WifiMapper and the wonders of data collection

If you’re already enjoying of WifiMapper on your phone, you might know that it is the world’s largest Wi-Fi community – of which you are now part! Indeed, WifiMapper is powered by a database of more than 650 millions Wi-Fi points, crowdsourced by our OpenSignal and WifiMapper users. That means that just by having WifiMapper on your phone, chances are that you’re helping increase the number of Wi-Fis in the database, making the app better for yourself and your fellow users.

Wondering what kind of data WifiMapper collects and how we do it? Then you’ve come to the right place!

Foreground and background data collection

On iPhone devices, data collection only takes place when the app is open. However, things get more interesting if you’re running the app on an Android phone, as you can also collect data on the background. In fact, the app is set to do so by default, though you can disable this feature on the Settings. Simply uncheck the box next to the option to “Automatically collect Wifi locations for the WifiMapper community”.

Say yes to background data collection!

Say yes to background data collection!

What we collect

While on iOS the data that the app can collect is limited to the SSID, BSSID and location of the Wi-Fi you’re connected to, once again the Android platform offers more collection possibilities. The data obtained enables us to create your personal stats page and can lead to a better understanding of Wi-Fi usage. It includes, among other things, the lapse of time spent on each Wi-Fi, the amount of data used and environmental variable readings taken at the beginning and the end of each session (such as pressure, magnetic flux, etc.). We’re currently working on data export capabilities that will allow you to save all your data to your SD card, to produce your own analyses and maps if you so wish!

The permissions we need for this

Some of the app’s permissions might have struck our Android users as a bit odd. They are in fact requested in order to collect the data mentioned above:

  • Device ID and call information: the device ID is employed exclusively for stats purposes. Set against the dataset, it lets us know how many people have seen a certain WiFi, where the data is coming from, etc. Your call information is never viewed and indeed not even needed, but because of the way Google groups permissions together, it is impossible to request access to your device ID without also asking for you call info!
  • Photos/Media/Files: it is only the “Files” bit in this case that we care about. To include in the app the data export capabilities that we’re currently working on, we need permission to save data to your SD card — that is, your files.
  • Identity: this enables Google+ login, which in turn allows you to share with us — and the community — your comments on Wi-Fi points.

Become a champion of data collection

If data collection is your thing, you might be pleased to know that in future versions the Android app will include a leaderboard of the users who have discovered and commented on more Wi-Fi hotspots. Any edits and comments that you leave now will already add up to your total. So, better not loose any time… get out there and start collecting!

Posted in Competition, Crowdsourcing, WifiMapper | Tagged , , , , | Leave a comment

Smartphones in the classroom 3: Learning from our apps

If you’re a frequent user of our apps, chances are that you chose OpenSignal to get better coverage and find the best carrier in your area, or downloaded WeatherSignal to check the temperature and pressure before leaving home in the morning. But apart from their immediate functions, these apps are also powerful data collection instruments that have successfully been employed by academics in different fields of research – from health and meteorology to economics and emergency response systems. Furthermore, we strongly believe that these collection capabilities make of OpenSignal and WeatherSignal potential teaching tools, similarly to other apps discussed in previous instalments of this series.

Why do we think so? What can OpenSignal and WeatherSignal bring to the classroom? How can you learn from them and what can they teach you? The answers to these and other questions can be found in a short video presentation we made with Teresa (in case you were wondering what we sound like, you’ll discover that too!). It corresponds to the contents of a conference we were going to give at the ISTE 2015 Playground via a telepresence robot. Unfortunately there were some technical issues with the WiFi at their venue and we were unable to present. However, we still wanted to share our thoughts on the topic with our users, so here they are.

Are you a teacher interested in using our apps with your students? Do you have any questions or suggestions? If so, or if you want a transcript of the video, send us an email to education@opensignal.com and we’ll get back to you shortly!

Posted in Academic, Crowdsourcing, Education, OpenSignal app, Sensors, WeatherSignal | Tagged , , | Leave a comment

Why have Apple invented a tiny island?

