GEOSPATIAL

INFORMATION

IN EMERGENCY

MANAGEMENT

INTEGRATING AND

DISSEMINATING SPATIAL

DATA IN FIRST RESPONSE

THE FIRM, MAY 2016

Jillian Browning

Noel Dyer

Hawk McMahon

Alison Regan

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1.0 Introduction

Cohesive and effective emergency response requires access and understanding of multiple types of

information. In a crisis, successfully integrating such a high volume of information may prove critical to

decision making and the success of an operation. The creation of a Common Operating Picture (COP) to

organize static and dynamic data can provide this key information pathway. Current efforts and research

in this area have highlighted the benefits of integrating real-time information to map crises. This data can

be used to generate additional information to influence response. The dynamic nature of geosocial

information in particular, and the knowledge that can be derived from it, is critical in emergency response.

When combined with other information, volunteered data may provide a powerful look into a situation.

This report summarizes The Firm’s proposed architecture and framework for integrating and

disseminating geospatial information in emergency response.

2.0 Proposed Framework

Real time geographic data analysis and dissemination during an emergency situation can be the difference

between life and death. The efficient dissemination of these data analyses and observations has the

power to save lives. Effective dissemination of geographic information requires a framework of guidelines

regarding the organization and management of geographic information system (GIS) data, the use of

models and cartographic products, and the distribution of services.

The premier emergency management agency in the United States is the Federal Emergency

Management Agency (FEMA). The mission of FEMA is to ‘support U.S citizens and first responders to

ensure that as a nation we work together to build, sustain, and improve our capability to prepare for,

protect against, respond to, recover from, and mitigate all hazards’ (FEMA, 2015). In support of their

mission, the agency has established and published the ‘FEMA Geospatial Standards of Operation (SOP)’

which was prepared to facilitate the production and distribution of GIS services during emergency events

(FEMA, 2015). The SOP assumes the prior establishment of a large emergency management

infrastructure; however, The Firm will institute the geospatial data guidelines established in the SOP to

prepare and facilitate the dissemination of appropriate geospatial information.

Emergency management activities can be grouped into five phases of operation: planning,

mitigation, preparedness, response, and recovery (Johnson, 2000). All of these phases are enhanced when

geospatial information is utilized. Planning is paramount to emergency management programs and all

begin with locating and identifying potential issues (Johnson, 2000). GIS’s simplify this process by allowing

users to view appropriate combinations of geospatial data. For example, identifying roads susceptible to

damage near a fault line allows emergency response personnel to plan alternate routes to existing

facilities. The prime objective of emergency response is to save lives, which can be mitigated by utilizing

a GIS during such an event. Identifying high risk areas following an event prioritizes resources to people

who need it most (Johnson, 2000). In response to an earthquake, identifying broken gas lines will inform

response personnel which locations to evacuate and to where. Preparedness involves activities that are

used to prepare for emergency situations (Johnson, 2000). In this capacity, a GIS can be used to identify

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prime locations for fire and police stations for specific response times, or even serve as a monitoring

system for early warning detection. Responding to the event is most critical in saving lives. Utilizing a GIS,

emergency response resources can be allocated to specific locations using real time routing applications.

Furthermore, the GIS can provide information to the responding personnel such as fire hydrant locations,

hazardous materials, or building floor plans. Recovery efforts begin when threat to life, property, and the

environment have passed and can be separated into short and long term phases (Johnson, 2000). The

short term recovery phase restores vital services and systems, which can optimized using a GIS. During

this phase, the GIS is primarily utilized in damage assessment and relocating recovery work to top priority

tasks (Johnson, 2000). The long term recovery phase restores all services to normal or better. Progress

during this phase can be tracked using a GIS and restoration costs can be estimated (Johnson, 2000).

The previous briefing by The Firm presented a simplified version of our operational workflow, as

illustrated by the diagram below. The following section will provide a more detailed outline of the

workflow updated to coincide with the SOP established by FEMA. This outline will serve as our proposed

framework for The Firm’s geospatial emergency management system.

