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Spatial Uncertainty: Data, Modeling, and Communication (R01)

Post Date

October 10th 2014

Application Due Date

January 7th 2018

Funding Opportunity Number

PA-15-010

CFDA Number(s)

93.113
93.393
93.399

Funding Instrument Type(s)

Grant

Funding Activity Categories

Education
Environment
Health

Eligibility Categories

State Governments
County Governments
City or Township Governments
Special District Governments
Independent School Districts
Public and State Controlled Institutions of Higher Education
Federally Recognized Native American Tribal Governments
Public Housing Authorities or Indian Housing Authorities
Non-Federally Recognized Native American Tribal Organizations
Non-Profits With 501 (c) (3) Status With The IRS (Except Higher Education Institutions)
Non-Profits Without 501 (c) (3) Status With The IRS (Except Higher Education Institutions)
Private Institutions of Higher Education
For-Profit Organizations (Except Small Businesses)
Small Businesses
Other

Other Eligible Applicants include the following: Alaska Native and Native Hawaiian Serving Institutions; Asian American Native American Pacific Islander Serving Institutions (AANAPISISs); Eligible Agencies of the Federal Government; Faith-based or Community-based Organizations; Hispanic-serving Institutions; Historically Black Colleges and Universities (HBCUs); Indian/Native American Tribal Governments (Other than Federally Recognized); Non-domestic (non-U.S.) Entities (Foreign Organizations); Regional Organizations; Tribally Controlled Colleges and Universities (TCCUs) ; U.S. Territory or Possession; Non-domestic (non-U.S.) Entities (Foreign Institutions) are eligible to apply. Non-domestic (non-U.S.) components of U.S. Organizations are eligible to apply. Foreign components, as defined in the NIH Grants Policy Statement, are allowed.

Funding

  • Award Range:

    $None - $None

Grant Description

The purpose of this funding opportunity announcement (FOA) is to support innovative research that identifies sources of spatial uncertainty (i.e., inaccuracy or instability of spatial or geographic information) in public health data, incorporates the inaccuracy or instability into statistical methods, and develops novel tools to visualize the nature and consequences of spatial uncertainty.

Contact Information


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