development of a decision support system for environmental indicators using visual analytics
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Development of a Decision Support System for Environmental Indicators Using Visual Analytics
Dr. James Hunter Jr., Lashaunda Johnson, Derek Riley, Andrae Francois & Olumayokun Odukale Department of Civil Engineering, Clarence M. Mitchell, Jr. School of Engineering; VACCINE Center, Purdue University
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 thinking, and information
visualization.
Information Visualization is a very important aspect when addressing the issues
we are researching during this project. For two weeks, we were sent to the
VACCINE Center at Purdue University to receive intense training on visual
analytics.
For preliminary exposure, we observed the BNIA (Baltimore Neighborhood
Indicators Alliance) data collected, visualized the data, examined the
correlations, and drew conclusions from the findings.
Background
Contact Information
Base Model for BMP
Implementation
What are ecosystem services/environmental indicators and how can
visual analytics be 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. 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 to
prevent further loss it is important to identify, quantify and assess their
benefits. 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
decision support systems that allow us to make informed decisions
about environmental impacts.
Problems
Loss of Ecosystem Services
Ecosystem services have been degraded as a result of several stressors. In order
to prevent further degradation of these services it is important to identify, quantify
them, and asses their benefits.
Assessing Environmental Indicators
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.
• Poor Water Quality
As indicated by the department of natural resources in June 2002 Herring Run
Stream, located directly behind the Mitchell-Schaefer Engineering complex at
Morgan State University, was assessed as poor quality.
Solutions and Expected Outcomes
Acid Neutralizing Capacity 1575.0
Dissolved Organic Carbon 3.2
pH (avg. lab and field) 7.94
Temperature 22.0̊C
Dissolved Oxygen 8.3
Conductivity 0.368
Proposed Area of Testing Assessment – Herring Run
& Chinquapin Run Stream
Our two current solutions for solving the problem presented is the use of participatory sensing, and
water quality testing. 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. The water quality
testing will take place in the Herring Run stream. The Troll MP9500 device will collect different
indicators and their data. Using these two system, 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 MP Troll, 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.
http://www.visual-analytics.eu/faq/ Morgan State University Department of Civil Engineering
Dr. James Hunter
Lashaunda Johnson
Derek Riley
james.hunter@morga
n.edu
http://www.designboom.com/weblog/images/images_2/2011/jenny/node/node04.jpg
http://www.in-situ.com/products/water-quality/troll-9500-instruments/troll-9500-water-quality-instrument
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Development of a Decision Support System for Environmental Indicators Using Visual Analytics
Lashaunda Johnson, Derek Riley. Andrae Francois & Olumayokun Odukale Department of Civil Engineering, Clarence M. Mitchell, Jr. School of Engineering; VACCINE Center, Purdue University
Morgan State University, Baltimore, MD
Goals & Objectives
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.
Decision
Support
Dashboard