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Essays in Computational Demography
Essays in Computational Demography
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151337
- ISBN
- 9798382843162
- DDC
- 320
- 서명/저자
- Essays in Computational Demography
- 발행사항
- [Sl] : Cornell University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 126 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Hall, Matthew.
- 학위논문주기
- Thesis (Ph.D.)--Cornell University, 2024.
- 초록/해제
- 요약Computational demography involves the use of new data and computational methods to improve measurement and understanding of population processes. This dissertation contains three examples of work in the field. Paper 1 tracks the migration decisions of partisan voters to tease apart the roles ideological and racial neighborhood context on individual mobility decisions. Paper 2 combines consumer trace data with administrative and survey data to measure migration flows at a new level of granularity. Paper 3 uses machine learning to improve methods of inferring neighborhood racial composition from datasets where race is not measured. These papers grapple with common issues that emerge when dealing with data that was not constructed for research purposes, including missing variables and non-representatives. Taken together, the work highlights the potential of this field, the challenges that still need to be overcome, and the many synergies obtained by combining together new and traditional approaches.
- 일반주제명
- Public policy
- 일반주제명
- Statistics
- 일반주제명
- Political science
- 일반주제명
- Demography
- 키워드
- Bayesian model
- 키워드
- Machine learning
- 키워드
- Partisan voters
- 기타저자
- Cornell University Public Policy
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382843162
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a320
■1001 ▼aDecter-Frain, Ari.▼0(orcid)0000-0001-9635-3334
■24510▼aEssays in Computational Demography
■260 ▼a[Sl]▼bCornell University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a126 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Hall, Matthew.
■5021 ▼aThesis (Ph.D.)--Cornell University, 2024.
■520 ▼aComputational demography involves the use of new data and computational methods to improve measurement and understanding of population processes. This dissertation contains three examples of work in the field. Paper 1 tracks the migration decisions of partisan voters to tease apart the roles ideological and racial neighborhood context on individual mobility decisions. Paper 2 combines consumer trace data with administrative and survey data to measure migration flows at a new level of granularity. Paper 3 uses machine learning to improve methods of inferring neighborhood racial composition from datasets where race is not measured. These papers grapple with common issues that emerge when dealing with data that was not constructed for research purposes, including missing variables and non-representatives. Taken together, the work highlights the potential of this field, the challenges that still need to be overcome, and the many synergies obtained by combining together new and traditional approaches.
■590 ▼aSchool code: 0058.
■650 4▼aPublic policy
■650 4▼aStatistics
■650 4▼aPolitical science
■650 4▼aDemography
■653 ▼aBayesian model
■653 ▼aComputational demography
■653 ▼aDiscrete choice modelling
■653 ▼aMachine learning
■653 ▼aPartisan voters
■690 ▼a0630
■690 ▼a0800
■690 ▼a0615
■690 ▼a0463
■690 ▼a0938
■71020▼aCornell University▼bPublic Policy.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0058
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161303▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
