Spatial Data Standards

-

In this lab, we were asked to compare the horizontal accuracy of two street datasets for the city of Albuquerque, NM. One of the datasets came from the city itself, and the other came from StreetMap USA. Below is a general outline of the process followed.  

  1. Selected 20 test points for both datasets evenly distributed throughout the study area. These test points were located at clearly defined intersections. 

  2. Created a reference for each test point based on orthomosaic images.

  3. Generated XY coordinate data for test points and reference points and calculated accuracy statistics using the following table. 


    Figure from the Positional Accuracy Handbook. 1999. Minnesota Planning, Land
    Management Information Center, St. Paul, MN


The map below shows the location of the 20 test points selected. 



Results


City of Albuquerque Dataset

Tested 13.206 feet horizontal accuracy at 95% confidence interval. 


StreetMap USA Dataset

Tested 241.367 feet horizontal accuracy at 95% confidence interval. 






Comments

Post a Comment

Popular posts from this blog

Isarithmic Mapping: Annual Precipitation in Washington State

-
Image
The second most common type of thematic map behind the choropleth map is the isarithmic map. While choropleth maps are great for mapping phenomena existing within hard boundary lines, also known as enumeration units, isarithmic maps are perfect for mapping continuous data such as temperature, precipitation, or elevation. In isarithmic mapping, contour lines are frequently used to connect locations with equal values. Hypsometric tints can then be added to color the areas between contour lines with different shades so that map readers can easily distinguish between areas with high and low values.  In this lab, our task was to create an isarithmic map showing average annual rainfall in Washington State. This rainfall data was interpolated using the PRISM (Parameter-elevation Regressions on Independent Slopes Model) method developed by Oregon State University. The map below was created using ArcGIS Pro. Areas with low precipitation (less than 10 in/yr) are shown in red, and areas with the
Read more

Spatial Enhancement, Multispectral Data, and Band Indices

-
Image
There are several techniques that can be employed within ERDAS Imagine to identify specific features using multispectral imagery. These features can then be displayed using different color band combinations to make them easier to distinguish visually. In general, the four steps used to identify features in ERDAS are: Examine the histogram for shapes and patterns in the data. Visually examine the image as grayscale for light or dark shapes and patterns. Visually examine the image as multispectral, changing the band combinations to make certain features stand out. Use the Inquire Cursor to find the exact brightness value of a particular area. In this lab, we were asked to identify three features within a multispectral image using these methods of interpretation based a given set of criteria. We were then asked to select an appropriate color band combination to display these features in a way that makes them clearly stand out from their surroundings.  The map below displays the first feat
Read more

Development Suitability Analysis

-
Image
For one of the scenarios in this week's lab, I was playing the role of a GIS Analyst for a property developer. My task was to create a map showing suitable land for development based on the following criteria:  Land Cover Soils Slopes Streams Roads  My general process for this analysis was to first convert all of these layers into rasters if they weren't already. For the rivers and roads layers, I used the Euclidean Distance tool to calculate the shortest distance from each cell to the nearest river or road. I then reclassified each of these layers based on a suitability scale of 1 - 5. Lastly, I combined the results of each of these five criteria using the Weighted Overlay tool. Below are my results.  The first map is an equally weighted average where each of the five criteria have an influence of 20% (or 1/5th). The second map shows an alternatively weighted output where each of the five criteria were weighed as follows:  Land cover (20%)  Soils (20%) Slope (40%) Distance to
Read more