a thesis presented to the department of … · joseph adduci northwest missouri state university...
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A CRITICAL ANALYSIS OF GEOSPATIAL TECHNOLOGIES AND EDUCATIONAL NEEDS TO SUPPORT HOMELAND SECURITY MISSIONS
A THESIS PRESENTED TO THE DEPARTMENT OF HUMANITIES AND SOCIAL SCIENCES
IN CANDIDACY FOR THE DEGREE OF MASTER OF SCIENCE
By JOSEPH JOHN ADDUCI
NORTHWEST MISSOURI STATE UNIVERSITY
MARYVILLE, MISSOURI JULY, 2013
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GEOSPATIAL APPLICATIONS HOMELAND SECURITY
A Critical Analysis of Geospatial Technologies and Educational Needs To Support
Homeland Security Missions
Joseph Adduci
Northwest Missouri State University
THESIS APPROVED
Thesis Advisor, Dr. Ming-Chih Hung Date
Dr. Yi-Hwa Wu Date
Dr. Mark Corson Date
Dean of Graduate School, Dr. Gregory Haddock Date
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A Critical Analysis of Geospatial Technologies and Educational Needs to Support
Homeland Security Missions
Abstract
This thesis examines the role of geospatial analysts in supporting emergency
responses. This support is increasingly critical and requires coordination among local,
private, state and federal organizations and agencies in an increasingly dangerous world.
This thesis analyzes the progression of GIS responses to recent disasters and emergencies
and examines the role and expertise of GIS modelers assigned to the United States Army
National Guard’s Weapons of Mass Destruction Civil Support Teams (WMD-CST). This
study led to development of three supplemental training modules, intended to bolster the
critical spatial skills and abilities of the WMD-CST modelers.
This research determined that the role of geospatial technologies has matured
tremendously since Hurricane Andrew in 1992. What used to be a haphazard spatial
response to natural and manmade disasters has morphed into a sophisticated coordinated
response from entities at all levels of government and private agencies. The WMD-CST
modelers are in a unique position to respond to a number of disasters in this country.
They report a dedicated interest in advancing their current skills and abilities to aid in any
response. The modules are designed to connect the needs of the response coordination
and modelers to facilitate a more secure and prepared country.
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Table of Contents INTRODUCTION .............................................................................................................. 1
Research Questions ......................................................................................................... 6 LITERATURE REVIEW ................................................................................................... 8
GIS in National and Homeland Security ........................................................................ 8 Pre 9/11 GIS Responses to Disasters .............................................................................. 9 GIS Response to 9/11.................................................................................................... 12 Post 9/11 GIS Responses to Disasters .......................................................................... 14 GIS Needs Assessment ................................................................................................. 23 The Role and Training of WMD-CST .......................................................................... 29
CREATION OF WMD-CST TRAINING MODULES .................................................... 32 Study Area .................................................................................................................... 32 Data Sources ................................................................................................................. 35 Methodology ................................................................................................................. 35
ANALYSIS RESULTS & DISCUSSION ........................................................................ 42 Questionnaire Responses .............................................................................................. 42 WMD-CST Modeler Education and Background ......................................................... 42 WMD-CST Modeler Geospatial Analysis Experience ................................................. 45 WMD-CST Modelers’ Mandatory GIS Course ............................................................ 47 Training Interests of the WMD-CST Modelers ............................................................ 50 Rationale for WMD-CST Training Modules ................................................................ 53
CONCLUSION ................................................................................................................. 58 Further Research Opportunities .................................................................................... 61
APPENDIX ....................................................................................................................... 68 Module 1: Argonne Population Rose ........................................................................... 69 Module 2: Hurricane Irene ........................................................................................... 77 Module 3: Argonne Sampling Data .............................................................................. 90
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List of Figures
Figure 1: Map Displaying the Weapons of Mass Destruction Civil Support Teams (WMD-CST) in Radiological Assistance Program Region 5 ........................................... 32 Figure 2: Map Displaying the Department of Energy Radiological Assistance Program Regions ............................................................................................................................. 33 Figure 3: Study Area of the Argonne Based Population Wind Rose…………………….34 Figure 4: Sample Questionnaire Presented to WMD-CST Modelers ............................... 36
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List of Tables
Table 1: WMD-CST Modeler Survey; Branch Rank and Education ............................... 43 Table 2: WMD-CST Modeler Survey; Military Specialty, Status and Length of Modeler Service............................................................................................................................... 44 Table 3: CST Modeler Survey; GIS Training and Training History ................................ 46 Table 4: WMD-CST Basic Spatial Modeler Course ......................................................... 47 Table 5: CST Modeler Survey; Skills and Certificate Responses .................................... 52
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ACKNOWLEDGEMENTS
This thesis would not have been possible without the help and support of the
faculty at Northwest Missouri State University, my friends, my colleagues and my
family. I would like to thank my thesis advisor, Dr. Ming Hung for all of his
encouragement and support during the thesis process. He was truly a wonderful advisor.
I would also like to thank my thesis committee members Dr. Eva Wu and Dr. Mark
Corson for all of their help, comments and support throughout this process. The guidance
of my thesis committee has been invaluable.
I would like to thank my colleagues and friends Dr. Dave LePoire, Steve
Bettenhausen and Dr. Bob Johnson at Argonne National Laboratory. They have helped
me achieve my goals and have been supportive towards these efforts beyond what I could
have hoped for.
The spatial modelers of the Army National Guard’s Weapons of Mass Destruction
Civil Support Team have my utmost gratitude for their heroic service and assistance in
this thesis. They are truly dedicated professionals who perform a brave service for their
country.
Lastly, I would like to thank my family for all of their support during my time at
Northwest Missouri State University. To my children Ellie and Max, you are my
inspiration for determination and perseverance. Finally, I would like to dedicate my
thesis to my beautiful wife, Katie. We have been through floods, kidney transplants and
multiple moves during this process, but we made it.
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INTRODUCTION
Geospatial data and the corresponding geographic information systems (GIS)
analysis are a critical and imperative part of the national effort to protect the United
States from terrorism and natural threats. It is imperative to identify the traits of a
successful GIS response, along with potential failures, in order to assess readiness, skills
and training of emergency personnel tasked with performing GIS functions in these
situations. Strategies can then be developed to reduce the gap between skills and needs to
improve planning and response to these events.
Effective geospatial responses to disaster and emergency management situations
increasingly relies upon on large volumes of accurate, relevant, on-time geographic
information that multiple organizations systematically, or at times not systematically,
create and maintain . The effectiveness and scope of geospatial responses has grown
over the past 20 years. Ad-hoc GIS responses to Hurricanes Fran (1996) and Andrew
(1992) proved that GIS was a critical tool in the recovery phase of these events. GIS,
spatial analysis and the related data have permeated many levels of government and the
private sector since these early events, as disasters and natural disasters appear to be on
the rise. Recent events, both natural and manmade, such as the 9/11 attacks, the Joplin,
Missouri tornado of 2011, the Gulf Coast hurricanes of 2005, the Madrid and London
terrorist bombings of 2004 and 2005, and the Japanese tsunami and subsequent nuclear
disaster of 2011 have strengthened the feeling that the world has entered a more
dangerous time. Potential threats of pandemic flu, dirty bombs, and smallpox reinforce
this perspective. Local, federal, state, academic and private entities are continually
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contributing spatial expertise to these events, creating a more efficient, safer response at
all levels (Blaschke & Schmidt 2006).
A 2007 National Research Council (NRC) report (Committee on Planning for
Catastrophe, 2007) illustrated the need to get geospatial operations up and running faster
in the event of a disaster. Yet, in many instances, the structure, knowledge, datasets and
data agreements that should support quick responses and immediate action in what is
called the “golden hours” (the first three days following a disaster) are not fully
operational. Local, state and government agencies including the National Guard, local
gas and electric companies and larger coordinating agencies such as the Federal
Emergency Management Agency (FEMA) and the Environmental Protection Agency
(EPA) are largely responsible for responding to large scale disasters during the critical
hours prior to the onset of an event, and during the first three days following a disaster. It
is during this time when the most lives are lost and the most destruction experienced.
During these “golden hours” when first responders will be most at risk and their
assistance most needed, they must have the best possible spatial information and
technology (Public Technology Institute and the Geospatial Information & Technology
Association, 2008).
In the Mid-Atlantic Region Geographic Information Systems Workshop held in
Towson, MD in July of 2008, Kenneth Ashe, Assistant Director of Geospatial
Technology Management, North Carolina Emergency Management Agency noted that
“lack of understanding GIS is a challenge. In some cases, there seems to be a lack of
knowledge of what data can do or offer. In other cases, there seems to be closed thinking”
(All Hazards Consortium, 2009). States such as California have recently recognized the
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need to identify and prioritize geospatial needs and capabilities, stating in the 2005
California GIS Strategic Plan that “This ongoing strategic process, to be closely aligned
with the state’s business strategic planning, will identify geospatial data needs and
development priorities with an emphasis on high-priority areas like homeland security”
(State of California, 2005). Murchison (2010) contends that training and education in
GIS, as it applies to homeland security and emergency management, is lagging far behind
endeavors in other areas, such as environmental science, urban planning, physical
geography and geology. Despite the creation of the Department of Homeland Security,
increased funding in the area of emergency management and the passage of the Patriot
Act, very little research has been performed on GIS based homeland security issues by
higher education institutions (Murchison, 2010).
In 2013, the National Academy of Science and the Committee on the Future U.S.
Workforce for Geospatial Intelligence published a report titled “Future U.S. Workforce
for Geospatial Intelligence” (National Research Council, 2013). The report cited the
evolving threats to the United States, including terrorism, asymmetrical warfare and
potential social unrest. The report stressed the need for the National Geospatial-
Intelligence Agency (NGA) to maintain a highly trained spatial workforce which will
accurately and efficiently deal with the threats facing the nation in the near future. The
goals of the report totaled four. These tasks included examining the current available
geospatial workforce, identifying gaps in the availability of geospatial intelligence
expertise, examining the academic infrastructure for geospatial intelligence disciplines
and lastly to suggest ways to build the necessary knowledge and skills in the U.S. for the
future geospatial workforce. The basic outcome and answer from the committee (covered
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in detail in the literature review of this thesis) was that the NGA was adequately staffing
all of their positions and critical areas with the exception of remote sensing experts and
geographic information scientists. Although the committee reported on an agency with
massive spatial analysis needs, capabilities and resources, the same lessons and findings
can be related to any agency, local, state or federal which has a role in responding to
emergency situations in homeland or national security. The need for expert spatial
analysts is growing, and the spatial education of the associated modelers is critical to a
quick and effective response.
Situational awareness is a basic and potentially life-saving requirement for all
entities, civilian, government and private companies involved in disaster operations. This
holds true for on-site first responders as well as managers and decision makers in the
offsite emergency operations center. In emergency responses, recognizing and
conveying spatial location is often the most necessary information for planning
coordinated responses, such as rescue measures and actions (Kevany, 2005). GIS and the
related technologies play a key role in conveying this potentially lifesaving information
to decision makers and first responders alike.
It is clear that geospatial technologies will continue to play a prominent role in the
security of the United States, both domestic and abroad. Wars in Iraq and Afghanistan in
the past decade have taken geospatial technologies to the limit by the necessity of
managing a tremendous amount of quality spatial data (Shroder, 2008). The military and
their supporting civilian counterparts have gained a copious amount of geospatial
skillsets and expertise at a strategic level, most notably at the tactical level in the wars of
the past decade. Yet, these skillsets and experience do not always translate to the United
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States’ focus of the American military, most notably the National Guard. Military and
civilian geospatial analyst need to know advanced technical methods in order to best use
these datasets towards the best possible outcomes for all. These spatial modelers must
maintain their current skills and expertise on a weekly, if not daily basis in order to fully
execute their mission to the best possible extent. This idea holds true for the spatial
modelers assigned to the Army National Guard’s Weapons of Mass Destruction Civil
Support Teams (WMD-CST), who must be prepared to deal with all emergencies and
disasters.
The formation of the WMD-CST’s was first announced by President Clinton at
the United States Naval Academy graduation in May of 1998. (GlobalSecurity.org,
2009). The certified WMD-CST’s provide unique capabilities, expertise, and
technologies to assist the governors in preparing for and responding to a chemical,
biological, radiological or nuclear (CBRN) situation. These WMD-CST units are
available 24 hours a day, seven days a week for rapid deployment for response
operations. The WMD-CST complements and enhances local and state capabilities. In
order to ensure that the WMD-CST’s are capable of a sustainable, rapid response in
support of a validated request for assistance, a response plan generated by the United
States National Guard Bureau outlines a standardized approach to provide WMD-CST
support anywhere in the United States (National Guard Bureau, 2011). Each WMD-CST
unit is required to have one main spatial modeler and one backup spatial modeler.
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Research Questions A fundamental component of security is information, specifically geospatial
information for homeland security and emergency management. Geographic information
systems and spatial analysis should play a major role in risk assessment and planning, the
mitigation of security threats, preparedness, response and recovery from natural or
manmade threats. This thesis will address the following questions:
1) What is the historical and current state of geospatial responses to homeland
security and disaster situations in local, state and federal agencies such as the
Department of Homeland Security agencies and the National Guard Weapons
of Mass Destruction Civil Support Team?
2) What are the current geospatial skillsets taught to the typical spatial modeler,
and how does this intersect their mission?
3) What training modules would help expand the scope and breadth of knowledge
for the WMD-CST spatial modelers and how would a web-based delivery of
these modules help them?
4) What future classes, certificates and training would help the WMD-CST spatial
modelers?
The focus of this thesis is the United States Army National Guard’s Weapons of
Mass Destruction Civil Support Team’s (WMD-CST) spatial modeling tasks,
capabilities, and training as it relates to these future challenges. This was accomplished
by distributing a questionnaire to a subset of the modelers which addressed the current
status of their training and capabilities, queried the skillsets they would like to obtain, and
surveyed the course delivery method these soldiers and airmen would prefer for
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additional training. This thesis analyzes the responses and documents the development of
three training modules which address their mission needs.
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LITERATURE REVIEW
GIS in National and Homeland Security
Geographic information systems (GIS) and the related spatial analytical sciences
have played a role in many of the recent natural disasters, emergencies, military actions
and terrorist attacks. What started as an unorganized and hasty response to hurricanes in
the early 1990s has matured into a coordinated and recognized asset to recent
emergencies such as the Joplin, MO tornado. These actions and the maturation of the use
of GIS in these events highlights the need for a first responder unit such as the WMD-
CST can greatly benefit from geospatial analysis in the planning phase, during the actual
event and in the resulting clean up and recovery efforts. As technology and access to
data increases over the years, it is critical that these WMD-CST modelers must be well
rounded and ready to deal with any type of situation. Whereas local government
agencies can prepare for specific disasters that are likely to happen in their jurisdiction
(i.e. hurricanes in Florida and the Gulf coast, terrorist attacks in larger cities, etc.), these
soldiers and airmen must be prepared to travel anywhere in the United States (and abroad
in the case of an emergency activation) and deal with any type of disaster the federal,
state and local governments may have to deal with.
