Especially in large cities, the user can play out the advantages here.

 

To give just a few examples, a typical situation that a tourist or a traveler experiences. Imagine you are visiting Berlin, Tokyo or New York. Whether by train, car or plane. All cities are the same in one thing. They are so big and therefore confusing. The language barriers should not be underestimated either. However, our travel time is limited, and of course you want to have the maximum experience, at least some travelers see it that way. Others like to be driven through the cities. No navigation, following the swarm of crowds,

letting progress pass you by.

Thanks to Google Maps, the whole thing has become much more convenient.

 

But in one point everything is still unclear, you zoom into the map from a certain place and you are overwhelmed by the multitude of shops, these many small points are more irritating than helpful. Before long, it's just a hodgepodge of everything, with no clear structure. As a result, we are more concerned with seeking than experiencing. Should that be so? You wanted to enjoy the moment and just sip your coffee. Ultimately, the wide range of options leads to a dead end and you decide on something. But the travelers don't want to experience anything, it should be the way you want it. Design everything yourself and lay out your daily program. Unless you are on the other side of the tourists, where a tour guide is planning your day and you only experience what 1000 others have already been able to experience in exactly the same way. No demands, no work and to see how a tour guide winds down his program can also be quite interesting.

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More structure and clarity would be the development goal here. Easier is easier, designed for everyone. Two big possibilities: One is personalization in combination with machine learning and algorithms.Each of us is individual, also in what we want to see in cities and what not. Our preferences for certain cuisines, or that some only visit boutiques, others prefer antique shops, monuments, museums, promenades, etc. Sports shops, market, textile, Expensive Medium inexpensive. Bookstore chain or bookstore small like a corner shop.Even on the plane we can type and type what we want to see and find in cities. So when you visit Tokyo you can set your preferences for this city.If you land in Tokyo and call up the app, all the shops that don't match your interests will disappear.With a tap in the menu, you can of course make them all visible again.Without individual personalization, we can rely on machine learning. If you are lucky you move through the city and at the end of the day or at the end of the journey we see where we have been. In a statistic, various data are available, including which stores we have been in the longest.This is how you get to know your behavior better when you let yourself drift in the cities.Technically, all of this is possible, although software adjustments have to be made here. You create your own profile after the trip.In another version, machine learning comes into play, the software remembers what we have visited and creates a profile from it. So why is this interesting? Quite simply, the next time you go to a big city, the app can ask you whether you want to see and find similar shops like e.g. in New York TOKYO Berlin. So the app/program/software/the system starts to get to know you better and you have it easier, digital assistance. After 10 trips or excursions you can see that you are more often

in places where you wouldn't have expected it.

The whole thing is mystical, but ok, it works the same way.This feature is part 1 of the problem.

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Let's now turn to part 2.

Keep everything that was just about on the tip of your tongue. The Google Maps app is still open. You've just got off at a train station somewhere in the world. With a feature I call "Compass Mode" the following happens. Take your smartphone and hold it in front of your body like a compass. Of course you won't see anything yet as the feature doesn't exist yet, but imagine seeing a long corridor with a width of 100-200m. A little wider than a typical shopping street. In addition, you will find shops and sights down to a depth of 1-2km. Then what would you see? Quite simply, everything that interested you from previous cities. If you have activated the profile and want to use it with the compass. On/Off, very simple.

So personalizing his interests in other cities also plays a role here. It should then also be possible to read distance information as well as walking or cycling time. Without a corridor you see everything, with a corridor you only see what you want to see or what is actually there on the mile.

 

In practice, that would be the case. You and your girlfriend or your buddies enter the shopping mall and you will initially only move in one direction. This is the classic. You follow the flow of the crowd. With a tap, the corridor only shows sights that are in front of you or only the restaurants, such as pizzerias. You might want to go eat something.

 

Feel free to think about how useful something like this could be and whether such features have added value in your everyday life.

The fact is, just because everything is so easy to read and feasible, only then does the programmer's work begin. That's a lot of writing and typing that comes up to development teams. For everyone who is in the Google Maps team and is familiar with everything, the whole thing is of course easier. You can see directly at which point in the software architecture the change has to be implemented. With a little search you will find one or the other video on YOUTUBE, what exactly has to be done during the conversion or feature expansion.

 

Could you follow me up to here, yes nice. No Unfortunately.

Here's another bonus that came to mind as I was writing this.

 

Most of us live in structures and in at least partly planned days, whether it is the afternoon or the morning is a matter of indeterminacy. Somewhere everyone finds their appointments in their calendar or in their everyday life. Club visits, fixed meal times, going out, cooking, etc. Dog walking.

 

How is it when you go on a trip ? A tour ?

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Do you need structure? For those who need one, the next lines can be interesting!

 

Already on the plane you talk to your girlfriend, the travel partners, the group what do we actually want to do when we arrive? Depending on the time, that is usually what food. So you take the Google Maps app and during the flight you write down a small order of the things you want to do after arrival in a chronological order.

 

Example: 1. Rent a bike 2. Go out to eat 3. Culture/festival/market 4. Visit a bookstore

5. Museum 6. Eat ice cream 7. Visit the park, relax. Then half a day is up.

 

Because the app knows what you are planning and where you are, you can conjure up a route on which you can experience all of these 7 points. A digital tour guide, just Made by Me/Google. If there are photographic highlights here, you can activate this and the app will send you information. You can also activate it if you want to be informed whether there are new openings of shops on the route. So a stopover is possible. You might turn around or stay on the path. The preferences of each individual mentioned at the beginning are also decisive here, and they should be mentioned again here. Personalization and machine learning as well as intelligent algorithms. If you have decided in point 2. for a restaurant where you want to eat pizza, then the app will take this type of restaurant into account when calculating the route.

 

Where we make use of the learning effect of the apps for ourselves in everyday life and ever more intelligent systems are used, you can also ask yourself, if we already have a digital assistant with a small app, what actually happens in the big companies? The same as on the lower levels. Where the little ones learn, the big ones learn as well. Everything is bundled up and converted into usable data floor by floor. A data cycle - the data highway at company level of a special kind.

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In detail, this means that if the feature is used successfully, the software can learn which paths we preferred to take, which cues we followed when jumping up and as a result it can then offer more routes as travelers can move through the cities in the future . Or you can simply see which type of traveler has which preferences. Everything we experience in the future will feel like a tailor-made suit that fits just you, you and your travelers. The opportunities that open up here for the advertising industry speak volumes. Here's an example. Examples are always good. After all, we grew up with examples at school.

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In Tokyo Berlin New York you've been to the museum and to the Italian for a pizza.

On your next trip, machine learning automatically negotiates attractive conditions for you with the local shops, of course also with the help of marketing strategists. They can look like this. You walk past participating Italian restaurants, but have not entered a daily program in the app for today. The dealers exchange your devices and the network via beacon and you get a voucher displayed, if you visit certain restaurants here again Italians are preferred, what else the world just loves pizza & pasta. If you visit by the end of the day or within a specified time, you get 25% off the total price. you wonder

No, that's business. The same can happen if you rent bikes or go to the cinema.

 

 

So where does the industry need to make some subtle changes? In the navigation of tomorrow. This largely determines whether we find travel attractive and pleasant and whether we will travel again.

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