How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
HIFLD Presentation Fall 2013
1. EPA, HSIP and Infrastructure Data
Perspectives
David Smith
Ana Greene
Aaron Meyers
Lee Kyle
10/30/2013
202-566-0797
202-566-2132
202-566-0690
202-564-4622
Smith.DavidG@epa.gov
Greene.Ana@epa.gov
Meyers.Aaron@epa.gov
Kyle.Lee@epa.gov
U.S. Environmental Protection Agency
1
2. EPA Emergency Response
• EPA ER focus has
traditionally been on ESF-10
(Oil and Hazardous Waste) –
shared with US Coast Guard
• Responses typically deal
with sampling, monitoring,
cleanup and remediation
after a disaster event
• Hurricanes, Flooding, BP
Deepwater Horizon,
Pipeline
3. EPA Use of HSIP
• Typically EPA has used
HSIP as a reference
layer, present in many
ER tools and Flex
Viewer applications
• Schools, Hospitals,
Vulnerable
Populations
4. Hurricane Sandy
• Hurricane Sandy highlighted a need for
infrastructure data – and exposed many
locational data quality issues in drinking
water/wastewater infrastructure
• Poor data quality hampered response analysis, triage and prioritization of
assessment of drinking water facilities
• EPA’s Facility Registry Service aided EPA
Region 2 Regional EOC for Hurricane Sandy
response
5. Many DQ issues
Vague, incomplete or
invalid address
Wrong county
Drinking water locational data
quality problems included
missing/invalid lat-long values,
wrong county, missing or vague
locations and other locational
data problems
Invalid lat-long data
6. Drinking Water
• Ultimately only a small percentage of SDWIS
facilities had any reliable, mappable
locational data at all, whether lat/longs, or
street addresses that could be geocoded
• Data gaps for infrastructure data can be filled
via data from other programs and states
7. FRS and Hurricane Sandy
Locational data gaps and
incorrect data in SDWIS, as well
as supplemental contact
information were filled in via EPA
Facility Registry Service (FRS)
data from other EPA program
data sources (TRI and others),
and by research by FRS stewards
using other public data sources.
8. Drinking Water
• The emergency response community needs
reliable data on drinking water infrastructure,
but currently that need is not easily being
met.
• Better locations could be found via other
systems, but this was a labor intensive,
manual process.
• Lack of reliable data slows response time.
9. Wastewater
• Permitting data collected by states via
National Pollutant Discharge Elimination
System (NPDES) – but is incomplete and poor
logic for querying Publicly Owned Treatment
Works (POTWs)
• New data coming online via Clean Watershed
Needs Survey (CWNS)
10. ER Infrastructure
Data Needs
• Ideally ER community should be able to
prioritize and triage in a response, for
example assessing infrastructure condition by
population served
• We find data needs to be tied to other
identifiers, i.e. state ID or other programs
• May need some additional attributes
• FRS team is looking at how to help fill gaps