Geocoding - Translating between machines and humans

May 22, 2019 26 min read

Geocoding translating between geographic coordinates and address, from address to geographic coordinates

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An interview with Ed Freyfogleco-founder of OpenCage Data, entrepreneur, and the inspiration behind Geomob ( a geoinovation meetup ) 

Ed:
You have these technological explosions where so much innovation happens, and then we need to simplify it again and agree on standards, agree on best practices. You just need the time also for people to become more familiar with new things like that. And then, as a result, winners come out of that pack.

Daniel:
Hello and welcome to another episode of The MapScaping Podcast. My name is Daniel and this is a podcast for the geospatial community. Today I'm talking to Ed Freyfogle and he does a lot of work with geocoding, and he has a company called OpenCage Data. We're going to be talking a little bit about his work today with geocoding, the challenges around it, and use cases for it. And then, more generally we're going to be talking about geospatial, we're going to be talking about mapping and what the future might look like. I really hope you enjoy the interview.

Daniel:
Hi Ed. Thank you so much for taking the time to do this interview with me today. It's much appreciated. I realize you're a busy person, and you're busy because you're running a company called OpenCage Data. Can you maybe tell us a little bit about that?

Ed:
Yeah. Great to be here. Thanks for having me. My name is Ed Freyfogle, and I'm one of the founders of OpenCage. We provide a geocoding API, so we do forward and reverse geocoding. Geocoding is the process whereby you convert between addresses or human readable geographic information to coordinates, so longitude and latitude. Forward geocoding is when you take an address or place name and turn it into the coordinates, and reverse geocoding is the opposite, when you have the coordinates and you convert that into the address or other human understandable information about that location.

Daniel:
That's really interesting that you talk about it as being human understandable and machine understandable, but that's really what it is. An address is just a way that the human readable system for geocoding locations on Earth, right?

Ed:
Yeah. That's right. Humans have devised all kinds of ways to talk about places. One of the big challenges that we have is if I ask you where are you right now? You might say, "Oh, I'm in ..." For example, I'm sitting at my house in Barcelona, so I could give you my full address, I could say my postcode, I could say I'm in Barcelona, I could say I'm in Spain, I could say I'm in Europe. That actually ... one of the delightful, let's say topics we get to deal with is things like disputes about locations. So I could say I'm in Spain or I could say I'm in the Republic of Catalonia, I could say I'm in the EU, I could say I'm on Earth. All of these things are correct.

Ed:
And so, we have all kinds of different ways to talk about location, and those are great because humans are really good at understanding those, and want to have a rich syntax for depending on whether you want to be hyper granular or hyper broad. But for computers, of course, that's not so good, not so useful, and computers can struggle with that kind of free text. So computers typically use longitude and latitude which are hyper precise.

Ed:
So we need to have a way to convert back and forth between the two, and that's what our API lets people do. One of the key differentiators of our service versus other services is we do this using open data. So primarily datasets like OpenStreetMap which covers the whole world, but also lots of other ones that are perhaps more country specific or region specific and things like that. So happy to go into lots of detail on that.

Daniel:
Yeah. It seems to me if you're using lots of different datasets, and open datasets, that you might run into a few translation problems there. Can you tell us a little bit about some of the challenges you face using several different datasets, and how you solve them?

Ed:
Well, I guess for some context. So open data is a rapidly growing topic over the last couple of years. OpenStreetMap was founded in 2004, so this year it will be 15 years old. And that's just probably one of the best known open geo datasets, there are many others. This is an area where all kinds of things are happening, new data is being released from governmental bodies, data is being collected via crowdsourcing, and on the software side all kinds of things are happening around this to make the [inaudible 00:04:38].

Ed:
It can be a big challenge to stay on top of that, with all that's happening because it's moving so quickly. And so, as a result a lot of people get ... they just want simplicity, they want a way ... I have some addresses, how do I turn them into coordinates. And they don't want to have to first get deep into it. And so, what we're trying to do is provide a very simple interface to enable software developers to do that, and at high volume.

Ed:
So we have a couple of challenges there. One challenge is just behind the scenes we're aggregating all these different datasets and all these different open source geocoders, so you have a DevOps challenge just of managing all that. I guess one of the things that we always think about is that ... and one trap that software develops often fall into is that building things is a lot of fun and it's easy, the real challenge is maintaining things. And that's definitely the case when we talk about geo, because the world is constantly changing, so it's not enough to say, "Okay. I got the data, now let me build some software to interact with the data, and I'm done." That's definitely not the case because constantly new places are being created, places are being changed, the names of places are changing, postcodes are changing, all those kind of things.

Ed:
A good example of that is OpenStreetMap. The OpenStreetMap database, the global database now has many, many millions of edits every single day. So it really is a living beast that people need to stay on top of.

Daniel:
I can definitely see that being a challenge. That having a living dataset, a dataset that's constantly evolving and trying to keep up to date with that. I think just in terms of indexing or knowing what's searchable, and how things are changing over time, and maybe even who's changing them, and how they're weighted, how those changes are weighted and that kind of thing. I can definitely see that being a massive challenge.

Daniel:
But, I think you touched on a really interesting point before, you said that people just want this thing to work. That geospatial is just a part of what they're doing. They don't have time to sit down and reinvent the wheel, and build this machinery around geocoding, because geocoding is probably just a little part of what they're doing. A very important part no doubt, but it's just a small part of what they're actually doing in terms of their application.

Daniel:
I think that sums up a lot of what's happening in geospatial in general, is that it's integrated into lots of different things. We're not doing geospatial for the sake of geospatial, we're doing it because it helps us out here, it adds value to different datasets all over the place.

Ed:
That is absolutely the case. One of the big trends in the industry, of course, is that whereas it used to be the geo industry was few very highly trained experts who would work on their special GIS software, and those people certainly still exist. Now the reality is everyone is walking around with a supercomputer in their pocket that knows where they are at all times. As a result of that, we have a huge explosion in the amount of data, and not just in the amount of data, but the amount of data that has a location context in some way, shape, or form because these devices are moving all the time.

Ed:
On the one hand it's, of course, smartphones, on the other hand it's also tracking devices on vehicles and things like that. The cost of these devices keeps coming down, the functionality in terms of battery life and power consumption, all these kind of things, keeps improving. So more and more of these devices are constantly out there gathering data with the location context.

Daniel:
If we get back to what we were saying right at the start, that idea of translation between machine and human, that's becoming more and more important I take it? If we've got more devices, they're collecting information, geolocation in their own language, in their machine language and we need that translation over to something humans can understand if we're going to actually make use of that data.

Ed:
Well, that's exactly right because on the one hand we're collecting all this data, generating all this data, and then on the other hand we have software developers who are trying to utilize that data in their application, so whatever way, shape, or form. Be that showing where a vehicle is on a map, be it in the context of games, be it applying it to other datasets. So if you collect someone's address, a customer's address and then at some point you want to display that on map so you need to go through the process of converting the address into coordinates.

Ed:
That end result is you have more and more software developers who do not have particularly detailed geo background, yet they still want to make use of this data. And so, at some point they need tools to help them do that. Of course, there are people who enjoy geo as a hobby or as an interest or because of their professional work, and I fall into that category and probably you can tell us more about that as people like to look at maps and reminisce about where they were, or where they're going to go and all these kind of things.

Ed:
There is a subset of people who really enjoy geo and want to get deep into geo and really understand it, and maybe start contributing to OpenStreetMap and map their neighborhood. Those people are great, that's the core of the community. But around that, you have a whole universe of people who basically just want to build their application, who just want to analyze their data, and geo is only a piece of that, a tiny piece of that.

Ed:
So these people need tools to help them do that because ... and one area that I think, as I said, because open data is so new and moving so quickly, and the tools are changing a lot and improving a lot, the amount of data that's available is growing, it can be very difficult for these, let's say, casual geo developers to keep a handle on it. And so, our service, we hope to provide a very straight forward and simple tool that they can use to get all the benefits of open data, but also to have the benefits of an enterprise level reliable service that they can depend on, and they can accomplish whatever task they're trying to accomplish. And so, that's what we do.

