Esri technology has been in full force in Louisiana during both the search-and-rescue phase and cleanup and disaster recovery efforts following massive flooding in Baton Rouge. Esri’s ArcGis Online is providing a way to collect, monitor and report field activities to be sure all departments are on the same page using real time imagery, data and apps.
In an emergency situation, location is a key component in response efforts — from maps showing affected areas to first responders; to where relief supplies are located; to evacuation routes and impending weather.
The Esri Story Map references the locations of civil air patrol photos.
Esri’s Disaster Response Program provides software support, data support, and consulting/technical support for active disasters. The program is available to any agency supporting a disaster, regardless of whether they are an Esri customer.
Currently, the Esri Disaster Response Program is supporting the efforts to respond to the wildfires in California as well as the flooding in Louisiana, but the tools are called into action on countless disaster situations and are available at any time.
For the flooding, a Public Information Map is updated continuously with multiple data streams such as social media and weather reports. There are also a Flooding Story Map and a Local Impact Map available. Similar resources are available for the wildfire emergency.
Last month I raised my anxiety level by writing about a revenant threat from terrorist-initiated biological attacks.
The same concerns were also cited by Director of National Intelligence James Clapper during recent Congressional testimony. These “poor man’s nukes” could potentially be more devastating than 9/11 and reach into every community and even our own homes. Additionally, the threats are not easy to ferret out and just as difficult to stop in our very complex and interconnected world.
From bioterrorism to natural disaster emergency management, predictive analytics used with geospatial tools and big data is proving to be a powerful new intelligence tool that may help counter global threats.
TransVoyant Predictions
TransVoyant CEO Dennis Groseclose.
Last year there was a lot of buzz at GEOINT surrounding a relatively new company in this field called TransVoyant. Several weeks ago, I visited TransVoyant’s Alexandria, Virginia, headquarters to learn more about their capabilities first hand. I was fortunate to be able to speak with TransVoyant CEO Dennis Groseclose, an Air Force Academy graduate who, with Tim Fleischer, a Naval Academy graduate and successful entrepreneur (Radian, PD Systems), co-founded TransVoyant.
Previously, Dennis led industrial base optimization restructure for the $37 billion dollar unmanned space launch program for the U.S. Air Force; directed and implemented Worldwide Supply Chain Optimization for IBM; and served as vice president at Lockheed Martin. These experiences built his expertise to solve complex supply chain and global risk management problems using advanced analytical intelligence. In 2011, Dennis and Tim put their collective experience together to form TransVoyant, a company that specializes in creating live and predictive insights from real-time big data.
The Internet of Things (IoT) has been a key component of their operation. In the mid-80s, connected remote sensors numbered in the thousands. In 2016 that number is expected to reach 6 billion connected “things” worldwide with estimates of 30 billion by 2020.
TransVoyant collects, cleanses and analyzes over 200,000 external events around the world every minute (such as severe weather, natural disasters, labor strikes, inventory locations, news, terrorism incidents, disease outbreaks and energy prices) from real-time IoT data sources such as sensors, radar, GPS, satellites, smartphones and meters. It then continuously applies advanced data scientist-crafted analytics to these data streams to assess important current and future behaviors, impacts, correlations, patterns and exceptions that deliver live and predictive insights ranging from forecasts of port disruptions and precise shipment arrival times to forecasts of economic flows to real-time and predicted threats to people and assets. The resulting insights — provided via cloud services, system API connections, email and mobile applications — improve mission-critical decision making.
The geospatial grid connection
This was all sounding like science fiction and black magic until an “aha moment” for me, as Dennis explained how they use a “multi-dimensional grid cell mathematics” based data structure to apply complex algorithms to real-world data and events. This put the very complex process of continuous real-time machine analysis that “understands” normal and abnormal behavior, both current and future, into something that was familiar to me.
Decades ago, I used the first release of ArcINFO GRID, now called ArcGIS Spatial Analyst, to complete my master’s thesis. For those of you that haven’t used a grid-cell-based GIS, let me highlight the differences between that and traditional GIS software.
Traditional GIS software describes our world as points, lines or polygons with topology describing the mathematical spatial relationship between each geographic element and its linked record in a database. This topological model is somewhat cumbersome and slow because the shapes and topological relationships are complex.
