Lab 4 - Building Networks

Results from running a route on a network with historic traffic trends added.

This week's lab had us building a road network with the ArcGIS Network Analyst extension.  The road network was built twice - once with historic traffic pattern data added, and once without. 

The additional functionality was added during the network building process by electing to model traffic patterns.  Associated with the roads dataset is a set of two tables that contain historic traffic pattern data, with such information as what percentage of normal (ideal) driving speeds a particular road segment has at certain times of day, for every day of the week.  Once this data was linked to the network the overall results became more realistic. 

Not shown above are the various results obtained from making minor changes to the route start and stop times, and on what day of the week the route was created for.  The resulting route travel times changed by only a few minutes... and surprisingly so did the overall distance traveled.  These route changes were so small that I could not detect them on my network, only on the route properties screen (such as in the example shown above).

Lab 3 - Land Use / Land Cover Classification Mapping

This week we got to try our hand at classifying an aerial photograph.  Using a Land Use Land Cover classification system, which was developed in 1976 by Anderson et al. for the USGS, we classed a single aerial photo to the second level.  My minimum mapping unit (MMU) was at 1:4,000. 

Aerial map showing Level II Land Use and Land Cover Classification.

What does all this mean, exactly?  To start, land use is a bit different from land cover.  Land use shows human-based uses of the landscape (urban areas, agriculture), and land cover shows primarily natural settings (forests, water).  To classify a map one first needs to differentiate between farmland, urban areas, forested areas, etc.  That is a primary classification level.  To map something at the second classification level is to specify if, for example, the areas within an urban area are for residential use versus an industrial use.

The map above shows various classifications within a small section of Pascagoula, Mississippi.  For consistency I had digitized my classification polygons at the 1:4,000 level only.  While I may have zoomed in or out to double check on a classification type or my overall location on the aerial photo, when I digitized an area it was always at 1:4,000 (also known as my MMU).

Completing this map was a bit rough at times - occasionally I felt like I was adding to much detail, and other times like I wasn't adding enough.  Since a Level II classification is meant to be a bit coarse perhaps my biggest lesson was in learning to let go of the details!  For example, a high-tension wire crosses through the lower left of the aerial photo.  This was not mapped in mainly because to do so would have been very difficult given the level of detail that it would require... if my MMU was a bit larger then perhaps it would have been possible, however I would also probably still be working on this map!

Reference:
Anderson, J.R., and E. E. Hardy, J.T. Roach, R. E. Witmer
1976     A Land Use and Land Cover Classification System for Use with Remote Sensor Data.  Geological Survey Professional Paper 964.  United States Government Printing Office, Washington D. C.

Lab 3 - Determining the Quality of Road Networks

One way to measure the quality of a road network is to evaluate its overall completeness.  The idea behind this is that the more roads mapped within a given network, the greater the likelihood that the network has better coverage of a given area.  This was the focus of our lab this week.

Do note, however, that just because a network has more coverage does not necessarily make it more spatially accurate... those lines still need to be in the right place!  Our lab focused only on comparing the completeness of one road network against another for the same area - testing the spatial accuracy of a road network using points was covered last week (Lab 2).

Technical Notes

The first comparison metric is exactly what one might think: we totaled the collective line segments lengths per road network, and compared the results.  At first glance the TIGER Roads data is more complete than the Jackson Co. street centerlines data.

After determining these lengths we then needed to break down just how complete each road network was per grid cell.  We overlaid a grid (a series of square polygons) covering the whole of Jackson County, then split up the road network polylines by grid cell.  This was done using the Intersect (analysis) tool. 

Once the road segments were separated their respective lengths per grid cell were then updated using the Calculate Geometry tool.  The grid cell data was then joined to the road segments - this made it easier to obtain the overall road length totals per grid cell (per road network). 

The TIGER Roads were also shown to be more complete in terms of overall length per grid cell... but the Jackson County street centerline data is more complete in more grid cells than the TIGER Roads data.  The results are depicted in the map below.  A choropleth map using (Jenks) Natural Breaks was created, and the results are explained in terms of percentage variance for the Jackson County street centerline network from the TIGER roads data.




The final result, explained in terms of how it relates to the Jackson Co. street centerline data.

Lab 2 - Visual Interpretation

This week’s lab focused on the elements of aerial photo interpretation.  To accomplish this we viewed two separate photos, then identified various elements within each. 

Map 1 - Comparing tone and texture.

