Module 2.3: Scale Effect and Spatial Data Aggregation

-

This module involves the concepts of scale effect on vector data, resolution effect on raster data, and gerrymandering of congressional district boundaries.

Scale effect refers to the relationship between how real world of features are represented and the change in accuracy as the scale is changed. The shape of hydrographic features can be represented differently based on the scale and the density of the points collected. The larger the scale, the more detailed and realistic the feature becomes, which increases the length and accuracy of the feature. The numeric results in the table above illustrate that as you go from a larger to smaller scale, the measurements decrease. 

The map below shows three different hydrographic datasets that were created at different scales. The smaller the scale the more detailed and closer to realistic representation of the actual hydraulic feature.





















Resolution effect on raster data refers to the cell size of the grid and how well it represents the true earth features. Higher resolution provides more detail and better represents the features on the ground and lower resolution is more generalized the surface of the earth. Below are three Digital Elevation Models (DEM) at 1-, 30-, and 90-meter resolution. The 1-meter resolution provides more detail and better represents the surface of the ground.




























































Gerrymandering is a term used to describe boundaries that are drawn to give an advantage to one political party or group of individuals. One way to measure Gerrymandering is by determining compactness which is a ratio of the circumference of a district and its total area. The Polsby-Popper score uses the formula (4π * AREA) / (PERIMETER ** 2) which produces a score range 0 to 1. Scores closer to one are more compact and lower scores are less compact. The below map shows the North Carolina Congressional District 12 which was the worst compactness offender of all the other districts in the US with a score of 0.03.




Comments

Post a Comment

Popular posts from this blog

GIS Programming Module 1 – Python Environments & Flowcharts

-
Image
Module 1 examines “The Zen of Python” which is a poem by Tim Peters a software engineer. This poem can be seen by typing “import this” into any Python interpreter and describes what it means to write Python code. “The Zen of Python” is the principals or guidelines on how programmers should write code. These principles suggest to me that we should write code in a way that is easy to read and understand, even though the code may be complicated. I see this as writing code that is modular where the complexity can be broken down into smaller functions that do a specific task. Complexity can also be handled by simplifying the code, by finding ways to reduce the number of lines it takes to accomplish the same task. We should follow these principles not only for others who might later need to interpret the code, but for ourselves to help us in debugging and future alterations. It is up to the programmer to write code that provides the required functionality, but they should make a conscious ef...
Read more

GIS Programming Module 2 – Python Fundamentals

-
Image
  Module 2 assignment covered some of the fundamentals of Python. This included learning about the different Python data types, working with string variables, understanding loops and conditional statements, and iterating through variables.   The screenshot below shows the output of the script written for the assignment. Step 1 was to create a variable to hold my full name separated by spaces, split the variable using the spaces, and then print my last name using the index -1. The index -1 is the last item in the list which was my last name. Step 2 was to fix two errors in the code that was provided. The code uses a list of the first names of players and calculates the number of characters in players’ name. Each player rolls a dice which is randomly selected from a list of integers from 0 to 2 times the number of characters in the name. Players win if the dice is greater than the number of characters, tie if they are equal, and lose if the dice is less than the number of ...
Read more

GIS 5935 Lab 1 - Calculating Metrics for Spatial Data Quality

-
Image
The first lab of GIS 5939 concerned spatial data quality and being able to determine if the data is accurate, precise, or has any bias. This involved understanding the difference between accuracy and precision. The horizontal accuracy of a point is determined by how close the collected points are from the true location. This is calculated by measuring the distance from the average of all points collected and the reference point. The larger the distance means, the lower the accuracy. The horizontal precision is how close different measurements are from each other. Points that are clustered together have high precision and points that are spread-out have low precision. The image below illustrates accuracy and precision. Image Source: https://byjus.com/physics/accuracy-precision-measurement/ The map below shows red dots for waypoints that were captured using an unknown GPS device. These 50 waypoints were captured using the same GPS and at the same location. The points should be at the sam...
Read more