Grid: David Kidner, Mark Dorey & Derek Smith, University of Glamorgan, Wales, U.K. CF37 1DL
The other kind of GIS is a grid cell or raster-based GIS. The data model is significantly simpler because — unlike a traditional GIS of points, lines and polygons — the grid-based GIS world is broken up into simple uniform grid cells.
The big advantage is that the data structure and tools lend themselves to very fast processing. Almost any mathematical formula can be used to operate on the individual or collective grid cells. Most grid-based systems use predefined mathematical operations such as shortest path analysis, interpolation including Kriging or very complex formulas using map algebra.
So, very similar to the famous Napoleon Hill quote, “Whatever the mind can conceive… it can achieve.” With a grid cell GIS, if an analyst can think of a way to describe an analytical process and predictive results as a mathematic expression or formula, it can be done very quickly in the grid cell environment. (See two previous columns for more in-depth information — “GRID Cell Modeling” and “Topology is not Topography”.)
So what does grid cell GIS look like in action?
Evacuations during a flood.
Proactive Emergency Response
In my discussions with Dennis, a TransVoyant customer segment that caught my attention was support of first responders. Emergency responders are using TransVoyant to help with very early disaster response. One specific example is evacuation of invalid patients before a flooding disaster becomes life threatening.
A hospital evacuation.
Using TransVoyant’s analytics on an extensive network of satellite imagery, 911 and 311 calls, water-stage sensors, street closures, weather forecasts, registries and more, responders can predict areas that are at high risk for flooding hours before flood waters rise. Among other essential emergency management actions, these early warnings provide emergency responders with the ability to identify specific neighborhoods and homes that have disabled residents who can be easily evacuated, increasing the safety and efficiency of their operations.
Here is a screen capture of TransVoyant Continuous Decision Intelligence (CDI) predicting a flood event.
TransVoyant Continuous Decision Intelligence (CDI) predicts a flood.
Other Clients
TransVoyant’s live and predictive insight solutions have attracted customers ranging from large multinational corporations to National Security and Intelligence agencies.
I know that one hears echoes of Minority Report when reviewing the tools and capabilities of TransVoyant. However, given the serious threat we face for a situation far worse than 9/11, I have no reservations about using open-source data aggregation and brilliant analytics that correlate and uncover patterns of life and global anomalies to divine a threat.
So, will predictive analytics be the tool that stops a bio terrorist or saves lives in critical emergency situations? I don’t know, but the potential threat is too grave not try every tool, including continuous precognition, in our collective toolbox.
TransVoyant will be an exhibitor at GEOINT 2016 this month, so stop by and learn more.
Since we are approaching Mother’s Day and Memorial Day, I’d like to call your attention to my May 2014 column. It’s the best column I ever wrote.
CoreLogic, a residential property information, analytics and data-enabled services provider, today released an expanded version of its natural catastrophe risk management solution, which features a new comprehensive probabilistic flood model that analyzes the potential damage and financial impact at the property-level from flood events in the continental United States.
This probabilistic flood model is unique to the industry because its riverine and flash flood risk components provide better risk estimation for areas outside the 100-year flood zones–areas responsible for 20 percent of historic flood losses but which represent only 1 percent of the flood insurance policies in force.
Measuring both severity and frequency of flood events, the probabilistic flood model loss calculations offer property, contents and business interruption analysis. The model also incorporates historical flood event footprints from the last 50 years and the accompanying property damage.
Additionally, the model incorporates detailed user-provided building information to derive vulnerability assessments driven by both water depth and water velocity. These building characteristics include construction type, occupancy, floor elevation, basements and elevated building configurations. The new CoreLogic flood model provides insurers with an unprecedented tool to more accurately underwrite the risk associated with this complex peril, especially the proprietary flash flood component.
With granular 10-meter elevation data, the catastrophe risk management solution incorporates the Digital Flood Insurance Rate Maps (DFIRMs) provided by the Federal Emergency Management Association (FEMA). It uses more than 80 different occupancy classes covering topography, land-use, stream coverage and inundation. In order to more accurately measure a property’s flood risk, more than 50 data layers ranging from elevation, hydrologic and catchment information are included, as well as data for over 6 million miles of streams and 20,000 stream flow gauges.
“The release of the U.S. Inland Flood Model means insurers can now use this advanced probabilistic tool to help them determine a property’s potential for flood damage,” said Tom Larsen, CoreLogic product architect. “The model’s unique ability to provide granularity down to the property level will offer insurers a complete view of flood risk, including contents and business interruption, for all types of properties.”
