Bicomponent Trend Maps
A Multivariate Approach to Visualizing Geographic Time Series
Bibliographic Data
| ID | 12751123 |
|---|---|
| Authors | Jonathan Schroeder (0000-0002-0538-0730, University of Minnesota, corresponding author) |
| Year | 2010 |
| Volume | 37 |
| Issue | 3 |
| Pages | 169-187 |
| Publication date | 2010-01-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Cartography and Geographic Information Science (JOURNAL) |
| Journal identifiers | ISSN: 1523-0406 • E-ISSN: 1545-0465 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1559/152304010792194930 |
| PMID | 23504193 |
| PMCID | PMC3595555 |
| OpenAlex | W2052159484 |
| Language | EN |
| Citations received | 1 |
| References cited | 9 |
The most straightforward approaches to temporal mapping cannot effectively illustrate all potentially significant aspects of spatio-temporal patterns across many regions and times. This paper introduces an alternative approach, bicomponent trend mapping, which employs a combination of principal component analysis and bivariate choropleth mapping to illustrate two distinct dimensions of long-term trend variations. The approach also employs a bicomponent trend matrix, a graphic that illustrates an array of typical trend types corresponding to different combinations of scores on two principal components. This matrix is useful not only as a legend for bicomponent trend maps but also as a general means of visualizing principal components. To demonstrate and assess the new approach, the paper focuses on the task of illustrating population trends from 1950 to 2000 in census tracts throughout major U.S. urban cores. In a single static display, bicomponent trend mapping is not able to depict as wide a variety of trend properties as some other multivariate mapping approaches, but it can make relationships among trend classes easier to interpret, and it offers some unique flexibility in classification that could be particularly useful in an interactive data exploration environment
Bivariate analysis · Cartography · Data mining · Flexibility (engineering · Geography · Machine learning · Multivariate statistics · Population · Principal component analysis · Statistics · Visualization · Computer Science · Data Visualization and Analytics · Land Use and Ecosystem Services · Mathematics · Remote Sensing in Agriculture · Artificial Intelligence
Algorithm as 136
The Cognitive Limits of Animated Maps
Strategies For The Visualization Of Geographic Time-Series Data
Dasymetric Estimation of Population Density and Areal Interpolation of Census Data
Different Places, Different Stories
Linking censuses through time
Patterning in Urban Population Densities
American Metropolitan Evolution
Street-Weighted Interpolation Techniques for Demographic Count Estimation in Incompatible Zone Systems
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,25 |
| Citation span | 2022 - 2022 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |