Just because it is on a map - does that mean it is accurate? What about Trap Streets for example?
Consider the following:
Why do we trust mapping data?
Is it because the computer says so? There is hardly a week goes by without some reference to the someone driving the wrong way up a 1 way street, onto the railway tracks, a lorry getting stuck in a space too narrow or under a bridge simply because they were following the SatNav.
Similarly - Utility companies have often discovered that even though their plans suggest it is fine to dig - all too often they severe a pipeline, cable, etc.
One thing all these incidents have in common, along with others of disastrous consequence is the assumption that the data they had available was correct.
As a GI company we have always insisted that the data should be appropriately accurate for the application in question. For example, if mapping mature trees in a park land environment - an accuracy of 1-3 or even 2 to 5m would be considered acceptable. This level of accuracy would be a huge liability within the Utility industry and invariably would result in severed cables and pipes dissatisfied customers and potentially costly implications.
The old adage 'Garbage In Garbage Out' is equally true in relation to mapping data and in some cases it is better to have no data at all rather than data which cannot be relied upon.
Only truly informed decisions can be made when we have faith in the data we are using. So how can we have faith?
Historically, the collection of meta data has been considered a tedious nuisance.
However, we are increasingly finding that organisations are now realising the worth of that meta data.
- Who collated the data?
- What sources were used?
- When was it captured?
- What degree of accuracy?
- What format is used?
Are just a few questions which should be inherent in any data set and available to the users of that data.
We have clients using data captured over 8 years ago but they can rely on the data because they know from the data provenance that the data is still valid.
At the other end of the spectrum we have seen all too many datasets without structure, consistency, provenance. The usual culprit here is information being recorded in a spreadsheet which a human may understand but requires a huge effort to enable it to be integrated into an information environment.
As with all information systems the lowest level of data must be uniquely identified otherwise confusion will inevitably result. That is why vehicle number plates are unique (unless someone has cloned your plate and you get the fine for speeding, etc.).
Consider the following scenario:
- All trees are tagged across a large area.
- What happens if an incident occurs at tree displaying tag number 2457?
- The response team arrives at the location of 2457 and discovers no incident.
- Is it false reporting? or are there two trees tagged 2457?
In the latter case vital minutes would be lost which could ultimately cost someone their life.
Behind the Tech
- Eye-TREE Lite
- Westonbirt Interactive Map
- Eye-SPACE (BIM)
- Mobile GIS
- Eye-TREE Analytics
- Eye-ASSET Analytics
- Eye-TERRIER Compilation
AddressAIT Spatial Ltd
Wrest Park Enterprise Centre