Relational Reprojection Platform
Non-linear distance transformations of spatial data in R
Bibliographic Data
| ID | 21247516 |
|---|---|
| Authors | Will B Payne (0000-0002-2223-7864, Rutgers, the State University of New Jersey, corresponding author), Evangeline McGlynn (0000-0003-4273-2245, Harvard University Press) |
| Year | 2024 |
| Volume | 51 |
| Issue | 2 |
| Pages | 546-552 |
| Publication date | 2024-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning B Urban Analytics and City Science (JOURNAL) |
| Journal identifiers | ISSN: 2399-8083 • E-ISSN: 2399-8091 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/23998083231215463 |
| OpenAlex | W4388734478 |
| Language | EN |
| Citations received | 1 |
| References cited | 8 |
When mapping relationships across multiple spatial scales, prevailing visualization techniques treat every mile of distance equally, which may not be appropriate for studying phenomena with long-tail distributions of distances from a common point of reference (e.g., retail customer locations, remittance flows, and migration data). While quantitative geography has long acknowledged that non-Cartesian spaces and distances are often more appropriate for analyzing and visualizing real-world data and complex spatial phenomena, commonly available GIS software solutions make working with non-linear distances extremely difficult. Our Relational Reprojection Platform (RRP) fills this gap with a simple stereographic projection engine centering any given data point to the rest of the set, and transforming great circle distances from this point to the other locations using a set of broadly applicable non-linear functions as options. This method of reprojecting data allows users to quickly and easily explore how non-linear distance transformations (including square root and logarithmic reprojections) reveal more complex spatial patterns within datasets than standard projections allow. Our initial release allows users to upload comma separated value (CSV) files with geographic coordinates and data columns and minimal cleaning and explore a variety of spatial transformations of their data. We hope this heuristic tool will enhance the exploratory stages of social research using spatial data
Cartesian product · Data mining · Exploratory data analysis · Heuristic · Spatial analysis · Statistics · Visualization · Computer Science · Human Mobility and Location-Based Analysis · Land Use and Ecosystem Services · Mathematics · Spatial and Panel Data Analysis · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,5 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |