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Development of a Human Health Index for Ecosystem Disservices Using Visual Analytics
Dr. James Hunter Jr., Lashaunda Johnson, Derek Riley
Department of Civil Engineering, Clarence M. Mitchell, Jr. School of Engineering
Morgan State University, Baltimore, MD
Introduction
In conjunction with the US Department of Homeland Security, Morgan
State University’s VAST-MSI Program (Visual Analytics for Science and
Technology at a Minority Serving Institution) is a research program designed to
solve issues and create valuable product solutions for the country. The areas of
focus are Computer Science, Civil & Environmental Engineering, and
Economics. This program is a combination of interdisciplinary collaboration, and
information visualization. Information Visualization is a very important aspect
when addressing the issues we are researching in this project. Preliminary work
has been done through training at Purdue University and the analysis and
visualization of sample data sets from the Baltimore Neighborhood Indicators
Alliance (BNIA). Ecosystem services/environmental data collected by the
researchers and BNIA will serve as the primary sources of data in this project.
Background
Contact Information
Problems
Human health is defined as “a complete state of physical, mental and social well-
being, and not merely the absence of disease or infirmity.” When assessing
human health in an urban/coastal environment, there are many factors that
contribute to the analysis. In order to monitor human health, it is important to
identify, quantify, and assess ecosystem services and disservices. Ecosystem
services are monitored through its environmental indicator. A method must be
created in order to receive real-time data as a current indication of the quality of
each ecosystem service. The three categories that contribute to human health
are the following:
 INFRASTRUCTURE: the basic physical and organizational structures and
facilities (e.g., buildings, roads, and power supplies) needed for the operation
of a society or enterprise.
 BUILT ENVIRONMENT: “the human-made space in which people live, work,
and recreate on a day-to-day basis”.
 GREEN INFRASTRUCTURE: planting trees and restoring wetlands, rather
than building a costly new water treatment plant.
Solutions and Expected Outcomes
Our preliminary data collection began by focusing on the surrounding neighborhood where
Morgan State University is located. With a population of 16, 643, the Northwood area includes
the following neighborhoods: Hillen, Montebello, Morgan State University, New Northwood,
Original Northwood, Perring Loch, and Stonewood-Pentwood-Winston. The data collection was
a comprehensive gathering of participatory sensor data, and database results. The database
collection was from the Baltimore Neighborhood Indicator Alliance.
The Baltimore Neighborhood Indicator Alliance (BNIA), founded in 2000, is a study focus group comprised
of citywide nonprofit organizations, municipal government agencies, neighborhoods, and foundations. The
alliance focuses on answering two questions:
“If you knew you would leave your neighborhood and could come back in 10 years, what is the vision you
want to see?”
“What will tell you we are successful is getting there? What are the indicators and measures that will tell
us we are moving in the right direction?”
Answering these two questions, the database is composed of different vital signs. Vital Signs are “groups
of related data points, compiled from a variety of reliable sources. ”These vital signs are categorized into
these major signs:
 Census Demographics
 Housing and Community Development
 Children and Family Health
 Crime and Safety
 Workforce and Economic Development
 Sustainability
 Education and Youth
 Arts and Culture
Although the purpose of BNIA’s data collection is to form
a picture of a neighborhood’s quality of life and overall
Health, there is no specific index value given. In order to
begin the assessment of the services and disservices
that would be scored, we collected our NODE data
and BNIA’s data in order to create a list of
services/disservices:
Using Microsoft Excel, a spreadsheet was generated in order to input the values calculated from the BNIA
data. The values for the neighborhood were then scored based on the values obtained for Baltimore City
(in general), and the index score was taken out of 100. All services were positive numbers, while
disservices were entered as negative. The total index score was the sum of the positive services and
negative disservices. For now, a more general rating was established when evaluating the services score,
disservices score, or total index score:
Dr. James Hunter
Lashaunda Johnson
Derek Riley
james.hunter@morgan.edu
lajoh27@morgan.edu
deril2@morgan.edu
What are ecosystem services/environmental indicators and how can visual
analytics is used in conjunction? Environmental Indicators are measures that serve as a
gauge for environmental changes. These indicators are divided into three subgroups:
state of the environment, sustainability, and environmental performance. Examples that
fall within these subgroups include vegetation, air, and climate. Environmental indicators
help to monitor the conditions of our ecosystem services. Ecosystem services are the
benefits humans obtain from natural resources. These services can include mechanisms
for nutrient cycling and primary production in our environment. Ecosystem services have
degraded over time and it is important to identify, quantify and assess their
benefits/disservices in relation to human health.
Human health in a particular geographic region is also affected by disservices
within the built environment. These disservices include food options, the presence of
impervious surfaces, transportation, and buildings. Using the ecosystem services and
built environment data for a particular location, an index will be created where each of
these factors will have a different weight in determining the expected health of the
individuals living in that particular area.
In order to accomplish this, one can use visual analytics as a tool to analyze and
draw conclusions from the data collected in Baltimore on these two areas of focus. Visual
analytics translates the data into knowledge. It simplifies a relationship between humans
and computers where computers support interactive visual representations of data to
amplify cognition. This amplification enables the creation of indices that allow us to make
informed decisions about natural and built environmental impacts.
