Data visualisation 5 – GIS

Spatial analysis is how we understand our world—mapping where things are, how they relate, what it all means, and what actions to take. From computational analysis of geographic patterns to finding optimum routes, site selection, and advanced predictive modeling, spatial analysis is at the very heart of geographic information system (GIS) technology..”

Geographic Information Systems (GIS) are a invaluable platform for OSS developers to visualise data on. Many GIS can even portray the three dimensions that we interact with every day, namely height, width and depth.

When it comes to presentation of visual data, GIS are able to display data patterns with a spatial context, including:

  • Geographic maps (eg routes and locations of outside plant networks)
  • Cartographic maps (eg showing overlays of where potential demographic distributions are)
  • Dot distribution maps (eg exact locations of where customers are)
  • Contour / Topographical maps (eg showing land, building and potentially even vegetation terrain contours that may impact line of sight or radio / cellular signal propagation)
  • Proportional symbol maps (eg showing the number of alarms, customers, bandwidth utilisation, bandwidth availability, etc at a given location)
  • Other thematic maps (eg heat maps showing high prevalence, such as regions of higher market penetration, plus the closely related cool maps that could show regions where penetration is statistically much lower than surrounding areas)

Whilst it’s possible to generate all of these maps via other means, the great features of GIS are their ability to cross-reference any number of other attributes to visualised objects as well as showing real-time updates (if needed).

Attributes could include object-by-object drill-down from a cable map into specific details such as:

  • Cable identifier
  • Cable types
  • Impacted customers (ie customers traversing that specific cable)
  • Service types supported
  • Utilised or available capacity on the link
  • Ownership model (eg owned, leased, etc)
  • Cost / expense / depreciation models
  • Maintainer’s contact details
  • Route tracing to show circuits beyond this cable link
  • Predictive analysis (eg when capacity is likely to be fully utilised on this link)

As we’ve discussed recently, there are even augmented reality (AR) tools that allow these various data visualisation techniques to be overlaid onto what we see before us.

Angela Zoss has prepared this interesting review of visualisation types on the Duke University website that might provide further thoughts on how to visualise OSS data. This link on Wikipedia shows some other interesting examples of thematic maps. This link from Esri provides some helpful guidance on understanding spatial relationships and patterns.

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