The Problem of Missing Data on Spatial Surfaces
Bibliographic Data
| ID | 8376142 |
|---|---|
| Authors | Robert J Bennett (0000-0003-3940-1760, University of Cambridge), Robert Haining (0000-0003-3462-7218, University of Sheffield), R P Haining, Daniel A Griffith (0000-0001-5125-6450, University at Buffalo, State University of New York) |
| Year | 1984 |
| Volume | 74 |
| Issue | 1 |
| Pages | 138-156 |
| Publication date | 1984-03-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Annals of the Association of American Geographers (JOURNAL) |
| Journal identifiers | ISSN: 0004-5608 • E-ISSN: 1467-8306 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1111/j.1467-8306.1984.tb01440.x |
| OpenAlex | W2152171006 |
| Language | EN |
| Citations received | 12 |
| References cited | 110 |
Although the problem of missing data arises in most branches of the discipline, it has received little systematic treatment in the geographical literature. In an effort to overcome this deficiency, this paper reviews a number of methods for approaching the problem. Of the three classes of solutions—ad hoc, cartographic interpolation, and statistical—the statistical approaches appear to be preferable. In this review a modified version of the Orchard and Woodbury missing-information principle receives the greatest emphasis because it combines classical statistical theory with trend surface and spatial autoregressive models. Although the best solution of all is to return to the experimental situation in order to collect supplementary data, this is often impractical or impossible. The analyst should then consider the estimation techniques presented here. The methods used to address the missing data problem thus become an important stage in the overall process of experimental design, sampling, and hypothesis testing
Autoregressive model · Data mining · Econometrics · Interpolation (computer graphics) · Machine learning · Missing data · Process (computing) · Sampling (signal processing) · Spatial analysis · Statistical hypothesis testing · Statistics · Artificial Intelligence · Computer Science · Economic and Environmental Valuation · Mathematics · Soil Geostatistics and Mapping · Spatial and Panel Data Analysis
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| Unique citing works | 12 |
|---|---|
| Citations per year | 0,29 |
| Citation span | 1984 - 2026 (43) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 11 |