Choropleth Mapping with Uncertainty
A Maximum Likelihood-Based Classification Scheme
Bibliographic Data
| ID | 3775856 |
|---|---|
| Authors | Wangshu Mu (0000-0002-2171-8025, Arizona State University), Diane Tong (0000-0001-7005-5128, Arizona State University) |
| Year | 2019 |
| Volume | 109 |
| Issue | 5 |
| Pages | 1493-1510 |
| Publication date | 2019-09-03 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Annals of the American Association of Geographers (JOURNAL) |
| Journal identifiers | ISSN: 2469-4452 • E-ISSN: 2469-4460 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/24694452.2018.1549971 |
| OpenAlex | W2925299704 |
| Language | EN |
| Citations received | 3 |
| References cited | 37 |
Choropleth mapping provides a powerful way to visualize geographical phenomena with colors, shadings, or patterns. In many real-world applications, geographical data often contain uncertainty. How to incorporate such uncertainty into choropleth mapping is challenging. Although a few existing methods attempt to address the uncertainty issue in choropleth mapping, there are limitations to widely applying these methods due to their strong assumption on the distribution of uncertainty and the way in which similarity or dissimilarity is assessed. This article provides a new classification scheme for choropleth maps when data contain uncertainty. Considering that in a choropleth map, units in the same class are assigned with the same color or pattern, this new approach assumes the existence of a representative value for each class. A maximum likelihood estimation–based approach is developed to determine class breaks so that the overall within-class deviation is minimized while considering uncertainty. Different methods—including linear programming, dynamic programming, and an interchange heuristic—are developed to solve the new classification problem. The proposed mapping approach has been applied to map the median household income data from the American Community Survey and simulated disease occurrence data. Test results show the effectiveness of the new approach. The linkage between the new approach and the existing methods is also discussed. Key Words: choropleth mapping, map classification, maximum likelihood estimation, uncertainty
Classification scheme · Data science · Econometrics · Maximum likelihood · Statistics · Bayesian Methods and Mixture Models · Computer Science · Housing Market and Economics · Mathematics · Spatial and Panel Data Analysis
GeoDa
The Mahalanobis distance
Information and Persuasion
The Analysis of Spatial Association by Use of Distance Statistics
Geographically Weighted Regression
Analogs Between Class-Interval Selection and Location-Allocation Models
Modifying Objective Functions and Constraints for Maximizing Visual Correspondence of Choroplethic Maps
In The Matter Of Class Intervals For Choropleth Maps
Visualizing Geospatial Information Uncertainty
Incorporating Data Quality Information in Mapping American Community Survey Data
Guidelines for the Display of Attribute Certainty
A heuristic multi-criteria classification approach incorporating data quality information for choropleth mapping
The Selection of Class Intervals
Mapping Population Data from Zone Centroid Locations
Evaluation of Methods for Classifying Epidemiological Data on Choropleth Maps in Series
Generalization in Statistical Mapping
Contiguity-Biased Class-Interval Selection
Visualizing Georeferenced Data
Optimal Map Classification Incorporating Uncertainty Information
A GIScience Perspective on the Uncertainty of Context
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,75 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |