THE ENVIRONMENTAL MANAGEMENT INFORMATION SYSTEM (EMIS)
METHOD
TOOLS AND TECHNOLOGY;
DECISION SUPPORT FROM RELATIONAL DATABASES AND GIS
Making Large Amounts of Data Work
Methodologies utilized in assessing the characteristics
and interactions associated with environmental data need to be able
to consider all of the parts of the system simultaneously. This can
be a complex procedure, no matter what methodology is utilized. With
the relatively recent advancement of computer power has come the ability
to write more and more complex software suited to storing, manipulating,
and analyzing larger amounts of data. As a result, we now have the
ability to work with large amounts of data simultaneously.
Traditionally, data has been analyzed in smaller, more
manageable segments. Analyzing data as a body allows one to realize
trends and interactions that may not be obvious when they are considered
on a smaller scale. Analyzing larger sets of data is also more efficient
in that procedures that often need to be repeated for each set of
data can be performed for all data sets a single time. There are also
great advantages in the ability to store all the data associated with
a site in one location. This is not only a more efficient manner to
store data, but also provides a mechanism to query data for specific
desirable parameters associated with a specific project.
The Microsoft® Access® relational database and
Environmental Systems Research Institute’s (ESRI) ArcView®
program are two such applications that are today accepted as industry
standards in data analysis. Each caters to a different aspect of data
(i.e. a datum’s inherent relationship among another datum and
data’s spatial relationship, respectively). As discussed below,
the two can be used in concert to visualize regional groundwater and
contaminant characteristics.
Microsoft Access and the Summit Geographic
Environmental Management System (GEMS)
Microsoft Access is a very popular and time-tested relational
database. Access can accept input from many different data formats
(i.e., spreadsheets and Dbase formats) and save them in a relational
database in one homogenous format. The relational characteristics
of the database provide a mechanism to query the data from different
aspects and, therefore, to visualize larger trends that may not be
initially apparent in the data.
Forms can be developed to streamline data entry, queries
can be written to sort and select desired data, graphs can be generated
from selected data, formatted reports can be developed, and custom
graphical user interfaces (GUIs) can be developed that accent the
desired capabilities of Access for specific uses. Essentially, the
functions that a relational database provide to the user are the ability
to hold very large sets of data in one location, in one homogenous
format, and to use the data to provide information that is not present
in each datum itself.
GUIs can be developed within Access, utilizing Microsoft
Visual Basic for Applications, to customize the utility of the software
to the procedures most desired. This makes Access a stronger tool
in that processes can be streamlined, queries can be automated, and
output can be constructed in a format that is most useful. Summit
Envirosolutions, Inc. (Summit) has developed such a GUI called the
Geographic Environmental Management System or simply GEMS (Figure
1).
Figure 1: GEMS initial login
Graphical User Interface (GUI).
The GEMS database can be customized to allow the user
to enter and then analyze the data that would be derived from a groundwater
monitoring well field on many different sampling dates for many different
parameters. After data obtained from any number of sampling dates
from a monitoring well network has been entered into GEMS, it can
be queried for all wells or a specific well over a given time period
or on a specific date (Figure 2). Each well contains the numerical
results from various parameters obtained from multiple sampling periods
(i.e., ground water elevation, benzene concentrations, toluene concentrations,
etc…). This simply allows the user access to their data so that
less time can be spent manipulating the data and more time can be
spent analyzing it.
Figure 2: GEMS query interface
and the subsequent results of the query. Here benzene and groundwater
elevation data have been queried from the relational database.
Once queries have been performed on the data set, they
can be immediately graphed or saved for export into other application
as will be discussed below (Figure 3).
Figure 3: Graphed
query results from Figure 2.
GEMS also allows users to select a specific well and
immediately view well specific data including lithology, site location
and well construction characteristics. Boring logs can also be generated
from this data (Figure 7).
Figure 4: Example boring log
from generated in GEMS.
Spatial Data Analysis and ArcView
Most data have a spatial component. A datum can almost
always be tied down to a geographic location. The ability to see data
spatially helps one to understand the nature of the data, and the
benefit gained in the ability to visualize a complex phenomenon or
event is quite significant from a decision making perspective. It
is for this reason that the GIS platform has become such a prominent
tool in the environmental assessment field. The dramatic increase
in computer power has allowed this tool to evolve into a very powerful
spatial data analysis and visualization tool. ESRI has become the
industry leader in the development of user friendly GIS software and
ESRI’s ArcView is perhaps the most popular GIS software on the
market today.
