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addressing climate change adaptation in regional transportation plans

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Address<strong>in</strong>g Climate Change Adaptation <strong>in</strong> Regional Transportation PlansA Guide for California MPOs and RTPAsIt is anticipated that, while an asset geodatabase populated with state and federaldata would be useful to <strong>regional</strong> assessment, MPOs and RTPAs will want toenhance this <strong>in</strong>formation by add<strong>in</strong>g <strong>regional</strong>ly and locally specific layers. Thiscan be accomplished <strong>in</strong> several ways, <strong>in</strong>clud<strong>in</strong>g the construction of a separate,free stand<strong>in</strong>g geodatabase or “nest<strong>in</strong>g” of <strong>regional</strong> layers <strong>in</strong>to the statewidedatabase. Figure 9.4 shows an example of this “nest<strong>in</strong>g” method for bridgelayers from the National Bridge Inventory (NBI) as well as local bridge data.This approach is preferred, because it allows for the direct <strong>in</strong>tegration ofcomplementary layers – <strong>in</strong>clud<strong>in</strong>g the use of exist<strong>in</strong>g feature classes (e.g.,Roadways or Bridges). Advanced users with access to on-l<strong>in</strong>e mapp<strong>in</strong>g licenses(e.g., ESRI FlexViewer), can consider convert<strong>in</strong>g the f<strong>in</strong>al geodatabase product <strong>in</strong>order to display and dissem<strong>in</strong>ate the data on-l<strong>in</strong>e to a select group of sub<strong>regional</strong>stakeholders or the public at large.Figure 9.4Abbreviated Approach to Nest<strong>in</strong>g Data for a VulnerabilityAssessment, Example for BridgesState/Federal Data on Left, Regional/Local Data on Right)LocationState RoadwayNetwork + NBIStructureIdentifierSpatialExtentNBI DecklengthNBI M<strong>in</strong>imumOverclearanceLocal Bridge DataConditionScour CriticalLocal Bridge DataSource: Cambridge Systematics, Inc., 2012.The <strong>regional</strong> layers should be assembled and organized <strong>in</strong>to feature datasets byasset category. These categories relate to a broad <strong>transportation</strong> mode or<strong>in</strong>frastructure type. With<strong>in</strong> each feature dataset are one or more feature classes,represent<strong>in</strong>g <strong>in</strong>dependent systems or assets that are thematically similar (or, as isoften the case, overlapp<strong>in</strong>g datasets from multiple sources).Cambridge Systematics, Inc. 9-11

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