|Particulate Monitoring||Creating the Context: Data Analysis|
Results of Study
Results of Study
Means and Extremes
The methods and number of steps used in gathering scientific knowledge may vary from one
investigator to the next, but scientific methods usually involve the alternation of two types of
activities, the observational and the explanatory. So far we have been making observations about
particulates, but we have not yet participated in the explanatory part of science. We can continue
to explore the data we have collected so far to increase the accuracy of our observations. The data
set you downloaded does not really mean very much yet because we do not know whether this data is
typical, high, low, or how it compares to "normal".
See if you can do a mean for the data that you have. Find the extremes in your data set. Look for the highest and the lowest ozone levels.
Construct a two-variable line graph ( particulates over date ) to graph the data you have downloaded.
Using Geographic Information Systems for Analysis
A geographic information system (GIS) is a computer-based tool for mapping and analyzing things that exist and events that happen on earth. The data that we have collected as a part of this project is well suited to GIS technology because it has a critical geographic dimension. GIS integrates common database operations such as query and statistical analysis with the unique visualization and geographic analysis benefits offered by maps. These abilities distinguish GIS from other types of analysis.
Geographic information systems work with two fundamentally different types of geographic models - the "vector" model and the "raster" model. In the vector model, information about points, lines, and polygons is encoded and stored as a collection of x,y coordinates. The location of a point feature, such as a bore hole, can be described by a single x,y coordinate. Linear features, such as roads and rivers, can be stored as a collection of point coordinates. Polygonal features, such as sales territories and river catchments, can be stored as a closed loop of coordinates.
I would like to work with the data in a map-based format.
Using Systems Thinking - Modeling to work with data.
Models are an important part of the explanatory part of science. Science is a practical study of what can be observed, and the prediction from that, of what will be observed. Models support moving beyond assimilating content to actually building understanding and effectively sharing this understanding with others. Using modeling software for analysis will build your capacity for, evaluating your models' congruence with reality and seeing complex interdependent relationships. Modeling is another tool of the practicing scientist. The STELLA software used by this project, uses a building block language that allows you to model the system of interest. The structure of virtually any system can be represented using just four icons! Sophisticated mathematics is not required to capture sophisticated relationships, as the STELLA software automatically creates the framework of equations needed to simulate the model. Once the model is constructed, simulations provide the opportunity to test the theories, observe results, and modify assumptions, thereby increasing your understanding of how things really work and how to make them work better. Perhaps creating a model of how particulates enter and leave the atmosphere would be a good model to begin with.
As you explore your data a number of questions will no doubt come to mind. Many of them begin with "Why".......which is good because it means you are ready to really begin the explanatory part of science. This process begins with establishing and refining your questions as a research question . If you do not have much experience with this process the Guided Research will help you begin working in the explanatory part of science. If you ready to jump in on your own go ahead and begin your work. If you need some helpful suggestions for how to proceed, or if you are ready to Publish My Research, this area will help you share the information you develop.
|1999, KanCRN Collaborative Research Network|