Relevance, distance, and prominence are the three drivers of local search ranking, according to Google.  We've known for some time now that proximity has only been increasing in importance.  Starting around 2017, proximity became the most important of these factors.

You can see this reality at work when you search for a local business without specifying a city or neighborhood.  When I search for plumbers, the results that are returned are what Google labels as sorted by "best match."

The top two results have strong review profiles, but the last result has no reviews (and also doesn't seem to have a website).  It's fairly common to see these kinds of results pop up, presumably driven by proximity and not much else.  This heavy weight placed on proximity might be best for the searcher, but it also has the side effect of making it easier to rank spam listings.

In any local search efforts, it's important to see things as you would from the location of the searcher (especially if you're studying the effect of proximity!).  

Follow along and I'll show you the steps to simulate local search position.

First, open your browser and go to, and scroll to the very bottom of the page.  You should see your approximate location, as well as a link labeled "Use precise location."

Geolocation controls can be found at the bottom of the search page. 

Press Ctrl+Shift+I to open the Dev Tools before you click anything here.  The developer console should open.  From here you can set a custom GPS position via the Sensors page, as seen below.

Setting a custom location via the Sensors page in Developer Tools.

Select "Custom Location" from the Geolocation menu.  The Latitude and Longitude input boxes should become editable at this point.  Set the desired location and return to the search results page we saw earlier.

Click the "Use precise location" link.  The labeled location should update to reflect the latitude and longitude you entered earlier.  If it doesn't, try refreshing the page and checking the location again.  

Searching for "dentists near me" after setting a custom location.

Unfortunately, this setting only applies for a brief duration before your search location will revert to your real approximate position.  Although this is a handy trick to know, it's difficult to use for anything beyond a handful of searches.

How strong is the proximity factor?

We know that proximity is a strong search factor, but just how strong is it, and is that strength seen equally across business categories?

My hypothesis going into this was that, for some businesses such as a coffee shop, proximity would be incredibly important.  After all, are you willing to travel across town for better coffee, when there's a coffee shop just down the road?  In other categories, such as law offices, it would seem to make sense that searchers might be willing to travel further.

I used the rank tracker to test out this hypothesis.  I selected six high ranking businesses from each category and performed a 10x10 keyword scan, with each scan point 1000 meters apart.  The resulting rank grids show just how quickly a business falls off the radar as you move further and further away.

Ranking grids from the rank tracker

Although each grid is fairly small, you can see that low rankings (shown in red) appear rapidly for coffee shops as we move away from the location.  Dental practices seem to be somewhere in the middle when it comes to proximity, as their rankings also fall off with distance, but not as sharply as coffee shops.

Law offices have the most stable rankings of all three.  They seem to experience only a slight dip in rankings, even at the outer edges of the grid.  Of course, proximity plays a big role for law offices too, but I've applied the same settings to each category here.  If I were to define a much bigger grid we'd start to see rankings decline for law offices as well.

Here's another way to look at the data.  I've taken the three categories shown above and graphed how their average rankings relate to distance.

What causes these pronounced differences between the segments?  One intuitive hypothesis might be that there are just more coffee shops, and that natural competition causes the steep drop-off in rankings.

I decided to dig into that idea.  I performed a ranking scan on a coffee shop and a law firm that are close together in downtown Los Angeles and compared the results.  This time, I also tracked the location of their competitors.

Although the search results displayed almost twice as many unique coffee shops as law firms, it doesn't seem to explain the difference in ranking behavior on its own.

From the experimentation I've done, I believe Google adjusts the importance of proximity depending on the segment or category of the business.  

Feel free to convince me otherwise!  You can reach me at @zchtodd.