Figure 1 Operational Workflow

The Firm has in employ a team of geospatial professionals that possess an excess of the skills

necessary to provide the best geospatial system possible. These individuals have been trained through

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the prestigious geography and geoinformation science department at George Mason University. The skills

necessary provided by FEMA include (FEMA, 2015):

Collecting, processing, and disseminating incident-related geospatial data;

Creating new data as needed for incident operations;

Incorporating data from (GPS) units and other sources;

Keeping informed of any hardware, software, or data difficulties and concerns;

Providing maps and other products as requested by staff or leadership;

Complying with and maintaining standardized data structures;

Creating necessary products using defined symbology;

Properly documenting data and archiving work;

Dissemination of GIS data and products through FTP sites, web, sharepoint and file servers;

Manage the delivery of GIS products, projects, and data to other personnel on the incident or within the

agency; and

Complying with demobilization procedures.

The work of our analysts begins in the preparing phase of operation where analysts contribute to

the creation of the master database for a given location susceptible to emergencies. This process involves

the coordination and sharing of data across multiple agencies, which is headed by FEMA (FEMA, 2015). In

an effort to not duplicate efforts, The Firm will incorporate data provided by FEMA, but will also create

and maintain its own proprietary data set. The creation of this master database involves assessing,

integrating, manipulating, exploiting, extracting, and analyzing digital imagery, geospatial databases, and

other various sources with remote sensing, spatial analysis, and various GIS tools to construct a multi-

source geospatial intelligence database. Specific tasks of our staff include data conversion, digitization,

feature attribution, editing of GIS layers, and the performance of quality control procedures to ensure

geospatial accuracy. This master database will serve as the base dataset for emergency response

personnel of The Firm and can be updated on the fly.

ESRI provides a service known as ArcSDE, a component of ArcGIS Server, which integrates

geographic information queries, mapping, analysis, and editing within a multiple user enterprise

environment (ArcSDE, 2016). ArcSDE allows users to work in an integrated environment where geospatial

data can be managed as a continuous database and accessible to the entire organization simultaneously

and easily publishable to the internet (ArcSDE, 2016). Managing geospatial data in this type of

environment allows our analysts to pull data from the master database, analyze the data, upload findings

back to the master database, and finally disseminate the information. This methodology and environment

allows our analysts to provide information prior to the event with the master dataset (planning and

preparedness), uploading and dissemination of real time analysis (mitigation, response), and the

monitoring of costs and progress (recovery).

Another component of ArcGIS Server, ArcGIS GeoEvent, enables real-time data streams to be

integrated with your enterprise GIS (ArcGIS GeoEvent, 2016). Event data can be filtered, processed, and

sent to multiple destinations, allowing analysts to connect with personnel in the field. This includes data

from sources such as vehicle GPS systems, mobile devices, and social media (ArcGIS GeoEvent, 2016).

Utilizing this technology, field personnel can send and receive data allowing updates to the master

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database with on the ground observations. Our on-site analysts will clean the data, conduct whatever

analysis necessary, and be able to push information back out to field personnel.

In summary, the framework proposed by The Firm utilizes a multi-source geospatial intelligence

database maintained by a team of geospatial professionals for real-time data analysis and dissemination

to aid in critical decision making.

3.0 Information Sources

As referenced in section two of this report the Firm will incorporate data provided by FEMA in this

framework, but will also create and maintain its own proprietary data set. This master multi-source

geospatial intelligence database will include static and dynamic information from a variety of sources.

These will serve as the base dataset for emergency response personnel of The Firm and can be updated

incrementally and when needed in real time. The core of this database will be static data sets from a

variety of partner agencies, augmented by dynamic social media information.

Static information, defined here as information that would not change in the course of a response

(i.e. the location of critical infrastructure, place names, facilities, etc) is critical in the course of a response.

This information provides key context for response activities. This information may provide reference for

aspects of a response, such as locations for staging, triage, and other emergency management functions.

