you can never hide.

[00:00:00]You can never hide, especially in today's age where at your fingertip is every tool and resource imaginable to track your exact position, even with extremely limited clues. And this photo has been circulating around the internet for a couple of years now of someone gatekeeping a pizza with everything dramatically obscured with the goal for the pizza to never be found. But well, I found it. Here's how. And when you look at this photo for the first time, your immediate response is probably, "That's impossible." There's absolutely nothing in this photo that could ever make this remotely findable because it's quite literally just a sidewalk. But you couldn't be more wrong. There is more info in this photo than you even need to be able to find it. I'm going to go through everything that I used to help find this location. Okay, so immediately your first reaction when looking at this photo is probably this is just New York City. And you also probably couldn't explain why, but I don't think I ever worked under the presumption this wasn't going to be New York City. But we also have the red tactile paving here with this light box, which if you like just go into any random street in New York, you can see the red tactile paving all across New York with the same exact post on the bottom here. Very, very common across New York. But that's also probably more of a subconscious thing.

[00:01:08]Even outside of that, it just looks like it's going to be New York. But we also have a four-way intersection right here.

[00:01:13]A person eating a pizza with other types of food at the corner in a restaurant that serves more than pizza, it looks like, with a car parked right here. And we also have the pavement changing colors right here. And right here you have a couple pixels that looks like a white square in the corner that's curved. But those are all things that are going to be used to confirm the location and not really help find the location. What's going to help find the location is this bus lane right here.

[00:01:39]You can see on the road here the color is well different and there is a white line right here separated by a gap which is also pretty rare for bus lanes in New York City. But there is a bus lane on the curb in New York City. So then we can use this bus lane map which is a map of every single bus lane in New York City that we then know is going to be at least on an intersection somewhere in New York City on one of these bus lane routes. But also that's extremely difficult to be able to find because that is a ton of intersections to go through all across the city and it's just not very efficient. But also when I mentioned in the intro that you can never hide, that's true. But what's extremely true is that you can never hide in New York City. But that's the case because New York City has some of the best data publicly available than any city I've ever seen in the world. So what we're looking at here is on the New York City's government website a Excel sheet that we can download with data about every single bus lane in New York City. So what I'm going to do is I'm going to download this as a CSV, import it into Google Sheets, and we can see as it loads here, we have tons of data.

[00:02:39]This is approximately 4,069 points of data for bus lanes in New York City that actually has the exact data that we need to be able to filter this down to find the exact location in this photo. And this is how I did this. Okay, so when we look back at this photo, there's a couple things here that obviously help confirm the location like I mentioned like it's at an intersection. You have the red tactile paving. You have the different pavement.

[00:03:01]You have the white box. You have the gap in the bus lane here. But none of that is filterable in this Excel sheet. But there's a couple things here that are.

[00:03:09]And the fact that this is all organized and labeled publicly is amazing because I'll show you exactly why. Okay, so when we look at this, we can use a couple things. The first thing that we can use here is using the bus lane, obviously, is the fact that it's painted red. So, we're going to filter the lane color to red. You can also see that the bus lane is curbside, but also there's not like a parking lane or a bike lane disconnecting the bus lane. It's directly on the curb, which if we go back to the lane type, you can also only show you bus lanes that are curbside.

[00:03:40]So, we're filtering it down again. But, as you can see, we keep going. This is still thousands of rows of data that it's not really quite feasible to go through quite yet. But, there's still one filter left that we can use that will filter this down significantly, and that is the street width filter. And your first thought is probably, well, how are you going to know the street width? In the same way that New York City also has data on their bus lanes and everything is categorized, they also have a website on their design on how everything is designed and why it's designed and tons of information behind all of the roads in New York City. So this is the curb bus lane page on that website that mentions the design is a minimum of 11 ft in width. So we know that this is going to be at least 11 ft.

[00:04:23]But there's one thing here that we can do that makes it a little more predictable. You know what else is predictable? A siliad in the middle of this video. If you guys know what Sale is, well, like I've said before, welcome to the channel. But it is an eim provider that allows you to get access to service in literally over 100 countries, including Tunisia.

[00:04:40]You guys know this bill by now. There's no roaming fees. It's super easy to set up. You don't have to go to the airport and have like constant fear of like, will this SIM physical SIM card at the airport work? You can land and automatically get access. And show you guys how easy it is. I have a 30 second timer. I'm going to load in. I'm going to see this is obviously Synagal. And this time I will get the right region on the coast. This is kind of ironic if I don't. We're going to go to Synagal.

[00:05:04]Click apply data. It's going to have code rainbow already applied at the checkout and redemption. Surely this time it's the car, not St. Louis.

[00:05:14]But but Sy actually does also now have SY phone numbers. With one instant click, you can now get your US phone number right inside the app with SMS and phone calling, which is obviously incredibly handy if you're always on the move. You're on the moving in San Jose, surely. Okay, I guess so. You can get 15% off by using code rain at the checkout or by scan the QR code on screen right here as I make a good region guess in the southern US sand.

