One object – different attributes

After dealing with attribute gaps and data inconsistencies, I want to focus on my favourite implication when it comes to using OpenStreetMap data for spatial modeling and analysis: attribute heterogeneity. Complicated term, easy-to-understand concept …

Somehow different from the two previous implications, which can basically occur in any data set, this one is quite characteristic of OSM data. As the individual mapper is relatively free in attributing objects, attribute heterogeneity is an inevitable consequence. In these cases it is not about right and wrong, but about different views on one and the same object. The mapper’s perception of reality is directly mirrored in how he or she assigns attributes to objects.

pic-gib8_pkt7_radweg-bsp1You can find this phenomenon frequently if you dig deep into the data set. Take for example a physically separated, mixed cycle- and footway along a primary road, as it is shown in the picture on the right. How can it be tagged in OSM? Basically there are several options. Here are just a few:

highway = cycleway
foot = designated
highway = cycleway
foot = yes
highway = footway
bicycle = designated
highway = path
foot = designated
bicycle = designated

None of these tags would be wrong. They are completely in accordance with the wiki’s recommendations. But a walking enthusiast might tend to tag the way as highway = footway with the corresponding bicycle tags. And a regular cyclist might want to emphasize the cycleway. And a third mapper prefers the general approach and simply tags the way as highway = path.

Attribut heterogeneity: two (correct) tag options for one and the same way.

Attribut heterogeneity: two (correct) tag options for one and the same way.

What seems to be irrelevant for some applications, can cause serious problems in the process of spatial modeling and analyses. Here, the attribute heterogeneity needs to be considered, if gaps and inconsistent analysis results should be effectively avoided! How can this be done?

In a first step it is necessary to check whether the tags are correct. For this combined queries (see last post internet) are a feasible option.

If different tag combinations are admissible and in accordance with the OSM wiki, the definition of derived attributes is a suitable approach. Such derived attributes are “virtual” attributes which can be “fed” by several, different tag combinations. Take for example the aforementioned example of a physically separated, mixed cycle- and footway. This type of road infrastructure can be defined as a derived attribute. To simplify matters, let’s introduce a new key sep.mixed for this attribute. Now we can define:

 (highway = cycleway AND foot = designated) OR (highway = cycleway AND foot = yes) OR (highway = footway AND bicycle = designated) OR (highway = path AND foot = designated AND bicycle = designated) etc.
 sep.mixed = yes

For modeling and analysis purposes this derived attribute is now considered when one wants to consider different types of bicycle infrastructure. Very plain approach, but with huge effects in models and analysis routines which are based on OSM data sets.

Of course, this approach is not restricted to bicycle infrastructure. It can be employed in anny case where objects are potentially heterogeneously tagged. In order to find these heterogeneous attributes in a data set we found a simple visualization approach and a plausibility check very useful as a starting point.

Concluding the last three posts, I hope it became clear, why it is of such great importance to not simply build applications on data sets, but to check the data sets’s quality and introduce modeling routines if necessary*. This of course requires an extra effort, but having an eye on analysis results and user satisfaction, the return of investment is striking.


* There is a very fine piece of work by Anita Graser et al. on this. It’s published in “Transactions in GIS” and can be accessed here internet.



One comment

  1. Pingback: Data quality: topology | gicycle

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