Erin Miller December 12, 2011

GIS/GPS Applications

Cycling the Continental Divide: Determining the Optimal Course

I. Introduction to Problem

I am an avid cyclist and I have noticed that the city of Austin bike maps are a welcome addition to the cycling

community. They improve one’s ability to determine a bike course based on traffic volume, road conditions

and elevation gain. While these maps are comprehensive and useful there is currently a lack of maps and

information for those seeking to bike outside of a single city. As I plan on cycling the United States continental

divide this summer, determining the best route from southern New Mexico to the U.S./Canadian border based

on types of roads available while minimizing the elevation gain over the course of the ride would greatly

improve my pending adventure.

II. Data Collection

The data needed in order to determine the best continental route is shapefiles of the state borders, cities,

continental divide and primary/secondary roads for New Mexico, Colorado, Wyoming, and Montana found at

the U.S. Census Bureau (http://www.census.gov/cgi-bin/geo/shapefiles2010/main) as seen in Fig. 1:

Figure 1: Locating/Downloading Data

Additionally, elevation data (DEM) of the area of interest is needed at a scale that is reasonable for the foot

print of the project, determined to be 90m. This was found at http://www.cgiar-

csi.org/data/elevation/item/45-srtm-90m-digital-elevation-database-v41 and was opened via GoogleEarth.

There were 9 files needed in order to cover the latitude and longtitude desired. On the website they were files

14_03-14_04, 15_03-15_06, and 16_04-16_06.

III. Data Processing

A. Loading Data

The first step was to load all of the data into the GIS map, entitled Final. This was done by right clicking on the

layers tab in the Table of Contents, selecting + Add Data, navigating to where it was saved (external hard

drive), selecting it, and clicking the button “Add” as seen in Fig. 2:

Figure 2: Adding Data

B. Combining DEM Data

For the DEM data, we need to combine the 9 files. For this project they were grouped into a Southern Zone

(New Mexico & Colorado) and a Northern Zone (Wyoming & Montana). In order to combine the DEM data we

use the tool Mosaic to New Raster located in Data Management Tools>Projections &

Transformations>Raster>Raster Dataset>Mosaic to New Raster as seen in the red box in Fig. 3:

Figure 3: Combining DEM Data via Mosaic to New Raster tool

In the “Input Rasters” select the data that covers New Mexico and Colorado (15_05-15_06 and 16_05-16_06).

For Output Location, place in your folder where you are keeping the project in a new folder labeled “SRTM”.

For Raster Dataset Name with Extension name it CONMUTM13. For Spatial Reference for Raster, select the

icon on the right, choose the select button, choose Projected Coordinate Systems, UTM, NAD 1983, and then

select NAD 1983 UTM Zone 13N.prj and hit “Add” as seen in Fig. 4 in the red box:

Figure 4: Selecting projection for new Mosaic

Say 1 for “Number of Bands” and leave everything else as it is. Repeat this process for the Northern portion

(14_03-14_04, 15_03-15_04, and 16_06) except this time on projection select UTM 12N and name it

MTWYUTM12. The project should look similar to Map Image 1:

Map Image 1: DEM data

C. Editing Road Data

1. Ranking Data

Next we need to prepare the road data so that when we rasterize it will be in the format we want. This

requires eliminating roads that are not defined. To start, in the TOC, right click on the roads shapefile and

select “Open Attribute Table”. From there, click the “Select by Attributes” tool. In Method leave it as Create a

new selection, and make the statement “RTTYP= “blank”. This will select all of the roads that are unclassified

and therefore should not be considered in our analysis. We need to delete this roads and can do so by

selecting “ Start Editing” on the Editor toolbar. When prompted, select the roads shapefile. From here, delete

selected. Make sure and click “save edits” on the editing toolbar afterwards.

