Interpolation

 This week we carried out different surface interpolation techniques.  To the right you will see:

1)IDW:  Inverse Distance Weighting.  This was performed with IDW tool.  Generally the process takes the known sample points and infers values at other points.  This is accomplished by taking the known values and assuming areas in proximity of this will have similar results.

 2)Spline Interpolation Regularized: This method utilizes minimum curvature to fit boundary divisions through and around sample values.  I imagine laying out a piece of yard around the data values to form the boundaries for classifications.

3) Spline Interpolation Tension:  This method again utilizes minimum curvature to fit boundary divisions through the sample values.  Tension setting causes the barrier lines to be a bit more stiff, displaying as less fluid.

TINs and Dems

This week we moved from data quality to started looking at surface information.  We created 3d visualizations of elevation models, and examined TINs and DEMs.   

TIN, Triangular Irregular Network, is a vector display of triangles formed between elevation points the sides or edges of the triangles cover the study area without overlap or gaps to interpolate changes in elevation over distance to provide estimates of slope. Displayed above is a TIN.  The triangular network is visible with graduated color of slope and contour lines with darker index contours.

DEM, Digital Elevation Model, utilizes raster data stored in the raster cells.  The cell data is processed utilizing a model like nearest neighbor, 8 cell window, or other to provide elevation data.

This week we discovered how complicated it can be to calculate completeness of road networks.  First to calculate completeness is a comparison to another source of data (so depends on how complete/correct that information is, more about this in a minute).  We read a few articles on methods and data sets of comparing road centerlines.  Then in lab we compared a data set from Jackson County, Oregon to the 2000 TIGER Road data.  Seo, 2009, reports 2000 TIGER data was so poor that it required an extensive overhaul to be able to better meet the needs of the 2010 Census (Seo, 2009).  Going into this lab with the knowledge of the 2000 TIGER data I had expectations that the County Centerline data would be far more complete.  The choropleth above shows 297 grid cells, of those 162 show Local info to have less road kilometers than Tiger data, 134 cells show the local data to have more kilometers and 1 cell had no road kilometers in either data set.  The cells in green show where more kilometer or roads were found in the TIGER data than in the local County.  The darker the color the higher the percentage of  difference.  This depiction does not question if more kilometers of roads is a good method of calculating more complete data. 


Reference:

Seo, S., & Ohara, C. G. (2009). Quality assessment of linear data. International Journal of Geographical Information Science, 23(12), 1503–1525. doi: 10.1080/13658810802231456

Data Quality-Standards

This week in class we examined the accuracy of two road networks, Street Map and City of Albuquerque.  We calculated accuracy with procedures provided by the National Standard for Spatial Data Accuracy (NSSDA).  We selected 20 street intersection locations and marked the location of the intersection provided in the Street Map data as well as the same intersection location for the City data.  We referenced each of these data sets with ortho-rectified imagery.  The test points are shown above in green for the city data, red for the street map and blue (slightly larger symbol) for the reference.  Finding appropriate test points was more challenging that I initially thought.  20 minimum points, with a minimum of 20% in each quadrant with minimum separation between points of 10% of the diagonal of the study area.

Accuracy Statistics were calculated seperately for City data and Street Map each utilizing the Ortho points as the reference.  The XY data of each point was obtained in ArcPro.  Those points were exported to Excel.  The calculations included differences for X & Y coordinates, each difference squared, and then the sum of X & Y.  Those sets were summarized as Sum, Average, RMSE, and NSSDA.

Formal accuracy statements as per the NSSDA guidelines are:

Horizontal positional accuracy for Street Map:  Tested 70,116.2 Feet horizontal accuracy at 95% confidence level.

Horizontal positional accuracy for City of Albuquerque: Tested 605.8 Feet horizontal accuracy at 95% confidence level.

 
The accuracy position at 95% confidence is quite different:  City data at 605.8 feet; Street Map at 70,116.2 feet.  The importance of knowing the source of your data as well as its accuracy became abundantly apparent! 

Calculating Metrics for Spatial Data Quality

Horizontal Precision @68% 5.65meters

Vertical Precision @68% = 5.71meters

Horizontal Accuracy from the average point is 5.82 meters

Vertical Accuracy from the average point is 5.96 meters

Week one of Special Topics:  Learning Outcomes 1)understand the difference between precision and accuracy 2) calculate vertical and horizontal position accuracy and precision 3) calculate root-mean-square error (RMSE) and cumulative distribution function (CDF).

What is the difference between Accuracy and Precision?  Our text reports "Positional accuracy measures how close a representation of an object is to the true value." (Bolstad, 2017, p. 264).  And "Precision refers to the consistency of a measurement method." (Bostad, 2017, p. 264).  Another source describes it as "The accuracy of a measurement means getting a value that is close to the actual answer. Precision, on the other hand, refers to the reproducibility of this result that is you get the same result every time you try." (Nedha, 2015, para 1).  So precision is the repeatability of the results, and accuracy is how close to the referenced point (actual location).  

Precision for this lab was measured by calculating the % of precision desired (common is 68%) of the total sample points.  Sorting sample points from lowest to highest values and utilizing the measurements of the nth(68% of 50 sample is 34) sample.

Accuracy for this lab was measured by calculating the average of all sample points and comparing it to the referenced/actual point.

Reference:

Bolstad, P. (2017). Gis fundamentals: a first text on geographic information systems. Acton, MA: XanEdu.

Nedha. (2015, July 13). Difference Between Accuracy and Precision. Retrieved from https://www.differencebetween.com/difference-between-accuracy-and-vs-precision/