A common theme in recent events reveal that the primary government agency in
which the incident took place was partially or fully incapable of handing the spatial
aspects of the disaster, often times having to wait for private entities to come to their
assistance in spatial matters. This is sometimes the case as a major component of the GIS
response was negated during an incident (i.e. the main GIS center may have been
seriously compromised during an attack or natural disaster) and sometimes this may be
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the case due to a lack of a basic GIS infrastructure (i.e. a small community or county with
little or no GIS capabilities). These phenomena decreases as GIS matures; the
government response to events in the post 9-11 era seem to run parallel if not ahead of
the private sector response and assistance. These lessons have significant ramifications
for the WMD-CST modelers as they must be prepared to deal with a lack of spatial data,
a lack of infrastructure and potentially a devastated landscape in the instance of a WMD
event. These modelers, much like other emergency spatial responders, must be trained
and equipped to act independently if called upon as the first spatial responder in an
emergency (Kevany, 2005).
Pre 9/11 GIS Responses to Disasters
GIS has been an important tool in responding to natural disasters such as
hurricanes since the 1990s. Hurricane Andrew represents one of the first large scale uses
of a GIS by FEMA (Dash, 1997). After Hurricane Andrew struck Florida in 1992, the
Federal Emergency Management Agency (FEMA) utilized mapping damage and
mapping neighborhood demographics in a recovery effort. The realization that GIS and
the associated spatial analysis could be hugely useful in not only a recovery phase but in
a preparedness phase prompted FEMA to invest in a Unix based MapInfo GIS system
with a 13 gigabyte hard drive, focusing on the recovery efforts in Florida in the year after
the hurricane. The disaster prompted FEMA GIS to collect and store basic GIS
information that could contribute to disaster response before the disaster occurred. These
datasets included evacuation zones and routes, hurricane and disaster shelters, nuclear
power plant evacuation zones, etc. FEMA’s GIS was also compelled to collect and store
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data which could assist in the effort to measure and track recovery efforts after a major
disaster. These datasets included electric service disconnect locations, postal service
forwarding data, FEMA public assistance data and the Dade County tax assessment files
(Dash, 1997). However, research conducted after Hurricane Andrew demonstrated that
the use of GIS for emergency response was still not accepted by many emergency
managers, despite the apparent success of the technology in the post event damage
assessment and clean up (Dymon, 1993).
A year after FEMA responded to Hurricane Andrew, the use of GIS in an
emergency situation matured when Hurricane Fran made landfall in North Carolina in
September of 1996. The hurricane made landfall on September 5, 1996 and quickly
intensified to a Category 3 storm. The storm surge caused extensive coastal flooding.
More than 10 inches of rain fell in less than 12 hours, lowlands were saturated, and rivers
swelled, eventually severely flooding inland areas as well (Dymon, 1999). GIS support
for the event began on September 5th and 6th, as the North Carolina Center for
Geographic Information and Analysis (CGIA) prepared Hurricane Storm Surge
Inundation Area maps. The maps produced by the center displayed historic storm surge
predictions for four southeastern coastal counties in North Carolina. A computer model
named SLOSH (Sea, Lake and Overland Surges from Hurricane) was enlisted to produce
maps showing potential areas of flooding for both fast and slow velocity hurricanes. The
maps showed land susceptible to flood inundation according to the severity of different
hurricane categories. The SLOSH model enabled the center to prepare basic evacuation
maps delineating the areas predicted to flood from the hurricane. The CGIA produced
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maps and datasets to various agencies using ARC/INFO, ArcView, MapInfo and Atlas
GIS (Dymon, 1999).
The efforts of the center did not go unnoticed as multiple agencies, both federal
and state, requested data and mapping products from the center. FEMA quickly
identified datasets of value at the CGIA. Even before Hurricane Fran arrived, FEMA
requested historical data related to hurricane storm surge inundation areas, state-owned
complexes, historical sites and districts, and natural heritage element occurrence sites and
sought county road maps with municipal boundaries. Other agencies recognized the value
of the CGIA’s pre-event efforts. The North Carolina Division of Forest Resources
requested maps of forest damage, which were eventually produced by overlaying various
forest cover layers with the inundation data. The Geographic Information Coordination
Council requested both a map of the declared disaster counties and a map depicting the
path of the storm. The North Carolina Department of Environment requested maps of
the damage which were later used for mosquito abatement efforts, and non-governmental
agencies such as the American Red Cross requested maps and data in the recovery effort
(Dymon, 1999).
These responses to natural disasters using GIS in the pre-9/11 era were genuinely
successful although not very widespread in use. Large agencies such as FEMA relied
upon local agencies to produce geospatial products and analysis at a local level. It
appears that these hurricanes prompted large city, state and federal agencies to see the
potential of GIS in emergency response, and led to the creation of such systems at a large
scale. Future events would prove that the efforts to construct these systems and collect
the data beforehand were well spent (Bliss, 2001).
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GIS Response to 9/11
The attacks of 9/11/2001 marked a major turning point in the area of emergency
response and homeland security. The Office of Homeland Security and the Homeland
Security Council were established in direct response to the terrorist attacks on the United
States on September 11, 2001. The mission of the Office “…shall be to develop and
coordinate the implementation of a comprehensive national strategy to secure the United
States from terrorist threats or attacks. The Office shall perform the functions necessary
to carry out this mission...” (Bush, 2001). This act of terrorism and the ensuing response
of GIS professionals demonstrated the value and utility of spatial data, analysis and
cartographic output in unmistakable ways (Cahan and Ball, 2002).
The GIS response to the 9/11 attacks in New York City were seriously crippled
from the outset as the city New York City lost its Emergency Operations Center (EOC)
and its GIS capabilities during the collapse of 7 World Trade Center (WTC) (Cutter,
2003). There were three main components directly supported the 9/11 response and
recovery efforts in New York City. The Urban Search and Rescue teams, supported by
the National Incidence Management Team provided detailed local maps around the
World Trade Centers. The Phoenix Group from the New York City Fire Department
used GIS and remote sensing, similarly supporting the efforts at the World Trade Center
Site, primarily in support of search and rescue. Finally, the major mapping activities for
the response were conducted at the makeshift Emergency Operations Center and were
coordinated by the New York City Information Technology (NYC IT) department. Many
private entities, private companies (including the Environmental Science Research
Institute - ESRI), academic institutions and private volunteers contributed to this effort
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(Thomas et al., 2003). Responding to the urgent and ongoing need for maps and spatial
analysis, Hewlett Packard, Esri, professors and graduate students from local colleges
were among those who supplied GIS support and equipment to the EOC, in addition to
GIS specialists from within the city government.
Initially, the GIS operations formerly operated out of 7 WTC were relocated to
the New York City Police Academy at 235 E. 20th St on September 11, 2001. This was
decreed by the Mayor Giuliani’s office, which placed a heavy emphasis on the role GIS
would play in the recovery effort. The GIS relief efforts quickly enlisted the help of the
Hunter College GIS Department in the area of site relief mapping, cartographic plotting
of large scale maps and the analysis of LIDAR data (Bliss, 2001). Hunter College had
played a large role in the creation of the city’s GIS system and was in an excellent
position to assist in these efforts. Hunter College had recently backed up and duplicated
the entire city’s GIS database, so the destruction of the EOC was not a complete
catastrophic blow to the spatial efforts (Walsh, 2002). These students and faculty
repeatedly demonstrated their usefulness in the processing of remotely sensed data in
support of the relief effort (Kendra and Wachtendorf, 2001). By September 14, a full
cadre of GIS analysts, GIS servers and plotters were working in full force at Pier 92, an
empty terminal on the Hudson River set up as an emergency mapping center. The
development of the map production and distribution capabilities at these temporary
centers quickly exceeded the capabilities that existed at 7 WTC, and the center quickly
resembled a mapmaking factory ((Kendra and Wachtendorf, 2003). This incident and
the improvised response is an excellent case study of a geospatial response to a disaster
despite a lack of central authority and command. The need for such volunteered
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expertise highlighted the lack of immediate geospatial expertise immediately available to
the disaster.
The geospatial actions performed after the 9/11 attacks in New York City provide
an excellent study of a geospatial response to a disaster. Although the response was vast,
many believe that the response was hasty and unorganized (Kendra and Wachtendorf,
2001). During the 9/11 attacks in New York, the spatial efforts which directly supported
the response and recovery efforts placed GIS in the spotlight as a valuable tool in support
of emergency management and response to major disasters. This seemingly prompted a
major boost for the emergence of GIS as a mainstay in responses to major disasters and
homeland security events, as future events relied heavily on this technology. Despite the
relative success of the GIS response during the post-event actions, a few shortcomings
were identified. These deficiencies included a lack of capability for real-time data
acquisition and management for the changing conditions in the response, a reliance on
paper plotted maps and GIS expertise for any GIS analysis and output (Kevany, 2005).
These shortcomings appear to have slowly been rectified over the next decade.
Post 9/11 GIS Responses to Disasters
In the post 9/11 era, GIS in emergency and disaster management has seen a
dramatic increase in use and sophistication. In 2003, the Center for Geographic
Information Sciences (CGIS) at Townsend University (Maryland) provided volunteer
GIS support to the Maryland Emergency Management Agency (MEMA) and the
Emergency Operation Center (EOC) during Hurricane Isabel in September, 2003
(Morgan, 2003). The CGIS, in an after action report, listed four major recommendations
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in the following four areas: data, hardware and software, procedures, and people. The
recommendations in relation to the “people” aspect of this report focused on the fact that
the state agencies represented at the EOC were not aware of the capabilities and data
available for the mission. Most notably, the CGIS recommended a training course for
interested state agencies in the aftermath of the hurricane. This exemplifies and
highlights the belief that many emergency response agencies are lacking in the proper
training and fundamental understanding of geospatial technologies.
In August of 2005, Hurricane Katrina surged through the Gulf of Mexico and
struck the Gulf coast areas from Florida to Texas. Massive damage was inflicted upon
the area due to the hurricane’s destructive winds and the resulting storm surge. The most
notable damage and fatalities occurred in New Orleans, Louisiana, which flooded as the
levee system catastrophically failed and the storm surge flooded many districts.
Although much of the media attention focused on the major urban areas such as New
Orleans, countless other municipalities and parishes suffered extensive damage and
losses. Nearly all areas affected by Katrina were mapped in one way or another with
GIS. Some were successful from the start whereas other areas were unprepared for such
a large scale disaster and relied on outside help for GIS assistance. Finally, outside
agencies such as the American Red Cross were compelled to deploy online GIS systems
in the relief effort (Boyd and Mills, 2007).
Whereas most of the media focused on the destruction and carnage caused by
Katrina in large cities such as New Orleans, smaller parishes, counties and cities were
equally devastated by the Katrina’s force. Hancock County (home of the Stennis Space
Center) in Mississippi was one such county that was hard hit by the hurricane. The
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county did not have an emergency GIS system in place, and relied upon federal, state and
private resources (NVision Solutions) to run a GIS in the relief effort (Boyd and Mills,
2007). Although the county possessed a fledgling GIS system in the tax assessor’s office,
it was readily apparent that the recovery effort would require an organized GIS effort.
NVision Solutions began assisting Hancock County a week after Katrina centered her
fury on the small county, quickly producing map books and placing hard copy maps in
the hands of first responders and recovery specialists (Adam et al., 2006). The company
developed a semi-formal relationship with the County Emergency Management Agency
(EMA) and eventually set up a lab in the Emergency Operations Center (EOC). A GIS
representative became an active participant in the daily EOC status meetings and offered
spatial solutions to daily problems or requests. Often times, the common request to the
GIS section was a simple road atlas, as many of the streets and street signs were rendered
unrecognizable during the event (Boyd and Mills, 2007). The creation and distribution
of county map books, including an accurate compilation of rural streets, waterways and
bridges, was completed weeks after the disaster struck (Adam et al., 2006).
Although the effort greatly assisted the recovery effort in Hancock County, a
number of valuable lessons were gleaned from the experience. First and foremost, there
was little in the way of an established GIS system, and the makeshift GIS lab was forced
to gather, scrounge or create much of the data themselves. These lessons learned are
something to be considered when training and preparing the CST modelers for future
events they may have to respond to. It is likely that these soldiers and airmen will be the
first “boots on the ground” and may encounter a similar situation as was found in
Hancock County after Katrina destroyed much of the infrastructure.
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Closely following the government response to Hurricane Katrina was a solid core
of private sector GIS efforts. The most notable of these entities was the American Red
Cross. Soon after the Hurricane made landfall, Esri specialists at the Redlands,
California headquarters helped guide GIS specialists with American Red Cross to
develop a web based “Shelter Locator ArcWeb Services Web” site (located at
arcweb.esri.com/redcross). This website provided critical mission information such as
address, capacity, population, and other descriptors available to both internal American
Red Cross staff and the public (ArcNews Online, 2005). The private sector cooperation
between these organizations exemplifies the growing ranks of non-government entities
who contributed effort, training and support to disaster relief in the post 9/11 era.
Another example of the maturation of geospatial support to disasters was the
Deepwater Horizon incident. On April 20, 2010 the Deepwater Horizon oil rig exploded
triggering an oil spill in the Gulf of Mexico. During the crisis first responders,
government officials, environmental experts, and commercial companies used GIS and
related technologies to monitor the oil spill, and identify the potential impacts to natural
resources and wildlife. Esri's disaster response team provided aide to users in local, state,
and federal government agencies as well as in the private sector by supplying software,
technical support, GIS data, and personnel. Dozens of agencies responded to the oil spill
using GIS for situational awareness, data collection, and analysis (Theodore, 2010). As
was common to previous disaster responses, academia stepped up and supported the
response with spatial assistance. Texas A&M University sent a team of GIS
professionals to a command post located in Houma, Louisiana. Devon Humphrey,
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geospatial intelligence officer and instructor at Texas A&M University in Corpus Christi
explained that:
"We came in to fight the spill and set things up so that others can rotate
through over the next several years. The plan is for these GIS
professionals to train replacement GIS staff as they rotate through the
Houma command post and provide them with certification from the
National Spill Control School at Texas A&M University. The training will
be conducted here in the GIS lab and will include a combination of GIS
for oil spills and the National Incident Management System [NIMS]
training required to work on this spill." (Theodore, 2010).
The state of Alabama also contributed to the relief effort, deploying new mobile
GIS technology to obtain response information. The Alabama Department of
Environmental Management used ArcGIS Mobile 10 to collect the locations and
condition of deployed booms. The application and the accompanying technology
allowed the marine police and resources officers to stream GPS coordinates to a laptop,
enter the appropriate attributes, and then edit the attributes. The data was then sent to a
server in near real time to provide the data to the planners. The group was able to
consolidate all collected data (imagery and maps) from state and federal agencies on oil
boom and oil slick information. The geographic information was portrayed to allow the
situation to be rapidly assessed in a geospatial manner by senior officials in the field
(Theodore, 2010).
Despite the well-planned strategy and well equipped staff, there were some limits
in the application of GIS largely due to ignorance and disregard of the National Incident
19
Management System (NISM) and Unified Command. The Oil Pollution Act of 1990
(OPA 90), following the Exxon Valdez oil spill in Alaska, mandated collaboration
between the government and company involved, the “responsible party.” Also mandated
were the Incident Command System (ICS) and the Joint Information Center (JIC).