We want to rank the remaining data in order to aid us in our least cost path analysis. We can do this by first

making a column in the attribute table in order to convert the letters defining the roads into values that can be

ranked. In order to add a column, open the attribute table, in the table options select “Add Field” naming the

field “Rank”, leave Type as Short Integer (Fig. 5).

Figure 5: Adding Field in Attribute Table

Now we need to fill in the Rank column based on data in the RTTYP column. The 6 letters we have are U:

Major highways which are a 4 in desirability, S: State highways, a 1 in desirability, M: municipal roads a 3 in

desirability, C: county roads a 2 in desirability, and O: back country roads which are a 6 in desirability. These

rankings are based on our personal goals for the road trip, but may vary depending on the goal one is focusing

on. The easiest way to go about filling in the Rank column is to “Select by Attribute” in the table by the RTTYP

column starting with “Letter U”. From here go to the frame that only shows the selected entries (blue box at

the bottom of the table). Since we are working with Major Highways, we want to fill in the Rank column with

the number 4. We can do this by placing our cursor where the column is labeled rank and right clicking. This

gives us a list of options. We want to use field calculator as seen in Figure 6:

Figure 6: Filling in Rank column with appropriate values

Once Field Calculator is open, double click on “Rank”, click on the equals sign and enter the #4, clicking “OK”

afterwards seen in Figure 7:

Figure 7: Filling in the Rank column via Field Calculator

This has filled in the Rank column with the #4 for only the entries that were labeled “U” in the RTTYP column.

Repeat these steps for S-O with the assigned numbers given in the paragraph above.

Since we have two different projections in our project, we need to make two different roads rasters and define

the projection for both the North & South Portion of the map. To do this, simply go to ArcCatalog, and

navigate to the Roads file. Select the roads file, right click on it, hit copy, and past in the same directory.

Rename the file and its copy, the original should be North and the copy as South. Make sure that when we

define the projections that, the appropriate projection goes with the appropriate file (North is UTM12, South

UTM13).

2. Defining Projection

In order to define the projection of the roads data, we will use a tool in Data Management Tools>Projections &

Transformations>Feature>Batch Project. Double Click on “Batch Project” and you should get the window seen

in Fig. 8. In the Input Feature Class, Select the original road file (named North), in Output Workspace place in

the same folder, and name NorthUTM12. For “Output Coordinate System” navigate to NAD 1983 UTM 12N

and select. Leave everything else blank. Repeat the steps for the copy file which we have allocated to be the

roads for the Southern part of the map (UTM13).

Figure 8: Defining Projections for the Roads (North & South)

3. Buffering the roads

Next we need to buffer the roads so that they can be recognized when we are processing to make our least

cost path raster. If the roads are too narrow or a line they will not be recognized. The tool is located in

Analysis Tools>Proximity>Buffer. Double click on buffer, which should look like Fig. 9.

Figure 9: Buffering the Road Files

Select the first roads file (NorthUTM12) for Input Features, for Output Feature Class, name it

BufferRoads_UTM12, in Distance, make sure “Linear Unit” is selected and the units are “meters”, and type in

500. This will give us a buffer of 250 meters on either side of the road. For Side Type, select FULL, End Type

select ROUND, Dissolve Type NONE. Click “OK” and repeat for the Southern Roads file, naming it

BufferRoads_UTM13. We need to make sure that the buffer was wide enough that the roads are connected

and that there aren’t any gaps. Zoom in and make sure the roads are continuous and that your map looks

similar to Map Image 2; 500 meters should have worked, but double check just to be sure.

Map Image 2: Buffered Roads

4. Make a Raster

Next we want to take the polygon road file we have been working with and convert it into a raster so that

further analysis can be conducted. In order to do this we will use a tool located in Conversion Tools>To

Raster>Polygon to Raster as seen in Fig. 10. Select your first Buffered Roads (BufferRoads_UTM12) for Input

Features, for Value Field select “Rank”, for Output Raster Dataset, place it in the roads folder and name it

North _RD, for Cell Assignment Type select “maximum area”, priority field select “none”, and cellsize enter

“90” and then click “ok”. Repeat these steps for the southern buffered file (BufferRoads_UTM13) and name it

South_RD.