Daniel:
That leads nicely onto the next question which would be what are some of the typical use cases you see for this translation service between machine and human readable addresses?

Ed:
Right. As I said, there are two ways, so it's forward geocoding which is you have an address and you want to know the coordinates. That common use case is you've somehow collected addresses from your customers, from your users, from some database and now you want to do some analysis on that, be it displayed on a map, be it maybe break that address into component pieces and try to understand how many customers do I have in region A or region B, things like that. That's probably a very common use case on the forward side.

Ed:
On the reverse geocoding side the challenge is mainly we have these devices that are recording longitude and latitude and now we want to know where that is and get information about that location. It's not just that we want to get it ... As I said, humans have a very rich vocabulary of how we talk about places. If you give me a longitude and latitude, you might want to know the exact precise address, but you might also be interested in getting that in a useful hierarchy of continent, country, state, sub state whatever that is, town, neighborhood, postal code, all these kinds of things.

Ed:
One of the challenges there of course is that different parts of the world do this very differently. Some countries have very, very precise addresses and postcodes that can pinpoint your address down to a hundred meters or whatever, other parts of the world have no addresses at all. So that's one of the big challenges we deal with, and actually it's not just even about human made addresses, I mean people want to know bodies of water, oceans, things like that. So there's a lot there. And of course, when I say humans have a rich vocabulary for talking about location, we have this hierarchy of places which may be more or less comprehensive in different parts of the world, but of course humans speak many different human languages, so we do that in all kinds of different languages as well.

Daniel:
It just occurred to me when you were talking about that, that in longitude and latitude we can be very, very, very precise and come down to millimeter accuracy, but I guess we could just keep going if we want to, just keep adding more digits, if that would make sense that's another question. But it seems to me, in terms of addressing we come down to almost like street level, house, property and that's it. Could you imagine a time where we'll be able to address people in a, I'm on this street, at this number, in my house, standing in front of my desk?

Ed:
Yes. Definitely. You're correct, that's certainly possible with longitude and latitude just by adding more decimals. There are a whole bunch of [inaudible 00:14:21] that are working on that challenge of what you described, a more precise location. So there are various ... one well known one is What3Words that has been getting some traction over the last couple of years. There are a couple of other ones as well, and Google has one called plus codes. Each of these systems has their pros and cons. They're also addressing slightly different things.

Ed:
It's interesting because another reason this has become so big is delivery services. So now with E-commerce more and more things need to be delivered to different places. So in logistics if a driver ... if you give someone an address but that's a big building and if the driver wastes five minutes trying to find the entrance, that adds up and costs a lot of money. That's one of the reasons a lot of these services have come about now, to try to improve logistics. And of course, you have people who, for whatever historic reason they live at an address that's hard to find, or sometimes if you live at the corner of two streets and your address is on one street but it's not marked there, it's marked on the other street and so the driver can't find it [inaudible 00:15:42]. You have all kinds of crazy stories like this. Or a house on a farm out in the middle of nowhere, things like that.

Ed:
And actually, one of the services that we provide under our API is what we call annotations. So when you send us coordinates or an address we give you back the opposite, but then we also add a bunch of data. So for example, we add the What3Words code, we add things like what time zone is this. Lots of information about that country like what currency did they use there, all this kind of thing so that you as a software developer, you don't need to dispend the time and effort to do that. Many of these calculation, I mean it's not difficult to figure out, given the longitude and latitude, which time zone is it.

Ed:
But as I said, now we have more and more of these casual geo developers who ... something that for a geo expert is a 10 minute project, for this other guy ... if you're not familiar with the pleasure and pain of time zones, you can waste a lot of time trying to figure all that out and understand the implications and all that kind of stuff.

Daniel:
I think, is it software developers, they need all the help they can get, because consumers are expecting more and more. And especially in terms of geo, like of geospatial, of location, we're expecting that things are seamless, we're expecting that everything we can do outside in terms of geolocation and routing and navigation, that we can do inside. We're expecting that my phone knows where I am and switches the time zone accordingly and tells me what language and tells me what currency, and all that kind of thing. We're expecting all of that information that's attached to location just to be there.