Therefore, government agencies and private sectors involved, including the responsible
party, would work together to provide a unified response. Devon Humphrey and all the
GIS experts brought in were familiar with NIMS and Unified Command, but many of the
people at the Incident Command Post (ICP) in Houma were not. Humphery further
described the situation:
“I saw a real disconnect among some of the players on NIMS and that
became a constant battle in GIS because the people we brought in to work
in the GIS lab were familiar and experienced with NIMS, and a lot of the
people working for BP or as BP contractors were not. We were constantly
educating people on basic concepts like unity of command. BP treated it
more like a corporate security issue than a NIMS information-sharing
issue. They threw up all sorts of roadblocks relating to firewalls and
security and said, ‘Let’s manage the data from Houston.’ Things like
booms, and wildlife samples and water samples; that was all proprietary in
that it was behind their firewall and they weren’t necessarily sharing.”
(McKay, 2010).
Following a growing trend, the private sector responded quickly to the crisis with
data, online map applications and technical support. Esri set up a disaster response web
site that included continuously updated maps, data, and applications as well as links to
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related web sites. An interactive map application was also launched that allowed users to
add volunteered geographic information (VGI) in the form of links to photos, web sites,
and YouTube videos. ArcGIS Online also provided applications, services, data, and maps
to assist with response efforts. Services included an oil spill plume trajectory model, an
environmental sensitivity index map, fishery closure areas, and stranded marine life
(Theodore, 2010).
Another excellent example of the emerging and maturing use of geospatial
technologies in a disaster response is the Joplin, MO tornado of 2011. During the
afternoon hours of May 22, 2011, the small city of Joplin, in southwestern Missouri was
struck by an EF-5 tornado. The system produced winds in excess of 250 mph and tore a
swath mile-wide through the southern part of the city. It took the lives of 158 people and
injured more than 1,150. When it was over it had caused over $3 billion in damages to
the community (ESRI, 2011). It was the deadliest tornado strike in over 60 years and the
costliest in American history. People’s homes, businesses and lives were transformed
into roughly two million cubic yards of the debris, the equivalent of 400 football field
stacked three-feet high. Immediately following these events, the U.S. Army Corp of
Engineers, the Department of Homeland Security, the Federal Emergency Management
Agency (FEMA), U.S. National Guard, the City of Joplin were on the scene utilizing GIS
to aid in the construction of emergency temporary housing and facilities such as schools
and fire stations, and to spearhead the massive debris cleanup effort (Castagna, 2012).
The first step for the GIS specialists was to create maps using pre- and post-
disaster aerial photography to gain an understanding of the path and extent of the
destruction, and provide search and rescue and cleanup crews with roadmap for how to
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reach areas isolated behind the mountains of debris. Parcel boundary information and
city and county sewer, water and gas line information from the utility providers was also
utilized to provide crews with background information regarding where houses,
energized power-lines, gas lines, and water lines should be. Field crews were armed with
GPS units which could be used to record spatial data in the field. This information was
added to the existing maps created from the pre- and post- aerial photography, parcel, and
utility line information to create a more accurate representation of conditions on the
ground. Maps were updated daily and provided to the search and rescue and cleanup
crews who were maneuvering 500 trucks around a city clearing properties and removing
debris. These maps were crucial as the city was left largely devoid of street signs and
recognizable structures and intersections, making blind navigation through the city nearly
impossible. The team utilized GIS street data, aerial photography and parcel layers to
create dynamic maps in which they could color-code land-owner parcels. The team used
a distinct red-outline symbology to indicate that the property owner has signed a right-of-
entry form, allowing cleanup crews the authority to access the property and begin
removing debris, yellow-outline for properties which haven’t been signed, orange-outline
for properties that were in the process of being cleared, and green-outline for properties
that have already been cleared.
Using the tools provided by modern day technology such as GIS software and
GPS receivers, the task of searching and clearing a city of debris was done efficiently and
more systematically than relying on a strictly “boots on the ground” method. Nicholas
Laskowski, GIS specialist for the Galveston, Texas District, U.S. Army Corp of
Engineers who worked on the Joplin recovery has remarked that “One hour at the desk
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can probably save a whole day for one person out in the field” (Castagna, 2012). The
dynamic maps created for the field crews not only provided accurate and up-to-date
picture of the conditions on the ground, but also provided them with information
regarding the safety and suitability of the areas to be surveyed.
The GIS team was also tasked with determining appropriate locations for
temporary housing sites and critical public facilities, such as fire stations and schools.
For this they used the previously discussed data to locate areas away from the devastation
and outside of flood zones, but near utility lines so these structures could be powered and
plumbed. In some cases it was important to find facilities in close proximity to where
they once stood. For example, temporary school structures should be close to their
original locations to more easily provide for students in that area. The same can be said
for firehouses, finding temporary locations near where they once stood are essential in
providing an adequate level of coverage for the community. Not only could GIS provide
the team with areas that met these criteria, but also afforded them with parcel background
information, such as the owner of the property, tax ID number and square footage.
Further streamlining the process and enabling a single user or team of users the ability to
analyze several different features quickly and precisely. With this information land
owners could then be contacted and asked if they would be willing to rent or sell their
property so it could be used for a temporary housing or critical public facilities location
(Castagna, 2012).
23
GIS Needs Assessment
In 2013, the National Research Council released a 243 page report entitled
“Future U.S. Workforce for Geospatial Intelligence”. The larger task of the committee
was to determine if the current geospatial workforce demands were in line with the
available workforce expertise that exists both in current times and in the future for the
National Geospatial Intelligence Agency (NGA). Although the NGA has a bit of a
different mission than those involved in the first responder sector of the GIS field, this
study is worth examining as these field’s, base skills, qualifications and ultimate output
goals are very similar and often times overlap. The NGA is tasked with providing data
and resources to the homeland security effort through programs such as the Homeland
Security Infrastructure Program (HSIP). Therefore, first responders and emergency
response GIS personnel should at least somewhat possess the same skillsets as the NGA.
The committee, which was made up of a mix of academic representatives and
National Research Council members, approached the task by addressing four major areas.
The committee set out to:
1. Examine the current availability of U.S. experts in geospatial intelligence
disciplines and approaches and the anticipated U.S. availability of this
expertise for the next 20 years. The disciplines and approaches to be
considered include NGA’s 5 core areas and promising research areas
identified in the May 2010 NRC workshop.
2. Identify any gaps in the current or future availability of this expertise relative
to NGA’s need.
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3. Describe U.S. academic, government laboratory, industry, and professional
society training programs for geospatial intelligence disciplines and analytical
skills.
4. Suggest ways to build the necessary knowledge and skills to ensure an
adequate U.S. supply of geospatial intelligence experts for the next 20 years,
including NGA intramural training programs or NGA support for training
programs in other venues.
The five core NGA areas discussed in the first area include geodesy and
geophysics, photogrammetry, remote sensing, cartographic science and GIS. The
promising academic research areas identified in the May, 2010 conference included
GEOINT fusion, crowdsourcing, human geography, visual analytics and forecasting. The
committee found that the current number of citizens with appropriate education in the
core areas is most likely on the order of hundreds for geodesy and geophysics, on the
order of tens for photogrammetry, hundreds to thousands for remote sensing, hundreds to
thousands for cartographic science and thousands for GIS and geospatial analysis. In the
emerging areas cited by the committee, the current number of citizens with appropriate
education in specific areas was likely tens to hundreds for GEOINT fusion, tens to
hundreds for crowdsourcing, tens to hundreds for human geography, tens to hundreds for
visual analytics and hundreds to thousands for forecasting. The committee also reported
that more than 100,000 U.S. citizens are employed in jobs and occupations closely related
to the core areas. Assuming on the job training were an option for the NGA, the
committee decided that the available workers in these areas would increase to 200,000
new graduates and 2.4 million experienced workers. Assuming the accepted 10 year
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growth trends continue, the number of new graduates in these fields could possibly range
between 312,000 to 649,000 by the year 2023.
In response to the second area of concentration, dealing with the identification of
any gaps in the current or future availability of this expertise relative to NGA’s need, the
committee’s conclusion revealed that there would be gaps in the future. Specifically,
these gaps would occur in a lack of candidates with what the committee called ideal
skillsets. These skills include spatial thinking, scientific and computer literacy,
mathematics and statistics, languages and world culture, and professional ethics.
Although the current supply of U.S. experts is higher than the demand of the NGA, future
competition from private industry and lower graduates in core areas will likely lead to
shortages in the NGA workforce of experts in all of the emerging areas in addition to the
core areas of cartography, photogrammetry, geodesy.
The third area addressed by the committee described U.S. academic, government
laboratory, industry, and professional society training programs for geospatial
intelligence disciplines and analytical skills. Examples of such programs were cited by
the committee, most notably universities which have a long history in these areas, a
number of accomplished instructors and professors, a large number of students
participating in these programs and programs that provide the opportunity to solve real
world problems encountered by the NGA. A number of large universities were cited in
the report, largely because they filled one or more of these characteristics. For example,
the University of Colorado’s Department of Geography was cited as an institution which
offered comprehensive coursework in areas important to the NGA. Carnegie Mellon
was referred to as a university which instills in the students the ability to work and think
26
spatially across multiple fields of study. Other universities, such as North Carolina State
and George Mason University, were noted to combine scientific knowledge and spatial
skills.
In addition to the academic institutions referenced in the report, the committee
cited a number of non-academic entities which provided professional training and skills.
The Environmental Science Research Institute (Esri) was noted to offer practical software
training. Governmental agencies such as the National Weather Service’s Warning
Decision Training Branch offer specialized training unique to the agencies’ needs and
missions. Short courses and workshops were cited as a means to provide short, intense
and immersive training in specialized areas related to the security mission of the NGA.
The fourth and final area of concern for the committee dealt with the issue of
finding ways to build and train the necessary workforce the NGA will need for the next
20 years. The committee suggested a number of actions, including those that would have
long standing implications (i.e. long standing research partnerships with industry, support
to notable universities and curriculum development) and actions which would have more
immediate impacts and results (i.e. support of professional workshops, the creation of
virtual centers and an expansion of current outreach programs). The committee posited
that the need in this fourth area is greatest in the emerging areas, which are producing the
fewest number of new graduates and would need the most support to the academic
organizations which would ultimately take over this role. Finally, the committee
recommended that if the NGA did increase their support to all of the mentioned
organizations, they could hold a certain amount of influence over the direction these
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organizations took geospatial education, training and professional development into the
future.
In essence, the committee reported that there are going to be some gaps in the
needs for geospatial analysts in the near future. The committee posits that the NGA is
most likely successfully finding an ample supply of experts in the core areas, although
the committee hints that they may have some shortfalls in the area of GIS and remote
sensing experts. The areas of photogrammetry, cartography, and geodesy are likely to
see labor shortages in the near term. Finally, the emerging areas are likely to see
shortages in the long term. The committee believes that although these potential labor
shortages should be of concern to the NGA, the agency possesses the mechanisms and
clout to ensure that this trend reverses. These mechanisms will strengthen existing
academic and government training programs, influence institutions and agencies to
promote education in the emerging areas and to enhance their recruiting in emerging and
deficient areas. If these areas are addressed, the committee believes the NGA can
achieve their long term labor needs.
The committee presents the geospatial labor issue in an interesting manner,
although it may be argued that they have overlooked a number of key factors in this
report. The committee appears to be a bit self-serving when surveying the landscape of
potential hires. The committee tended to look at only the larger universities, not
surprisingly many of the universities the members of the committee teach at. Whereas
these institutions are very important to the overall geospatial workforce, both present and
future, it may be argued that smaller universities and junior colleges may be equally as
important for the future of the workforce. Note that Hunter College in New York, a
28
relatively small entity, was responsible for the major initial thrust in the city’s response to
the 9/11 attacks. Increasingly, geospatial programs are becoming more prominent in
mid-level universities and junior colleges. The committee somewhat ignored the
potential contributions of junior college programs. The study completely overlooked the
potential of the military to train and produce future geospatial workers for the NGA, even
though the WMD-CST modelers may appear to be a very good fit for potential
employment.
This report is an interesting examination of the workforce needs for the NGA. A
similar study could be undertaken to look at the current and future geospatial needs of
emergency responders in the United States. The study could be modeled after this
committee and address the potential skillsets needed to staff units such as the WMD-
CST, local, state and federal emergency operations centers and larger agencies such as
FEMA, the Federal Bureau of Investigation and other agencies with an emergency
response or law enforcement mission in the United States. The education of this
specialized labor force could also be conducted at local universities, smaller colleges and
junior colleges. This proved true in the case of Hunter College’s response to the attacks
of 9/11 and in the case of the GIS support provided by Townsend University to the
Maryland Emergency Management Agency (MEMA) and the Emergency Operation
Center (EOC) during Hurricane Isabel in September, 2003. These institutions, with the
help of private entities such as Esri and the Red Cross, often prove to be the backbone of
support during a crisis.
29
The Role and Training of WMD-CST
The WMD-CST are authorized by Congress and designed to provide a specialized
capability and to respond to a chemical, biological, radiological or nuclear (CBRN)
incident or other natural or manmade disasters within the 50 United States, the District of
Columbia, and its territories and possessions. These units are authorized to support
emergency preparedness programs and respond to any emergency incident involving
1. the use or threatened use of a weapon of mass destruction;
2. a terrorist attack or threatened terrorist attack in the United States that results,
or could result, in catastrophic loss of life or property;
3. the intentional or unintentional release of nuclear, biological, radiological, or
toxic or poisonous chemical materials in the United States that results, or
could result, in catastrophic loss of life or property; or
4. a natural or manmade disaster in the United States that results, or could result,
in catastrophic loss of life or property (National Guard Bureau, 2011).
According to National Guard Regulation 500-3, a WMD-CST consists of 22
Army or Air National Guard soldiers and airmen on Full-Time National Guard (FTNG)
duty status; also known as State Active Guard/Reserve (AGR) status. A unit is made up
of six sections, including command, operations, administration/logistics,
medical/analytical, communications, and survey (National Guard Bureau, 2011). They
are available 24 hours a day, 7 days a week and 365 days a year to respond to the events
described above. Often, the WMD-CST units are the first WMD specific units to arrive
on the scene of a disaster, as they are spatially located in the state of the emergency.
Federal assets often rely upon the units to act as their first responders when it comes to
30
the analysis of chemical, biological, radiological and nuclear (CBRN) materials or events
(Viana, 2012). They are a federally funded but state controlled unit, meaning that the
respective state governors are usually the authority approving the deployment of these
units.
The WMD-CST teams have been activated to perform a variety of missions in the
past few years. From October 2010 through September 2011 (the fiscal year 2011), the
WMD-CSTs were activated and deployed a total of 128 times in response to potential
WMD incidents. These missions included responses to “white powder” incidents
(suspected anthrax attacks), chemical emergencies, clandestine laboratories (i.e.
hazardous chemicals abandoned or left behind from illicit drug laboratories). Outside of
the typical WMD related responses, the units were called upon to perform more
traditional National Guard duties, such as a response to the BP Deepwater Horizon oil
spill disaster and in support of the recovery efforts after Hurricane Irene. In addition to
these emergency responses, the WMD-CST units were called upon 504 times for what
are described as standby missions, in which the units are called on to support local, state
and federal agencies for large scale events and national security events (NSE). These
events typically include stadium and sporting events, political events and other special
security events in conjunction with hosting government agencies. The WMD-CSTs
participated in 1,092 training events in which they trained with and met local, state and
federal agencies with similar missions (Viana, 2012). The WMD-CST units typically
provide expert support in relation to air sampling and chemical, radiological and nuclear
detection. In the first half of the fiscal year 2012 (October 2012 through March 2013),
the CST missions remained consistent with the levels from the previous year. The units
31
were called on a total of 349 times for standby missions, 51 times for potential WMD
events and 685 assist missions (Viana, 2012). Multiple WMD-CST teams were on the
ground and responded to the Boston Marathon bombing in April of 2013 (Grimm, 2013).
