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Earthquake Updates
and Enhancements to
Processing for 3.2
TROY SCHMIDT, SENIOR SOFTWARE DEVELOPER
FACTOR, INC.
1
Outline
 Goals of Hazus 3.2 release
 Hazard Factors Processing
 Probabilistic Analysis
 ShakeMap Anaysis
 Performance comparison
 Import Updates
 Questions
2
Goals of Hazus 3.2 release
 Migrate ArcObjects calls to Geoprocessing Tools
 Eliminate pGDBs still needed for Earthquake
 Leverage Spatial SQL capabilities
 Improve Processing Speed
 Simplify processing where possible
 Make internal processing more accurate
3
Hazard Factors Processing
 New spatial tables created in study region database:
eqSrSoil, eqSrLQF, eqSrLND and eqSrWaterDepth
 Values updated when default value updated, user datamap
loaded, or as first step of analysis
 ArcObjects uses ICursor an iterative row by row loop
processing method
 Spatial SQL utilizes set based approach updating all values
in a single step
4
Soil, Liquefaction, Landslide, WaterDepth
Hazard Factors Processing
v3.1
 Create fields dynamically
in pGDB (if not existing)
 Loop over all features
and load the default
value
 Loop over all features 1
by 1 and update values
by performing spatial
lookup
 Push updated values
from pGDB to SQL via
stored procedure
v3.2
 Use Spatial SQL via stored
procedure to update
features value in a set
based fashion
5
Soil, Liquefaction, Landslide, WaterDepth
Probability & ShakeMap
Analysis 6
Probability Analysis
v3.1
 Processed central USGS grids to
USGS grids just of the study
region at aggregation
 Loop over all pGDB tables and
add fields (PGA, PGV, SA03,
SA10)
 Loop over all features 1 by 1 and
update values by performing
spatial lookup
 Loop over all features and
compute their soil amplification
value in code
 Push updated values from pGDB
to SQL via stored procedure
v3.2
 Stored procedure to update
values directly using
central USGS grids
7
ShakeMap Analysis
v3.1
 Loop over all pGDB tables
and add fields (PGA, PGV,
SA03, SA10)
 Loop over all features 1 by
1 and update values by
performing spatial lookup
 Push updated values from
pGDB to SQL via stored
procedure
v3.2
 Stored procedure to update
values directly using spatial
SQL against new spatial
tables
8
Performance Comparison
Study Region Hazus 3.1 Hazus 3.2 Performance
increase
Charleston, SC 1hr 43min 10 min (Win10 32GB) 10.3x faster
Greater Los
Angeles area
1hr 58min 58 min (Win10 32GB) 2.03x faster
Kenai Alaska Could not import
50K AEBM
1hr 2min (Win10 32GB)
9
Aggregation times enhanced to less than 5 minutes*
Importing (UDF & AEBM)
v3.1
 Tabular data was supplied as
input with lat long values as
fields
 Geometry was created for
each row as it was imported
 Temporary tables were
created and then joined and
records add to SQL through
a convoluted process
 For some processes, 3.x
users needed elevated ESRI
license level to complete
v3.2
 Views for each feature type
were created as templates
of the data structure
 pGDB Feature classes are
the input and their
geometry is imported
directly by projecting to
Hazus wkid 4326
 Import done in a single
step via parameterized SQL
10
Impacts of Import Changes
 Spatial data is imported directly and not recreated
 Paves the way for importing line and shape data
 Process is greatly simplified for maintainability and future
enhancement
 Speed is increased dramatically. Approx. 49,000 records in
5 minutes
11
Questions?
12

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Earthquake Updates and Enhancements to Processing for Hazus-MH 3.2

