GIS5935 M1.2 Data Quality Assessment

 Accuracy Assessment of Road Network Completeness

Goal of the Accuracy Assessment

This accuracy assessment aims to compare the completeness of two road network datasets: Street_Centerlines and TIGER_Roads shapefiles for Jackson County, Oregon. The objective of evaluating the total length of roads within a uniform 5km x 5km grid is to determine which dataset provides more comprehensive coverage for the area. This analysis allows for identifying areas where one dataset may be more detailed than the other, contributing to a better understanding of road network quality in the region.

Analysis Methodology

The analysis methodology involved multiple steps to compare the two road networks. First, the road datasets—Street_Centerlines.shp and TIGER_Roads.shp—were clipped to the boundaries of a 5km x 5km grid that spans Jackson County. The Clip tool ensured that the roads outside the grid cells were removed. The next step used the Intersect tool to divide road segments at the grid boundaries. Now, only the segments within each grid cell will be counted towards the total of a specific grid polygon. A new field was created to store these measurements, and the Calculate Geometry tool was used to compute the lengths in kilometers. The Dissolve tool was applied to summarize the total road lengths in each grid, aggregating road segments based on their grid codes. The final step involved comparing the total road lengths between the two datasets and calculating the percentage difference for each grid, enabling a clear visualization of how the networks differed in completeness. 

Summary of Results

Based on the analysis, the TIGER_Roads dataset was more complete overall, with a total road length of 11,382.7 km, compared to 10,805.8 km for Street_Centerlines.

The negative percent difference values are when the TIGER dataset has a greater total length than the Street_Centerlines. The positive values are the Street_Centerlines with greater total lengths. In the middle, from -5% to 5%, there is a near-equal or minimal difference between the total length of roads between the two datasets. Overall, the choropleth map is an excellent visual summary of the analysis.






Comments

Post a Comment

Popular posts from this blog

M1 - Python Environments & Flowcharts

Image
  Python Module 1 Recap: From Scripts to Flowcharts and Zen Principles Converting 3 Radians to Degrees To convert 3 radians to degrees, we use a simple formula: degrees = radians * 180 / pi Below is the flowchart illustrating the conversion process: Running the calculations in IDLE gives us:  ≈ 171.887 Interpretation of the Zen of Python The "Zen of Python" showcases the philosophy and guiding principles of Python programming. It emphasizes simplicity, readability, and clarity in code. In other words, “Zen of Python” promotes straightforward and elegant solutions over complicated ones. By valuing explicitness over implicitness, the basis of Python encourages clear and understandable code. Moreover, The principles highlight the importance of practicality and avoiding unnecessary complexity— why use 4 lines of code when 1 will do the same thing? This mentality helps to balance between purity and practicality. Overall, the “Zen of Python” advocates for writing code that is easy
Read more

GIS Map Projections

Image
  This week's lab exercise looked at the importance of map projections in GIS, especially when using multiple datasets. The map above shows four counties in Florida that are displayed using the Albers Conical Equal Area, NAD 1983 UTM Zone 16N, and NAD 1983 HARN State Plane Florida North FIPS 0903 (US Feet) map projections. As the table within the map shows, the areas of the counties vary depending on the map projections. The best map projections are ones that shows the least distortion between map features. For example, The UTM projection is best when used the map features fit within one UTM zone, which is not the case for showing the entire state of Florida. The same can be said for the Florida State Plane that is dependent on the region of interest. Therefore, the map projection would need to be Albers since it minimizes distortion between its standard parallels.  Overall, this lab illustrates how important it is to use accurate map projections since it can cause distortions betw
Read more

ArcGIS Pro Orientation

Image
  Module 1 Lab Assignment This first lab assignment's objective is to prepare and show what is expected for future lab assignments. The primary learning outcomes involved: Getting familiar with accessing the ArcGIS software through Argo Apps. Assessing data storage drives. Preparing project folders for each project. The map shown above is a visualization of the Cities and World Countries data files found in the OverviewArcGIS file in the R drive. In order to view the shapefiles in a way that is informative, we had to add a Folder Connection to the data we downloaded as well as update the symbology to show graduated colors for the field POP_2007 with Natural Breaks and 7 classes. It is important to use a single color from light to dark when using sequential data. While I wish this had been a smooth and uncomplicated process, I struggled with learning UWF's Argo Apps since that is how we access ArcGIS Pro without downloading it to our personal computers. While I am familiar with
Read more