University of WashingtonCampus Pedestrian Path Network Analysis

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University of WashingtonCampus Pedestrian Path

Network Analysis

Evan SirokyIon Arai

UDP 422June 2, 2006

University of WashingtonCampus Pedestrian Path Network Analysis

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Project Summary& Project Questions

Project Summary:

Our team built a GIS network whose primary function is to analyze the shortest distance to walk from point A to point B on the University of Washington campus. We wrote procedures providing users written and visual directions corresponding with the shortest route. In addition, we analyzed the potential impediments for mobility impaired persons and the most efficient routes around these obstacles.

Project Questions:

• Find the most efficient pedestrian routes on the University of Washington campus.

• Find the most efficient pedestrian routes around potential barriers.

• Identify the most efficient pedestrian routes based on multiple points of origin to a destination on campus.

• Identify potential barriers for mobility impaired persons and find the most efficient alternative routes.

University of WashingtonCampus Pedestrian Path Network Analysis

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Methodology

Methodology:

• Created pedestrian path network.• Merged City of Seattle street network (snd.shp) with our created pedestrian path network.• Identified and connected campus buildings to pedestrian path network.• Identified impediments for mobility impaired such as staircases.• Identified and connected bus stop locations to pedestrian path network.• Created network dataset from pedestrian path network.• Employed Network Analysis Tools – New Route and OD Cost Matrix

Find Best Route Flow Chart

University of WashingtonCampus Pedestrian Path Network Analysis

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Creating the Pedestrian Path Network

We created the pedestrian path (arc) network using Digital Orthophoto Quads and visual inspection. We merged the our pedestrian path data with the City of Seattle street network (snd.shp) to provide network paths outside of the campus boundaries. We identified potential impediments for disabled persons such as stairs and non-paved surfaces.

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UW campus path network

City of Seattle Street Network

City of Seattle Street Network

UW Path Networks

pedestrian path network creation flowchart

University of WashingtonCampus Pedestrian Path Network Analysis

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Most Efficient Route

Using our created pedestrian path network, we built a network dataset using ArcCatalog. Once created, using the dataset we were able to use the Network Analyst tool – New Route to find the best route. To use this operation, we first need to input the stops (origin, destination and any other points of interest along the way). The Solve tool in Network Analyst toolset then calculates the most efficient route based on distance along the network paths. Directions can be derived by right clicking the desired Route and selecting the Directions tool. Distance traveled is given in the directions as well as a map for each step.

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most efficient route

merged shape filesUW campus paths& Street Networks

buildingfootprints

networkintersectionsnodes

most efficent route

UW pedestrian path network

origin

destination

generated directions window

University of WashingtonCampus Pedestrian Path Network Analysis

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Most Efficient Route with Barriers

This operation is similar to the Most Efficient Route operation, but we add Barriers to our network. For example, if Klickitat Lane is closed to pedestrians for pavement improvements, we can use the Barrier tool to place these barriers along the line segments that are affected. The Network Analyst Solve tool then recalculates the Most Efficient Route around the impediments.

most efficient alternate routedue to barriers

barrier

most efficient route without barriers

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origin

destination

most efficient route without barriers

most efficient alternate route due to barriers

UW pedestrian path network

University of WashingtonCampus Pedestrian Path Network Analysis

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Route Efficiency From Multiple Origins Using OD Cost Matrix

The Network Analyst toolbox contains the OD (origin/destination) Cost Matrix tool, which can determine the most efficient route to a destination from a set group of origins. For example, we set our origins as bus stop locations and the destination as Johnson Hall. The OD Cost Matrix calculates the distance traveled along our

UW campus pedestrian path network from each origin to destination and outputs the data into a table (see above). Using the table, we determine that Bus Stop4 would be the most efficient route to Johnson Hall because the total length is the shortest.

Note: although the routes are presented as a straight line, the calculated distances are same as if you used the

Bus Stop�

Bus Stop2

Bus Stop�

Bus Stop4

Johnson Hall

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UW pedestrian path network

Matrix lines showing connections between origins and destination

bus stop

destination

output attribute table for OD Matrix

University of WashingtonCampus Pedestrian Path Network Analysis

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Most Efficient ADA Route

This operation is similar to the Most Efficient Route with barriers, but we add a restriction to our network. While inputing path data, we added a field to all paths called “STAIRS.” If the physical path we are representing has stairs, then we put a value of �, if not then 0. When building the network dataset, we set the stair attribute as a restriction. Once we opened up the network dataset in arcmap we now have the option to check or uncheck the stairs option in the analysis settings.

most efficient ADA route

stairsUW campus path network

most efficient ADA route

stairs UW pedestrian path network

University of WashingtonCampus Pedestrian Path Network Analysis

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During the course of our study, we learned a lot about network analysis functions and successfully implemented a dataset of all campus paths on the UW Campus. We learned data creation techniques, including setting up cost and restriction attributes. We also learned how to perform route analyses to determine the best route and to perform cost matrixes to determine the best route among multiple origins to one destination. Combining this knowledge with the data we produced a working database of UW campus paths to find the best walking route.

The knowledge we gained will be extremely useful in future GIS ventures. Since we know how to perform network analyses, we can add more datasets to allow

Conclusion

for more functionality and to make the data useful to more stakeholders. For example, the possibilities of adding point features of any type to the project is almost limitless ranging from a points representing trees to points representing men’s bathrooms with accessible stalls.

Another benefit of our data is the ability for it to be used by multiple parties. If our data is posted on a server, other parties can access the data to get walking directions and information about spatial attributes on the campus. As long as any third party has an idea, they can manipulate the data and display it in user friendly ways. Although our class project is finished for the quarter, our project data will continue to be useful in the future.