The WMD-CST units are required to train and maintain a primary and backup
spatial modeler. These modelers are trained by the National Guard Bureau (NGB) in the
basic use of the ArcGIS software program and various plume modeling software and
programs. The training occurs in a two week period with the basic modeler’s course. As
is discussed in chapter four of this thesis, the main job of the spatial modeler is to provide
the command staff of the CST-WMD teams quick and accurate geospatial products
related to potential WMD events. These spatial modelers are required to support the
mission of their respective units various deployments. As was discussed, these
deployments may include standby missions (i.e. stadium type events) and responses to
natural disasters.
Higher education institutions and government agencies such as the Department of
Energy are well positioned to respond to this need through the integration of spatial
technology education into programs that provide instruction in the principles and tools
associated with homeland security as well as in their research and community outreach
activities. In addition to training professional GIS personnel, there is an opportunity to
train emergency managers and others already integrated in the system. This may be
accomplished by offering a comprehensive curriculum in a certificate form. This thesis
will address potential circumstances where both professionals and fledgling GIS
personnel may receive basic and advanced training in the area of homeland security and
GIS.
32
CREATION OF WMD-CST TRAINING MODULES
Study Area
The study area (Figure 1) for this thesis will focus on the National Guard WMD-
CST from the area covered by the Department of Energy’s Radiological Assistance
Program (RAP) Region 5 (Figure 2). This area includes 10 states in the north central
portion of the United States. These states include North Dakota, South Dakota,
Nebraska, Minnesota, Iowa, Wisconsin, Illinois, Indiana, Michigan and Ohio. The
WMD-CST Modelers in these states responded to the surveys and the modules were
Figure 1: Map Displaying the Weapons of Mass Destruction Civil Support Teams (WMD-CST) in Radiological Assistance Program Region 5
33
tailored to their needs and concerns (note that these teams are often called outside of their
own state, so an inundation module in New York is still relevant).
The geographical regions covered by the developed training modules will focus
on the greater Chicago area, New York City and Argonne National Laboratory. The first
training module included a 50 mile buffer starting at the Advanced Photon Source
building at Argonne National Laboratory and affecting the great Chicago area. The
buffers were intersected with census data to determine potential hazard and population
zones in the event of a hazardous materials release (Figure 3). The second module
focused on a hypothetical storm surge on the Atlantic Ocean and the potential effects of
Figure 2: Map Displaying the Department of Energy Radiological Assistance Program Regions
34
the storm on New York City. Even though this module does not fall within the
boundaries of the fifth RAP region, the CST teams in this region are often called outside
of their states to assist other CST units. Finally, the third module focused on radiation
walkover data artificially placed on the Argonne National Laboratory grounds to simulate
data entry and interpolation of sampled data. The laboratory is often used in these
exercises for the WMD-CST teams in the area.
Figure 3: Study Area of the Argonne Based Population Wind Rose
35
Data Sources Data for the developed training modules will partially be user generated and
partially gathered from official sources. The following data will be downloaded from the
website specified, and be provided to the users along with the modules:
Census 2010 GIS data, downloaded at http://www.census.gov/geo/maps-
data/data/tiger.html.
Boundary data (boroughs), for New York City downloaded from the New York
City Department of City Planning at
http://www.nyc.gov/html/dcp/html/bytes/districts_download_metadata.shtml.
New York City elevation data, evacuation routes, hurricane evacuation zones and
emergency shelters downloaded from New York City Open Data Center at
https://nycopendata.socrata.com/.
Hurricane Irene’s predicted path GIS data downloaded from the National Weather
Service Hurricane Center at
http://www.nhc.noaa.gov/gis/archive_forecast_results.php?id=al09&year=2011&
name=Hurricane IRENE.
New York City elevation data downloaded from the Columbia University spatial
data catalogue at http://sedac.ciesin.columbia.edu.
Methodology The thesis was completed in three major stages: research, surveying the WMD-
CST modelers and creating and refining the training modules. The first stage involved a
detailed search through related literature. The second stage involved the creation and
administration of survey questionnaires to selected spatial modelers from WMD-CST.
36
The third stage involved the creation of the training modules based on the findings of the
first two stages.
The first stage involved researching the various geospatial responses to disasters,
natural and terrorist related, within agencies such as the Department of Homeland
Security (i.e. FEMA), local responses to such disasters and the WMD-CST spatial
modeling sections. Past responses were studied and examined and the evolution of these
responses was noted with the intent of examining and comparing the current and potential
skills of the CST modelers. The summarized findings of this first stage can be found in
the literature review chapter of this thesis.
The second stage involved the creation of a questionnaire with the intent of
researching the demographics and training of the WMD-CST modelers (Figure 4). These
Figure 4: Sample Questionnaire Presented to WMD-CST Modelers
37
soldiers and airmen were questioned on various factors in their roles as the geospatial
modeler for their respective unit. These questions focused on the demographics of the
modelers, the official training they have been provided by the United States military,
their level of expertise and comfort level while conducting spatial analysis, their
education level, the software and hardware they are comfortable using and the type of
advanced training they would like to receive. In anticipation of the hypothesis that these
modelers would most likely be open to training in a web based format, the modelers were
asked if they would be interested in online training sessions and a possible certificate in
GIS for Homeland Security. It was assumed that the online format would be the training
method of choice due to the numerous deployments these soldiers and airmen experience
on a yearly basis. These results of the study are reported in chapter four of this thesis.
The complete exercise modules, featured in the appendix, focus on the specific requests
and skills identified in these surveys.
The third stage of the thesis involved creating a series of three training modules
for the WMD-CST modelers. The training modules were written in a step by step format,
with specific discussions placed on the various spatial analyses and geoprocessing tasks
highlighted throughout these modules. The emphases on these modules focused on data
acquisition, data storage in geodatabase format and the relationships between these
different datasets and how they can be analyzed and processed into meaningful mission
intelligence. These modules were created with the specific needs of the WMD-CST
modelers in mind, mindful of the successful responses and skillsets studied in the second
stage of this thesis. The specific responses to the questionnaire are discussed in detail in
the analysis results and discussion section.
38
The first module involves the creation of a windrose at Argonne National
Laboratory using Esri’s ArcMap GIS program. The windrose, which will be created as a
surrogate for a plume model, will intersect with census population data to estimate the
affected populations at multiple distances and directions from the laboratory. Although
the preferred method of instruction would be to have the modelers interact with the
census data through the use of the Hazardous Prediction and Assessment Capability
(HPAC) model preferred by the military, the use of this program is restricted to
government use only, and any output generated by the program is considered official use
only. The module begins with the creation of a primary geodatabase and a “scratch”
geodatabase, used for geoprocessing output. The modeler will identify a central location
at Argonne (the center of the Advanced Proton Source), create a center point, followed by
a multiple ring buffer at distances of 1, 2, 3, 4, 5, 10, 20, 30, 40, 50 miles. The buffers
will be edited and the polygons will be cut, using the snapping, direction and distance
tools in ArcMap. The cuts made to the polygons will be made in the 16 cardinal
directions (N, NE, NNE, etc.). The attribute table of this layer is then edited to reflect the
direction and distance of each section of the resulting windrose.
The windrose, now attributed with distance and directional data, is unioned with
census 2010 population data. The resulting feature class is then recalculated and
summarized for a total population in each sector using the population density field of
each census block group and the resulting area of the new polygons. This module
provides an introduction of a number of geoprocessing skills and spatial functions in the
process. These skillsets include the creation of geodatabases, data management, multiple
buffers, spatial intersections and unions, table calculations, advanced editing techniques
39
and table summarizations and statistical analysis. The rationale behind the creation of
this module based on the responses of the WMD-CST modelers is discussed in the
analysis results and discussion section of this thesis and the full module can be found in
the appendix.
The second module created for this thesis involves a hypothetical spatial response
to Hurricane Irene in New York City using Esri’s ArcMap GIS program. The module
assumes the modeler is deployed to New York City and is working with FEMA to create
geospatial products for the use of decision makers. The module guides the modeler
through the creation of a primary geodatabase and a “scratch” geodatabase, used for
geoprocessing output. The modeler is then tasked to create a city base map using the Esri
online streets layer, borough boundaries and the appropriate projection. The modeler is
then instructed on the creation of a customized layout view in ArcMap, including the
creation of a proper scale bar, north arrow and logo. This base map will serve as a
template for the other maps created in this module.
The next product created for the second module involves a predicted hurricane
path map. The modeler is instructed to visit the National Oceanic and Atmospheric
Administration (NOAA) website and retrieve multiple days’ worth of predicted path data
and ultimately store them in the user created geodatabase. Using this data, an additional
product is created for the module.
The third product generated for this module involves the retrieval of emergency
management data from the New York City Open Data Center. The modelers are guided
through the process of locating, downloading and transferring evacuation route data,
evacuation zone data and emergency shelter data to the previously created geodatabases.
40
These layers are added to the base map, a proper table of contents is created and a third
output graphic is created.
The final product of the second module involves the creation of an inundation
model for the New York City, displaying the potential areas of concern should a storm
surge strike the city. The modeler is instructed to download a digital elevation model
from the Columbia University spatial data catalog. And the process of extracting this
data to the geodatabase is explained in this module. The elevation model is classified
into multiple break values denoting potential inundation levels of one foot intervals up to
five feet. The modeler is then instructed to use a masking technique to clip the raster
dataset for aesthetic purposes. A final product displaying potential areas of inundation is
then created.
The third module involves the conversion and analysis of field collected radiation
data. The radiation data is presented to the modeler in excel format. The data is real and
was collected from an unidentified contaminated site. The data was transformed to fit on
the Argonne National Laboratory site and the coordinates were recalculated and
presented in this module. The modeler is initially instructed to create a final and
“scratch” geodatabase in Esri’s ArcCatalog to store the eventual geospatial data. The
modeler is then guided through the process of examining the data in Excel and an
explanation of the “counts per minute” (cpm) field is explained. The data is then
imported into Esri’s ArcMap program.
Once the tabular data is imported into ArcMap, the modeler is instructed to covert
the data into a feature class. The resulting point file is then examined and symbolized
with breakpoints of 1,000, 1,800, 2,500 and greater than 2,500 cpm. These break points
41
are described as significant as they represent the lower and upper trigger levels of the
dataset as defined by health physicists who analyzed these locations using a non-
parametric statistical approach.
The next steps of the third module involve the interpolation of these point files
using Inverse Distance Weighted (IDW), Kriging and Natural Neighbor methods. The
modeler is first instructed to create a product using the IDW method. An explanation of
the IDW methodology is presented to the modeler and detailed instructions are provided
on the method used to create the resulting raster file. The same break values (1,000,
1,800, 2,500 and greater than 2,500 cpm) are applied to the raster grid as were applied to
the original point data for comparison purposes. The same steps are taken for the Kriging
and Natural Neighbor methods. Finally, the modeler is asked to compare and contrast the
three methods with the original point dataset.
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ANALYSIS RESULTS & DISCUSSION
Questionnaire Responses
The WMD-CST questionnaires were gathered and the results were tallied and
personal correspondence was conducted to follow up on the questionnaires when
appropriate. The information gathered from the questionnaires and personal contacts
were used to construct three training modules which satisfied both the perceived and
voiced needs of the WMD-CST spatial modelers. The three modules included lessons
and processes involving various spatial, geoprocessing and data acquisition tasks, all
tasks or skills identified as being needed by the modelers and skills that were in line with
best practices identified in open literature.
One detail of the collected information will not be discussed for confidentiality
and security purposes. The names, states and units of the individual spatial modelers will
be kept confidential for security purposes and as to not indict them for a perceived
deficiency in skills or training. They will be referred to as CST modeler #1, CST
modeler #2, etc. throughout this thesis.
WMD-CST Modeler Education and Background
In the 10 states covered by the Radiological Assistance Program, Region 5, there
are 10 CST-WMD units (as displayed in Figure 1). Of these units, there are 10 full time
spatial modelers and seven backup modelers as three of these positions are currently not
filled. Of the 17 possible spatial modelers, 13 modelers responded to the survey,
representing 75% of the potential responses. Of the 13 modelers, 10 were members of
the Army National Guard (associated with the U.S. Army), and three were associated
43
with the Air National Guard (associated with the Air Force) (Table 1). 10 of the 13
modelers possessed the rank of Sergeant First Class (SFC), otherwise known as an E-7
(enlisted grade 7). On a scale of E-1 (“Private”) to E-9 (Command Sergeant Major), the
majority of these modelers are very high in rank. The remaining three modelers possess
the rank of Master Sergeant, otherwise known as an E-8. Four of the modelers identified
that they had “some college” or less than an associate degrees worth of college. Four of
the modelers have earned an associate degree and four have a bachelor degree. One of
the modelers possesses a master’s degree. There is a large degree of homogeneity in
terms of military service and rank in this group, but a somewhat large disparity in
educational degrees earned.
Table 1: WMD-CST Modeler Survey; Branch Rank and Education
CST Branch Rank Education
#1 Air National Guard E-7 BA
#2 Air National Guard E-7 AD
#3 Army National Guard E-7 AD
#4 Army National Guard E-7 Master's Degree
#5 Army National Guard E-7 Some College
#6 Army National Guard E-8 BS
#7 Army National Guard E-7 BS
#8 Army National Guard E-8 Some College
#9 Army National Guard O-3 BA
#10 Army National Guard E-7 Some College
#11 Army National Guard E-7 AD
#12 Air National Guard E-7 BS
#13 Army National Guard E-8 Some College
44
Table 2 displays the questionnaire results as they relate to the modeler’s military
background, current modeler status and tenure as the spatial modeler. In terms of
military experience, the modelers shared a similar set of military training backgrounds.
Nine of the 13 modelers identified their primary military occupation specialty (MOS) as
“Operations NCO/Modeler”. Two of the modelers identified their MOS as being related
to education and training. The other two modelers had backgrounds in cyber security and
signal systems. Of the questionnaire responders, nine identified themselves as the
primary spatial modeler for their unit, and four identified themselves as the backup
modeler. The average length that the modelers have acted in that role is approximately
3.6 years. The expected tenure as their unit’s primary or backup spatial modeler is 6.4
years (with one respondent stating he wished to stay a modeler as long as he could).
