Scale & Spatial Data Aggregation

GIS 5935 Special Topics in GIS 
Lab 6

 

Student Learning Objectives:

• Speak to the effects of scale on vector data
• Explain the effects of resolution on raster data
• Understand the effect of the Modifiable Area Unit Problem (MAUP) using OLS analysis
• Identify multipart features
• Measure compactness

There are two objectives of this lab:  To examine the effects of scale and resolution on the properties of spatial data and to become familiar with the Modifiable Area Unit Problem (MAUP)

Part 1: Effects of Scale

Compare different scales of vector and raster data to each other in order to determine the effects.

Vector Data: 

As discussed in the lecture for this module, the total perimeter increases as the smallest measurement possible increases. At smaller scales, vertices are closer, creating a more detailed representation of the observed feature. This is reflected in the first row in the table above. It is possible to capture more details at smaller scales because small features are clearly visible. These finer details and small features are lost as the scale increases and they fall below the threshold for being distinctive enough to be recorded. As scale increases data can become generalized and excluded, meaning that fewer vertices are used to represent the data or features may not be represented at all because they are not distinguishable at that scale. 

Raster Data: 

According to this scatterplot, as the cell size increases the slope represented becomes more flattened out.  As the cell size increases changes that happen below the cell size threshold are excluded. A smaller resolution allows for the capture of more slope information thereby representing a steeper average slope. 

Part 2: Measuring Compactness (Gerrymandering) 

Gerrymandering is when the borders of any given district are manipulated to favor one group or another. Below is an example:

This can be measured using the Polsby Popper Scale. This equation determines the compactness of a given area. The closer to 1 the more compact an area is. The example above comes in at .08, while the one below comes in at .29 .  I created the necessary new fields and used calculate geometry and calculate field to populate the Polsby-Popper values.