All language subtitles for 10_zero-values-on-your-map.en

Here's a useful tip that might come in handy when you're making your quantitative maps.

You may end up having some values that are zero in your data set.

As long as those are real values,

you know that there are zeros, if that makes sense that they're supposed to be there.

Maybe, you have census tracts where there's nobody living there,

it could be an industrial area or something like that.

That's fine but it can have an effect on

the color scheme for your data classes and how that data are interpreted.

So, one technique that can be a useful tip is to actually exclude

the zero values from your data classification. I'll show you how to do that.

So, here we have the classification dialog box in ArcMap.

We can just click this little button here for Exclusion.

All that does is you are going to select data that we

don't want to include in our data classification.

So, if we click on that Exclusion dialog box,

we'll end up with this other dialog box here

and we can build a query which is essentially what's happening here,

where it says SELECT FROM Median Income WHERE,

median income equals zero.

So, all we're doing is saying, select the values that

meet the criterion that we've set up here.

All that is, is a very simple one saying if the value equals zero,

then it's going to be excluded.

So, here's the results of my doing that is I now have,

this is a diverging color scheme for median household income here.

So, I've indicated the median here on the map in PowerPoint. You can do this.

Sometimes, it's useful to put that in a map or in Legend in some way,

in order to be able to tell people even though it is diverging,

where the point is,

where they're diverging from.

But the main thing I wanted to point out here is I've added

this Legend category which is unpopulated.

So, you can see that there's a few census tracts around the city,

where there really isn't anybody living there.

So, why is this important?

Well, imagine if you were showing this map to,

say a policy maker of some kind,

and they're interested in providing social services for

people who are in low-income areas.

Well, if you have those as zeros,

so in other words, on the map,

this would be shown as an area where the median income is zero,

if somebody just looked at that quickly they'd say, "Well,

that must be a very poor neighborhood because the median income is so low."

It would just show up on the map as being a really dark red.

So, somebody may misinterpret that map and look at it and say,

"There these areas where there's very low income,

we should do something about that.

This should have an influence or an effect on the policy decisions that we make."

When really, there's just nobody living there.

That's perfectly fine.

It's not that there's people living there with low-income,

except there's nobody there at all.

So, it may seem like a small point but I do think these kinds of little attentions to

detail can make a difference in terms

of the way that your map is perceived, the way it's interpreted.

So, it's really takes a few seconds to change it and to add

that a little bit of nuance to

your map and it will just make it a little bit better overall.