Table 2: WMD-CST Modeler Survey; Military Specialty, Status and Length of Modeler Service
CST Military Specialty
Primary or Backup Modeler
Length as Modeler
How Long Expected to Stay as Modeler
#1 Cyber Security Backup 3 4 #2 Education and Training Primary 4 4 #3 Signal Systems Specialist Back‐up 4 8 #4 Operations NCO / Modeler Primary 7 8 #5 Operations NCO / Modeler Primary 1 4 #6 Operations NCO / Modeler Primary 2 4 #7 NCO/modeler Primary 7 13 #8 Operations NCO / Modeler Primary 2 As long as I can
#9 Medical Backup 0 4 #10 Operations NCO / Modeler Primary 4 8 #11 Operations NCO / Modeler Primary 4 8 #12 Education/Training Primary 1 4 #13 Operations NCO / Modeler Backup 5 8
45
These results state that the modelers have a decent amount of experience in the
job of modeler and would probably have a good idea of what they need to know in terms
of geospatial skills and knowledge.
WMD-CST Modeler Geospatial Analysis Experience Table 3 displays the questionnaire results as they relate to the modelers current
and past spatial training history. Of the 13 modelers, only two reported having any
spatial training before becoming a modeler for the CST teams. Modeler # 7 had a
previous MOS of “Army Surveyor” and Modeler #8 reported previous experience was
simple mapping programs such as Regional and State Online Resource for Emergency
Management (RaSOR-EM), a program that is a contributor to the National Guard’s
Common Operating Picture. These responses show that most of the baseline experience
for incoming WMD-CST modelers is minimal, proving the need for constant retraining
and continual training exercises beyond the basic modeler course which every modeler
must take.
Table 3 also addresses the spatial training the modelers received from the military
and that which they may have pursued on their own. In regards to the official GIS
training received from the military, all spatial modelers must take the required eight day
training course. Of the 13 responses to the survey, eight claimed this course as their only
formal spatial education. Other modelers claimed “ArcMap I” and “ArcMap II” as
official training.
Aside from the required eight day training course (described in detail below) all
modelers are required to take, the extent of extra training and education in geospatial
studies is minimal in most cases. Some of the modelers profess to subscribe to the ESRI
46
Table 3: CST Modeler Survey; GIS Training and Training History
CST Previous GIS GIS Training from Military GIS Training on Own
#1 None Attended CST Modelers Course via Battelle
Subscription to GIS periodicals
#2 None Basic Modeler Course and ArcMap 1 & 2 ESRI User Conferences
#3 None
ARC GIS I &II, GIS for Law Enforcement
Additional skills from other modelers like plotting, using military analyst, also training from DTRAs GIS personnel
#4 None
ArcGIS I and ArcGIS II, as well as some specific Hazardous materials modeling software
Home studies on geospatial databases and GRASS
#5 None Basic Modeler Course
Have attempted to attend courses but they are currently not offered
#6 None
A very basic overview of the aspects of GIS that we require. It was part of our Modeler Course and also included hazardous plume modeling training. Additional courses are available and I plan on attending those in FY14.
None, just OJT type training and some side by side training with GIS specialists from other departments.
#7 Army Surveyor
10 days of ArcGIS courses at PEC (Professional Education Center)
Training put on by other CST modelers
#8
Simple mapping programs and tools such as Rasor‐EM
Very basic ArcGIS training thus far, with additional hazard modeling programs like Hazardous Prediction and Assessment Capability (HPAC) and Vulnerability Assessment and Protection Option (VAPO).
Small group training and local conferences
#9 None None None #10 None ArcGIS I and ArcGIS II Online ESRI modules
#11 None GIS for CST modelersSome training from DTRA and DOE
#12 None GIS course for modelers
Learned from other government modelers (DOE)
#13 None CST Modelers CourseESRI conferences & training
periodicals and online courses, and a few list local and national conferences as a source
of training. The following paragraphs describe the basic modeler’s course and the skills
and software they are taught in this training.
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WMD-CST Modelers’ Mandatory GIS Course
Every WMD-CST modeler must complete an eight day basic spatial modeler
course. The information describing the modeler course was received from personal
correspondence and note sharing with CST Modeler #5, who shared the course modules
and class notes. The course is divided into four sections spanning eight days
(summarized in table 4). The first three days deal with an introductory module which
explains the course expectations, introductions and the final exam for each module. The
final exam includes a scenario based practical exercise in which the modelers are
required to produce a number of maps and data to supply the appropriate information to
their unit. The modelers must use all or a portion of the software programs (whichever is
applicable) in order to accomplish this task. The modelers are required to score a 75% on
the evaluation in order to receive a “Go” (or pass) for the course. The second module of
the first three day section deals with ArcGIS software. The terminal learning objective of
Section Length Description
1 3 Days ArcMap Basics, course expectations
2 2 Days Plume modeling using the Hazard Prediction and Assessment Capability (HPAC) tool
3 1 Day Computer-Aided Management of Emergency Operations (CAMEO) software
4 2 Days Three final exams
Table 4: WMD-CST Basic Spatial Modeler Course
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this module is to “Operate the ArcGIS standardized CST hazard modeling software in
accordance with user manual”. The instruction includes nearly 300 slides dealing with
the ArcGIS program, dealing with all of the basics of the program (map navigation,
adding and removing data, ArcCatalog, symbolizing features, the Military Analyst
extension, etc.). The three days of lessons are filled with 19 exercises pertaining to the
military and WMD missions of the WMD-CST.
Days four and five of the WMD-CST spatial modeler course deals with one of the
modelers’ major tools for assessing plume and various aerial releases of toxic and
radiological materials, the Hazard Prediction and Assessment Capability (HPAC) tool.
The terminal learning objective of the two day module is to “Operate HPAC hazard
modeling software in accordance with user’s manual to produce hazard mitigation
products”. Produced by the Defense Threat Reduction Agency, the program and all
results generated from the program are considered official use only and will not be
further discussed.
Day six of the WMD-CST spatial modeler course deals with CAMEO (Computer-
Aided Management of Emergency Operations), which is a software tool for emergency
planning and response. The terminal learning objective of this one day module is to
“Operate CAMEO software in accordance with user manual to produce hazard mitigation
products”. CAMEO is a collection of software programs designed to assist first
responders and emergency planners to access chemical property and response
information, model potential chemical releases, display key locations and release
predictions on a map and manage planning data. The CAMEO model, developed by the
National Oceanic and Atmospheric Administration's Office of Response and Restoration
49
(NOAA OR&R), and the U.S. Environmental Protection Agency's Office of Emergency
Management (EPA OEM), contains eight databases related to facilities, chemicals in
inventory, contacts, incidents, special locations, routes, resources, and
screening/scenarios. The modelers are taught how to use CAMEO to access, store, and
evaluate information critical for developing emergency plans and quickly, safely and
efficiently respond to these incidents.
The final two days of the WMD-CST spatial modeler course are the final exams.
There are three total exams, and the modelers must score at least a 75% on these exams in
order to pass the class. The first practical exam presents a scenario where a massive
accident involving a hazardous chemical has occurred on a major road in Missouri. The
scenario states that the CST has been asked to assist in the incident. The commander
asks the CST modeler to provide map and directions to the CST staging area from Fort
Leonard Wood, and a weather report. Additional information injected in the scenario
states that a vehicle has slammed into the truck carrying the hazardous material, causing
deaths, injuries and a leak. The modelers are asked to provide a map of the initial plume
(HPAC) and define layers, and a chemical report. This exercise displays competency in
the areas of basic cartographic skills, the use and integration of outside models within a
GIS and the identification of possible routes and the location of potential roadblocks.
The second practical exercise sets up a scenario where a local Wine maker of
Columbia, Missouri has called the 911 dispatch to report that a fork lift operator has
damaged the 2 ton sulfur dioxide storage tank. The CST has been asked to assist. The
modelers are given the specifics on the damage to the tank and asked to perform a
number of tasks. The modelers are required to provide a map and directions to the CST
50
staging area from Fort Leonard Wood, a weather report, and a map with the initial plume
using the Areal Locations of Hazardous Atmospheres (ALOHA) program, and a chemical
report. The modelers are also required to create a plume in HPAC and define layers
displayed in ArcMap. Much like the first practical exercise, this scenario demonstrates
the modelers’ ability to create finished cartographic products, identify and map exclusion
zones via the ALOHA program and integrate plume models with other spatial data within
ArcGIS via the use of the HPAC modeling program.
The third and final practical exercise states that the Missouri State Highway Patrol
has been notified of an unusual looking container at the steps of the Missouri state capital
building in Jefferson City, Missouri. Radiation detectors alarmed and further
surveillance with cameras identified an improvised explosive device (IED) with
approximately 10 pounds of explosives and a detonator. Activity and isotope estimates
are given to the modelers, who are asked to provide a map and directions to the CST
staging area from Fort Leonard Wood, and a weather report. The modelers are then
asked to provide a map with the initial plume (created with HPAC) and define layers with
recommended exclusion zones and road blocks. Much like the first two, the third
exercise tests the modelers’ aptitude in the areas of plume modeling, the identification of
road blocks and routes while examining the modelers’ ability to present them in a
finished product.
Training Interests of the WMD-CST Modelers The basic course completed by all of the WMD-CST modelers provides the modelers
with the basic skills to complete their mission and report geospatial information to their
51
command staff. However, as table 4 shows, the WMD-CST modelers would be
interested in a certain degree of extra training to enhance their spatial skills and aptitude.
In addition to their duty as WMD specialists, these units are often called upon to respond
to various natural disasters and events such as floods and tornadoes. The desire to obtain
skillsets related to these issues is clear when viewing the results of table 4, which displays
the results of the response to the question “What type of GIS skills would you like to
learn to enhance your abilities?”
The results of this section of the questionnaire reveal a variety of skills the
modelers would like to obtain. For example, Modeler #7 stated the desire to learn how to
start “Utilizing elevation and terrain data for predicting flood plains, etc.” Modeler #9
stated the desire to learn “Population data intersection” and Modeler #5 stated the need to
learn “Geoprocessing tasks on population data and externally retrieved data”. Modeler
#10 stated the desire to become more versed in “Flood and disaster analysis” and
Modeler #11 replied that they would like to receive “Weather related GIS skills”.
Multiple modelers requested basic training in the use of the spatial analyst extension and
the 3D analyst extension. These modelers clearly are not only concerned with their
WMD mission, but also concerned with skills related to other disaster responses they may
have to respond to.
Finally, the questionnaire asked the soldiers and airmen if they would like a
potential “Homeland Security Certificate for GIS”. Table 4 shows that each and every
one of the responders replied with a “Yes” answer in one way or another. In the same
manner, every modeler answered positively to the suggestion that this training be
52
Table 5: CST Modeler Survey; Skills and Certificate Responses
CST Types of GIS Skills Want to LearnHomeland Security Certificate for GIS? Online?
#1 As many as possible As many as possible Yes #2 Spatial Analyst Yes Yes #3 As much as possible Yes Yes
#4
I would like to improve database management, and be able to model traffic flows and population movements throughout the day. Tactically, I’d like to be able to model how crowds disperse after a given event, such as a football game or concert. Yes Yes
#5 Geoprocessing tasks on population data and externally retrieved data Absolutely
#6
I certainly could use a basics refresher and then more advanced training. GIS offers so many more capabilities than what we are initially trained on – we capitalize on an extremely small portion of the overall capabilities of GIS – it would be beneficial to utilize GIS to its fullest capabilities. Yes Yes
#7 Utilizing elevation and terrain data for predicting flood plains, etc. Yes
#8
Installation and software management of ArcGIS; and integration and use of ArcGIS suite to include Global, Catalog, and more. Yes Yes
#9 Population data intersection Yes Yes #10 Flood and disaster analysis Yes Yes #11 Weather related GIS skills Yes Yes
#12 Additional geoprocessing skills and data acquisition Yes Yes
#13 Use of spatial and 3D analyst Yes Yes
53
administered online. As was stated earlier, this question was posed with the thought that
the modelers would be more open to these types of training if it were offered online, so as
to not interfere with possible deployments, which occur frequently. When queried on
the manner in which this would fit into their schedules, the overwhelming response
related to the fact that it would be very beneficial to take these training sessions via the
internet. Modeler # 2 replied “With our operations tempo (OPTEMPO), online is the
only feasible option. After being introduced to the world of GIS I really enjoy it and
could see pursuing it as a career after the military”. Modeler #5 stated that online option
would be best, saying “Yes due to the rigorous and unpredictable nature of the Civil
Support Team job”, whereas Modeler #11 replied “We are always deployed, this is a
great option”. Although Modeler #6 stated that online delivery of advanced spatial
training is the best option, he had reservations, stating “Unfortunately, yes online classes
would be the best fit. I would prefer an actual classroom setting but it is not feasible”.
Rationale for WMD-CST Training Modules
The results of the questionnaire and an examination of the training given to the
WMD-CST spatial modelers result in a few conclusions. First, the results of the
questionnaire and personal correspondence with these soldiers and airmen proved that
they are ambitious and talented individuals. They appear willing and eager to obtain
extra training to increase their skillsets and enhance the mission of the WMD-CST units.
Secondly, the modelers voiced the opinion that they need and want to work with outside
datasets and spatial data, the most prominent of which is the United States Census Bureau
data. These modelers also voiced the desire to utilize advanced analytical programs and
54
geoprocessing tasks such as spatial analyst and 3D analyst to further examine these
datasets. Three training modules were created to respond to the needs for additional
trainings.
The first module (Argonne Population Rose) is located in its entirety in the
appendix. This module focuses on the introduction and manipulation of external data in
the ArcGIS program. Census data is provided for the modelers, and the module focuses
on the interaction of the modeler with this data. The module guides the modelers through
the process of creating geodatabases, populating the geodatabase with data and guides the
modelers through the complex process of creating a windrose. This module provides a
suitable alternative to the plume models used by the modelers and offers the introduction
of a number of geoprocessing skills and spatial functions in the process. The creation of
the windrose incorporates a number of geoprocessing tasks and requires the modelers to
complete multiple steps to complete the resulting feature class. The process exposes the
modelers to such skills as multiple ring buffers, projections, intersections, the creation of
multiple feature classes and the eventual union of the user created windrose with census
data. The resulting dataset is used to calculate new populations for each direction and
buffer distance, exposing the modelers to advanced table functions and calculations. As
is shown in Table 5, this module addresses multiple responses from the WMD-CST
modelers, including the desire to improve database management (CST #4), geoprocessing
tasks on population data (CST#5) and population data intersections (CST #9). Other
related responses addressed by the first module include the need to gain additional
geoprocessing skills (CST #12) and the request for a basic GIS refresher (CST #6).
55
The second module (Hurricane Irene), located in the appendix, focuses on
potential disaster areas in New York City after a strong. As was previously discussed,
these CST units are frequently called away from their stated WMD mission to assist in
local and regional disasters. Although a modeler may be stationed in Illinois or Iowa, he
or she must always be prepared to deal with a hurricane in Louisiana or a wildfire in
Colorado. The module walks the modelers through the process of obtaining DEM data
for an inundation model, population data and other relevant data such as subway
openings and hurricane evacuation zones from various websites. The module guides the
modelers through the process of collecting these datasets and analyzing them in a manner
meaningful to the mission of the CST. Lastly, this module guides the modelers on the
process of estimating the impacts of a potential storm surge on the City of New York.
This module also leaves room for the modelers to collect and experiment on their own
with various datasets.