The first map is a study in texture and tones.  We were to identify various tones (ranging from very dark to very light) and various textures (ranging from very fine to very coarse).  I found the tones to be a bit of a challenge, as to me the gradation of very dark to dark was a bit subjective.  Never fear, I was able to tell the basic difference between light and dark!

 

Map 2 - Picking out features based on specific criteria.

The second map required that we identify features within the photo that correspond to a specific criteria: association, shadows, shape and size, and pattern.  It was interesting to see just how many findings based on association I was able to make… I didn’t map all of them in as the assignment only called for two examples, but there are quite a bit as can be seen above.

Lab 2 - Determining Quality of Road Networks

This week's lab continued in the theme of spatial accuracy and data quality.  Using the National Standard for Spatial Accuracy (NSSDA) statistics we compared the accuracy of two street datasets in Albuquerque, New Mexico.

Street test point locations in Albuquerque, New Mexico.

The first data set consisted of streets data provided by the City of Albuquerque - this was our 'truth' layer.  The second data set was a portion of the USA Streets layer provided by ESRI.  Both data sets were compared at 55 different test point locations.  Test point locations were selected using an ArcGIS Desktop Add-In called the 'Sampling Design Tool'.  These sample locations were then moved to the nearest four- or three-way intersection on the City of Albuquerque data.  Sample locations were discarded if the closest City of Albuquerque streets intersection did not also have a corresponding ESRI USA Streets intersection (the ESRI USA Streets layer was not as complete as that provided by the City of Albuquerque).

The reference layer was then hand digitized using aerial orthophotos of the study area (outlined in blue above). All reference points were taken in what can be considered the 'centerline' of the street, as viewed in the orthophoto image.  The two data sets were then compared to this reference location using an Excel table to compute the Euclidean distance difference between the reference x and y locations and the streets layer under comparison.  After following the NSSDA worksheet (the details of which can be viewed here: https://www.fgdc.gov/standards/projects/FGDC-standards-projects/accuracy/part3/chapter3) the results were as follows:

USA Streets Positional Accuracy:  Using the National Standard for Spatial Data Accuracy, the data set tested 264.7 feet horizontal accuracy at 95% confidence level.

Albuquerque Streets Positional Accuracy: Using the National Standard for Spatial Data Accuracy, the data set tested 51.5 feet horizontal accuracy at 95% confidence level.

Lab 1 - Calculating Metrics for Spatial Data Quality

The first lab covered how to calculate error in x, y data.  We specifically examined how 50 GPS points taken at the same location related to each other as well as to the 'true' point location, otherwise known as the reference point.  Our goal was to calculate the amount of accuracy and precision our sample of 50 GPS points has.

Accuracy represents the absence of error, or how close a point is to any given reference or 'true' point location.  Precision represents variance around a given point location, or how many times the same result occurs around a given point location.

Map layout of the GPS points in relation to the average point location.

The above image shows the 50 sample GPS points in relation to their average point location.  The rings around the average point location represent what percentile the points located around the average location are in - this is a measure of the overall precision of the GPS points.  So, roughly 50% of the points are 2.9 meters from the average point location, 68% of the points are 4.4 meters from the average point location, and 95% of the points are 14.8 meters from the average point location. 

As shown above, the sample GPS points were not very accurate, nor were they very precise.  The points are scattered all around the average point location, and while 68% of the points are within 4.4 meters of the average that level of accuracy can be considered too course if one requires sub-meter accuracy for their project.

Module 11 - Sharing Tools

The final lab of the semester was short and sweet - we created parameter help messages associated with a script tool, then embedded the main python script within the tool in order make it easier to distribute.  To keep our scripts 'safe' we password protected the embedded script - which means that only those who know the password can see or modify the python script.

The script tool that we modified does the following: creates a series of random points within an extent as defined by the input feature class, then creates a buffer around the randomly created points.  The random points are offset from each other by a certain distance, as defined by the tool end user.  A screenshot of the tool dialog box and output results is shown below.

Tool dialog box with custom parameter help messages on the left, and the tool results on the right.

Parting Thoughts...

This course has proved to be very useful... I'm still in shock over being able to actually create a custom script tool that actually works!  Learning the basics of how to code in Python has been very cool and I definitely think the lessons 'took'.  Even if I never get a chance to write another custom tool in my life, just by being able to understand what all those code examples mean on the ArcGIS Help page will come in handy.  In a strange way I think I have a better understanding of how most tools run now that I can get the gist of their associated code.