Ed:
Well, you're absolutely right. I mean, in that regard, of course we need to give full credit to our friends like Google. I think they, with the ubiquity of Google Maps and the prevalence of Google Maps, they set a very high bar, of course, of how people expect to interact with a digital location service. Which doesn't mean it's perfect, of course. But you're absolutely right, the consumer demands keep growing continually.

Ed:
We saw it when, for example, a couple years back when Apple Maps came out and people didn't find it ... it got a lot of negative press because maybe it wasn't quite ready, or there were a few examples of where it performed poorly. And the thing is, if Apple Maps had come out just a few years before, it would have been absolutely revolutionary, we forget how quickly the technology is moving. This actually raises a good point because a lot of people ...

Ed:
As I said, full credit to our friends at Google, but there is one thing that's not good about Google, and that is that it's not open. So it's a proprietary system, if you want to use it as a developer, A, it's fairly expensive and B, you need to agree to their terms and conditions. And a lot of people don't want to do that, or they don't really want to share their data with Google, and so they think, "Well, let me use open data," and they start digging into OpenStreetMap and they'll say, "OpenStreetMap, it doesn't have every single address in the world, how can I use this? It's not good enough. I can't use it."

Ed:
We're trying to help change that perspective by, A, providing an enterprise level reliability around our service, but also what we're finding is there are many, many use cases where open data is more than good enough. I can give you one example where one of our customers was a social service for sharing videos between people. So I make a video of myself and I post it and you can see the video. And they wanted to have some context so that you can see where I am, but what we don't want is to show my exact address, because then that raises privacy concerns. So this is a case where they have the longitude and latitude where I record the video, and then they want to convert that into an address or a location description but not at a hyper level specific. They just want to say, "Ed's in this neighborhood, in this town." So that you get some context, but we don't have really the privacy concerns.

Ed:
This is a use case where open data is fantastic, absolutely fantastic. Using something like Google in that case and paying Google's high prices, but also having to agree to their terms and conditions and things would be way overkill. And so, as a result, right now we're seeing more and more use cases like this where we're able to meet their needs perfectly at a very reasonable price. And as a result, they're able to rely on open data. And then, in the medium term what we see is that as more and more companies are using open data, relying on open data, they become more interested in where does this data come from? How can we fix it when there are problems? How can we give back? How can we get involved in the community?

Ed:
And so, we see that as a virtuous circle that step one, people use open data, then they learn about open data, then they start contributing to open data, and depending on open data, and then hopefully they start giving back to the community so that open data gets better and more people start using it, and so on and so forth. So that's really what we're doing.

Daniel:
I think most people listening to this podcast can really relate to that around open data and data access, and how important it is. And how much we've gained with the likes of OpenStreetMap, what an amazing project it's been for us, really for everyone in the community. But just getting back to what you said there about open data being good enough, and what you were talking about there I think is, in terms of accuracy, like lots of times we don't need that hyper accurate data or hyper accurate location.

Daniel:
Don't even need it, and maybe it's not even desirable because we want to protect things like privacy, but it would be enough to say that, "Hey. Ed is in London, and he shared a picture of something, something, something." We maybe don't want that accuracy, but we want all the stuff that comes with it, we want that personalization, we want that relationship to the place.

Ed:
That's right. First of all, I also don't want to create the wrong impression, there are many parts of the world where OpenStreetMap specifically and open data in general is the best data available at any price. And there are other parts of the world, like in Northern Europe where the OpenStreetMap communities have been very active for a long time and the data is very good, and it's probably the equal of commercial services.

Ed:
I think this is a trap many engineers or technical people fall into, into thinking, it's always, "It's going to get more precise and better and faster, and how can I improve the functionalities?" It's just all about getting the data more and more precise, more and more precise. And they miss the point of how is this data actually going to be used? And so, there are many, many use cases for which OpenStreetMap and open data are absolutely more than adequate even at the current level, and that's our customer base.