Based on the research, questionnaires and personal correspondence conducted in
this thesis, there is a need for skills and experience related the second module. This
module fulfills the stated needs of the CST-MWD modelers. CST #10 responded that he
wished to obtain flood and disaster analysis skills (Table 4). CST # 11 stated the desire
to obtain weather related GIS skills. CST #7 specifically mentioned the need to learn
how to use elevation data and terrain data to predict flooding. Multiple modelers
expressed the need to gain skills in spatial analyst and other geoprocessing tasks.
The final module, also located in the appendix, focuses on hypothetical radiation
walkover data at Argonne National Laboratory, where many of the local WMD-CST
teams come to train throughout the year. Although the spatial extent of this module is not
56
near any of the subject states, these teams are often called to assist states all over the
country, often times in geographic areas, climates and disasters in which they are
unaccustomed. The radiation data is real data gathered from a walkover at a
contaminated site (not identified in this thesis or in the lab for security and privacy
purposes) and was transposed and recalculated to fall on the laboratory property. This
module guides the modelers through the process of translating a large amount of tabular
data (over 18,000 points) gathered from a radiation walkover survey, turning this tabular
data into a spatial feature class and interpreting this data in a meaningful way. The lab
instructs the modelers in methods of displaying the point data and then using three
methods of spatial interpolation (including kriging, inverse distance weighting and
nearest neighbor analysis) to analyze the data. This fulfills the need to use and
understand the spatial analyst extension as requested by the modelers throughout the
questionnaire.
Much like the first two modules, the third satisfies the stated needs of the
modelers based on the research, questionnaires and personal correspondence conducted
in this thesis. CST #12 responded that he wished to gain further geoprocessing skills and
data acquisition techniques, which is accomplished in the third module as the data is
compiled in excel and then imported into ArcGIS. The modeler is then prompted to
create a feature class from the tabular data, furthering his or her geoprocessing skills.
Modeler #4 stated that he wished to “improve database management”. The third module
prompts the modelers to create geodatabases and populate them with radiation walkover
data and the interpolated raster files created during the process. Finally, the interpolation
57
methods explored in this module fulfill the stated needs of modelers #2and 13, who
replied that they wanted more experience using the spatial analyst extension.
58
CONCLUSION
The purpose of this thesis was to examine the historical and current state of
geospatial responses to homeland security and disaster situations in local, state and
federal agencies such as the Department of Homeland Security agencies and the National
Guard Weapons of Mass Destruction Civil Support Team. The thesis examined what
current geospatial skillsets are taught to the typical modelers and examined how their
training intersected the team’s mission. The thesis then examined and produced three
training modules which would ideally expand the scope and breadth of knowledge for the
WMD-CST spatial modelers for current and future missions.
The research was conducted via a survey that was presented to the modelers
located within the states covered by the Department of Energy Radiological Assistance
Program Region 5. Of the 20 potential responders, three positions were vacant and 13
modelers completed the survey for a 75% response rate. The modelers were
predominately upper level enlisted (generally E-7 or E-8), typically had a few years of
college experience and had been their unit’s primary or backup spatial modeler for about
3.6 years.
The responses indicated that the modelers received basic GIS training through the
National Guard, but felt extra training would keep their skillsets fresh and strengthen
their expertise. The basic course completed by all of the WMD-CST modelers provides
the modelers with the basic skills to complete their mission and report geospatial
information to their command staff, yet the questionnaire responses indicate that
additional training would be welcomed. The most poignant skills and training identified
in these responses included spatial analysis training, skills involving elevation and terrain
59
data in order to estimate flooding, the use and manipulation of population data, weather
related GIS skills and flood/disaster analysis. The identification of these desired skillsets
led to the creation of three modules designed to convey these skills to the WMD-CST
spatial modelers.
The first module focuses on the introduction of population data into the ArcGIS
program and guides the modelers through the process of creating new data layers through
geoprocessing tasks. The module instructs the modelers on the process of creating a
complex windrose for the purpose of intersecting the directional data with census based
population data. Tabular data is completed based on the intersection and spatial unions
of these datasets. Much of the current GIS processes conducted by the WMD-CST
spatial modelers involves the creation of new spatial data via an automated model (i.e.
HPAC); guidance and instruction in creating new data through the geoprocessing tools
helps these modelers gain critical GIS skills.
The second module focuses on potential disaster areas and flooding in New York
City after a strong hurricane. This scenario is well within the possible deployment
scenarios for the WMD-CST units. Based on research, questionnaires and personal
correspondence with the spatial modelers within the RAP Region 5, there is a need for
skills and experience related to flooding and potential urban inundations. The module
walks the modeler through the process of obtaining DEM data for an inundation model,
population data and other relevant data such as subway openings and hurricane
evacuation zones. Once again, this module serves to guide the modeler through the
process of obtaining outside datasets and creating new datasets on their own. This
60
empowers the modelers to produce spatially relevant data without the use of canned
models.
Finally, the third module focuses on the manipulation and interpretation of
hypothetical radiation walkover data. This is an especially appropriate module for the
WMD-CST modelers as it focuses on the act of collecting radiation data, converting
tabular data to a GIS file and then analyzing the resulting file through the use of various
interpolation methods. This fulfills the questionnaire responses which asked for
additional instruction in the area of spatial analyst. This module allows the modelers to
import external datasets and manipulate these feature classes, allowing for the modelers
to gain supplementary geodatabase management skills in the process.
The modules may indeed fill a gap in the continuing education of the WMD-CST
modelers, but the delivery method must be carefully considered. The research conducted
in this thesis indicates that the best delivery method for this type of instruction is web
based due to the frequent deployment schedules of the WMD-CST teams. Although a
few of the questionnaire responses indicated that they preferred a live classroom setting,
all respondents admitted the online delivery system would be the best option. The
questionnaire responses indicated that the training would be well received if it was placed
within the context of an academic certificate from an accredited college or university.
The three modules created for this thesis could be expanded into a number of modules; an
entire course could be derived from these modules and presented in an online certificate
program related to “GIS for National and Homeland Security”.
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Further Research Opportunities
This thesis focuses primarily on the mission of the WMD-CST modelers and their
specific mission needs, yet this study and a similar questionnaire could be distributed to
local, state, federal and private GIS entities involved with national and homeland
security, disaster response and related activities. The results of such a study could be
compiled and reported in a study similar to the “Future U.S. Workforce for Geospatial
Intelligence” which was published for the National Geospatial Intelligence Agency. A
study of this size and magnitude could be commissioned by a large, national entity such
as the National Guard or FEMA. Both of these entities have a large stake in disaster
response at a local and national level, and have a vested interest in the creation and
dissemination of accurate and relevant spatial data. The resulting publication could
provide guidelines for academic and other training institutions when creating curriculum
and certificate programs in GIS and related fields.
The basis for the three modules created for this thesis was a questionnaire
distributed to the WMD-CST spatial modelers. The input from the modelers was
invaluable to this thesis, but this methodology did not take into account the needs of the
WMD-CST Commanders, Deputy Commanders and other senior officers. A separate
questionnaire could be generated and distributed to these officers, and the results could be
compared and contrasted with the perceived needs assessments provided by the spatial
modelers, who largely consist of non-commissioned officers. This approach would allow
for a more complete assessment of the WMD-CST spatial needs. The combination of
these responses could form the basis for a complete advanced spatial training course
addressing the stated needs of the spatial modelers and the command staff. This new
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questionnaire and the questionnaire distributed to the modelers could be most useful if it
was circulated nationally to all units in the country, providing complete needs assessment
in all regions.
The idea of generating advanced geospatial training based on the stated needs of
the spatial modelers and the command staff of the WMD-CST would be bolstered if an
annual meeting were held at a national level discussing recent advances, techniques and
success stories related to the use of spatial modeling. This could facilitate an open forum
where modelers could interact with their counterparts in different states and regions, and
modelers and command staff could openly discuss mission needs and plans in a dedicated
forum. The idea of an annual meeting dedicated to WMD-CST spatial modeling could be
further improved if academics and private sector entities participated in the meeting.
These individuals could expose the members of the WMD-CST to the latest technologies
and methods. Additionally, this forum would allow academics and private sector
individuals to contemplate and discuss the mission of the WMD-CST and the emergency
response community in general.
A growing number of events, natural or terrorist related, national and
international, could be examined as a basis for determining the future needs and skillsets
deemed necessary for the spatial analyst dedicated to national and homeland security.
For example, climate change may increase the number and intensity of hurricanes
affecting the northwestern United States. Wetter than normal winters have triggered
Midwestern floods in areas which have never seen such events in the past 50 years.
FEMA regions and local National Guard units could benefit from a study examining
future impacts of these events and which spatial skillsets would help mitigate these
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effects and hasten the initial response when such events inevitably occur. In the process
of examining these events, advances in geospatial technologies could be identified and
taught to spatial modelers in particular regions to increase the effectiveness of the
response. An example of these technologies would be a class or online training related to
harvesting geospatial intelligence from social media feeds (i.e. Twitter) in areas which
increasingly experience flooding and other natural disasters. This type of innovative and
advanced spatial training would increase the overall effectiveness and data acquisition of
first response agencies.
Finally, this thesis implies that although larger, prestigious academic institutions
are well prepared to serve as breeding grounds for advanced topics such as
photogrammetry, geodesy, crowdsourcing, etc., there is a place for smaller universities
and community colleges in GIS and spatial analysis training. In addition to training,
these institutions are well poised to assist government agencies in times of crisis. This
proved true in the case of Hunter College’s response to the attacks of 9/11 and in the case
of the GIS support provided by Townsend University to the Maryland Emergency
Management Agency (MEMA) and the Emergency Operation Center (EOC) during
Hurricane Isabel in September, 2003. These institutions, with the help of private entities
such as Esri and the Red Cross, often prove to be the backbone of support during a crisis.
There is an opportunity for institutions such as the College of DuPage (Illinois)
Homeland Security Education Center to create certificate programs in GIS for Homeland
and National Security. The College of DuPage is located approximately 20 miles from
Chicago and would provide an excellent center for such a training program, as would
similar institutions across the country.
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Hagel Reverses Decision to Cut WMD Response Unit. [Online]. Available at: <http://grimm.house.gov/press-release/rep-grimm-saves-ft-hamilton%E2%80%99s-cst-chopping-block-hagel-reverses-decision-cut-wmd>. [Accessed 20 June 2013].
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Oosterom, Peter van; Zlatanova, Siyka; Fendel, Elfriede M. (Eds.): Geo-information for Disaster Management. Springer, Heidelberg, pp. 443-464.
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Center Disaster: Reconstituting New York City's Emergency Operations Centre. Disasters, 27(1), 37-53.
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[Online] Available at: http://www.emergencymgmt.com/disaster/Gulf-Oil-Spill-Debate-NIMS-Unified-Response.html [Accessed 29 March 2013]
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Lessons Learned During Hurricane Isabel. [Online] Available at: <http://pages.towson.edu/morgan/files/Hurricane_Isabel.pdf> [Accessed 20 July 2012].
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for Higher Education Institutions, New Directions for Institutional Research, 146: 75-86.
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APPENDIX The appendix contains the three modules completed for this thesis.
• Module 1: Argonne Population Rose
• Module 2: Hurricane Irene
• Module 3: Argonne Sampling Data
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Module 1: Argonne Population Rose Assignment: Determine population counts for a 50 mile area surrounding Argonne National Laboratory. Estimating counts based on mile radiuses of 0-1, 1-2, 2-3, 3-4, 4-5, 10, 20, 30, 40, and 50 miles in conjunction with the 16 cardinal directions.
Data: The only data provided for this module will be census blockgroups for multiple counties surrounding Argonne National Laboratory. A 16 direction windrose divided by a multiple ring buffer will be created and unioned with the census data.
Scenario: A windrose is an effective GIS layer for estimating affected populations and land uses in the case of an accidental chemical or hazardous material release. When the windrose is divided into multiple distances and unioned with census data, it can be a powerful tool for incident commanders to estimate intersected populations based on wind conditions and the type of materials released. The following module creates a windrose and unions it with 2010 census data to allow Argonne Site Emergency Management the ability to estimate potential affected populations should the site experience a hazardous material release.
Starting from the beginning:
Before creating a geodatabase, create a folder to store two geodatabases. Navigate through the local and network drives create a new folder on the working drive named Argonne_Population_Rose. This folder will contain the geodatabases created for this exercise. Geodatabases are created in ArcCatalog; open ArcCatalog and navigate to the Argonne_Population_Rose folder that was just created. In that folder, right click and select 'new' then 'personal geodatabase.' After naming the geo-database Argonne_Final, repeat the process to make a second geodatabase, naming it Argonne_Scratch. The scratch geodatabase is created so that geoprocessing results can be run and placed in the database without crowding the final geodatabase.
The next step in the process is creating a center point. This center point will be the point used to run the different mile radius zone's buffers. This step is done by selecting a location at the center of the Argonne National Lab, namely the Advanced Photon Source (APS) building. To do this, create a new feature class in the Final geodatabase, by right clicking, and selecting 'New,' then 'Feature Class.' A dialogue box appears for New Feature Class. Name the feature class APS. Set the feature class type to point. Setting the type is a crucial part of creating a new feature class. (It can be point, line, polygon, etc. In this case, APS is a point.) Select 'Next.' Set up the coordinate system that will be used for XY coordinates in the data by navigating through 'Projected Coordinate Systems' (the second option in the list). Extend 'State Plane.' Next, extend 'NAD 1983 (Meters).'
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Within that list, select the 'NAD 1983 StatePlane Illinois East FIPS (Meters) ' projection. Click 'next' and 'next' again, then 'finish' to complete the dialogue box.
Creating the center point (APS building) in ArcMap:
Keeping ArcCatalog open, launch ArcMap, and start new, with a blank map. Drag the APS point from ArcCatalog into ArcMap. Although no point is shown on the map (remember, a blank feature class was created but not populated), the projection has been set with the defined projection of the APS point created earlier (NAD 1983 StatePlane Illinois East). The first feature class added into ArcMap sets the coordinate system for the map. Click the Add Data+ button dropdown, and add a basemap. Add the “Streets” basemap. Zoom into Argonne National Laboratory.
Zooming into Argonne National Laboratory using Streets basemap.
Next, repeat the basemap
process and add a second basemap, 'imagery.' Zoom in to the APS building. Continue to zoom to the center flagpole of the building.
Open ArcToolbox and select editor. Docking the editor station can help find tools quickly and efficiently. Click Editor's drop down menu, and select start editing. The only editable feature is the APS point. (It's the only feature created thus far.) Under construction tools at the bottom of the screen, select 'point.' Click the part of the map where the point is desired. In this case, where the shadow of the flagpole meets the concrete. Click on editor and 'stop editing'. Save the edits.
Zoomed into APS Building using imagery basemap.
Zoomed into flagpole using imagery basemap.
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Creating Buffer Zones:
Simple buffers can be done through geoprocessing tools, but multiple ring buffers need to be done through the "toolbox" menu. Extend 'Analysis Tools' in the toolbox menu. Extend 'Proximity' from the given list, and choose 'Multiple Ring Buffer'. A multiple ring buffer dialogue box appears. The input feature is the point, line, or polygon feature to be buffered. Since the APS building is our center point to be buffered, choose APS for input feature. Output feature class is very important. The user must navigate to the desired location for the output features (namely the buffers). Although the APS point was created in final, the buffers' destination should be the scratch geodatabase because further analysis will be done on the buffers in later steps. Navigate through the flash drive's folders to Argonne_Scratch. Name the output feature class Buffers. Under distances, list the desired buffer ring distances dictated by the lab assignment. (1, 2, 3, 4, 5, 10, 20, 30, 40, 50). Buffer unit needs to be done in miles. Dissolve Option determines if buffers will be dissolved to resemble rings around the input features. Select ALL as the desired effect will be rings that do not overlap one another. Click Ok. After the processing is complete the buffer layer will appear. Zoom to layer for the full view.
