Charlie O’Neil3-6-2015

ENVS421: Advanced GISInstructor: Aquila Flower

Lab 6: Network AnalysisWithin the ArcGis tutorials the Network Analyst toolset provides spatial analysis through a

network. A common applications for this system are modeling of stream and water networks such as

watersheds. Another common use for this dataset is the analysis of road and city networks to solve

complex routing problems. The Esri tutorials listed in this report deal are designed to give the user the

ability to plan and solve route finding analysis in order to gain efficiency, increase organizational

Intelligence, and increase user interface through mobile technology. The objectives of the tutorial are

designed to teach the user simple applications including building a network dataset and defining rules of

connectivity.

Exercise 1: Creating a network Dataset

In the initial stage of this tutorial we begin by creating a network dataset within a geodatabase

using historical traffic and street network data from San Francisco. We will use this network dataset

through out later stages of the analysis in order to solve time dependent routing finding information.

Figure 1.0 this is simply a

preview of our working stage

network dataset for streets_ND. It

was taken directly from arcatalog

using a the snipping tool. This

preview simply shows our edges

as well as traffic that will be

furthur defined.

During this first stage of analysis the user is instructed in how to set up connectivity within our datset

assuming that a third dimension is assigned to the X-Y plane system. Setting connectivity will allow the

software to interpret the data using elevation fields. We also add a new attribute field titled

RestrictedTurns and assign values and evaluators.

Exercise 2: Creating a multi-modial network dataset

The second exercise in the series allows the user to create a network for each single mode of

transportation whether it be by walking, train, or bus. In this analysis we are setting multiple coumns for

each of the connectivity groups allowing us to model both the road and metro network. The user is also

instructed in setting up attributes within the network dataset as well as configuring elevators to remove

and correct the problems that arcatalog has identified within the dataset.

Figure 2.0 In this section

the user is working with

data showing Parisian

metro and road networks.

This preview shows the

users newly created

multimodial network that

will allow the user to

furthur the route analysis

using multiple

transportation methods.

Exercise 3: Finding the best route using a network dataset

` In part 3 of this tutorial we are using our prevoiusly created networks to begin finding thr

quickest route to visit a set of stops in a predetermined order. Back to San Francisico analyis the user

begins by defing stops within the street network by usung the network analyst toolbar to place “stops”

points at chosen locations. These stops are then used to create the quickest route between them as if they

were stops along a bus route. It also shows written directs if the “directions” link is clicked on the

network analyst toolbar. In this ecercise using the created stops arcmap automatically creates the

shortest route by click the create route button. The final stage of this exercise allows you to tweak the

route by placing barriers representing roadblocks. Using these barriers arcmap once again reroutes to a

new route without interference.

Figure 3.0 this is a screenshot of the route finding analysis and directions box displayed through the

network analyst toolbar.

Exercise 4: finding the closest fire stations

In this exercise the user is is instructed in finding routes for fire response that will provide the

shortest response time to a given address from multiple fire stations. Routes are identified as well as

directions that will allow emergency response to quickly navigate.

Figure 4.0 Using San Francisico fire

stations an incident is placed a cost

distance routes are formed showing

response time from several stations.

Ecercise 5: Calculationg Service area and creating an OD cost matrix

This excerise is used to teach the user to create a series of polygons that represent the distance

the can be reached from a facility within a specified amount of time. These polygons will use increments

of 3,5,and 10 minutes to show service areas to parisian warehouses. The user is instructed in creating a

new service area that encopasses several of the stores that are farther from a warehouse then any of the

time increments. The user solves this problem by placing a new warehouse unit that is within the central

aspect of the map so that all stores can be served within the travel distance time intervals.

Exercise 6: Creating a model for route analysis

This exercise is used to model delivery routes connecting 21 stores in Paris. The resulting model

creates a path that shows the route connecting each of the 21 stores through the shortest connectiong

route.