Ed:
I can give you another example on the reverse geocoding side. One of our customers is a car rental service, and so in each car they have a tracking device that's recording where the vehicle is. Because of maybe insurance reasons or things like that, when you rent the car you have to say are you going to leave the country. So for example, I live in Barcelona, we're not very far away from France, and if I rent a car here I have to pay a bit more if I want to drive into France. So at some point they need to determine did the car go to France or not?

Ed:
For someone with a GIS background this is trivial. This really, really trivial to say, "Here are the boundaries of France, give me the coordinates, let me see if any of those coordinates fell inside the boundaries of France. This is not complex thing, but that's the use case that they need. They don't need all the overkill of a hyper complex system, and paying high prices for someone who's like, "We have ultra precise data that we're updating every single day," or whatever. That's way overkill for that use case.

Daniel:
Yeah. Absolutely. And again, I think we're talking about missing the point a bit in terms of the geospatial industry. Who are we building these things for? Who's going to use them? Who's it going to be relative to? What is good enough, what is precise enough? Instead of getting bogged down, and it has to be the best of the best of the best and the most accurate possible. Maybe it's fine, maybe it's even more desirable to have it like this, at this level of accuracy.

Ed:
I know there are a lot of people working on the indoor mapping space, and how can I get it down to centimeter level accuracy, and I guess there are definitely use cases where that's highly valuable and needed and people are willing to pay for that type of service. But, I think there are many, many other use cases where that's not needed at all. And so, if they can get something that meets their need at a highly affordable price, there's a market for that.

Daniel:
Yeah. We talked briefly before about machines and the greater number of computers out there in the world collecting this data, and I think that's going to lead to more and more tracking. And tracking, probably is going to be like, we're going to say, "We'd like to track everyone all the time because we'd like to provide the personalized experience." Do you think that privacy is going to win the day? That we're going to say, "Hey. We don't actually want our experience to be that personalized. I don't really want Google to know that I'm standing in my home office in front of my computer right now. It's enough for them to know that I'm on this street." Where do you think the ball's going to land in terms of that in the future?

Ed:
That's so hard to say because we have a couple pieces there, we have the technology and then we have the cultural piece of what is acceptable to track, and then you have the, I guess, legal piece of what's actually allowed to be done. Certainly on the technological side, the technology keeps getting better and better. The cost of tracking, the precision of tracking, it used to be you only had fixed line internet, now you always have high speed internet all the time, be that via WiFi, via mobile networks. Certainly the technological capability will be there to track things much more.

Ed:
Now we've moved then, into the cultural piece of what should we track, what do I want to track? You have cases where you say, "This is horrible, we should never track people." We don't want to build systems that know where people are all the time. But then, on the other hand, you have cases where someone says, "I want to know where my kid is. I want to know where my child is." Actually, it's funny because one of our customers, they do ... it's a dog walking service. So someone will walk your dog for you if you're traveling, or you're at work or whatever. And so, they put a tracking device on the dog, so that you can ... on their apps, you go, "Where's my dog right now? Did he go to Park A or Park B with the dog walker?"

Ed:
That's a use case where you think, "Okay. That makes sense," right? Then there's another piece on that, of course, any type of cultural thing like that, of should you be able to know where your spouse is? Should you be able to know where your kid is? This gets into culture, and cultures around the world have, obviously, very different perspectives on that and what is acceptable and what is normal. And the thing that might be normal in one place is abhorrent in another.

Ed:
I think ultimately the solutions will have to be ... I think there are things that we can do on the technology side. I can remember in, you may recall there was this project about 10 years ago, or maybe even longer now, from Yahoo!, this service called Fire Eagle that was attempting to ... It was way ahead of its time, but the idea was you would have one service that would be kind of, I think they called it a location [inaudible 00:29:04]. You would feed your location information into that and then with some very easily understandable levers and dials say, "This service, it should know exactly where I am. This service, I just want it to know what country I'm in." Giving consumers control over who gets this information and at what level of granularity.