Slicing into 16 Cardinal Directions
Start by making a new feature class, lines by using ArcCatalog. Navigate to the scratch database and right click new, feature class. In the pop up dialog box, name the feature
ArcToolBox and Buffer rings around the APS center point.
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class, "lines." Change the type of feature stored in the feature class to 'Line' and click next. Now click import on the right and import the projection from the APS point in the final database. (It will be 'NAD 1983 StatePlane Illinois East FIPS (Meters).) Continue to click next on the next two menus until the “Finish” button appears.
Viewing ArcCatalog and ArcMap at the same time, drag the newly created lines feature class into the layers table in ArcMap. Click on the editor and select 'start editing.' Choose lines, as we will be drawing lines to create the 16 cardinal directions. (Make sure lines are selected on the side menu bar as well). Select Editor again and navigate to the snapping tools options. Toolbox will appear after clicking. Select point to snap to the only point in our database, namely the APS building.
Before laying lines, do a quick calculation to accurately place lines as the cardinal direction dividers. 360/16 = 22.5. The first line will be half that distance above 0 on an X plane so that the direction East's middle will align with 0. The absolute value of -11.25 +11.25 will equal 22.5. All other lines may be calculated simply by adding 22.5 to the previous line.
1. 11.25° 2. 33.75° 3. 56.25° 4. 78.75° 5. 101.25° 6. 123.75° 7. 146.25° 8. 168.75° 9. 191.25° 10. 213.75° 11. 236.25° 12. 258.75° 13. 281.25° 14 303.75° 15. 326.25° 16. 348.75°
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Click on editor once again, and start editing. Choose Buffers this time. In the snapping toolbox, choose 'snap to ends.' Select "cut polygon tools and open the attribute table and select all the buffers. Starting with one end point of a line click, then click the APS point, and click the opposite end point across the buffer circle. [Selecting the APS building in the middle for each line will increase accuracy]. Once the second end point has been selected with a double click, notice what happens. The polygon is split into twice as many segments. Repeat this process for all 16 lines to produce 160 separate polygons. Each time the process is done, the attribute needs to be opened and polygons selected or the process may not cut all of the polygons. (Open the attribute table to verify the 160 polygon count when all lines have been selected for cutting.) If the lines layer is unselected, the viewer will see the 160 polygons as seen below. Stop editing and save edits.
Each line created divides the polygon into 22.5° slices. One slice for each of the 16 cardinal directions.
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Adding the Direction to the Attribute Table
After closing ArcCatalog, click the drop down menu in the attribute table and select 'Add Field.' when a dialog box appears, type "Direction" for name, and change type to text. Limit the text characters to 3. (Cardinal directions are denoted by a maximum of 3 characters such as NNE.) Start editing once again. Select buffers again. Select all 10 polygons in a direction. Right click on the Direction field in the attribute table and click 'Field Calculator.' With those ten polygons selected (they should be selected in the attribute table as well) assign a direction to them through the Field Calculator by typing: quote mark N quote mark. By typing "N", N will be assigned to those 10 polygons for North in the direction field after clicking ok. Do that for all 16 directions and end editing:
N, NNE, NE, ENE, E, ESE, SE, SSE, S, SSW, SW, WSW, W, WNW, NW, NNW
Adding Census Population Data:
The next step is to import the census data into the geodatabase. Add the seven counties of blockgroups to ArcMap. Go to “Geoprocessing” and the select “Merge”. Under the “Input Datasets” dropdown, select the seven counties one by one. Under the “Output Dataset” tab, select the navigate folder and go to the “Argonne_Final” geodatabase and name the output file “Block_Group_2010”.
160 polygons
listed
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Add Block_Group_2010 into ArcMap. This is the created data. Also bring population_rose in. These are the 160 polygons. The color was changed to hollow and a red outline was given. A field was added in the Population_Rose as DIRDISTID. This is a value that will be calculated using the field calculator. Select all 160, and run the field calculators. Typing this expression [Dir_Name] + " " +[Dist_Name] will create a unique identifier for each of the 160 polygons.
Opening the block group attribute table create a new field to calculate the population per square meter. In the same manner as above, select all features and open field calculator for the Pop_SQMeter field. Select the following expression [Pop2010]+[Shape_Area]. Verify the calculation by testing Shape_Area * Pop_SQMeter for any given feature and it should equal Pop2010 for that same feature. If it equals, the calculation done by Field Calculator was correct.
Create a Union:
Create a union to merge the windrose and the census data. Click Geoprocessing Tools and Select Union, a dialog box will appear. Input features will be Pop_Rose and Block_Group_2010. Output the feature the scratch database as Test_Union. Join all attributes in the union. Run the union.
For a block group area that is in two different polygons in the population rose, the area is divided into the two polygons, but the population data is the same even though the block was divided. The new population will now need to be calculated.
[An intersect geoprocess could have been run as well. The resulting difference would have been that the city block groups outside of the parameter of the population rose would have been limited to the rose's boundaries. Union leaves them visible. Either process will result in the correct data for the Population Rose.]
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Creating New Population
In the test_union attribute table create a new field, "New_Pop." Using the field calculator once again, select the expression, " [Pop_SQMeter] * [Shape_Area]". Change the symbology to test if the union worked. Change the colors based on DirDistID. There will be 160 unique sections.
Summarizing the Findings
In the Test_Union table, Select Summarize. This will collect all of the unique identifies for an area and summarize them. Under “select a field to summarize”, choose DIRDISTID. Now chose the attributes to be included in the output table. In this case, New_Pop. Save Final_Population to the final database, as these results are the answers the lab is seeking.
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Module 2: Hurricane Irene Assignment: Create maps to support the response to Hurricane Irene on the East Coast of the United States.
Data: All data for this module will be gathered and analyzed by the analyst
Scenario: You are a GIS analyst working with FEMA deployed to the East Coast of the United States in preparation for Hurricane Irene. You have been given a last minute assignment: Determine the most likely scenario for Hurricane Irene’s impact on New York City. This includes: Basemap of the city, best predicted path of the hurricane with estimated landfall times, geo-referenced evacuation zones, and potential water inundations as low lying areas.
Starting from the Beginning and Setting Up the File Structure:
Before creating a geodatabase, create a folder to store two geodatabases. Navigate through the local and network drives create a new folder on the working drive named Hurricane_Irene. This folder will contain the geodatabases created for this exercise. Geodatabases are created in ArcCatalog; open ArcCatalog and navigate to the Hurricane_Irene folder that was just created. In that folder, right click and select 'new' then 'personal geodatabase.' After naming the geo-database Irene_Final, repeat the process to make a second geodatabase, naming it Irene_Scratch. The scratch geodatabase is created so that geoprocessing results can be run and placed in the database without crowding the final geodatabase.
Within the Hurricane_Irene folder, make 2 new folders. Name one Figures and the other Downloaded_Data. Within the Figures folder create a subfolder entitled Basemap. This is the location the mxd files will be stored. Once the file structure is in place, the project can easily be assembled
Creating the Basemap for the Project in ArcMap:
Creating a base map is the first step in production of the mxd's. Once a base map and a desired layout is created, it can be used as a template for all the other maps within a series, simply by changing out data and editing title names. This method streamlines the production efforts, making it quick, easy, and efficient.
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To create this base map, open ArcMap to display a blank page. Navigate ArcCatalog on the side to the Hurricane_Irene folder. Further Navigate to Figures and Basemap. Before adding any data to map, save the map. Clicking on File, Save As, and name the file Basemap (making certain to save the mxd file under the Figures Basemap folder. As a part of the saving process, it is good practice to store relative paths by clicking once again on File, at the top of the page, navigate to the bottom of the menu and select Map Document Properties. This will open a dialogue box. Check the "Store Relative Pathnames" and change the Default Geodatabase to Irene_Scratch, and save the mxd.
Slicing into 16 Cardinal Directions
Now that the mxd structure is in place, add data through ESRI's basemap Streets. ESRI's map will load with a pre-defined coordinate system. To view this information, right click and select Data Frame Properties. Selecting the Coordinates tab will display the information about the maps currently defined coordinate system (WGS_1984_Web_Mercator_Auxiliary_Sphere). The map creator must decide what projection will be used for the series of maps. In this case a UTM zone projection will be selected. As seen below, New York is within the 18th UTM Zone.
The map is now centered on New York. Zoom into New York, to the desired area. The map displays the area, but does not show the boundaries of New York City. Although the
File structure seen using ArcCatalog in ArcMap
Check the Store Relative Paths
box and
Change Default Geodatabase to Irene_Scratch
Select Predefined, Projected
Coordinate System, UTM,
NAD 1983, Zone_18N, Click
OK, and Ok again.
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map creator can zoom to the general area, data must be added for lines to visually designate the boundaries.
A few simple Google searches will provide a wealth of GIS layers for New York City. Not all GIS layers are credible, and not all layers are free to use. Research the data. Where did it come from? Why was it produced? What information is within the data? Is it credible, etc. The data set used in this module originated at: http://www.nyc.gov/html/dcp/html/bytes/districts_download_metadata.shtml. The Borough Boundaries and Community Districts layer has the information needed on Borough Boundaries (clipped to Shoreline). The data is credible and free to use. Download the data into the Downloaded_Data folder created earlier within the Hurrincane_Irene folder. All downloaded data will be stored here for easy access. After downloading the data, unzip the files, as they have been compressed for downloading purposes.
After unzipping, 5 files will be extracted. View the nybb.shp file in ArcCatalog. Viewing the file in arc catalog will offer a preview of the shapefile before importing. A creator
may also view the metadata associated with the information in the description offered by ArcCatalog. Note: ArcCatalog must be opened separately. The ArcCatalog feature within ArcMap is limited and does not offer preview or descriptions.
Once the data has been confirmed as a solid and desired resource for the project, import the data into the Irene geodatabase. This will not be imported into Irene_Scratch as the data has been deemed credible and can be included in the final project. The scratch database is for geo-processing tasks that may corrupt good data or create undesired results.
To import the data into the Irene database (the final geo-database), right click on Irene's database in the side view of
ArcCatalog within ArcMap. Click Import, and then select Feature Class (single). Navigate to the Downloaded_Data folder and select nybb.shp to import. For Output rename the data NYC_Boroughs. Click ok.
Download the nybb_13 file into the
downloaded_data folder. Then extract
all to access the files.
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Double-click the symbology of the nyc_boroughs in the table of content. Make "hollow" with an outline of 1. Click ok. Now a black outline appears displaying the boundaries of the boroughs of New York City. Right click on Boroughs once again and "zoom to layer." Save the project. The map should look similar to the image below.
Customizing the Layout for the Basemap
The basemap is complete, but the layout view still needs to be adapted so that the map portrays a professional appearance and displays important information. The map layout will have a title, legend bar, scale, and space for logo, etc. The following layout is an acceptable, professional layout, but layouts may vary depending on the modeler’s style and purpose.
At the bottom of the screen select the layout view icon. Add the draw tool bar and anchor it to the bottom of the screen. To make the layout a landscape orientation, select File, and then Page and Print Setup. When the dialog box appears, select Landscape. Turn Printer Paper settings off by un-checking the box. Click ok. The page is now sideways.
Create a boundary for the page using guidelines. Select the ruler on the top and side of the layout view to place a guideline. Bind the layout page by placing a guideline at 0, 11, 0 and 8.5. Set another guide line at 2 on the vertical ruler and at 2 and 9 on the horizontal. Expand the dataframe to fit the top portion of the page.
Right click on symbology icon to
change the look of the map, then right click to select "zoom to layer."
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Now create boxes to fill each of the guideline sections. Using the draw tool 'square,' draw a square over the dataframe. Double click and change the fill color to no color and the outline to back. Repeat for the remaining three sections.
Using the text tool, add the text Hurricane Irene. Click on the text and change the font size to 36 and bold. The next line will read New York City font size 28 and bold. Then the third the line will Basemap, font 20 not bolded. The title of the map is cascading with the event, area, and type of map.
Set guidelines by clicking at the ruler at 1.5 inches, 1.1 inches, and the third at 0.8 inch. Rest the text on each of these corresponding lines. Drag a box around all three areas of text, right click, select Align, and Align Center.
Insert a legend by clicking on Insert along the top menu bar, select legend. A dialogue box appears. Choose the layer desired for the legend items. (In this case NYC_Boroughs, but not World Street Map). Click Next. Delete the word Legend for Title, click next, next, ok. Change the text in the legend by changing the name in the table of content. This is not changing the file name. It is only changing the appearance in the table of contents and the legend. Drag the legend to the far left box, and place in a desired location.
Label the boroughs. Right Click on NYC Boroughs in the table of contents and open the Layer Properties. Choose the label available in the list, BoroName. Make it grey, bold and font 12. Select Symbol. Select Edit Symbol. Click the mask tab and choose Halo. Open Layer Properties again and under Labels, select placement properties and select remove duplicate labels.
Insert a scale bar by clicking insert at the top of the tool bar. Click scale bar, and choose a miles scale bar. Use miles that are appropriate for the map for example 10 miles. Repeat a similar process to add a north arrow. Click Insert, click north arrow. After selecting the type of north area, drag the image to a desired location on the map.
Many projects will have an organization, company, or agency associated with them. The right hand square has been reserved for a logo. Use Google to search for a logo of New York City. Save an image as a jpg file in the downloaded_data folder under Hurricane_Irene as NYClogo. Select Insert from the top menu bar in ArcMap and select picture. Navigate to the Downloaded_Data folder and select the NYClogo.jpg file. The
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image may have to be reorder to force the image to the front of the layout. (To do this simply right click on the image and select Order, Move to Front.
Save the basemap as a mxd, but also export the file as a Jpg to the figures folder. Save it as Basemap.jpg. The template is now ready for custom content, and can easily be adapted to themed data for new maps, cutting the production time required to make a map.
Creating New and Different Maps Using the Basemap
Create new folders for each new map awaiting creation. Navigate to Hurrican_Irene, Figures. Within the figures folder, a Basemap folder exists housing the mxd and jpg files for the basemap recently created. Create 3 new folders within the Figures Folder (Predicted_Paths, Evacuation_Routes, Inundation). The data for these future maps will live within the geodatabase, but the output files belong in these well-organized folders. This type file structure keeps data management neat and clean--a must when producing large projects.
Save this base map in each of the Figure's Folders, but save the map's title as the corresponding folder name. Save this map (without further development of data), as Predicted_Paths.mxd within the Predicted Paths folder. At this point in time there is no need to produce a jpg export. That will occur after additional data has been added. Repeat the process for the other two types of maps, evacuation_routes, and inundation.
The completed Basemap serves as a template for the other maps in the project's
production
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Predicted Paths Map
Open the mxd for predicted_paths. It should look identical to the basemap recently created. Begin to edit the layout of the map. Rename the third line of text Hurricane Irene Predicted Paths. Save the mxd at this point. The only steps left for this map is to insert the new data and change the legend.
