Methodology

Purpose

The effectiveness of emergency response systems is measured in their ability to respond to a variety of incidents with the proper resources as fast as possible. The tools of spatial analysis can help to identify the demand for such services and assess the capabilities of the response system. By describing the demand for and capabilities of emergency services quantitatively, efficient systems can be tailored to the needs of the community.

 

This analysis attempts to demonstrate the effectiveness of the distribution of these resources spatially. 

Constructing the Network Dataset

The first step in this analysis was to create a GIS representation of the Mansfield Fire Department response model based on the location of Fire and EMS resources. To accomplish this, a network dataset was constructed and response polygons were calculated in intervals that correspond to national standards (i.e. 4 and 8 minute polygons). To construct the dataset, a road centerline feature class for Richland County, Ohio was used. In order for the feature class to be used for network analysis, fields needed to be added and calculated to determine travel time and to construct hierarchy levels based on road classification. A geo-processing model was constructed to perform these functions.

 

 

 

Once the road centerlines feature class had been prepared for network analysis, a network dataset was then created.  The network analysis needed to determine travel time to incidents; therefore the cost attribute assigned to the network dataset was in minutes.

 

The network dataset can determine routes based on a predetermined hierarchy. When the hierarchy is used, the modeled routes will follow more closely to realistic routes of travel. The hierarchy for this network dataset was assigned based on road classification. Without assigning a hierarchy system, routes of travel may follow unconventional paths, such as through residential collector streets rather than main roads. This is not ideal, as the model is meant to represent reality to the closest degree possible.

 

To create accurate response time calculation the global turn delay function was added. This feature allows for a predetermined penalty to be assigned to each modeled turn based on the angle of the intersection. The following screen capture demonstrates the parameters set for the turn penalties.

 

 

The final step in the creation of the network dataset was to restrict flows on one-way streets. The road centerline feature class contained a field that identified which road were posted as one-way and specified the one direction in which traffic could pass. In order for the network dataset to account for these restrictions, a script evaluator needed to be assigned using pre-logic VB Script Code. The code was written as follow: 

 

 

Restricted = False                          

Select Case UCase ([DIR])

Case “T”: restricted = true

End Select

 

Value = Restricted

 

Restricted = False                          

Select Case UCase ([DIR])

Case “F”: restricted = true

End Select

 

Value = Restricted

 

 

Distance cost attributes were also added to the network dataset for feet and for miles (feet/5280).

 



Geo-Coding Point Feature Classes

The second step in the analysis was to locate the resources in the Fire and EMS system and describe their capabilities.  The Fire and EMS resource location tables were constructed using the given data and imported into the project geodatabase. Furthermore, Fire and EMS incidents from 2008 where located using. Emergency medical incident data was obtained directly from the Mansfield Fire Department. This data was exported from the Firehouse database server. The information received contained no patient identifiers. The relevant data fields that were used contained the address of the incident for address locator matching and the nature of the incident. A geo-processing model was then constructed to create an address locator and then match the locations based on the given table.

This address locator used an address point feature class obtained from OGRIP. The locator type was One Address with Zone, in order to match the format of the Input Reference.

How Well Does the Model Fit?

Service area polygons were constructed once the network dataset was complete. NFPA and OSHA standards require 4 and 8 minute polygons. To suite these standards, polygons were created for each station. The polygons were then merged to create a district wide layer.

 

The following steps were used to calculate road network coverage within the 4 minute response polygon:

1.      Geoprocessing Intersect function was used to overlay the Road Network with the Fire District.

2.      Attribute table was summarized by District and Sum of Road lengths.

3.      Sum Road lengths were added to the Fire Districts using a Tabular Join.

4.      Geoprocessing Intersect function was used to overlay the Road Network with the 4 Minute Polygons.

5.      Attribute table was summarized by District and Sum of Road lengths.

6.      Sum Road lengths were added to the Fire Districts using a Tabular Join.

7.      Field Calculation was completed to determine the percent of the streets actually covered by the 4 Minute Polygons.

The 4 minute polygons used in this analysis was compared to a point feature class of fire responses from 2007 in which actual response time was calculated. The 2007 layer was created by the Richland County GIS Consortium. The results give a visual confirmation of the polygon accuracy:

 

This overlay demonstrates that the 4 minute polygon created through a network analysis closely mirrors the extent of actual 2007 responses of 4 minutes or less.

Quick Navigation