Ed:
There are a lot of challenges there with that in terms of usability, and do people really understand what information they're sharing, who's getting, and what it means? I think one good thing is, not just with location privacy but with this topic of online privacy, be it through GBPR or be it all the stuff with Facebook or whatever. Obviously it's become a mainstream issue over the last couple of years. I don't think we've solved it, but at least now we're having the discussion about it as societies, and I think ultimately that will be reflected in legal ramifications. So GDPR is one example of that already.

Ed:
But cultural things move very slowly. It takes a long time for people to, as a society, to understand new technologies, understand the implications of those new technologies. I really think this is going to be a journey and something we measure in generational time frames.

Daniel:
I think you're absolutely right there. I'm a strong believer in the idea that we, in the geospatial industry, people that have worked with maps, GIS professionals, we have a big job ahead of us in terms of communicating what we actually do and how it fits into things, and making sure that we take the user with us. I think we're really, really, really good at building networks, and building applications, and building systems, and coming up with great ideas of things we can do. But, I think we haven't been very good at communicating those ideas out to the end user, "Hey. This will help you because of ..." And really looking at them and saying, "What's the needs here?" Because technology, it seems to be running away from most of us, really.

Ed:
Well, I think obviously the technology will keep getting better and more powerful. But, if you look at some of the companies that have had major success over the last couple of years, a good one to study is Apple. Apple had this huge breakthrough with the iPhone. It wasn't because of any particular amazing technology on the iPhone, it was because they were the first company that made the smartphone simple to use. You know, one button, the concepts of apps was very clear, they had an app store where the apps have to be approved so you have some level of quality control. And it just works, it just works.

Ed:
And so, as a result they're now one of the biggest companies in the world. I think we need companies and organizations and thinkers who are going to apply that kind of concept to the challenge of privacy. Of how can we create interfaces and tools around location data, around other types of data, that are going to be easy enough for consumers to understand, to interact with, so it just works?

Daniel:
Yeah. Of course, of course. That's why it's so difficult for people to solve. Hey, I've just got a couple more questions before we have to say goodbye to each other. I can see that we're slowly but surely running out of time. I was wondering if you had some insights to, what's the biggest thing or the biggest challenge facing your industry at the moment, or maybe within the next five years? What things can you see on the horizon, like, "Oh. That's going to be difficult to solve"?

Ed:
Well, I think the [inaudible 00:33:04] of technical complexity. There's so many things now that are possible and that's led to a huge explosion in people innovating in the space. I mean, if you look at a space like indoor mapping, there's so many startups now working on this with all kinds of different technological solutions, and some of them using beacons, some of them using visual recognition. All kinds of different ways to try to do indoor mapping. And the end result is just a very, very cluttered and crowded field.

Ed:
When I talk to people about it they're like, "It's overwhelming. There's too much choice and it's too hard to understand." You have these technological explosions where so much innovation happens, and then we need to simplify it again and agree on standards, agree on best practices. You just need the time also, for people to become more familiar with it and things like that. And then, as a result winners come out of that pack. But I think right now we're in period of we're still in, not just indoor mapping, but if you look at data visualization there are all these companies now that will help you take your data and put them on a map and draw insights from them. And not small companies, these are companies that have raised big funding and things like that.

Ed:
But, I just think it's too cluttered of a space, people get lost. The person who says, "Okay. I have some data, I want to understand it." They're overwhelmed with offerings right now. It's probably the same around concepts like augmented reality and things like that. There's so much happening, and now I think it needs to back into a ... That level of innovation will keep happening, but we need to agree on some standards and some best practices and some clear winners need to emerge, I think.

Daniel:
Yeah. I completely agree. I think probably we don't even need to agree on the standards in terms of file formats and APIs and things like that. But I think, if you look at Google Maps for example, what they did for the consumer, for the everyday person, was say, "Hey. This is possible. This map can be fast, it can move around, and you can do these things there." And by doing that they really set the standard for everybody else because they were the first to do it, and they were the clear winner. Even in terms of the Mercator projection, that popped up all over the place, and I wouldn't mind betting that a lot of that was because of we were so used to seeing it on the standard web map which was Google for a long time. They really set the standards and they forced everybody else who was making web maps to really lift their performance level and service level.

