Data Accuracy and Data Currency

The Currency of Data is Currency.

Yet how to maintain accuracy in the face of ongoing currency?

Oh yea, and who’s paying for all this?

That sums up about every conversation ever had by any human about Open Data. People in the industry of map making and data visualization have struggled since 2005 and the advent of Google Earth, to compete with the common perception that any information on a map is current and accurate. Navigation systems that lead travelers astray are the first to receive the brunt of the layperson’s realization that things in space and time change.

Cartographers should relish that, after a 2 century stalemate of industrialization, people are actually thinking of maps as having value in the common and current marketplace. Not that they haven’t always been present and underpinning all that society rests on, its just that they’ve made their way back into the hands of daily users. They have reclaimed an essential stance in the habits of life.

So, the problem. A map is only as useful as it is accurate and legible (some might employ the word beautiful here), and accuracy comes with knowing the vintage of data. The time has sunset on accuracy being primarily a challenge of the instrument of collection. It is now as much of a function of medium and context as it is spatial location and contiguity.

Like anything else, there is a certain amount of production that can be batched and done by computers without laborious human intervention, and then there is the inevitable quality assurance stage of the process. This quality assurance is the mechanism for accuracy in attribute data, and its association with spatial location.

Making this reliable and high value data available to the public is the foundation of democracy, but there remains the challenge of finding funding for the quality assurance and curation of the data.

This continuum of data accuracy somehow captures the essence of spatial and attribute accuracy in one spectrum.

  • Survey Grade GPS (centimeter)
  • Prof Grade GPS (sub-meter)
  • Consumer Grade GPS
  • NAIP aerial photography (year)
  • Local/higher res. photography (year)
  • Satellite (SPOT, LandSat, etc.)
  • DRG (scale)
  • Vectorization from maps (scale)
  • Point address based geocoding
  • Zip Code
  • Computer aided design/planimetric
  • TIGER (year)
  • Metes and bounds
  • Parcels
  • Utilities and Infrastructure
  • Public Land Survey (Eighth Section, Sixteenth Section, Quarter Section, Footing)
  • Census (Block, Block Group, Tract)

This is the tip of the iceberg for defining quality data, and these photos require credit to this business article that nicely sums up the importance of data quality – Key Steps to Quality Data. The point being – let the data tell the story, don’t do this:

wma_dataQuality2

 

Margaret Spyker

Trackbacks & Pings

Leave a Reply Text

Your email address will not be published. Required fields are marked *

Powered by WishList Member - Membership Software