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National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME [email protected] MOTF/Risk Analytics Training July 27-31, 2015

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National SAPA Steps toward standardization 1)Define SAPA operational requirements 2)Define standard processes based on hazard, scale, and required turnaround time 3)Establish a framework for successful practices 4)Strengthen our analytic credibility

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Page 1: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

National Situational Awareness Predictive Analytics (SAPA)

Jesse RozelleFEMA Region VIII GIS Coordinator

FEMA Modeling Task Force [email protected]

MOTF/Risk Analytics TrainingJuly 27-31, 2015

Page 2: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

National SAPA

How do we create consistent national situational awareness predictive analytics?

Page 3: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

National SAPA

Steps toward standardization

1) Define SAPA operational requirements2) Define standard processes based on hazard,

scale, and required turnaround time3) Establish a framework for successful practices4) Strengthen our analytic credibility

Page 4: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Defining SAPA Operational Requirements

Who is our audience?

FEMA? State, County, and local partners? All of the above?

Page 5: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Defining Situational Awareness Requirements

What analytics do we need? Some examples

Primary List:Hazard ExtentPopulation ImpactsBuilding ImpactsCIKR (Critical Infrastructure/Key Resources) ImpactsTransportation Impacts

Secondary List:Mass Care RequirementsNFIP Impacts/CoverageOther?

We can create a very long list of potential impact analytics, but should we first all agree on basic, then extended lists?

Page 6: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Defining SAPA Operational Requirements

What is our goal?

• Situational awareness?• Expediting declarations?• Expediting IA rental assistance?• Expediting response resources?• Public outreach and risk communication?• Projecting long term economic impacts?

Page 7: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

What does SAPA Look Like? Product Formats

Operations Dashboard Viewer

GeoPlatform Viewer Spreadsheets

Static Maps

Page 8: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Define Standard Processes Based on Hazard, Scale, and Turnaround Time

Develop SAPA SOP’s for each hazard, for large scale and small scale events.

SOP’s should cover basic and advanced analytic component lists

Page 9: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Scope of the Event/Turnaround Time

First, what scale of SAPA is appropriate (and possible) for a given event? What is the extent of the disaster?

• Is our product time dependent? (most likely)

• Is the scale of a request realistic with the given time frame?

• The answer for deriving analytics for Minot and Hurricane Sandy will be very different

Page 10: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Is our disaster striking a small, rural town? Minot, ND 2011

Page 11: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Is our disaster causing impacts across a state? Colorado Floods of 2013

Page 12: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Is our disaster causing devastating impacts across the entire east coast? Hurricane Sandy

Page 13: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Depending on the scale, phase of an event and time for completion, the analysis method will vary

• The goal is always to provide the most detailed analytics possible, in an acceptable timeframe

• Managing expectations for what can be provided is critical• The larger the extent of an event, the lower the detail of the

analytics possible• The shorter the turnaround time allowed, the lower the detail of the

analytics possible• Finding a middle ground is key

Page 14: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Weather Watches/Warning, Pre Activation Example

Page 15: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Your RRCC is activated for a flooding response, flood gages show major to historic flooding, and the extent of impacts is still unknown.

Some questions you’ll want to ponder before beginning your analysis:

• How many communities could be affected based on USGS stream gages? One community, or 50? 100? Multiple states?

• How soon do you need to provide estimates? Two hours, two days, or two weeks?

• Do you want to provide basic impact analytics (number of households/people affected, CIKR)? Or do you want detailed economic impact projections?

Imminent Flooding Event Sample Request:

Page 16: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Small Scale Event – One Community (2 FTE)

• 1-3 hours – DFIRM based exposure

• 1 week – custom inundation mapping

• 2-3 weeks – event based inundation mapping, economic impacts and percent damage per structure

• Custom inundation mapping availability dependent on access to hydrologist support, high resolution terrain data (LIDAR)

Page 17: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Statewide Event – Regional – (3-4FTE)

• 1 day – DFIRM based exposure

• 2-3 months– custom inundation mapping

• 3-6 months– event based inundation mapping, economic impacts and percent damage per structure

• Custom inundation mapping availability dependent on access to hydrologist support, high resolution terrain data (LIDAR)

Page 18: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

National Level 1 Event – Full MOTF Activation (8 FTE)

• 1 day – SLOSH/NHC advisory based exposure – updated daily

• 1 week - high water mark based event inundation mapping

• 3 weeks – final custom storm surge inundation mapping for entire event

• 3 months– event based inundation mapping, full suite of impacts to all sectors and programs

• 1 year– event based inundation mapping, full suite of impacts to all sectors and programs

Page 19: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Other Federal Agencies Are the Authority on Estimating the Hazard

• Always defer to the authoritative science agency for estimating the extent and severity of each hazard

• USGS, NOAA, NWS, SPC, NHC• These agencies are the authority on hazard extent, but they do not

estimate impacts (SPC is beginning to deliver very rough impact information)

• FEMA is the lead on impacts• Flood inundation mapping is currently unsolved for

Page 20: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Common Pitfalls When Estimating the Hazard in Hazus

The level of accuracy you get out of Hazus, is dependent on the accuracy of your inputs, time spent setting up your model, and SME experience with Hazus prior to the request. Garbage in, garbage out. Quality in, quality out.