Figure 10: Converting Road Polygon to a Raster

Your map should look similar to Map Image 3:

Map Image 3: Polygon to Raster of Roads

5. Clip Road Raster by DEM

The final step with the road data is to clip the two road rasters to their appropriate DEM. Remember that

North_RD is going to be clipped to MTWYUTM12 and that South_RD is going to be clipped to CONMUTM13.

In order to clip with a raster we are going to use the “extract by mask” tool located Spatial Analyst

Tools>Extraction>Extract by Mask. For input raster, select “North_Rd”, for Input Raster of Feature Mask Data

select “MTWYUTM12” and for Output Raster, name it NorthRD_Clip. Select “ok”, your map should look similar

to Map Image 4:

Map Image 4: Clipped Road Raster to the correct UTM zone (UTM12 shown here)

Repeat for the South_RD, selecting CONMUTM13 for the Feature Mask Data and naming it SouthRD_Clip.

D. Converting elevation DEM to Slope

In order to work with the elevation data we need it to be in terms of slope. In order to convert this we use the

tool “Slope” located in Spatial Analyst Tools> Surface>Slope. Once the tool is open, select CONMUTM13 for

the Input Raster, save in the DEM folder as Slope_South, select DEGREE for Output measurement and click

“ok”. Repeat for MTWYUTM12 naming it Slope _North. The slope layer should look similar to Map Image 5:

Map Image 5: Slope layer, from DEM data

E. Converting elevation DEM to Aspect

We want to know which direction we are traveling and having an Aspect of the DEM elevation data allows

us to do that. Combined with the roads, and slope we will be able to control the direction of travel and

select the least amount of elevation gain while remaining on the most desired roads. In order to complete

this step, locate the tool “Aspect” in Spatial Analyst Tools> Surface>Aspect and Select CONMUTM13 DEM

for the Input Raster and name the file South_Aspect placing it in the DEM folder. Repeat for MTWYUTM12

naming it North_Aspect. The map should look similar to Map Image 6:

Map Image 6: Aspect of DEM Data

F. Reclassifying Data

1. Reclassifying Slope

We need to reclassify slope so that we can determine the easiest route. By assigning a lower value to a

lower slope we will be able to determine the path that requires the least amount of climb. Navigate to the

Reclassify tool, Spatial Analyst Tools>Reclass>Reclassify. In the box, select the Slope_North, for Reclass

Field select “Value”, and in the Reclassification dialog box, click on the button “Classify…”. Under the

Classification, Method select “Equal Interval” and “6” for classes, click “OK” bringing you back to the initial

dialog box, save it as ReSlope_North in the DEM folder (Fig. 11), press “OK” repeating for Slope_South

naming it ReSlope_South. The new layer should look similar to Map Image 7:

Figure 11: Reclassifying Slope

Map Image 7: Reclassified Slope

2. Reclassifying Aspect

To reclassify aspect we follow the same rules to locate the tool, selecting Aspect_North for Input Raster,

Value for Reclass Field, but this time we need to select “Defined Interval” in Classification>Method and

enter 60 for Interval Size when we click the “Classify…” button from the initial Reclassify window. This will

break the Aspect into ranges from 0-60, 60-120, 120-180, 180-240, 240-300, and 300-360; your box should

look similar to Fig. 12. Press “ok” and return to the main Reclassify window. Now we need to assign

numbers to the breaks we just made. Since we want to generally head Northwest, we want to rank the

such that 300-360=1, 0-60=2, 240-300=3, 60-120=4, 180-240=5, and 120-180=6; your box should look

similar to Fig. 13. Save in the same folder that the initial aspect file is in and name it ReAspect_N. Repeat

these steps for Aspect_South naming it ReAspect_S.