An example of an index was one focusing on assessing water quality. The NSF Water
Quality index is a 100 point scale summarizing results from different measurements:
 Temperature
 pH
 Dissolved Oxygen
 Turbidity
 Fecal Coliform
 Biochemical Oxygen
 Total Phosphates
 Nitrates
 Total Suspended Solids
Using weights, and index values, quality ratings were developed such that 0-100 (100
being considered excellent) scoring scale was used. Although this focuses on one
aspect, our index is to incorporate both field testing, and multiple sources of data analysis.
Our current solution for solving the problem presented is the use of an index. This index
incorporates a collection of services that contribute to a general rating for the human
health. These services are classified under infrastructure, green infrastructure, and the built
environment. The index will generate a certain value, along a scale which will determine a
qualitative description for human health in a specific are.
Services will be evaluated using participatory sensing, as well as data collection/analysis.
Participatory sensing uses established applications to collect data. Variable Technologies has
a device called a “NODE”, where different factors such as temperature, climate, and air
quality can be collected from a device and saved on an iPhone device. Using these two
methods, data charts can be formed, indicators can be graphed, relationships and
correlations will be assessed, and conclusions will be drawn.
Based on the information compiled, we can create a network or system to assess our
ecosystems in our major urban areas, and create monetary value and importance to them.
From there, we can monitor some of these ecosystem services through participatory
research and sensors (which can also be used in assessing the value) and create an
application approach to a set of protocol in certain scenarios. Scenarios most relevant to our
area of study are severe weather and technological hazards such as major transport,
industrial, facility, or hazardous material mishaps. With the use of GIS, plans and alert
systems can be created to make the public more aware and detailed in preparation for any
evacuations during such attacks.
Preliminary Tasks and Data Collection
SERVICES DISSERVICES
• Available healthy food options
• Tree cover
• Literacy
• Use of public transportation
• Climactic data (Natural
Phenomena)
• Abandoned/vacant homes
• Licensed liquor store vendors
• Available fast food options
• Dirty street
• Clogged storm drains

2. Poster template by ResearchPosters.co.za
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Development of a Human Health Index for Ecosystem Disservices Using Visual Analytics
Dr. James Hunter Jr., Lashaunda Johnson, Derek Riley
Department of Civil Engineering, Clarence M. Mitchell, Jr. School of Engineering
Morgan State University, Baltimore, MD
The primary research objectives are to:
• Measure the potential vulnerability of an area of interest to natural disasters.
• Quantify provisioning and regulating ES in existing stand-alone models;
• Identify potential environmental stressors and means to “sense” and relay these
parameters to the DSS
• Identify actions and management practices that promote urban infrastructure
and green infrastructure resilience.
• Estimate the economic value of selected provisioning and ecosystem services.
The objective of the decision support tool developed through this project is to:
• Enable DHS and other stakeholders to easily evaluate management options for
crisis and recovery scenarios by accounting for ecosystem services lost,
maintained, or recovered due to action or inaction.
NEIGHBORHOOD NORTHWOOD
Indicators
Neighborhood
Raw Score
Baltimore City
Score Index Percentage Index Score
Available Healthy Food
Option Index 8.9 10.3 0.86407767 86.40776699
Tree Cover (Percentage) 33.8 27 1.251851852 125.1851852
Literacy (Library Cards
per 1000 residents) 265.8 299.1 0.888665998 88.8665998
Use of Public
Transportation to work
(Percentage) 16.7 18.8 0.888297872 88.82978723 Services Total
Climatic Data (Home
Weatherization -
Percentage) 0.7 0.6 1.166666667 116.6666667 505.9560059
Liquor outlet density
(per 1000 residents) -0.1 1.2 -0.083333333 -8.333333333
Fast food outlet density
(per 1000 residents) -0.4 1.4 -0.285714286 -28.57142857
Rate of Dirty Streets
and Alleys Reports (per
1000 Residents) -40.3 70.5 -0.571631206 -57.16312057
Rate of Clogged Storm
Drain Reports (per 1000
Residents) -4.3 6.2 -0.693548387 -69.35483871
Disservices
Total
Percentage of
Residential Properties
that are Vacant and
Abandoned -0.4 8 -0.05 -5 -168.4227212
TOTAL 337.5332847
-400
-300
-200
-100
0
100
200
300
Available Healthy Food Option Index
Tree Cover (Percentage)
Literacy (Library Cards per 1000 residents)
Use of Public Transportation to work (Percentage)
Climatic Data (Home Weatherization - Percentage)
Liquor outlet density (per 1000 residents)
Fast food outlet density (per 1000 residents)
Rate of Dirty Streets and Alleys Reports (per 1000 Residents)
Rate of Clogged Storm Drain Reports (per 1000 Residents)
Percentage of Residential Properties that are Vacant and Abandoned
Human Health Index - Neighborhood Comparison
Northwood
Belair-Edison
SW Baltimore