So we sent a team to coordinate some installs in Santa Monica last week. Since Google Earth makes city boundaries available, it’s not hard to quantify when we were there, and how much time was spent in Santa Monica and what stops were made in neighboring cities. This takes no time at all in GPS Insight. Here’s how:
First, turn on City Boundaries, turn off roads (to make it easier to see the city boundary) and then “fly to” Santa Monica:
Then trace the outline using a Google Earth Polygon and name it “Santa Monica” and color it however you may like:
To be precise while tracing the city boundary, you can Zoom Down/Up, and pan around using the Pg Up/Pg Down and Arrow Keys ( Help -> Keyboard Shortcuts gives you this information) – just click all the defining points until you complete the polygon:
It’s up to you how accurate you want to be — 1 minute and you should be 99.9% accurate:
After clicking on “OK” you can then right-click/cut [or copy to be safer -- either is fine] the definition from Google Earth and paste it into “Quick Import” under Mapping -> Landmarks on the GPS Insight top navigation bar:
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Then click on “Import Now” and repeat for any bordering cities in question.
To find out which ones we need, grab the history for roughly the time that vehicle (Scion 4000) was out in California.
I forget when they were there so I’ll just grab 9 days or so toward the end of April:
It looks like they spent some time driving through Venice, having breakfast in Playa Del Ray, and in WestWood, Sawtelle, Culver City, etc.
I’ll just add a couple other cities for the sake of this article, but if these were important county or city boundaries, you would only need to add them once, and if it was a LOT of data, we have ways of automating this for our customers — just ask! [sometimes that means we do it for you...]
Here are 3 I quickly traced (they’re not 100% — there are crazy in & out borders which are irrelevant for our purposes here):
Now we can group them as “CA Coastal Cities” under GPS Insight -> Mapping -> Landmark -> Groups:
Click on “Create New Landmark Group”:
Then name it and save it:
Now here’s the timesaver:
Draw a quick “throw-away” polygon around all three in Google Earth and cut/paste it into “Filter by Polygon”:
We quickly see only our 3 Coastal Cities — this is down from 966 landmarks we would otherwise need to look through to find all three and drag them into the new group we’ve created:
Now shift-click between the top & bottom to select all 3, then drag onto our new “CA Coastal Cities” group:
Now click on the “edit landmarks” icon to change if necessary or just verify the group is correct:
OK, enough about grouping landmarks, but that’s necessary for the final report:
Make sure to refresh or hit F5 on your browser to pick up the new landmark group, and run a landmark report for Scion 4000 for ONLY the landmark group called “CA Coastal Cities” — make sure to click on the “Passing Through” checkbox to ensure you get driving activity which does not begin/end a stop as well.
And .6 seconds later, here is the answer:
103.8 hours in Santa Monica, 9 minutes passing through Venice, and 25 minutes in Playa Del Ray with 9 minutes stopped there for something (Breakfast? — too quick):
Turning on the “Places of Interest” layer we see it’s a McDonald’s (which explains why it’s only 9 minutes):
You get the idea — this article has gotten long…
Hopefully it gives you a good example of how to use Google Earth, along with GPS Insight landmarks/landmark groups, and our landmark report.
One nice feature I’ll mention though is this — if you want to remove a particular landmark from a group because it doesn’t belong there, just click on the “minus” sign near the landmark name in the report:
You are given a chance to “OK” or cancel the deletion:
This is a great example of how our reports allow you to interact to create/delete/remove landmarks, pull up maps, etc. We are always trying to make the product more able to answer questions about your fleet, and the interface easier for you to do so efficiently.
It truly took me 3 minutes to get the answer to my initial question — but about an hour & 15 minutes to document it in this article. It’s a long one, thanks for reading it.
Thanks,
Rob.
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