Reducing Uncertainty in the American Community Survey through Data-Driven Regionalization
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{"title"=>"Reducing uncertainty in the American Community Survey through data-driven regionalization", "type"=>"journal", "authors"=>[{"first_name"=>"Seth E.", "last_name"=>"Spielman", "scopus_author_id"=>"35938922600"}, {"first_name"=>"David C.", "last_name"=>"Folch", "scopus_author_id"=>"36813030800"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"602565981", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0115626", "scopus"=>"2-s2.0-84923818020", "pmid"=>"25723176", "sgr"=>"84923818020"}, "id"=>"bd4e70eb-1d94-3395-b848-3a373a1e0d1f", "abstract"=>"The American Community Survey (ACS) is the largest survey of US households and is the principal source for neighborhood scale information about the US population and economy. The ACS is used to allocate billions in federal spending and is a critical input to social scientific research in the US. However, estimates from the ACS can be highly unreliable. For example, in over 72% of census tracts, the estimated number of children under 5 in poverty has a margin of error greater than the estimate. Uncertainty of this magnitude complicates the use of social data in policy making, research, and governance. This article presents a heuristic spatial optimization algorithm that is capable of reducing the margins of error in survey data via the creation of new composite geographies, a process called regionalization. Regionalization is a complex combinatorial problem. Here rather than focusing on the technical aspects of regionalization we demonstrate how to use a purpose built open source regionalization algorithm to process survey data in order to reduce the margins of error to a user-specified threshold.", "link"=>"http://www.mendeley.com/research/reducing-uncertainty-american-community-survey-through-datadriven-regionalization", "reader_count"=>39, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>4, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>1, "Student > Master"=>9, "Other"=>1, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>4, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>1, "Student > Master"=>9, "Other"=>1, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Environmental Science"=>4, "Medicine and Dentistry"=>2, "Design"=>2, "Philosophy"=>1, "Arts and Humanities"=>2, "Social Sciences"=>18, "Earth and Planetary Sciences"=>2, "Economics, Econometrics and Finance"=>4}, "reader_count_by_subdiscipline"=>{"Design"=>{"Design"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Social Sciences"=>{"Social Sciences"=>18}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>4}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>4}, "Arts and Humanities"=>{"Arts and Humanities"=>2}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"United States"=>2}, "group_count"=>1}

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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