Name
Title | ||
|---|---|---|
Geospatial socio-economic/demographic data: The existence of spatial autocorrelation mixtures in georeferenced data-Part I |
Abstract | ||
|---|---|---|
A conjecture recently began to materialize in the GIScience and spatial statistics literature asserting that many georeferenced attribute random variables (RVs) contain a positive-negative spatial autocorrelation (SA) mixture, rather than the widely recognized purely positive SA based upon routine cursory observation. Another newly emerging supposition maintains that, after controlling for the already well-known tendency for SA to increase with increasingly fine geographic resolutions, georeferenced socio-economic/demographic attribute RVs have roughly the same magnitude of positive SA as that degree frequently computed for remotely sensed pixel data, an extent suppressed in its net amount by the negative SA in a variable's mixture. This article, the first of a two-part series, summarizes research exploring these two propositions using population density as a common georeferenced RV across a diverse set of geographic landscapes. The principal population density findings support these two suppositions. Part II presents parallel analyses for a wide range of georeferenced RVs across the same set of geographic landscapes. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1111/tgis.12826 | TRANSACTIONS IN GIS |
DocType | Volume | Issue |
Journal | 26 | 1 |
ISSN | Citations | PageRank |
1361-1682 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel A. Griffith | 1 | 91 | 23.76 |
| Khushboo Agarwal | 2 | 0 | 0.68 |
| Meifang Chen | 3 | 0 | 0.68 |
| Changho Lee | 4 | 0 | 0.68 |
| Emily Ann Panetti | 5 | 0 | 0.68 |
| Kyunghee Rhyu | 6 | 0 | 0.68 |
| Lasya Venigalla | 7 | 0 | 0.68 |
| Xiaohe Yu | 8 | 0 | 0.68 |