics is weather impact analysis. The more weather pattern data we gather and analyze, the better we can understand global warming and improve disaster response and preparedness. Vegetation, soil, water availability, agriculture production, and humans are all impacted by weather. Many natural hazards are triggered by extreme weather conditions, such as droughts, floods, heat waves, and rainfall-induced landslides. Analyzing and visualizing weather information with Earth observations provides excellent insight into the impact of climate and weather and how we can prevent or mitigate catastrophic events.
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Advancing Earth Analytics with Hardware Acceleration

Earth data is as complex as it is massive. Big Earth data is multi-source, heterogeneous, multi-temporal, multi-scalar, highly dimensional, non-stationary, and unstructured. Combine that with fluctuating and streaming weather data, and you’ve got an analytical challenge that only the most advanced tools can handle. HEAVY.AI’s hardware-accelerated analytics tools advance earth analytics and weather impact analysis so that critical decisions can be made fast and informed by all the latest, most accurate observations.

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HEAVY.AI enables analysts to cross-filter billions of location data records and polygons alongside other features in milliseconds. Analysts can collect, monitor, store, visualize, and analyze data from an environment at scale with the rapid integration of continuous monitoring data and dynamic map adaptation. Geospatial capabilities are a top feature in HEAVY.AI. Analysts can connect to or load native raster data like GeoTiffs into HEAVY.AI; incorporate Earth observation data into the workflow to monitor upper air, marine, and landscape changes; and model climate risk.
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Analyzing the Earth with HEAVY.AI Tools

Heavy Immerse’s interactive visual analytics and geospatial data platform enable exploration, cross-filtering, layering, and joining massive amounts of earth observations without downsampling. This helps unlock spatial context and reveal hidden insights from the largest datasets without lag impeding time-sensitive weather impact analysis. 

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HEAVY.AI’s Rendering Engine provides state-of-the-art graphics capabilities that allow analysts to visualize and monitor vast, disparate earth and weather datasets, such as: 

• vegetation management imagery and utility grid maps
• active fires and burn scars
• locations with weather sensitivities
• precipitation and surface temperatures
• Arctic sea ice, sea levels, and ocean heat
• incoming sunlight, greenhouse gasses, and carbon dioxide  
• mountain glaciers and spring snow   

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HeavyConnect allows HEAVY.AI to work seamlessly as an accelerator for data in other data lakes and warehouses, intelligent linking to enterprise data (HeavyConnect), and support for raster geodata. The HeavyDB SQL Engine natively supports Open Geospatial Consortium (OGC) compliant geospatial SQL types and operators to enable programmatic big earth data analytics.    
                     

Earth Analytics Applications 

HEAVY.AI provides an Earth analytics environment that instantly and seamlessly blends Business Intelligence (BI) and Geographic Information Science (GIS) into one dynamic, immersive dashboard. Analysts, scientists, and decision-makers can quickly analyze data related to catastrophic events and develop effective strategies for mitigating natural disasters. Some events in nature for which HEAVY.AI can accelerate earth and weather data analysis include:

• Wildfires: Fire science groups and utility companies rely on atmospheric modeling, interactive web maps, web GIS intelligence, visual analytics, hardware-accelerated SQL, and advanced analytics & data science frameworks to support granular, real-time visualizations of circumstances on the ground that may lead to fire outbreaks.
• Floods: NOAA’s data on melting ice sheets, the expansion of warm water, precipitation, and regional tidal variability can help emergency management teams develop proactive flood warning systems and help the US Department of Agriculture and Farm Service Agency determine where and when destructive soil erosion and soil salinization may take place.
• Freezing Temperatures: Weather forecasts and historical data on power outages and energy grid vulnerabilities can help utility companies anticipate energy use needs and backup power strategies, which can help avoid devastating loss of power and heat during freezing temperatures. 
• Droughts: Data from the Standardized Precipitation Indices (SPI) and soil moisture (SM) levels are used as indicators of agricultural drought. Climatologists also use historical data to understand the likelihood of a drought in a specific region, which can help inform mitigation and preparation strategies such as artificial precipitation, groundwater recharge, water treatment and recycling, desalination of brackish water, and small-scale water harvesting. 
• Tropical Storms: In addition to monitoring real-time doppler radar storm data, analyzing historical data such as meteorological statistics, casualties, and damages is critical to helping local governments and utility companies anticipate the behavior of a developing tropical storm, its likely damage, and proactive strategies to implement ahead of landfall.
• Landslides: NASA’s Landslide Hazard Assessment model for Situational Awareness tracks potential landslide activity over time by combining and monitoring factors such as rainfall, recent road construction, and steep hills. These data sets require constant ingestion and consistent, frequent, automatic refreshing.

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The HEAVY.AI SQL engine rapidly executes queries and enables rendering, visualization, and interactive exploration of constantly changing data. This enables earth analytics, weather impact analysis, real-time environmental monitoring, and interactive data visualizations that empower decision-makers to see the big picture, make connections, identify trends and patterns, and derive decision-quality information.

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