Historically, the federal government has been a primary provider of authoritative geospatial information; however, geospatial information has become crucial to a wide range of federal applications and online services across multiple domains — military, law enforcement, intelligence, emergency response, agriculture and weather-climate prediction — there’s a significant cultural shift underway.
Now, federal agencies are consuming rather than providing geospatial information from a variety of sources. As a result, the federal government’s role also has shifted toward coordinating and managing geospatial data and facilitating partnerships among the producers and consumers of geospatial information in government, the private sector, and academia.
The volume of geospatial data and its multiple sources — ranging from phones, health devices, vehicles, private satellites, drones, and many other types of sensors, both fixed and mobile — creates a reality where geospatial information is dynamic and ever-changing. As government organizations face a wide range of significant challenges from wildfires, hurricanes, COVID-19, social unrest, traffic congestion, service delivery, utilities and many more, the importance of accurate, timely, and informative geospatial data will continue to increase.
If data is not consumable, is it real?
A challenge arising from this shift to being more of a consumer of third-party geospatial data and dealing with the extreme variety of available sources is that agency leaders, especially CIOs and CDOs, need to focus on how their platforms are utilized to make this data available and consumable.
The CIO from the U.S. Army Corps of Engineers put it this way, “We want to be able to ensure that the data is consumable, regardless of what form it’s in. So we’re assessing the various repositories, assessing the various sources, and we’re trying to consolidate or collapse our enterprise to begin to pull from those respective repositories of what is authoritative and begin to put out an output that’s consumable by the end user.”
An article in NextGov profiled some of the forthcoming technology needs that the National Geospatial Intelligence Agency (NGA) needs to address in order to stay ahead of near-peer adversaries that have much of the same access to datasets and are making investments in new technologies. As NGA CTO, Mark Munsell, explained, “To stay ahead of these adversaries, we must bring together our world-class experts at NGA, industry partners with exquisite domain expertise and technical capabilities, and companies who have never worked with government before but whose products could help advance NGA’s mission.”
The article goes on to highlight some of the specific needs and capabilities that agencies like the NGA and others need to acquire. In the area of advanced analytics and modeling, analysts working with data need the ability to rapidly discover and integrate diverse data types from multiple sources to discover and characterize relevant patterns. These data teams also require a user-friendly assisted modeling capability to recommend statistical approaches based on observed workflow data to ensure they are aware of analytical options that may be of use.
When it comes to managing the veracity and volume of data, data teams also need the ability to rapidly aggregate diverse data types and schemas from sources across multiple domains to quickly extract insight at scale. From a geospatial perspective, data teams at the NGA and in other agencies need quick and ready access to location-based insights with the ability integrate large amounts of data from commercial location-based services into existing workflows to improve their spatial and temporal insights about the physical environment.
These required abilities sound like a lot, but with the Alteryx APA Platform, these abilities are built in to enable the modern-day government analyst, citizen data scientists, traditional data scientists or any level of data worker to access and leverage these capabilities and more in a unified advanced analytics platform.
Advanced Geospatial Analytics in Disaster Response
The proof of this comes from
some robust geospatial related real-world uses
cases developed by an Alteryx partner, Atkins, a
contractor to the U.S. Federal Emergency Management Agency
(FEMA). When dual hurricanes hit Puerto Rico in
2017, Atkins used the
predictive analytics capabilities found in
the Alteryx APA Platform to perform automated substantial damage estimates
for close to 150,000 structures, identifying and
prioritizing those structures most in need of a
The strategy was to use Alteryx to estimate the structural damage that had occurred,
prioritize areas that still needed some sort of human inspection, and ultimately
reduce the total number of in-person inspections so that the recovery process
could begin quickly. Alteryx was used to blend over a dozen datasets and the
following variables to predict damage:
Atkins determined that they needed to get three functional groups of teams out to the
field quickly. The first team used a geographic information system (GIS) to
find the locations of damaged structures. The second team collected the
information on the structures, and the third team built the analytics model in
The data that was feeding the model came from very disparate sources,
including data from the European Union, NOAA, the National Weather Service,
FEMA, and the Army Corps of Engineers. This caused some significant
data challenges, such as overlapping building stock data,
numerous databases of different ages and sources, an inconsistent
addressing system, and multiple sources of inundation and flood
depth data. Additionally, this data was stored in various formats that
required cleansing prior to blending, and creation of indices between the
datasets. Alteryx was used for these data prep and blend tasks.
At the heart of this work was the ability to use the Alteryx APA Platform’s
predictive modeling capabilities to overcome some of
the incomplete or missing data, by filling in
the gaps. Additionally, the team leveraged a Boosted
Regression Model to look for patterns in the data across thousands
of iterations, selecting the most relevant variables and providing a
high degree of control over the entire predictive model.
With this model, 146,039 structures were evaluated and sorted
into priority groups for actual physical inspection. From
those structures loaded into the model, it
was determined that just over 30,000 of them required
the dispatch of inspectors. With this geospatial and
predictive capability, inspection resources were
deployed where they were most needed, saving tens of
millions of dollars in inspection costs and most
importantly — speeding up delivery of recovery support to people and
Staying Ahead of the Flood of Data at FEMA
This same ability to ingest, prep, join, and analyze
massive geospatial datasets and apply predictive analysis in
a unified platform enabled Atkins to work with FEMA
to analyze and identify flood risk across 90,000,000
structures, throughout all 50 states and six territories. The Alteryx
workflow ingested and blended over 300 datasets, with 4000 internal
layers. The workflow contains over 3600 automation building
blocks and produced actionable insight to
inform federal, state, and local authorities on
the risk faced by individual structures across different types of flooding
Both of these use cases are real-world examples that show how data teams with the NGA, IC, DOD, and other federal agencies can seamlessly harness and expand the use of commercially available datasets and leverage an easy to use spatial and predictive analytics capabilities within a unified analytics platform. What the Atkins use cases prove is that even with large datasets and multiple sources and high complexity, agencies can create the actionable insight they need to accelerate mission outcomes.
Want to hear more on the subject?
Watch our webinar on-demand: Automating Geospatial Analytics
and Predictive Analytics with
Alteryx, featuring Michael DePue, Principal Technical
Professional, Atkins Global.