Measuring State and District Ideology with Spatial Realignment
Bibliographic Data
| ID | 6342192 |
|---|---|
| Authors | James E Monogan (0000-0002-1968-6052), Jeff Gill (0000-0002-8592-8996) |
| Year | 2016 |
| Volume | 4 |
| Issue | 1 |
| Pages | 97-121 |
| Publication date | 2016-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Political Science Research and Methods (JOURNAL) |
| Journal identifiers | ISSN: 2049-8470 • E-ISSN: 2049-8489 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/psrm.2015.5 |
| OpenAlex | W2048812707 |
| Language | EN |
| Citations received | 8 |
| References cited | 50 |
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
Algorithm · Cartography · Covariate · Data science · Econometrics · Economics · Geography · Geospatial analysis · Ideology · Lag · Political science · Politics · Public opinion · Regional science · State (computer science · Statistics · Computer Science · Electoral Systems and Political Participation · Law · Mathematics · Media Influence and Politics · Opinion Dynamics and Social Influence
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| Unique citing works | 8 |
|---|---|
| Citations per year | 0,89 |
| Citation span | 2017 - 2023 (7) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 8 |