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Essays in Computational Demography /

Decter-Frain, Ari,

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Essays in Computational Demography /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Essays in Computational Demography // Ari Decter-Frain.
作者:	Decter-Frain, Ari,
面頁冊數:	1 electronic resource (126 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
標題:	Public policy. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31241974
ISBN:	9798382843162

Essays in Computational Demography /
Decter-Frain, Ari,

Essays in Computational Demography /Ari Decter-Frain. - 1 electronic resource (126 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Computational 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.

English

ISBN: 9798382843162Subjects--Topical Terms:

532803
Public policy.
Subjects--Index Terms:

Bayesian model

Essays in Computational Demography /
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520 $a Computational 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.
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653 $a Bayesian model
653 $a Computational demography
653 $a Discrete choice modelling
653 $a Machine learning
653 $a Partisan voters
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