Recently I got to work, as a subcontractor, on a street map for a US educational institution. My task was to label street centerlines, using MAPublisher and LabelPro. This was my first real project using LabelPro so this would be a good test case.
I loaded up the road centerline data, which was not classified by type, and set up some rules for this. Using 9 pt Helvetica Neue, 55 Roman, as font.
When I examined the results of this first run, I noticed that it seemed to be very “leader line happy”. I have not shown the unplaced labels in this particular sample (for the sake of the argument that I’m trying to make, they don’t really matter that much anyway).
It quickly became obvious that if I’d want the best results out of LabelPro, I would have to prepare my data. a “one set of rules fits all” approach clearly wasn’t going to work. I decided to split my data into 4 different categories.
These categories are then each set up with a different set of rules, so that I can more accurately influence the way they’re being labelled.
The rulesets I created for these layers are as follows:
Major roads:
Regular roads:
Short roads:
The main LabelPro interface, where I’ve specified the priorities and fonts to be used, looks like this:
Finally, the result that we’ve all been waiting for. As you can see, there still is a need for some manual editing, but the overall image is a lot less cluttered than the first run.
I did change the color for the streets for legibility. Instead of 100% black it’s now only 75%.
Reducing the number of roads where leader lines are a valid option makes for a much clearer result. In retrospect, I think I might even want to limit it to dead-end streets and cul-de-sacs.
I think it’s important to keep in mind that LabelPro is a tool, not a magic box that’ll deliver perfect cartographic text placement. Even with the manual editing that’s necessary to make this map look finished, it’s still saving me a lot of time. Also, since the label text is taken directly from the attributes, there’s less chance of typos or other mistakes (assuming the source data is good of course). Of course the rules you have to set are very much influenced by the local topography and what I’ve done here may not apply to your maps. Still, it pays to experiment.