Exercise #4 — Measuring Distance and Connectivity

Introduction to GIS Modeling, GEOG 3160, University of Denver, Spring 2000

Name _____<enter your name>_____

Date _____<enter the date>______

Part 1 – Calculating Simple/weighted proximity

Question 1. Complete the analysis below that calculates “simple” and “effective” proximity. Include screen grabs of the maps and your responses to the “notes” and ‘questions” embedded in the instructions. Be sure your answers clearly explain the similarities and differences between the proximity surfaces you generate (map values and geographic patterns).

Access MapCalc using the Tutor25 database.

Viewà More Windows…, select Roads

…and note the “discus-hurler” shape of the road network (2-D).

What do you think the two-digit map values mean?

Grid Analysisà Distanceà Spread, then enter

SPREAD Roads TO 50 Uphill Only Simply FOR Roads_simpleprox

…and note the differences in the 2-D lattice; 2-D grid; 3-D lattice and 3-D grid views.

In your opinion, which map display is best in conveying the information in the Roads_simpleprox map?

What is the interpretation of the value 0?

Of the value 10.7?

SPREAD Roads TO 50 OVER Elevation Uphill Only Simply

FOR Roads_uphillprox

…display in the best form (above) and comment on the visual differences between Roads_simpleprox and Roads_uphillprox using your favorite display.

Which map has the farthest away location?

Is the most distant locations on both maps at the same location?

Which map is more like the “buffer” operation in a desktop mapping system.

RENUMBER Covertype ASSIGNING 0 TO 1 ASSIGNING 2 TO 2

ASSIGNING 5 TO 3 FOR Hiking_friction

…What do you think the values 0, 2 and 5 mean in terms of hiking in different cover types?

SPREAD Roads TO 50 THRU Hiking_friction Uphill Only Simply

FOR Road_hikingprox

…How far away is the farthest land location?

…If the Hiking_friction map is calibrated at 1 minute per base-weighted cell, how long would it take you to hike to the farthest location?

…How long would it take you to hike to the Cabin identified on the Locations map?

Question 2. Prepare a brief discussion of the differences in the distance algorithm (SPREAD) used to generate “simple” and “effective” distance. Be sure your answer makes reference to the affects of the Hiking_friction map.

Question 3. On your own (or in your group), evaluate the following GIS Model that determines the effective proximity from the Ranch to all other land locations. Prepare a narrative flowchart with embedded maps that identifies the Input map(s), Analysis operation and Output map including discussion of map values and data types for each of the five steps.

What was the effect of the COVER operation in Step 3?

How far away is the Cabin from the ranch? …in effective cells? …in minutes?

Part 2 – Identifying optimal path(s) and path density

Question 4. Complete analysis operations below and describe the processing that took place. Be sure your answer contains embedded maps and discussion that identifies the Input map(s), Analysis operation an Output map including map values and data types.

RENUMBER Locations ASSIGNING 0 TO 1 ASSIGNING 0 TO 3 THRU 10

FOR Cabin

STREAM Cabin OVER Ranch_prox Simply Steepest Downhill Only

FOR Cabin_route

COMPUTE Cabin_route Times Ranch_prox FOR Cabin_route_details

COMPUTE Cabin_route Times Roads FOR Cabin_route_deatils2

DRAIN Entire OVER Elevation Simply Steepest FOR Flowmap 

Part 3 – Determining visual connectivity

Question 5. Complete the analysis below (not verbatim commands) that calculates visual exposure to roads and housing. Prepare a narrative description of each processing operation.

Explain the meaning of the values on the “ve_roads” and “ve_housing” maps, the difference between “completely” and “weighted” and an interpretation of the values on the “ve_slope” map.

Suggest a real-world application that might use this visual exposure analysis.

Radiate Roads over Elevation to 100 completely for Ve_roads

Slice Ve_roads into 5 for Ve_roads_sliced

Radiate Housing over Elevation to 100 completely for Ve_housing

Slice Ve_housing into 5 for Ve_housing_sliced

Analyze ve_roads_sliced with ve_housing_sliced mean for Vexposure

Slice Slope into 3 for Slope_sliced

Compute Slope_sliced times 10 plus Vexpose for Ve_slope

…on your own (or in your group), determine the average visual exposure (Vexposure map) for each of the administrative districts (Districts map). Describe your solution with a narrative flowchart.

…on your own (or in your group), identify the visual exposure (vexpose map) for a 500m simple buffer (5 cells) around the ranch (Locations map). Describe your solution with a narrative flowchart.