Figure 12: Defining Reclassification Interval for Aspect Raster

Figure 13: Defining Reclassification for Aspect Raster

G. Applying Buffered Roads to Aspect and Slope Rasters

1. Extract by Mask, Aspect

Before we can combine the rasters to get a least cost surface we need to extract by mask the Aspect raster

with the buffered roads as the mask. In order to do this navigate to the tool “Extract by Mask” located at

Spatial Analyst Tools>Extraction>Extract by Mask. For Input Raster, select your recently reclassified aspect

raster, ReAspect_North, for Feature Mask Data select the NorthRD_Clip (this was the roads file that was

buffered, projected for UTM 12 and clipped to the North DEM). Repeat for the South Aspect Raster,

naming SAspect_Mask Name it NAspect_Mask. It should look similar to Fig. 14; press “ok”:

Figure 14: Extract by Mask the Aspect Raster to Roads Raster

The NAspect_Mask and SAspect_Mask should look similar to Map Image 8:

Map Image 8: Aspect Raster extracted to Road Mask

2. Extract by Mask, Slope

We are going to repeat step one, except this time, for the Slope Raster. Select ReSlope_North for Input

Raster, NorthRD_Clip for the Feature Mask Data, and save as NSlope_Mask. Repeat for the

ReSlope_South, naming it SSlope_Mask.

H. Combining Rasters

We want to combine the Masked Slope Raster with the Masked Aspect Raster with the Roads Raster. To

do this we are going to use Raster Calculator located in Spatial Analysis tools>Map Algebra>Raster

Calculator. In Layers & Variables, double click on NSlope_Mask, NAspect_Mask and NorthRD_Clip. Save in

a new Folder called “Combined Rasters” as North_Raster. Do the same for the Southern data

(SSlope_Mask, SAspect_Mask and SouthRD_Clip) and save as South_Raster. We know have our two cost

surfaces.

I. Starting & Ending Points

We have to tell the software where we want to start and end up. Since our project is existing in essentially

two pieces we will have 3 points, starting point, the middle point, where South ends and North begins, and

the Final point, where our bike tour will end. To do this Select By Attribute the cities we want, one at a

time (Hatch, Denver, Longmont, and Whitefish from the cities polygon. Once a city is selected, right click

on the polygon, choose data, Export Data and select yes when promoted to add as a layer. Do this for all

four cities.

J. Determining Route

In order to do this we will need the tools “Cost Distance” and “Cost Path” located at Spatial Analyst

Tools>Distance>Cost Distance/Path. Click on “Cost Distance” and select your destination city (Whitefish

for the North and Denver for the South) for Feature Source Data, the Input Cost Raster will be your

North_Raster or South_Raster. Name the Output distance Raster and Output backlink raster accordingly

and say “ok”. Now click on “Cost Path” and select the starting city for Feature Destination Data, and the

two files that were created from the “Cost Distance” step will be selected for Input Distance Raster and

Input Cost backlink Raster. Name the output raster, as Route and press “ok”. The route is now created.

The map should look similar to Map Image 9:

Map Image 9: Cycling Route from Cost Distance and Cost Path Tools

IV. Conclusion

Cycling is a wonderful activity that can be enjoyed by almost anyone. Biking can be used for more than

entertainment though and is a wonderful form of transportation. Often times traveling by bike can be

difficult and scary due to interacting with automobiles and step slopes in certain areas. That creates a

genuine need for an analysis like the one presented above in order to determine the least cost path for a

cyclist to take in order to get from one point to another. The route created here utilizes a mix of road

types, while minimizing the elevation change a cyclist will encounter. Simply looking at a map would not

give the route created here, which is a really nice asset in improving one’s ability to cover the greatest

distance in the least amount of time in the safest way. This route, which parallels the continental divide,

provides an example for anyone searching for the best bike path. With easily accessed road and elevation

data, requiring only some amount of manipulation, this is a reasonable solution to a common problem.