The Firm’s central proposed database relies heavily partnership with the Homeland Infrastructure

Foundation-Level Data (HIFLD) Working Group. HIFLD partners with several Federal agencies, including

the National Geospatial Intelligence Agency (NGA) to compile foundational data for use by the Homeland

Security; Homeland Defense; and Emergency Preparedness, Response, and Recovery communities. These

datasets allow for nationwide infrastructure information access to assist decision makers in analyzing

threats. They also provide a common picture in disasters and emergencies, whether they are natural or

manmade. This data set is known as the Homeland Security

Infrastructure Program (HSIP). HSIP is free for use to local, state,

federal, tribal, and private sector mission partners, and can be

downloaded online via the Homeland Security Information

Network (HSIN). This foundational data is updated annually

(Infrastructure Information Partnerships 2016). This data set

includes hundreds of data layers ready for use, divided into core

categories (see figure 2). This baseline data will drive the

creation of an operational picture to support response and

recovery efforts in an emergency.

Capturing more dynamic information is equally

important in emergency response. Dynamic information, which

may change quickly in the course of response, provides insight

into what is happening at an incident site, and the area where

response efforts are being conducted. Dynamic information

helps to derive additional knowledge and information for use in a response. The Firm has specific interest

Figure 2 The HSIP Model

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in data sources that may provide information on weather, traffic, and other activities happening in real

time. Given the fast pace and high volume of information in an emergency response, it is critical that the

tool and solution developed to provide situational awareness consider these sources of information, to

provide decision-makers and responders with the most complete picture of the situation possible.

Weather and traffic are self-explanatory. Both can impact decisions on response efforts at a

moment’s notice. As such, both should be integrated into the solution proposed here. AccuWeather,

OpenWeatherMap and Weather Underground all provide access to Application Program interfaces (APIs)

that allow real time information to be paired with a separate application. This would provide a simple

solution to integrate real time weather information with an emergency response application. It is also

possible to integrate real time traffic information via Waze, a Volunteered Geographic Information (VGI)

based traffic application, updates traffic based on user information in real time. Waze recently launched

a Software Development Kit (SDK), which allows other applications to integrate information from Waze

with their respective apps (Waze SDK 2016). The Firm would use this Kit to leverage the massive amount

of information collected by Waze for emergency responders. When paired with static information and

other sources of dynamic data, the traffic data could prove to be a valuable tool.

Other real time, high volume data sources would be valuable in an emergency response. Social

Media information for instance can be especially beneficial. Useful situation awareness information can

be derived from social media analysis. Verification or validation of ground truth, burst detection, and

classification of social media information may all prove useful in emergency response (Yin et. al 2012).

There are several social media streams that could be leveraged. Twitter, Flickr, Instagram, and others may

provide first responders with valuable information on an incident. User generated content could be mined

to provide critical understanding of an incident, and improve decision-making.

In order to develop concepts for using volunteered content and social media, The Firm obtained

Twitter datasets from George Mason University for two terrorist events: the bombing of the Boston

Marathon on April 15th, 2013 and the Brussels airport on March 22, 2016. Jacek Radzikowski, an engineer

at GMU’s Center for Geospatial Intelligence, has been studying twitter data and the dataset he provided

The Firm included a sentiment quantification of the text field for each tweet. This mood value was not

specifically designed for application in an emergency response framework. However, such algorithms are

in development by other researchers. Michael Kaiser at AGT is developing AI evaluation of tweets for

emergency response and event detection (Kaiser, 2013). The Firm’s analysis of the dataset provided by

GMU provided insight into the information flow and overall nature of social media data. Rather than a

mood value, a final product developed by The Firm would include an algorithm to measure the pertinence

of a tweet to a known event or to apply the machine learning to correctly identify an event as emergent

in real time before authorities are notified via normal channels like emergency dispatch and 911. A key

aspect of such an algorithm would be harvesting of images posted in social media like Twitter. This

information leverages the dispersed citizen population as remote sensors and an image can reveal

important details as well as act as a key accuracy confirmation to other sources of information.

