One critical visual task when using choropleth maps is to identify spatial clusters in the data. If spatial units have the same color and are in the same neighborhood, this region can be visually identified as a spatial cluster. However, the choice of classification method used to create the choropleth map determines the visual output. The critical map elements in the classification scheme are those that lie near the classification boundary as those elements could potentially belong to different classes with a slight adjustment of the classification boundary. Thus, these elements have the most potential to impact the visual features (i.e., spatial clusters) that occur in the choropleth map. We present a methodology to enable analysts and designers to identify spatial regions where the visual appearance may be the result of spurious data artifacts. The proposed methodology automatically detects the critical boundary cases that can impact the overall visual presentation of the choropleth map using a classification metric of cluster stability. The map elements that belong to a critical boundary case are then automatically assessed to quantify the visual impact of classification edge effects. Our results demonstrate the impact of boundary elements on the resulting visualization and suggest that special attention should be given to these elements during map design.

Quantifying the Visual Impact of Classification Boundaries in Choropleth Maps

The article investigates the possibility of using cartographic redundancy to reduce the change blindness effect on spatio-temporal maps Unlike in the case of previous research, the authors take a look at various methods of cartographic presentation and modify the visual variables in order to see how those modifications affect the user’s perception of changes on spatio-temporal maps The study described in the following article was the first attempt at minimizing the change blindness phenomenon by manipulating graphical parameters of cartographic visualization and using various quantitative mapping methods Research shows that cartographic redundancy is not enough to completely resolve the problem of change blindness; however, it might help reduce it

Cartographic Redundancy in Reducing Change Blindness in Detecting Extreme Values in Spatio-Temporal Maps

Choropleth map animation is widely used to show the development of spatial processes over time. Although animation congruently depicts change, the rapid succession of complex map scenes easily exce...

https://www.tandfonline.com/doi/pdf/10.1080/15230406.2017.1308836

Complexity reduction in choropleth map animations by autocorrelation weighted generalization of time-series data

The present paper presents the performance of an experimental cartographic study towards the examination of the minimum duration threshold required for the detection by the central vision of a moving point symbol on cartographic backgrounds. The examined threshold is investigated using backgrounds with discriminant levels of information. The experimental process is based on the collection (under free viewing conditions) and the analysis of eye movement recordings. The computation of fixation derived statistical metrics allows the calculation of the examined threshold as well as the study of the general visual reaction of map users. The critical duration threshold calculated within the present study corresponds to a time span around 400msec. The results of the analysis indicate meaningful evidences about these issues while the suggested approach can be applied towards the examination of perception thresholds related to changes occurred on dynamic stimuli.

/pdf/detection-of-moving-point-symbols-on-cartographic-4q1e7wvxg8.pdf

Detection of moving point symbols on cartographic backgrounds

To address unsolved issues of change detection in animated choropleth maps, we proposed the concept of ‘gross change detection’ and performed an experiment that empirically verifies the incidence of change blindness stems from the ‘magnitude of change (MOC)’, spatial distribution in animated choropleth maps. We generated experimental materials using the change-characterization arrays and the global Moran’s I. Participants had 108 cases of changing maps with time duration (1 to 3 sec) and had questions. The results showed that MOC and duration affect gross change detection, but the most interesting result from our experiment was that different spatial distributions between two adjacent choropleth maps may lead the map reader to under- or over-estimate the level of gross change in the map. It implies that we should consider spatial distribution of change when we design animated choropleth maps.

Effects of Spatial Distribution on Change Detection in Animated Choropleth Maps

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Kim

Author Tools

Chat about Author

Papers

Effects of Spatial Distribution on Change Detection in Animated Choropleth Maps