Project 702

Climate Chip Background

Working through the background section of my project proposal. I apologise if these posts are somewhat muddled, they serve as a dumping ground to collect my thoughts before crafting them into the proposal. The background is divided into 4 sections:

  • The Climate Chip Project
  • Geospatial Data Management (and how it is unique from traditional database models)
  • The role of normalisation, indexing and spatial datatypes (and database solutions that support this)
  • The Big Data view

This post will describe the Climate Chip Project.

The Climate Chip project is a non-for-profit group that aims to provide information and resources about heat stress and other health impacts associated with climate change. Their vision is to provide easily accessible information for a wide range of people and organisations, from high school students and academics through to health organisations and the media. Information is readily accessible via interactive web maps at climatechip.org. The Climate Chip team is made up of 6 international researchers, including NMIT senior lecturer Matthias Otto and former-NMIT lecturer Ryan Clarke.

The web front end, developed by Matthias and Ryan, provides an interactive tool to navigate the data set which is segmented into an even 50 Km x 50 Km grid of the earth. By clicking on any single grid segment, the user is able to view a graphical summary of the climate data {longitude, latitude, year, month, temperature unit, T(max), T(mean), WBGT(max), MBGT(mean), UTCI(max), UTCI(mean). Ryan and Matthias have done an incredible amount of work to make this easy-to-use and seemless. The generation of the data summary is blazingly fast. It seems to me that the largest challenge (from a data perspective) is the size of the data and the ability to return results quickly.

Observations of the interactive web map

The web map is simple to use and blazingly fast. It is a static map, in fact it is almost a direct reference map. The data is static and rigidly parceled within the collection regions across the earth. By clicking any single region, the observed data for the region is returned. In this sense, the data does not have to manage spatial relationships or thematic relationships. From a useability perspective, it would be interesting to overlay a thematic representation of the data, allowing the use to view local and global trends. However this is probably outside the scope of this current project.

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