The following are common mistakes which can lead to inaccurate analytics when using Hazus. Hazus can estimate the hazard when OFA data isn’t available, but its real value lies in loss estimation

• Creating your own earthquake scenarios in Hazus earthquake vs. using USGS shakemaps

• Using the Hazus level 1 flood methodology – inundation mapping for an event is very challenging

• Using Hurrevac wind field data from outdated advisories when using the Hazus hurricane wind model

Page 21: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Hazus Flood Model Time Investment/Limitations

There are limitations for all three Hazus models, but the flood model limitations are most prominent

• Hazus level 1 flood analysis for 1 county takes approximately 2 days to run full H and H with 10 meter NED.

• Detailed site specific Hazus flood estimates for one county takes about 2 weeks

• DFIRM exposure estimation for 1 county takes about 1 hour

Page 22: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Hazus Level 1 Flood Methodology

• LIDAR derived level 1 flood hazard methodology is very time consuming, if it works at all. 30/10meter DEM derived flood hazard is low resolution.

• Over estimation for level 1 flood model aggregated general building stock. This is minimized with the dasymetric inventory.

• Time required from start to completion? Approximately 2 days of processing per county, assuming no problem reaches or unresolvable errors.

• Issues are being addressed in the Hazus modernization effort for the future, but for now is not applicable for response

Page 23: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

National Hazus Level 1 2009 Archive

• National Hazus level 1 analysis run in 2009 for every county in the US; offers a means to skip level 1 hydrology

• Lower level of confidence, intended for general county by county risk

• Utilized 30 meter resolution terrain data, outdated hydrological and hydraulic methodology, and homogenous distribution aggregated building stock loss estimation methodology

• Average AAL reported by NWS (Verify) $6-8B• Hazus AAL from national study $60B• Use of year 2000 census data vs 2010

Page 24: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Analyzing Flood Losses Using FEMA’s Hazus Flood Model

Aggregated vs. Site Specific Building Losses

Page 25: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Establishing a Framework for Successful Analytical Processes

1) Build out an Org Chart of SME’s2) Train and Certify those SME’s,

invest in new SME’s3) Establish QA/QC processes

Page 26: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Who is providing SAPA and when?

Build out an organizational chart for contributors to SAPA

• Includes names, titles, and organizational location; not just titles

• Establish roles/responsibilities for regional staff, MOTF, AGTS, EAD, HQ Recovery

• Build out a chart for regional and national hypothetical events; not just level 1, but level 2, 3

• Plan strategically for how this would look in the future (NHAP/institutionalizing the MOTF and other staffing initiatives), and how it would look next week.

Page 27: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

How do we standardize SAPA?

Develop New SME’s, and Maintain Current SME’s

• Identify key players for the future, and next week• Make sure they’re qualified• Train them• Train them again• Not just Hazus training! • Focus on this steady state, not only during response• Certify and credential SME’s• GISP’s, ArcGIS Professional certification, Hazus certification

Page 28: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Establish Credibility in our Analytics

Consistency

• Establish pre defined products we’ll provide as a cadre• Establish pre defined product formats we’ll provide as a cadre

(Geoplatform viewers, ArcGIS Operations Dashboards, static pdf/jpg map templates)

• Delivery – upon activation, don’t wait for requests, implement our SOPs

Page 29: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Establish Credibility in our Analytics

Transparency

• Clearly document your loss estimation methods so they can be provided to our customers.

• This includes data sources, impact estimation methodologies, and how recent your information was generated.

• The more details page in the FEMA GeoPlatform provides a great place to do so.

Page 30: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Establish Credibility in our Analytics

Validity

• Are you prepared to stand behind your analytics?• Are you willing to publicly put your name on it?• Are you willing to answer press inquiries?• Are you documenting your methodology in detail in Geoplatform?• Are you practicing proper QA/QC on your numbers before they go

out?

Page 31: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

QA/QC processes are crucial! A second set of eyes on your results before distributing can often catch easily identified issues.

Page 32: National Situational Awareness Predictive Analytics (SAPA) Jesse Rozelle FEMA Region VIII GIS Coordinator FEMA Modeling Task Force SME

[email protected]@fema.dhs.gov

Questions?