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The Firm began with appraising the spatial extent of the sample social media data in response to

the two terrorist bombings. Figure 3 shows the global nature of information on social media. This was

observed in response to the attacks on both Boston and Brussels. The Firm initially hypothesized that

there would be a regional difference in response to these events. By analyzing the mood with respect to

the geospatial information associated with the tweet it could be expected that Americans and Belgians

would share expressions condemning the attacks while perhaps more radicalized countries would

demonstrate ambivalence or support for the attackers. This hypothesis was shown false in this study but

this methodology of evaluating a tweet attribute could be applied to other purposes as discussed above.

Figure 3 Global Twitter Activity in Response to the Brussels Airport Bombing (24 hour period)

An initial plot of mood value with respect to distance from the event was performed - distance

was determined using ArcMap. The scatter plot shown in figure 4 represents this information for the

Boston and Brussels attacks respectively. No obvious pattern was observed, but the data could be

displaying Tobler’s Law for the Boston data where both strongly positive and negative mood tweets

more likely to occur nearer to the event. This trend was less evident for Brussels.

Figure 4a & 4b Mood value by the Radzikowski algorithm as a function of distance from the terrorist attack on Boston.

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The periodicity of the scatter plot was hypothesized to be attributable to gaps in twitter

information over the Atlantic Ocean or similar areas of low population or low twitter usage. The full

Brussels data primarily keyed off of keywords relating the attack to the terrorist attacks on Paris

November 13, 2015. A smaller subset of data in response to Brussels is shown in Figure 5. This subset

provided more easily viewable general clusters to test the hypothesis about gaps. Figure 6 shows these

delineations shown geospatially and drawn with respect to Geodesic distance. The twitter data within

the red band is closest to the Brussels attack and includes all of Europe. The band between red and

orange covers a low twitter volume of the Atlantic Ocean, Northern Africa. The band between orange

and green gathers the high volume area of the United States and India.

Figure 5: Mood value with Breaks Figure 6: Great circle distance breaks

Both positive and negative mood tweets were observed at every distance band. There existed

the possibility that while the average mood was near zero there could still be concentrations. For

example a high volume of negative moods in US data could be balanced by a high volume of positive

moods in India for the Brussels subset data shown in Figures 5 and 6. To evaluate this the mood data

was joined to countries and their mean mood value mapped. Figure 7 shows the mean mood response

to the Boston bombing.

Figure 7: Mean mood value of Twitter response by country to the attack on Boston.

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The Firm’s hypothesis was that there would be strong positive reactions in areas like

Afghanistan, Iran, North Korea, and Gaza or other areas that are traditionally anti-American or more

likely to support radical Islamic attacks on the west. Positive values of mood represent approval of the

attacks and are colored red in this map set to signify a disagreeable position. This trend was not

observed and our hypothesis disproved. Congo and Uzbekistan stand out for having a value of 1 and

Swaziland had a value of 4 but each of these had very few tweets so it is still very difficult to propose any

sort of national or regional trends. The process was repeated for the Brussels data set (See Fig. 8). The

general trend appeared to be more neutral (closer to value of 0) on a global scale but again there were

no obvious regional support for the event based on the mood criteria and the one country that

responded differently - Equatorial Guinea - had only a single tweet with a mood of two.

Figure 8: Mean mood value of Twitter response by country to the attack on Brussels.

The final data analysis performed was a measurement of randomness for the overall dataset as

applied on a national basis. This yielded Moran’s I values of 0.185 with a p-value of 0.001773. This

indicates that there is a high degree of certainty that the data is correctly described as clustered and not

random, but that the clustering is not perfectly positive. Taking this a step further the Anselin Local

Moran’s I was used to develop a local indicator of spatial association (LISA) map. As shown below in Figure

9 the overwhelming majority of the globe did not display significant local clustering. A few low-low

neighbor trend clusters were identified (here in light blue). Interestingly one occurs in the area of Niger,

Libya, and Chad. With low mood being associated with tweets expressing negative emotions it would

have been more expected to find such LISA indications for the US/Canada. Stronger LISA trends were

noted for the Brussels data set as shown in Figure 10. Mozambique may be overly affected by a few

tweets but it still displays a higher value than its neighbors as does Isreal. These outliers may be an

indication that there is a different mood about the Brussels event but further analysis is needed to draw

possible conclusions or follow up inquiries. For Brussels it is observed that the USA is part of a low-low

cluster of mood.