Ed:
Absolutely. Absolutely. I mean, clearly that was the watershed moment. I can remember back in 2005, 2006 when Google maps came out. Actually, I was living in London at the time and I started a company that we did real estate search with. The key feature on day one when we launched was simply we can put the properties on the map, we can put pins on the map. I mean this sounds absurd today, but that was clearly a revolution at that time. It was a complete user experience breakthrough. It's pretty amazing how quickly things are moving.

Ed:
I went to a presentation a couple of months ago and someone put up a slide, so I guess it was last year in 2018, and they put up a slide showing a picture of the very first iPhone when that come out, and it came out in 2007. I can't believe that this was only 11 years ago. And now everyone has an amazing map of the world in their pocket at all times. Not just a map but aerial images. And now increasingly, things like you can see real time traffic, you can see how long's it going to take for my bus to get here, public transport. All these kind of things, I mean it's absolutely phenomenal, phenomenal how quickly that has come about.

Daniel:
I agree. And think in terms of navigation, every summer ... I live in Denmark with my family and every summer we drive south for summer holidays. Increasingly we don't even use our GPS in the car, just put the phone on the dashboard, "Hey Google, take me to this address here." It does the geocoding on the fly, gives us a route, which way to go, and tells us about traffic updates, and you take it for granted.

Ed:
Yeah. Yeah. And you get all that for free, it's [inaudible 00:37:55].

Daniel:
Yeah. We probably give away a little bit in terms of privacy I'd imagine, but the point is that it's possible and it's accessible, and I think that's amazing.

Ed:
Yeah. It really is phenomenal. Honestly, I think in many ways that we're still just at the beginning because I think right now ... Any time there's a new technology, a technological change, the human instinct is just you use the new technology but you still do it the old way. So a good example is when the internet first came out, we took the newspapers and made web pages that looked like newspapers. Basically we took that content and just stuck it into pages. And even in the terminology we use, we describe a page.

Ed:
And now, increasingly as a new generation comes through and they aren't stuck in the cultural models of the past, and they're native to the new technology they start engaging in completely new ways to do things, and completely new ways to connect all these tools. I think with location we're only entering that phase now, where you have truly digitally native location tools. The idea of having a map, of course the map is a powerful tool, but it's still basically, "Okay. We're taking the paper map and now we're sticking it on digital screen and you can move it around."

Ed:
But as we get into being able to layer more data on top of that, and things like that, we're starting to create truly new experiences. And that's really what gets me excited about the industry. But there's still a long way to go. It's a mixed thing where on the one hand it's so exciting of all the cool new things that are possible, like I said, now we start talking about things like augmented reality and all this kind of stuff. It's so exciting now it's possible. And then, at the other end of the spectrum, I see that we need to make sure the underlying infrastructure is there and solid and can be depended upon, and that software developers and end consumers can understand it and use it.

Daniel:
Ed, this has been truly an enlightening conversation for me. I really enjoyed it and I'm confident that the people listening to this podcast have also really enjoyed hearing your thoughts about the future, about the geospatial industry. It's been really great. Before we say goodbye, where can we go to learn more about you and your work?

Ed:
Right. So anyone who's interested in geocoding should, of course, check out our service that's at opencagedata.com. For me personally, they can check out my webpage which is freyfogle.com, that's F-R-E-Y-F-O-G-L-E.com. And likewise, anyone can ping me on Twitter where I'm pretty active, and my Twitter handle is also Freyfogle, F-R-E-Y-F-O-G-L-E. Yeah. Anyone out there who's interested in geocoding, interesting in geo stuff, please get in touch, and I would love to hear from you. And thanks a lot for having me on the show.

Daniel:
You are more than welcome. Thanks for coming on.

Ed:
My pleasure.

Daniel:
And that's it for another episode of the MapScaping Podcast. My name is Daniel and I really want to thank you for listening to the show today, it's much appreciated. As always, full transcripts of these episodes are available at mapscaping.com. And you are more than welcome to reach out to me on social media, on Facebook and Twitter it's MapScaping, and on Instagram it's Map_view, I'd love to hear from you Thanks very much, and we'll talk soon.


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