Exercise 7: Servicing a set of orders with a fleet of vehicles

This exercise is designed to teach the user to create a route with maximum efficiency for a small

fleet of vehicles. This application could be used in the delivery of goods such as a set of grocery stores

served in this exercise. It is assumed that each of the stores has a specific demand and each vehicle has a

limited capacity for carrying those goods. The objective of this exercise is to minimize transportation

costs while providing adequate supply

to match the demand of each store.

Figure 7.0 this custom made map

shows the results of this fleet analysis.

The two vehicles routes are displayed

in red and blue while the delivery

locations are green and the resupply

depot is a yellow square.

Exercise 8: Finding best routes to service paired orders

This portion of the exercise deal with route finding for a fleet of vans looking to serve people in

different areas and transport them to hospitals. This will be accomplished by running a Vehicle Routing

Problem (VRP) analysis which will relate and sequence ordered pairs. Appointment times will be

factored into the analysis as well so that the patients will not miss appointments. The analysis begins

with geocoding addresses for patients and hospitals in order to load the results as ordered pairs. Once

this has been done ordered pairs are added the points are assigned predetermined hospital addresses

correlating to the appointments. The analysis also assumes that the company running this service has a

limited number of depots so in order to account for this the user must set three depot points. Routes are

then calculated assuming each van can only carry 6 passengers at a time.

Figure 8.0 the resulting VRP shows

the three districts (North Bay, East

Bay, and downtown) each serviced by

one of the three vans. Routes are

shown in purple (notice that they

sometimes loop back to the hospital

point reflecting carry capacity).

Exercise 9: Choosing optimal store locations using locations-allocation

This exercise trains the user in creation of a network showing the most profitable locations for a

retail business. This will be accomplished by locating these locations near large population centers in

order to provide the highest financial yield for the retail chain. The analysis will be run using three

different problem types: maximum attendance, maximum market share, and target market share.

Figure 9.0 this map shows

one of the intermediate

step of locating optimum

locations serving the most

number of people. This was

run using the maximum

attendance problem and

shows the locations within

san Francisco that would

serve the highest number of people for a given location

Exercise 10: Configuring live traffic on a network dataset.

This application within the tutorial teaches the user to create a network dataset that is capable of

processing live traffic at a variety of different times of the day. This will create a model that will be

capable of processing real time destination travel problems based on the shortest and most convenient

routes to or from a given location. For these steps it’s essential that the user attains the user name and

password for traffic data in order to complete this analysis. Once acquired this data will be updated

using the traffic data geoprocessing tool to create a format that the network analyst extension will be

capable of processing. The analysis begins by creating a network dataset similar to previous stages of

the tutorial. With this completed the next step would be to update the traffic time zone data using the

update traffic tool and by editing the time zone data. The tutorial gives you the output data for this

process as a file within the data provided. With this created the user must then run a python script in

order process the traffic data in the next steps of the analysis.

Exercise 11: Performing network analysis using traffic data

With exercise 10 completed the user is now ready to begin solving service area analysis given

different times of day and the associated level of traffic. For this analysis the user is instructed to

configure the service layer to 9am to simulate morning rush hour. Once this has been accomplished and

the service layer has been solved a similar process is then run for one hour later so as to have a

secondary model for comparison. With levels or transparency set layers can be toggled and overlaid to

view the service area polygons and visualize how they change over time during the morning commute.

Exercise 12: performing network analysis using restriction attributes

In the final exercise of the tutorial the user will now be instructed in using restriction attributes

with different restriction usage parameters to solve a simple route problem for the San Diego area. The

section teaches the user the value of using prohibition restrictions such as avoiding toll roads and how

that can shape your analysis. The section also shows how moderate levels of avoidance can be set so that

the parameters are saying “avoid these roads as much as possible unless there is no alternative route”.

Figure 12.0 shows the results of setting

the toll bridge avoidance to high instead

of prohibited so that these raods can be

used but only if necessary and for the

shortest amount of time possible.