In performing these analysis the biggest take away for the Firm was the importance of the

algorithm. Tweet text analysis to obtain a quantitative value commensurate with its content – and in

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particular the existence of primary source photographs on the scene is a definite area of future

development in this field. While sentiment based on keywords as defined in the GMU algorithm seems

to indicate that the global response to terrorist events like this are similar and that geospatial

concentrations of one particular mood (positive or negative) do not occur as strongly as might be

expected.

Figure 9: Local Indications of Spatial Association for mood sentiment in response to Boston.

Figure 10: Local Indications of Spatial Association for mood sentiment in response to Brussels.

These same modes of analysis could be conducted on a regional or local scale and with a larger

temporal coverage. Utilizing the dataset provided by GMU The Firm was able to disprove our initial

hypothesis about regional response. This methodology could be applied to a smaller scale to see how

various neighbors in Mexico City respond to a terrorist attack by a narcotic trafficking gang. It could also

be applied with entirely different algorithms. For example the tweet text could be analyzed for how

personal an event is perceived. This could yield important insights in a crisis such as identifying

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communities, families or individual persons affected by an attack in order to deliver support services like

counseling, victim identification or medical intervention. The algorithm of social media analysis can be

tailored to a specific concern. The work of analysts to determine if the algorithm is providing insights that

are useful is of key importance and The Firm is interesting in developing such algorithms for testing with

regards to a specific emergency response toolset.

These sources of information, when combined in the framework proposed in section two may

lead to effective decision making, rapid staff actions, and appropriate mission execution. All would be

valuable to integrate with a final situational awareness tool for emergency response.

4.0 Information Collection and Dissemination

In addition to the dissemination of GIS data and products as discussed in section 2, The Firm has

determined that server based information sharing should be augmented by web based field portals of

information and applications running on mobile devices – to include the cell phones of volunteer

responders and private citizens in the area of importance. The determination of what data is sent to

whom is considered more on the basis of useful versus overloading information rather than on

preconceptions of information control versus delegated tasks. If a fire captain with a laptop computer in

her engine needs access to Waze traffic information to reroute to the incident it should be accessible. If

a nurse running a volunteer responder app on his iPhone can be sent data about the specific location of

an injured person in need of first aid then that information should be pushed out via the system. Likewise,

information collection can occur at each end user. The Command and Control elements will retain quality

control and verification, but as much as possible automatic verification steps should be inherent in the

system to identify an event or object that has been marked by several information gatherers (such as

remote sensing drones, private citizens with an app, or police calling in notification to their dispatcher).

The Firm’s recommendation for this is Constellation tm. Constellation tm is a web based application

designed to work on laptops and cell phones. It has an install version for professional members of the

emergency response community with a higher level of information control and communication and has a

private citizen’s version. This second version is operated on a volunteer basis. When the application is in

passive mode it may receive information alerts and warnings similar to the FM/AM/TV emergency

broadcast system. If an event occurs geospatial near the user though they are prompted and asked if they

are willing to go into active mode. Or the user can play the application in active mode themselves. This

allows a greater amount of information to be shared but more importantly turns the end user into a

remote sensor.

One of the key pieces of information studied by the Firm in the twitter datasets for the Boston

and Brussels terrorist attacks was the quantity and location of images taken by a user on scene. There is

not special tag for this for twitter – at least as shown in the datasets Twitter shares externally.