  • 1. Earthquake Updates and Enhancements to Processing for 3.2 TROY SCHMIDT, SENIOR SOFTWARE DEVELOPER FACTOR, INC. 1
  • 2. Outline  Goals of Hazus 3.2 release  Hazard Factors Processing  Probabilistic Analysis  ShakeMap Anaysis  Performance comparison  Import Updates  Questions 2
  • 3. Goals of Hazus 3.2 release  Migrate ArcObjects calls to Geoprocessing Tools  Eliminate pGDBs still needed for Earthquake  Leverage Spatial SQL capabilities  Improve Processing Speed  Simplify processing where possible  Make internal processing more accurate 3
  • 4. Hazard Factors Processing  New spatial tables created in study region database: eqSrSoil, eqSrLQF, eqSrLND and eqSrWaterDepth  Values updated when default value updated, user datamap loaded, or as first step of analysis  ArcObjects uses ICursor an iterative row by row loop processing method  Spatial SQL utilizes set based approach updating all values in a single step 4 Soil, Liquefaction, Landslide, WaterDepth
  • 5. Hazard Factors Processing v3.1  Create fields dynamically in pGDB (if not existing)  Loop over all features and load the default value  Loop over all features 1 by 1 and update values by performing spatial lookup  Push updated values from pGDB to SQL via stored procedure v3.2  Use Spatial SQL via stored procedure to update features value in a set based fashion 5 Soil, Liquefaction, Landslide, WaterDepth
  • 7. Probability Analysis v3.1  Processed central USGS grids to USGS grids just of the study region at aggregation  Loop over all pGDB tables and add fields (PGA, PGV, SA03, SA10)  Loop over all features 1 by 1 and update values by performing spatial lookup  Loop over all features and compute their soil amplification value in code  Push updated values from pGDB to SQL via stored procedure v3.2  Stored procedure to update values directly using central USGS grids 7
  • 8. ShakeMap Analysis v3.1  Loop over all pGDB tables and add fields (PGA, PGV, SA03, SA10)  Loop over all features 1 by 1 and update values by performing spatial lookup  Push updated values from pGDB to SQL via stored procedure v3.2  Stored procedure to update values directly using spatial SQL against new spatial tables 8
  • 9. Performance Comparison Study Region Hazus 3.1 Hazus 3.2 Performance increase Charleston, SC 1hr 43min 10 min (Win10 32GB) 10.3x faster Greater Los Angeles area 1hr 58min 58 min (Win10 32GB) 2.03x faster Kenai Alaska Could not import 50K AEBM 1hr 2min (Win10 32GB) 9 Aggregation times enhanced to less than 5 minutes*
  • 10. Importing (UDF & AEBM) v3.1  Tabular data was supplied as input with lat long values as fields  Geometry was created for each row as it was imported  Temporary tables were created and then joined and records add to SQL through a convoluted process  For some processes, 3.x users needed elevated ESRI license level to complete v3.2  Views for each feature type were created as templates of the data structure  pGDB Feature classes are the input and their geometry is imported directly by projecting to Hazus wkid 4326  Import done in a single step via parameterized SQL 10
  • 11. Impacts of Import Changes  Spatial data is imported directly and not recreated  Paves the way for importing line and shape data  Process is greatly simplified for maintainability and future enhancement  Speed is increased dramatically. Approx. 49,000 records in 5 minutes 11

Notas do Editor

  1. Troy Schmidt with Factor INC Specialize in developing software that supports managing risk in both the private and public sectors <Importance of Hazus> Hazus project team for nearly 3 years Worked on Hazus for 2 years on the Modernization efforts Worked on all three hazards in various capacities Improve build steps Upgrade VB6 code to C# Remove pGDB dependency Lessen ArcObjects dependency Streamline code \ Holistic view
  2. Released just last week. Improvements as part of final stage of Modernization efforts.
  3. Goals predicated on migration away from ArcObjects and the elimination of the study region pGDBs that were only still around for Earthquake. Over the course of development additional goals were targeted.
  4. 4 variables that get loaded for every feature. Loaded from default values and / or user supplied data maps At 3.2 four new spatial tables exist. They are used in the new stored procedure to do set based SQL updates. Previously Hazus used ArcObjects and cursors to loop over each feature one at a time and update values. Now it uses a set based approach.
  5. Before processed every feature twice and the data was duplicated. Because the values are discrete it still updates based on point. At 3.2, everything is performed on a set basis once in the stored procedure using spatial SQL.
  6. First thing to talk about in the analysis is how the processing of the spatial lookup has changed. PGA displayed Centroid would indicate 1.24 but it computes out to 1.022. 3.1 this was point based 3.2 it is shape based Performance monitored SQL GetPrimaryKeyColumn 15x faster Can we improve things called A LOT Can we improve things that take a LONG TIME If we can, analysis runs faster UPDATE E SET E.PGA = D.AVERAGE FROM (SELECT A.SchoolID, AVG(C.PGA100) AS AVERAGE FROM [syHazus].[dbo].[USGS] C, [dbo].[eqSchool] A INNER JOIN [dbo].[hzSchool] B ON A.SchoolId = B.SchoolId WHERE C.Shape.STIntersects(B.Shape) = 1 GROUP BY A.SchoolID) D INNER JOIN eqSchool E ON E.SchoolID = D.SchoolID
  7. USGS grids are the National Seismic Hazard Map data. The soil amplification is part of the new process by using special views and pivot tables to achieve the same answer just set based. Aggregation from 20 min to 8 or less.
  8. Very similar to Probabilistic with the exception of the Soil Amplification math.
  9. Each study region impacts a different area of Earthquake analysis and results Charleston SC because of Census pipeline data (largest EQ in Eastern US 1886) Greater Los Angeles is perhaps the most important Earthquake study region in the world Kenai Alaska has 49,000 AEBM points Aggregation of 30 min for Greater LA down to 1min 44sec
  10. Limitation of ESRI license level for Basic they can only view Microsoft SQL data. Import functionality broken and custom solution had to be used.
  11. GetPrimaryKeyColumn