Much like the initial borough's boundaries, data can be found through a Google search. For predicted paths a Google search for "National Hurricane Center Archives" produced GIS layers for predicted paths for the days prior to the hurricane. The following link is a credible, free archive. http://www.nhc.noaa.gov/gis/archive_forecast_results.php?id=al09&year=2011&name=Hurricane IRENE
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Download two files to the Downloaded_Data folder under Hurricane_Irene. (al092011_5day_025.zip and al092011_5day_028.zip). Unzip the files, expanding them within the Downloaded_Data folder. In ArcMap, add data and import the line files for each. These files still need to be added to the geo-database. Right click on the first line file in the table of contents, right click data, export. Change the output location to the final database Irene (as oppose to Irene_Scratch). Save the data. Add the geo-databases line files to the table of contents. Right click on the old version and remove it from the table of contents. The only version left is the one stored in the geo-database. Repeat the process for the second line file.
Zoom out to view the lines. (Creating a bookmark can help easily navigate to different views if multiple views are used in the production of a map.)
Opening up the attribute tables, find the date for each path. Al092011028_5day is really August 27th's projected path. A1092011025_5day is really August 26's projection. Rename the files in the table of content. Notice the legend changed. Now, change the symbology of Aug 27th's line by double clicking on the line. Make it blue and increase the line size. Change August 25 to a red line and increase its line size to match the blue line. Adjust the legend's location in layout view if needed.
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This map is complete. Save the mxd, and export the file as a jpg as well.
Evacuation Routes and Centers Map
Open the evacuation_routes mxd within the evacuation_routes folder under figures. Immediately change the third line of the title to read, "Evacuation Routes and Centers." Re-center the text by holding down the ctrl key clicking the text and click the dataframe map, right click, align and center.
Once again, starting with a Google search, find data for the new map. Type "New York City Evacuation Zone GIS." NYC Open Data Center has a search bar on the side menu of the page. Type “Evacuation” in the side search. Select Hurricane Evacuation Zones. Click the External Link and save the data in the downloaded_data file. Extract files after download.
In ArcMap, import the evacuation shapefile. This is simply looking at the data to see if it is usable. The data still needs to be added to the geo-database. Viewing the data allows the map created to see that the data is good. It is not corrupt and is usable for this
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purpose. Right click on the shapefile in the table of contents, click data, export data. Change the output location to the final database Irene (as oppose to Irene_Scratch). Save the data as Evac_Zones. Add the geo-databases Evac_Zones to the table of contents. Right click on the old version and remove it from the table of contents. The only version left is the one stored in the geo-database.
Open the attribute table by right clicking on evacuation_zones in the table of contents, and become familiar with the data provided within the attribute table. Notice the categories of evacuation zones, A, B, and C. Reviewing the metadata in ArcCatalog will reveal that these letters are the order of evacuation zones. Close ArcCatalog and close the attribute table in ArcMap.
Opening the layer properties by double clicking on evacuation_zones, and clicking the symbology tab, opens many options for making dynamic maps, simply by changing their appearance and highlighting data within the attribute table. Select categorical in the left hand menu of the dialogue box. Add A,B, and C. Uncheck "all other values." Clicking on the colors change them to Pink, Red, and Deep Red. (Darker Red meaning
that if the inland evacuations are ordered, the storm is most severe). In the Display Tab, set transparency to 38%
Change CAT1NNE in the table of Contents to Evac Risk Zones. Move the legend to a desired location on the layout page.
Now collect emergency center data the same way evacuation zone data was collected. The NYC Open Data Site has the file "Consolidated_ShelterList_Mebmerc_wRole. Download to Downloaded data, unzip file. Import data's shapefile into ArcMap. Right click data, and expect to the Irene geodatabase as before. Save output name as Shelters. Remove the old version.
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Double click on the symbol for Shelters in the table of content. Select Cross2 in the symbology. Increase the size, make red, and click Edit Symbol. Click the mask tab. Select Halo. Make size 1, and click Symbol; choose the color black. Click ok. Now the red cross has a black border that helps it stand out more on the map.
Viewing the map with the legend, the shelters should be at the bottom of the legend list. To do this, double click on the legend. A dialogue box appears. Select Shelters from the Legend Items list and click the down arrow to move the item to the bottom of the legend.
This map is complete! Save the mxd, and export the jpg file to the corresponding figures folder, Evacuation_Routes.
Inundation Model
Open the inundation mxd within the Inundation folder under figures. Immediately change the third line of the title to read, "Inundation." Re-center the text by holding down the ctrl key clicking the text and click the dataframe map, right click, align and center.
Once again, starting with a Google search, find data for the new map. Type "New York City Digital Elevation Model." The second search result is New York City Area Digital Elevation Model, 1 Arc Second. Click the Link and save the nyc1 data in the downloaded_data file. Extract files after download.
In ArcMap, import the evacuation raster
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file. Again view the data to see if it is usable. The data still needs to be added to the geo-database. Right click on the raster file in the table of contents, click data, export data. Change the output location to the final database Irene (as oppose to Irene_Scratch). Save the data as Elevation. Add the geo-databases Elevation file to the table of contents. Right click on the old version and remove it from the table of contents. The only version left is the one stored in the geo-database.
Double click on the Elevation in the table of content to open layer properties. This raster has been stretched meaning that its highest points are white and its lowest are black. The highest elevation in NYC is 674.225 feet, the lowest is -28.9886 feet.
Click Classified in the side menu. This means that the data is broken up into classes. Select 7 Classes. Select Classify and enter the break values (0, 1, 2, 3, 4, 5, 647.) Click ok. Now change the "color ramp" to red to green, making the last value no color by clicking the color of the last range and selecting no color above the other color options. Click ok.
Notice that all of the water is shown in red and appears to be a hazard zone due to elevation. The raster needs to be clipped. This can be accomplished by creating as mask. Open ArcToolbox. Use the search and type mask. Use the Extract by a Mask (Spatial Analysis). It opens a dialogue box. Input raster is Elevation. Input raster or feature mask data is NYC Boroughs as the Borough borders will be used to mask off the desired areas. Output raster will be stored in the Irene_Scratch geodatabase as Extract_Elevation. This is a tool that is being processed
and should be stored in the scratch database to protect the integrity of the final database. Click ok and allow the computer time to process the request.
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The Extract_Elevation was added to the table of content as a black and white raster image. The color coded raster image, Elevation, peaks through the Extract_Elevation layer where the elevation raster was clipped. Turn off the original Elevation raster. Double Click on the new Extract_Elevation to open the layer properties, click the symbology tab. Click Classified and break into 7 classes once again, but this time change the break values to 0, 2, 4, 6, 8, 10, 381. Select Red to Green Color Ramp once again, and anything over 10 feet apply "no color." Zoom out the map for the full result. Adjust legend accordingly and this map is complete. Save as an mxd and export as a .jpg within the appropriate folder. All four maps have been produced.
Before the Raster was clipped using the masking technique
After raster was clipped using the masking
technique
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Module 3: Argonne Sampling Data Assignment: Convert radiation walkover data to a feature class and analyze the data using various methods of interpolation, including inverse distance weighting, kriging and natural neighbor.
Data: An excel file containing over 18,000 records will be provided. The file will contain an X-coordinate, a Y-coordinate (in State Plane Illinois East (meters) and a cpm (counts per minute) field.
Scenario: Radiation walkover data is a common way to collect and analyze potentially contaminated areas. In this module, real walkover data from a contaminated site was transposed to the Argonne National Laboratory site. The walkover data was collected using a Mini-FIDLER system with differential GPS for complete site coverage The data will be examined, converted to a feature class and analyzed using a number of interpolation methods in the spatial analyst extension.
Equipment used to gather data.
Starting from the beginning:
The first step in this module is to create a folder in which to store the data and geodatabases. Navigate through the local and network drives create a new folder on the working drive named Argonne_Walkover_Data. A geodatabase must be created (one does not already exist). Geodatabases are created in ArcCatalog; open ArcCatalog and
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navigate to the Argonne_Walkover_Data folder that was just created. In that folder, right click and select 'new' then 'personal geodatabase.' After naming the geo-database Argonne_Walkover, repeat the process to make a second geo-database, naming it Argonne_Walkover_Scratch. The scratch geodatabase is created so that geoprocessing results can be run and placed in the database without crowding the final geodatabase. Finally, add the file GammaWalkOver.xls to the new folder.
Examine the Data:
Open Excel and open the GammaWalkOver.xls file. The coordinates are in State Plane Illinois East (meters). The dataset contains 18,281 measurements, each with an X and Y coordinate and a cpm (counts per minute) field. The cpm field represents the measurement of ionizing radiation, expressed as a rate of counts per minute, at the specific X and Y coordinate. These counts are only manifested in the reading of the measuring instrument, in this case a Mini-FIDLER, and are not an absolute measure of the strength of the source of radiation. Even though the Mini-Fidler displays data at a rate of cpm, the operator did not have to detect counts for one minute.
Although this tabular data is useful, it is impossible to analyze at this magnitude in tabular format. The data must be turned into spatial data, and eventually analyzed in raster format through interpolation.
cpm POINT_X POINT_Y
393 ‐
9794949.6417 5117313.1661
420 ‐
9794949.1417 5117318.3661
431 ‐
9794952.1417 5117309.7661
436 ‐
9794953.0417 5117303.9661
444 ‐
9794950.3417 5117309.6661
446 ‐
9794949.0417 5117322.6661
450 ‐
9794947.9417 5117311.4661
452 ‐
9794952.2417 5117312.5661
452 ‐
9794950.1417 5117307.3661
Sampling data in Excel format.
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Convert the Tabular Data to a Feature Class in ArcMap:
Launch ArcMap, and start mxd with a blank map. Open the “Catalog” window in the top center of ArcMap. The Catalog view will open on the right side of the window. Navigate to the Argonne_Walkover_Data folder and right click on the first worksheet in the expanded GammaWalkOver.xls file (in this step, the table must be expanded to access the first worksheet in the excel file). Click “Create Feature Class”, the “From XY Table”. Select the X and Y coordinate fields and navigate to the Argonne_Walkover geodatase and name the resulting file “Walkover_Data”. Select the State Plane Illinois East (meters) for the coordinate system.
Symbolize the Point Feature Class in ArcMap:
Make sure the Walkover_Data feature class is added to ArcMap. Click the Add Data+ button dropdown, and add a basemap. Add the “Streets” or “Imagery” basemap. Zoom into Argonne National Laboratory, where the walkover data is located. Next, double click the Walkover_Data layer and click the “Symbology” tab. Since the cpm attribute will be symbolized feature, select the “”Quantities” level and “Graduated Color” (the data is quantitative because it is numerical and not a category). The classes of the initial point symbolization will number four, and the breakpoints will fall at 1,000, 1,800, 2,500 and greater than 2,500 cpm. The 1,800 to 2,500 cpm readings represent the lower and
Converting XY coords to a Feature Class
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upper trigger levels when the entire dataset was examined by health physicists and analyzed using a non-parametric statistical approach. It is important for the radiation experts to view the spatial characteristics of this data according to these guidelines. A green-to-red color ramp will be used to signify the potential hotspot areas. This plot displays the locations of hotspots in the study area.
Interpolate the Point Data in ArcMap
Interpolation predicts values for cells in a raster from a limited number of sample data points. It can be used to predict unknown values for any geographic point data, such as elevation, rainfall, chemical concentrations, noise levels, etc. The assumption that makes interpolation a viable option for these phenomena is that spatially distributed objects with these characteristics are usually spatially correlated; in other words, things that are close together tend to have similar characteristics. For instance, if it is snowing on one side of the street, one can predict with a high level of confidence that it is snowing across the street. Yet, one would be less certain when predicting whether it was snowing across town and less confident still about the state of the weather in the next county.
The radiation walkover data will be interpolated using three different methods; Inverse Distance Weighted (IDW), Kriging and Natural Neighbor. Although this dataset is fairly dense, it is worthwhile to perform interpolation operations to complete the surface analysis. Each method varies slightly and the analyst will have to choose which method is the best possible fit.
Walkover survey data points.
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Inverse Distance Weighting
The IDW (Inverse Distance Weighted) tool uses a method of interpolation that estimates cell values in the resulting raster dataset by averaging the values of sample data points in the neighborhood of each processing cell. The closer a point is to the center of the cell being estimated, the more influence, or weight, it has in the averaging process.
To run IDW, click “Windows” then “Extensions” and make sure the “Spatial Analyst” extension is checked. Open the ToolBox, then expand the “Spatial Analyst” toolbox. Expand “Interpolation” and double click “IDW”. The input point feature is the “Walkover_Data”, the Z value is “cpm” and the output raster should be stored in the “Argonne_Walkover” geodatabase and should be named “Walkover_IDW”. Keep the “Power”, “Search Radius” and “Number of Points” at the default settings. Once the process has run, add the resulting raster to ArcMap and symbolize the raster with the same breakpoints as was done to the data points.
Inverse Distance Weighting.
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Kriging
Kriging is an advanced geostatistical procedure that generates an estimated raster dataset from a scattered set of points with z-values. In this instance, the Z value represents counts per minute. Kriging is based on the regionalized variable theory that assumes that the spatial variation in the phenomenon represented by the z-values is statistically homogeneous throughout the surface (for example, the same pattern of variation can be observed at all locations on the surface). This hypothesis of spatial homogeneity is fundamental to the regionalized variable theory.
To run Kriging, click “Windows” then “Extensions” and make sure the “Spatial Analyst” extension is checked. Open the ToolBox, then expand the “Spatial Analyst” toolbox. Expand “Interpolation” and double click “Kriging”. The input point feature is the “Walkover_Data”, the Z value is “cpm” and the output raster should be stored in the “Argonne_Walkover” geodatabase and should be named “Walkover_Kriging”. Keep the “Power”, “Search Radius” and “Number of Points” at the default settings. Once the process has run, add the resulting raster to ArcMap and symbolize the raster with the same breakpoints as was done to the data points.
Kriging.
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Natural Neighbor
Natural Neighbor interpolation finds the closest subset of input samples to a query point and applies weights to them based on proportionate areas to interpolate a value. It is also known as Sibson or "area-stealing" interpolation and is based on Voronoi tessellation of a discrete set of spatial points (i.e. dividing a space into a number of regions).
To run the Natural Neighbor interpolation tool, click “Windows” then “Extensions” and make sure the “Spatial Analyst” extension is checked. Open the ToolBox, then expand the “Spatial Analyst” toolbox. Expand “Interpolation” and double click “Natural Neighbor”. The input point feature is the “Walkover_Data”, the Z value is “cpm” and the output raster should be stored in the “Argonne_Walkover” geodatabase and should be named “Walkover_Natural_Neighbor”. Once the process has run, add the resulting raster to ArcMap and symbolize the raster with the same breakpoints as was done to the data points.
Natural Neighbor.
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Analysis
Compare all of the different interpolation methods and determine which method might best represent the real life contamination based on the walkover survey data. This lab has the luxury of containing an overwhelming number of points. Consider which method might best interpolate data if a sparse sampling set was the only set available.
Points, IDW. Kriging, Natural Neighbor
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.
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Close Up Area of Points, IDW. Kriging, Natural Neighbor