Additionally, the tendencies of sharing and inactive status of some users geotag location services causes

some photos to be shown as taken very distant from an event. By using Constellation tm the user will have

access to a camera shutter button that not only transmits directly to our emergency response system it

also insures that the photo is geocoded. A mock-up of this application is shown in Figure 11.

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Figure 11: Concept Model of Constellation as used by a volunteer end user

As new technologies are developed they can be immediately integrated into Constellation tm. For

example, heavy duty GPS locators for firemen that can detect their location vertically in a building to assist

with coordination or rescue in the case of collapse.

Plans would be stored and prepared ahead of time by emergency personnel to develop SOPs. But

typically these cannot be mapped out in advance for every single potential attack. As a result, adaptable

models would be stored. Then in the event of an incident the on-scene personnel can share information

and insight back to the command center. Then an operation plan can be confirmed and disseminated to

all members of the team. Figure 12 shows how such a plan would be displayed.

Figure 12: Concept of Constellation disseminating hazmat areas & routes for evacuating wounded.

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Since private citizens are part of the response they would be shown a slightly altered version of

this plan. For example it could indicate where they should go for medical assistance. In a micro-scale

event like an attack on a subway station this may seem more trouble than it is worth when simple voice

commands and sirens would be enough. But in a distributed chemical or biological attack it would be

critical to insure citizens stay both out of the area of affect (blown by wind) and out of primary routes

used for fast movement by first responders. In a distributed event disseminating the information

quickly and accurately is key. Traditional news sources may prove effective but the modern information

consumer is getting more and more of their information from a mobile device and social media rather

than a TV station.

5.0 Outlook

The future is unpredictable, and no one knows when a disaster will occur. This is why it is so

important to pre-plan and have technologies prepared, to aid in emergency response. Disasters need

integrated solutions that combine on the ground emergency response with geospatial technologies. Being

able to visualize the problem before, during, and after such a disaster is critical to how that disaster is

handled. Specialized data, data networks, and information processing methods and technologies are

needed in a highly dynamic situation fraught with uncertainty and unpredictability.

Going forward, cyberspace will play a role in geospatial responses to disaster in the following

ways: (1) revealing the role of virtual communities in disseminating information via new and innovative

means (e.g., mobile phones, mashups, crowdsourcing); (2) illuminating the need for interdisciplinary

approaches to address disasters where geospatial approaches and technologies are at the forefront; (3)

identifying efforts to improve communication through spatial data; and (4) developing long-term

strategies for recovery efforts, risk reduction, restoration, and monitoring programs (Laituri 2010).

The use of social data—information about people such as age, income, ethnicity drawn from

sources likes the U.S. census, or data derived from social media—has become increasingly prevalent in

emergency response. Social media plays a huge part in disseminate messages and information from

emergency management in a top-down approach. This information can be used to assess the location of

vulnerable and special needs populations within a community. This is especially important for emergency

responders. Some areas will be more vulnerable to disaster than others. Knowing about the landscape of

this social vulnerability helps to identify which populations may need assistance in preparing for,

responding to and recovering from events (Holdeman 2014).

Researchers are exploring the role of geospatial technologies in disaster response. This includes

research into GIS and public safety; GIScience; and applications for emergency response, disaster recovery

networks, vulnerability mapping, and local responses to disaster using GIS. The amalgamation of the

Internet with GIS applications can be applied to 3D real-time emergency response, serving maps online

for emergency escape routes, and mobile GIS and digital video for urban disaster management. Geospatial

modeling can be used for determining evacuation routes, tracking hurricanes, and ascertaining refugee

populations.

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These technologies are modernizing emergency management. These are all things once can

complete from a specialized field device, or even from an iPad. Data can now be collected from the field

and uploaded to the cloud and servers at management facilities. This technology not only cuts down on

processing time and errors, but it also means that maps can be generated much more quickly. Real-time

damage data can be collected using this method post-disaster, meaning that preliminary damage

assessments can be produced within hours, not days when it might be too late. When you add in citizen

sensor data from social media and the possibilities of crowd sourced damage and recovery information

becomes endless.

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