• Gained insight on how data is stored and managed in the ArcGIS software package by familiarizing myself with various interfaces as well as previewing, managing and exploring properties of spatial data.
• Understood how coordinate systems and map projections affect geographic maps by changing map projections, repairing misaligned and improperly referenced spatial data, and learning how to follow the best practices so senseless mistakes do not occur.
• Learned the difference between spatial data models (raster, vector) and performed additions to attribute tables along with minor calculations.
• Explored the use of remotely sensed imagery by creating composite images using different bands of satellite data.

Q: How can one fix misaligned and improperly referenced data?  

Misaligned and improperly referenced data could be due to two things (1) the GCS, or geographic coordinate system is incorrect, or (2) the projection is incorrect. The GCS, or geographic coordinate system uses a datum from which all coordinates are referenced to. Some examples of these are WGS1984 and NAD83 (or the older version NAD27). Projections are representations of a 3D surface onto a 2D plane. They can drastically change the way your data appears if data layers in the GIS are using different projections. Some examples of popular projections are Lambert (conical) and Mercator (cylindrical).

Note: ESRI’s ArcGIS software package may utilize projection-on-the-fly, where your initial data set imported into the GIS is used as a reference to which all other data sets will be projected to.

To correct misaligned and improperly referenced data, the easiest way is to use the Projection too in the ArcToolbox. By selecting this tool, you can choose which data set you would like to correct and select the GCS and projection you’d prefer to utilize based on the goal of your map.

Q: What are the advantages to using remotely sensed Landsat data for geographic analysis? 

Using remotely sensed Landsat data is extremely useful when you need to be able to distinguish areas that, to the human eye, are not as easily distinguishable. Landsat data is a collection of different wavelengths (or “bands”) that have been reflected back from the Earth. The sun is the initial source of energy for the bands. If you were to combine the red, green, and blue bands, you would get a true colored image. This is the principle of Landsat data – combining different bands to get the best contrast between two or more areas that you want to compare. Another example (that was used in my lab) is to combine the green, red and near infrared bands to create a false color image where vegetation is displayed as shades of red, water as black or very dark blue and bare soils as grey with a cyan/greenish hue. The new color contrast created by the combination of the bands allows for easy geographic analysis.