ArcView provides a user friendly GUI that can be used
to view and edit large amounts of spatial data and it can create charts,
reports, and maps to support them. In ArcView, a specific real-world
"feature" (i.e., road, well, house, pole, river, lake, etc…)
is represented as a stand-alone layer (Figure 5). This enables the
user to overlay multiple features in multiple combinations. Each feature
is ‘geo-referenced’ using real-world locations according
to a chosen coordinate system such as Latitude and Longitude, State
Plane Coordinate System, or Universal Transverse Mercator (UTM). Geo-referenced
Images such as aerial or satellite photography may also be brought
into ArcView and used as a back drop from which to view other data.
Figure 5: ArcView GUI showing
multiple ‘features’ overlaid upon one another. This
particular project shows an oil refinery on the East Coast.
The real power in a GIS is that these geo-referenced
features each have a database associated with them called an attribute
table. For instance, a monitoring well feature, or layer, has an attribute
table associated with it where one record in the table corresponds
to one well in the map view (Figure 6). The user may choose to denote
any number of items, or columns, in the attribute table. Like Access,
ArcView has the capability to query these tables and then represent
these queries in the map views. Queries of multiple data sets, or
features, may be performed and then overlaid with one another. This
capability, once again, allows the user to bring information out of
the data sets that may not be apparent in each data feature or each
datum itself. When this spatially queried data is made into ArcView
generated high quality maps and used in reports, the user is equipped
with a very effective communicative presentational and interpretive
tool.
Figure 6: ArcView showing the
monitoring wells attribute table associated with the monitoring
wells view in the background. (Note: MW-1 is selected in the table
can also be seen in the background as a feature).
ArcView also has more process specific extensions that
are geared towards specific tasks. Spatial Analyst is one such extension.
Spatial Analyst allows the user to manipulate raster data (that is,
data that consists of a grid or a Cartesian framework as opposed to
vector) and use attributes from it, such as elevation or concentration,
to represent it graphically. For instance, groundwater scientists
could use the well feature mentioned above, containing groundwater
elevation in the attribute table, to quickly create contours of any
contour interval. This graphically demonstrates the relationship between
the groundwater elevation among the wells (Figure 7). This is a vast
improvement over manually reviewing log sheets and spreadsheets of
data and trying to visualize spatial relationships.
Figure 7: ArcView showing the
results of groundwater elevation data and DRO contouring.
Interaction between a GIS and GEMS and benefits
attained from a decision support system.
Access and ArcView as stand-alone packages are very
useful when searching for relationships and trends among large amounts
of data; however, each has a particular strength. Together, GEMS and
ArcView make entering, storing, sorting, querying, analyzing, and
ultimately presenting data a very user friendly, powerful, intuitive
and effective process.
Data can effectively be analyzed utilizing this combination
of software to accurately reveal groundwater and contaminant trends.
Data that resides in GEMS can be queried by date and parameter, imported
into ArcView, and then modeled or contoured with great ease and efficiency.
For example, a user wanted to know what the benzene concentration
in a down-gradient monitoring well was on a specific sampling date,
the process would be to: 1) query the GEMS database for benzene content
in all wells as sampled on the desired date; 2) save the query results
table; 3) import the query results into ArcView; 4) attach the results
table to an existing wells table in ArcView; and 5) contour the data.
Very realistically, such an exercise is a five-minute process or less.
Summit’s RealFlow® extension in ArcView automates this process
even further. This process could be repeated for any number or combinations
of parameter queries in GEMS and then visualized in ArcView.
Understanding the interaction between sets of data associated
with different parameters, as measured in monitoring wells, can be
done with greater ease by ‘seeing’ results. Plumes of
contaminants, for instance, can be measured against groundwater flow
direction to visualize plume travel direction and speed, thus aiding
the decision making process.
Perhaps the largest advantage of utilizing this data
processing tool with this approach is data visualization. Almost any
groundwater impact scenario in the past or future that one would wish
to assess could be visualized with these tools. This improves personal
understanding of the situation, increases speed of comprehension of
the effects of real world or hypothetical scenarios, and ultimately,
greatly aids in the decision making process.