The catastrophe risk management solution contains parcel-level geocoding through PxPoint from CoreLogic, which can convert physical addresses or locations into precise geographic coordinates for over 142 million parcel boundaries. A new visualization feature identifies details in the data as well as exceptions via easy-to-use charts and graphics. Other new components include updates to three risk assessment models including Italy Earthquake, the North Atlantic Hurricane Risk and U.S. Offshore Energy.
Highlights include:
The Italy Earthquake Model now incorporates an updated seismic source model based on the Seismic Hazard Harmonization in Europe (SHARE) to provide a current and more accurate view of seismic hazard in Italy. Increased maximum magnitudes, an updated magnitude-frequency distribution and a new ground motion model are part of the enhancements.
The North Atlantic Hurricane Risk Model update includes a high-resolution storm surge model and enhanced hazard risk assessment to more accurately capture the damage from storm surge as the surge attenuates inland (outside of the high velocity zones). It uses storm intensities from historical events based on the Atlantic hurricane reanalysis project by the National Oceanic and Atmospheric Administration (NOAA). Additionally, the North Atlantic Hurricane Risk Model includes a full set of default secondary structural modifiers by vintage and location for all hurricane states, which are based on the International Building Code as well as state-specific building codes to provide refined results. RQE 16 also includes a model version which was certified by the Florida Commission Hurricane for Loss Projection Methodology in June 2015.
The U.S. Offshore Energy Model features a distinctive wave model component and unique financial model which produces an improved estimate of potential damage to physical assets in U.S. territorial waters within the Gulf of Mexico. A network analysis is also built into the model to produce a better estimate of the lost production from oil wells.
“All of these enhancements will help insurers understand hazard risk in a more granular and comprehensive way, and this precision in risk modeling will help the industry overall fine-tune its underwriting, claims and reinsurance efforts,” Larsen said.
Water management company DHI used satellites to map Jan. 11-13 floods in Denmark.
On the weekend of Jan. 9-11, two storms passed over Denmark. During and after the storm flood warnings were issued in different areas of the country. To monitor and map the spatial extent of the flooding DHI GRAS asked Airbus Defence and Space to acquire TerraSAR-X satellite images over the areas. DHI then analyzed and mapped the flooded area.
The main advantage of using radar satellites like TerraSAR-X is the ability to acquire images independent of weather and light conditions, DHI said. It is possible to get an image of the actual conditions on the ground even during harsh winds (up to 35 to 40 meters per second in this case), massive clouds and rain, and during the night.
The illustration below shows parts of the flooded area around Limfjorden in Northern Jutland, Denmark. On the background radar satellite image the water is black while land area appears in white and grey tones. The light blue overlay indicates flooded areas.
The images were acquired over various parts of Denmark. By preparing and reacting to future and similar early warnings of flooding and storm events, it is possible to plan for new images to be acquired during the maximum extent of the water level.
CoreLogic, a global property information, analytics and data-enabled services provider, has released an analysis ranking Florida as the U.S. state with the highest level of comprehensive risk exposure to multiple natural hazards, with Michigan identified as the state with the lowest risk.
The analysis was derived from the new CoreLogic Hazard Risk Score (HRS), an analytics tool launched today that gathers data on multiple natural hazard risks and combines them into a single easy-to-use score ranging from 0 to 100. The overall score indicates risk exposure at the individual property and location level.
For every geocoded location across the U.S, the CoreLogic HRS is compiled using data representing nine natural hazards: flood, wildfire, tornado, storm surge, earthquake, straight-line wind, hurricane wind, hail and sinkhole. Locations with higher risk levels are exposed to multiple hazard risks and will, therefore, receive higher scores when the risk analysis is aggregated. Subsequently, locations with minimal risk levels have lower exposure and receive lower scores. Geocoded locations are generated at the property-address level using latitude and longitude coordinates and include both residential and commercial properties.
“Florida’s high level of risk is driven by the potential for hurricane winds and storm surge damage along its extensive Atlantic and Gulf coastline, as well as the added potential for sinkholes, flooding and wildfires. Michigan alternatively ranks low for most natural hazard risks, other than flooding,” said Dr. Howard Botts, vice president and chief scientist for CoreLogic Spatial Solutions.