[00:05:36]Bro, what is this? Is this Carolas or like Alabama? Sure, it's Alabama code.

[00:05:41]Let's get back to the video. There's other lanes here that aren't visible in the photo that you have to assume. But what we can do is using the crosswalk here and the zebra stripes on the crosswalk. We can see one two three four five 6 7 8 9 10 11 12 13 14 zebra stripes on the crosswalk. And we can use that to actually just reference other streets in New York City. So what I did was using this example that I was showing earlier, we have 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 zebra crossings on this intersection right here, but that's including a parking lane. So the assumption was that this is going to be a four lane road in New York City with one of the lanes being a bus lane. So one bus lane and then three other typical lanes of traffic. So if one bus lane is 11 ft as mentioned on this page, we can use the other page. The typical lane width for moving vehicles is 10 ft.

[00:06:39]Buses and trucks require lanes 11 to 12 ft wide. So with one bus lane and then three other lanes, we can assume that the width of this road is going to be no less than 41 ft. But what you could also do is like say for example using this road right here as an example that has a parking lane and four other lanes. We can actually just measure the distance of this road using Google Maps. Just using this as a reference point is going to be approximately 14 m it looks like which is about 45 ft. So using this reference point of this lane right here that has 15 zebra crossings at around 45 ft. And the minimum requirement if these are all the other three lanes are going to be typical lanes and this one having 14 zebra crossings which means it's probably slightly less wide than the 15 zebra crossing. That means that this has to be somewhere between 41 and 45 ft wide. So, we can actually filter down the data now to only show us 41 to 45 foot bus lanes in New York City. I think it does even give you 41 option. So, we're going to do 40. Yeah, 40 through 45. Also, I know that there's probably other ways to go about this that's way more efficient. This is just the way I did it because that's what I'm familiar with. So, once you apply that, you can see that we're left with just a few options. And each one of these options is actually it looks like a lot more than it is because this is one row to check. This is one row to check. So there's really only about 15 possible options that this location can be in New York City only using available what we have in the photo so far. So then obviously the next goal is to check the locations that we have left which is actually like I said not that many but we need to convert the string of coordinates that's given here in the first column here into something that we can convert to a JSON file. So I just use this script that allow me to make a column here. I'll do equal mapmaking which will make this column here exactly what I need to convert this into a JSON. So I'm going copy the code and I'm going to paste it here. Save as.

[00:08:32]And yes, I'm doing this all for a pizza that quite frankly looks quite mid, but it's something new and it's pretty fun.

[00:08:38]Okay, so now that I have the JSON saved, I can just drag and drop it into mapmaking.app, app, which as you can see here will give me every single point on the map that this can be, which is not that many. It says 113 locations, but you can see each road here, each intersection, there's multiple dots. So, it's really only like 27, I think, locations that need to be checked. And really, you don't ever think it's going to be somewhere in the Bronx or Manhattan, especially if we can like just go in here. We can see that this like obviously that's not going to work.

[00:09:08]There's no sunlight. You don't need to to spend much time on that. But when you start going to intersections over here in Brooklyn, you can start seeing that.

[00:09:14]Okay, this looks actually pretty pretty close. Come down here. Okay, wait a second. You know, is that it? But since we have actually so many clues in the original photo that allow us to confirm it, we know that there's not a trash can there. I mean, we can go back and see that the trash can is still there, but you know, it's just shifting. And I know that you guys are probably thinking at some point, wait, why didn't you just find the person that originally posted it? I did try. It's just one of those recycled internet memes that's from a close friend's Instagram story that it was I couldn't find where it originally came from. So, the only year I was going off of was 2022, which was the first time I ever saw it recycled across the internet. But, you can quickly see a couple things here don't match. You can see that the bus lane right here doesn't have that disconnect between the red and the white stripe. So, you can automatically eliminate this. Also, the white line doesn't go in there. You have the trash can, things like that. You keep checking different roads here and you see that. Okay, clearly not it. But you come to these four dots down here.

[00:10:07]You can zoom in. You can see that there's the bus lane on the curb. You can click and turn around. We see a couple things here. The white line is disconnected from the red red tactile paving discoloration in the pavement.

[00:10:23]The same light post, but more importantly, the same exact white square. And if we go back to 2021 coverage, we can see the exact chair she was sitting in. Uh it is unfortunate, but if we do go into the Google reviews, it has permanently closed down. But we can still use the photos on the reviews to scroll down here and see that the user uploaded photo right here. This black table right here is the same exact black table right here along with everything else included confirming the exact location of this photo right here. And you can also see that the manhole right here behind this cup is right there. Yeah. So clearly I have nothing better to do in my life.

[00:11:05]But it was fun and I hope that someone took something away from this that despite there being very little information in the photo, anything can be a clue and anything can be found, especially if you're in New York City.

[00:11:18]If you guys like this, subscribe or don't. And I'll see you guys next time.

[00:11:22]Goodbye.