The DOI matches its Crossref record.
Spatial Regression Models
Abstract
Spatial Regression Models illustrates the use of spatial analysis in the social sciences. The text includes sections that cover different modeling-related topics: mapping and making projections; doing exploratory spatial data analysis; working with models which have lagged endogenous right-handed side variables; using spatial error correction models; employing conditionally autoregressive models; and dealing with over-time panels exhibiting spatial structures. Each of the modeling-based discussions includes separate delineations of how to proceed when dealing with main variables that are quantitative as well as qualitative. In each section, the authors employ prominent and diverse examples, introducing readers to key literature in the field. The examples are presented along with relevant data and programs written in the R, which illustrate exactly how to undertake the analyses described. The book ends with a chapter that covers techniques for presenting spatial information.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
A multidisciplinary survey on discrimination analysis
Romei, A., & Ruggieri, S. (2014) · The Knowledge Engineering Review · doi:10.1017/s0269888913000039 · Cited by 288
Sustained drought, vulnerability and civil conflict in Sub-Saharan Africa
von Uexkull, N. (2014) · Political Geography · doi:10.1016/j.polgeo.2014.10.003 · Cited by 149
Food environment and obesity: A systematic review and meta-analysis
Pineda, E., Stockton, J., Scholes, S., Lassale, C., & Mindell, J. S. (2024) · BMJ Nutrition, Prevention & Health · doi:10.1136/bmjnph-2023-000663 · Cited by 125
(2016) · doi:10.1037/pspp0000104 · Cited by 111
Measuring the Spatial and Temporal Patterns of Police Proactivity
Wu, X., & Lum, C. (2017) · Journal of Quantitative Criminology · doi:10.1007/s10940-016-9318-5 · Cited by 52
Do they know something we don’t? Diffusion of repression in authoritarian regimes
Olar, R.-G. (2019) · Journal of Peace Research · doi:10.1177/0022343318822718 · Cited by 50
Draheim, H. M., Moore, J. A., Fortin, M., & Scribner, K. T. (2018) · Evolutionary Applications · doi:10.1111/eva.12617 · Cited by 29
Metadata
- Authors
- Ward, M., & Gleditsch, K.
- Venue
- Sage eBooks
- Publisher
- SAGE Publications, Inc.
- Published
- 2008
- OpenAlex
- W4299426141
- Cited by
- 418
- References
- 0
- FWCI
- 27.59
- Type
- book
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