The proprietary CoreLogic HRS is able to calculate risk based on a 10 x 10 meter grid, the lowest level of granularity available for the underlying hazard data. In calculating the overall score, both the probability of an event and the frequency of past events are significant contributing factors used to determine risk levels associated with individual hazards, as well as each distinct hazard’s risk contribution to total loss. The data is combined into an aggregated, consistent and normalized value that allows statistically valid combinations to be derived.
“In the past, natural hazards have been difficult to compare and combine in a meaningful way,” said Dr. Botts. “Hazard Risk Score is a single solution that measures risk concentration consistently and pinpoints the riskiest places in the U.S. with timely and granular accuracy. This insight is critical in conducting comparative risk management nationwide and fully understanding exposure to potential natural hazard damage.”
Insurers, risk managers and mortgage servicers can use CoreLogic Hazard Risk Score to improve decision-making and enhance a variety of business operations, including:
Business continuity and disaster recovery planning
Analyzing risk associated with a residential property or portfolios of properties
Measuring mitigation savings vs. total hazard potential damage
Evaluating and determining natural hazard risk levels of distribution and supplier networks
Recognizing which underinsured or uninsured properties may become at risk of default
Adverse selection avoidance and identification of “good risk” properties
U.S. Natural Hazard Risk by State* (Ranked by CoreLogic Hazard Risk Score)
Rank State HRS
1FL94.51
2RI79.67
3LA79.23
4CA75.56
5MA72.12
6KS69.51
7CT69.04
8OK66.82
9SC66.38
10DE65.38
11OR64.89
12NJ61.54
13IA61.02
14TX60.89
15NC59.72
16MO57.81
17DC57.33
18MS57.05
19AR56.7
20NH55.3
21ID52.75
22MD52.28
23CO51.88
24NE51.86
25IL51.8
26IN50.74
27GA50.58
28NV50.12
29AL49.42
30KY47.34
31TN46.48
32UT45.22
33NM43.76
34AZ42.81
35VA42.35
36WA42.3
37WI38.52
38SD38.24
39MT37.91
40MN36.42
41OH34.61
42ME31.64
43WY30.24
44PA28.79
45VT28.31
46ND27.5
47NY24.97
48WV20.67
49MI20.22
Source: CoreLogic 2014.
* AK and HI were excluded in the ranking due to limited natural hazard risk data.
As the Gulf Coast begins another hurricane season, researchers with the Conrad Blucher Institute for Surveying and Science (CBI) at Texas A&M University-Corpus Christi will be improving the data collection system to allow for more accurate planning and predictions for flooding and sea-level rise.
CBI has been awarded $1.35 million to enhance the National Spatial Reference System that helps model and predict sea level rise.
Forecasters are predicting a hurricane season with one or two major hurricanes, but flooding can still pose significant threat, especially to the vital infrastructure along the Gulf coast, which includes 10 of the 14 largest ports. The long-term stability of this region’s infrastructure is in question due to the impact of sea level rise and associated increases in risks of flooding. Growing Gulf coastal populations, up 32 percent from 1990 to 2008, compound the risks. Preparing for sea level rise, flooding and other impacts requires accurate data about what’s occurring at the water’s edge. Collection methods for this type of geospatial data will be enhanced through this project.
The funding, from the National Geodetic Survey, a project of the National Oceanic and Atmospheric Administration, provides the foundation for modeling along the northern Gulf of Mexico through the National Spatial Reference System.
The project focuses on an area that is most exposed to inundation from tropical storm surge and has a high risk of flooding and long-term effects of climate change and subsidence.
“We are excited to be part of this project to provide the latest geospatial data with information from tide gauges, sea level observations, land elevation reference points, and 3D positioning,” said Gary Jeffress, director of CBI. “This system will help local and regional leaders plan for improved resilience to the impacts of sea level rise and flooding and develop long-term strategies to address impacts along the northern Gulf of Mexico.”
The project will extend and improve monitoring stations from Texas to the Florida Keys to provide additional measurements, including more accurate data regarding elevations, 3D positioning, subsidence rates and sea level observations, that will establish ongoing monitoring of the relative sea-level change along the northern Gulf of Mexico in the coming decades.
Jeffress, Ruizhi Chen and James Rizzo, with CBI and Texas Spatial Reference Center, will lead the project for A&M-Corpus Christi. Researchers from University of Southern Mississippi, Louisiana State University and Florida Atlantic University are also partners in the project.
