CARTOmazing

TIN is a vector-based representation of the elevation compared to DEM which is represented as a raster from square pixel/grid squares. The most common used are DEM which is publicly available for the world at around 30m resolution which can also derive a TIN data as TIN is a type of derived DEM.  DEM visualizes a much softer edges (see left image), feather edge as what we can call it in Photoshop,  compared to TIN that has pointy edges or very defined/jagged edges (see right image).  Any DEM or TIN is as good input data that can help user to assess elevation, slope and suitability areas. TIN is more accurate in terms of you can take not of sudden change in elevation depending on how close your sampling points are, but DEMs provides real world view of the elevation compared to TIN especially if you have a high-resolution raster imagery of the DEM, which sometimes is hard to find unless captured by a drone. The advantages of DEM is its inability to adapt to areas of differi...

This week's lab objective is about data quality assessments by evaluating accuracy/completeness by road networks. We were given two (2) road networks in Jackson County, Oregon named TIGER Roads from the 2000 census data and street centerlines data created by the county itself.  First, we were asked to compute the total length of each road networks in kilometres, both data should have the same projection, to know which is more complete. With this method we used the Calculate Geometry Attributes to convert the shape length from feet to kilometres and then used the Summary Statistics to compute the sum. The result for this method was that TIGER roads was more complete than the centerline roads with the following values:  Street Centerline: 10805.8 km TIGER Roads: 11382.7 km The second method involved having a grid data which method was based on a study comparing OSM and Meridian networks in England (Haklay, 2010).  The goal here is to determine the length (in km) for TIGER a...

This week, still at Module 1 - Data Quality, we learned how to determine the accuracy of road networks using the methodology provided by the National Standard for Spatial Data Accuracy (NSSDA). The objective of this week's lab is to compare two different road network dataset, city level (ABQ) and national level (Street Map USA), in Albuquerque, New Mexico based on the NSSDA methodology. Based from experience, the city level usually provides a much more detailed information of the road network compared to the national level. This is because of the administrative level area that they usually work on has a smaller scale compared to a national level - which I think the national level generalizes their data most of the time.  To start things off, we followed the 7 important steps of the NSSDA accuracy assessment (just using Steps 1 to 6). Here are the following steps: STEP 1: Determine if the test needs horizontal (X and Y) accuracy, vertical (Z) or both. STEP 2: Select your TEST poin...

As we begin this semester's journey, this week we started with calculating metrics for spatial data quality. This is something that I really need to refresh my skills on as I can't seem to really meet statistics and GIS in the middle. So I had to refresh myself with the terminologies that rather confused me in the past - which upon finishing this week's lab helped me. The horizontal accuracy is 3.24 meters which is the spatial distance between the average way point  to a reference point. While we got our horizontal precision by getting an average of the data values/measurements collected equal to 4.5 meters.  difference of 1.26 meters between the two which for me still provides a reasonable difference though b y using the percent error formula of (Observed – True) / True then I would get a percent error of 38.89%. Furthermore, it tells us that our horizontal precision data is 61.1% accurate.  Horizontal precision is the dispersion or closeness of the value to the average ...