Murphy’s Law: What can go wrong will go wrong.

Let’s count the ways that your digital map can bite the dust.

1. Labels get cut off at tile seams. (Increase map buffer area.)
2. Effects at land/water boundary don’t work if the two datasets lack topology. Mouths of large rivers and canals? (Use data that was built to work together like Natural Earth.)
3. Layering isn’t correct. Trailhead symbols underneath park shading. (Do lots of testing and moving code and stylesheets around as needed.)
4. Symbols not appearing. (Remember to include them in the right folder and call correctly. Hello.)
5. You forgot to normalize thematic, population-related data. (Normalize.)
6. Line widths aren’t changing incrementally. When you zoom in they just get bigger and bigger until pretty soon the entire map is one road. (Specify a different line-width for every zoom level or groups of 2-3 zoom levels.)
7. Lines are jumbled or too thick at low zooms. (Generalize the low-zoom data. Simplify the lines using a simplifying algorithm.)
8. All features show up but with no styling, all black and Arial and width = 1. (Re-code, nesting isn’t working right.)
9. You published it but nobody cares. (Remove half the functionality and increase the prominence of the central purpose.)
10. You published it and everybody cares but in the wrong way. (Remember the most vocal voices are not always the majority opinion.)

Ever wondered what the map scale is at each zoom level in a digital map? Well, it’s not an easy answer, since almost all webmaps are in the Web Mercator projection (some are in Web Mercator Auxiliary Sphere), which has a greatly varying scale depending on your latitude.

The varying scales of a Mercator projection:

Assuming a 96 DPI*, we can translate the zoom levels to map scales and pixel sizes, and for the sake of simplicity they have been recorded only for locations at the equator. Here’s the very useful Esri chart for this. Obviously these will change depending on how far from the equator your features are. But it is a good start to get a handle on, say, what datasets will look good at what zoom levels. For example, you might want to use some Natural Earth large scale data that comes in 1:10m resolution. You’ll see from the chart that it will look good through zoom 6.

The Esri chart is, like I said before, quite handy. However, you might want to see just approximate scale equivalents as you go about looking at various datasets and plotting which ones are good at which zoom levels. Incidentally, if you’re doing an exercise like that, I highly recommend making a data chart in a spreadsheet and putting these values across the top, horizontally, and then xing out or coloring the squares that correspond to each dataset’s resolution. This gives a handy visual way to scroll through 100+ datasets to see what resolution they have.

REMEMBER these are just APPROXIMATE scales for each zoom level. Not only does it vary by latitude but these have been significantly rounded:

zoom approx. scale
0 1:500m
1 1:250m
2 1:150m
3 1:70m
4 1:35m
5 1:15m
6 1:10m
7 1:4m
8 1:2m
9 1:1m
10 1:500,000
11 1:250,000
12 1:150,000
13 1:70,000
14 1:35,000
15 1:15,000
16 1:8,000
17 1:4,000
18 1:2,000
19 1:1,000

You’ll note that in the Esri zoom level document they also wrote out how many meters, at the equator, one pixel is. Why would this be at all important? Simply put, if your features are less than one pixel in size, this may have implications for how much of that data you show and how you style it. For more on that and some other very interesting dot styling information, see Eric Fischer’s excellently illustrated Mapping Millions of Dots article.

*See this post on why I/we/everyone shouldn’t be assuming 96 dpi. Required reading!