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Measuring State and District Ideology with Spatial Realignment

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  • Monogan, James E.
  • Gill, Jeff

Abstract

We develop a new approach for modeling public sentiment by micro-level geographic region based on Bayesian hierarchical spatial modeling. Recent production of detailed geospatial political data means that modeling and measurement lag behind available information. The output of the models gives not only nuanced regional differences and relationships between states, but more robust state-level aggregations that update past research on measuring constituency opinion. We rely here on the spatial relationships among observations and units of measurement in order to extract measurements of ideology as geographically narrow as measured covariates. We present an application in which we measure state and district ideology in the United States in 2008.

Suggested Citation

  • Monogan, James E. & Gill, Jeff, 2016. "Measuring State and District Ideology with Spatial Realignment," Political Science Research and Methods, Cambridge University Press, vol. 4(1), pages 97-121, January.
    • Handle: RePEc:cup:pscirm:v:4:y:2016:i:01:p:97-121_00
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    Cited by:

    1. Deniz Aksoy & David Carlson, 2022. "Electoral support and militants’ targeting strategies," Journal of Peace Research, Peace Research Institute Oslo, vol. 59(2), pages 229-241, March.
    2. Jason S. Byers & Jeff Gill, 2022. "Applied Geospatial Bayesian Modeling in the Big Data Era: Challenges and Solutions," Mathematics, MDPI, vol. 10(21), pages 1-23, November.

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