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Time cartograms construction for effective visualization of scheduled movement data


Currently, high volumes of multivariable spatio-temporal data are available. The study and analysis of such data can help to explore interesting and unknown patterns that can improve our understanding of complex phenomena and systems associated with the data. The available data is underutilized in many domains due to its spatial and temporal complexity, and the limitations of visual analytical tools. Of the available visualization techniques many lack the capability to analyze the data across all dimensions (i.e., spatial and temporal). Most of these deals very well with the locational and attribute component of data, but options to also deal with the data’s temporal component are relatively new and limited.

This research concentrates on an alternative visualization approach of movement data from a temporal perspective: a visualization environment based on time cartograms to support the spatio-temporal analysis in order to explore temporal characteristics, temporal relationships, temporal regularities or patterns within data, and present them in an easy-to-understand form to support human interpretation, problem-solving, and/or decision-making. In a time cartogram the geographic space (network: distance) is replaced by time-space (network: travel time) to express the temporal effect.

General framework of the analysis

  1. Background: what is all about and give a key biblio reference if available
  2. keywords: give key terms used and algorithm

Project objectives

Describe the output you target (c.f.: either calculate something, apply a model, produce one or a set of maps, graphics or tables issued from the processing of one or several data sources).


Describe your input data and their license of use if you know them.

Vector data

Raster data

Other data sources


Describe your script. If you use a specific algorithm link the bibliographic references.

Computational implementation

Some further explanation of the overall analyses and of each step paste your code here

- mycode
# Comment your code to make it more easy to understand. 
wget ftp://something.rar 
for file in *.tif ; do 
  echo $file ; gdalinfo -mm $file  | grep "Min/Max" 

Describe your second code if needed, what it does …

# my R code here

If you are modeling you might distinguish between

Model parametrization

Model prediction



Show us the output of the analyses upload images if needed with “add images” link in the upper bar of this window while editing

  • Map 1. bla bla bla bla …

  • Graphic 1. bla bla ok ok yes yes fantastic
    My graphic caption

Later on after the training you could improve the script and upgrade your wiki project page

wikistudholland/utwente_rehmat.txt · Last modified: 2021/01/20 20:36 (external edit)