A few weeks ago one of our iOS users had a problem with our new app WifiMapper. Looking for Wi-Fi hotspots near the Eiffel Tower, and so typing ‘the Eiffel Tower’ into the search bar, they were redirected to a the middle of the ocean – near a small Island, shaped a little bit like South America if you cut out large parts of Peru and Bolivia. The island in question is pictured below, unfortunately no other information was attached beyond the latitudinal line, no clues to the size of the island or where in the world it could be – all we knew was that a search for the Eiffel Tower returned us here and none of us recognized it.

Slack for iOS UploadOn a hunch based on similar previous bugs (and guessing that the line you can see is the Equator), we decided to turn our search to the Gulf of Guinea – to the intersection of the Equator and the Greenwich Meridian – 0,0. The centre of the world.

At first we found nothing, there’s no island at 0,0. Zooming in further, however, saw the island rise into view, as though a submarine volcano was pushing its lava spew above the waterline. We immediately went to Google Maps to see if we could find the same thing – but we could not, the screen was completely ocean blue. Our first (and perfectly reasonable) thought was that we had uncovered Apple’s secret headquarters; until we noticed the size of the island – approximately 45 yards across – far too small to hold any respectable secret headquarters.

0,0 on Google Maps vs 0,0 on Apple Maps

0,0 on Google Maps vs 0,0 on Apple Maps

So what is this fake island? What’s it doing there? One possible answer can be found in the history of cartography, as mistakes have historically been a part of mapmaking (and in other works of reference) as a form of copyright protection.

The term for this is a ‘fictitious entry’ (although I prefer the term ‘Mountweazel’, based on a fictitious entry in the 1975 New Columbia Encyclopedia’). Creating maps from scratch has always taken a huge amount of work, as Apple Maps’ far from seamless launch in 2012 demonstrated, and it is paramount for their creators to be able to protect their work from infringement. Since Maps are based on the same physical world, proving infringement of a perfectly accurate map would be impossible, but by creating entries that differ from the real world in small but unmistakable ways, mapmakers are able to prove that their work has been ripped-off. One of the most famous examples of this in action was in 2001, when the Ordnance Survey Company successfully sued the Automobile Association for copyright infringement – with AA ordered to pay out £20m in compensation.

Apple may have created their own island as a mark of ownership. Maps remain huge business, with Google’s billion dollar acquisition of crowdsourced traffic app Waze regarded as a move to protect their own dominance in that space, and it is probable that Apple Maps is full of tiny mistakes designed to make proving copyright infringement easy. If only they’d been able to use this explanation back in 2012 when the mistakes were rather more than ‘tiny’.

Posted in Known Bugs, Mobile Trends | Tagged , , , , , | Leave a comment

WifiMapper for Android

Two months ago we released WifiMapper for iOS – an app to help people find free Wi-Fi anywhere in the world, based on our crowdsourced library of over 500 million Wi-Fi hotspots. WifiMapper was a huge success, with over 100k downloads in the first month and getting featured by apple in over 100 countries, and today, we bring the app to Android.

We think the app will be worth the wait, as it has a few cool Android-only features that will allow you to stay smug and superior over your iPhone using friends. The main differences, I hear you ask? As follows:

  • Connect to Wi-Fi directly from within the app
  • See all the Wi-Fi hotspots you’ve connected to (a kind of wireless location diary)
  • See cool Wi-Fi stats, such as the total data used per hotspot and your total time connected

WifiMapper (as the name might suggest) shows you a handy map of all your nearest hotspots, and lets you look further afield as well. Whether you are running low on data for the month, in a foreign city and don’t want to ring up a roaming bill worth thousands or just want to find somewhere nice to settle down with your laptop for the afternoon, WifiMapper can help you get connected. We’ve integrated Foursquare comments to give you better contextual information about the hotspot you are considering (it’s all very well knowing you can get Wifi in two different cafes, but which has the better coffee?). By including Foursquare comments we are able to give a more rounded perspective on the hotspot venue than just the binary ‘does this location have WiFi? Y/N?’

Never be without a WiFi connection again – get WifiMapper for Android today!

Tl;dr? Watch the video!

 

Posted in Android Development | Tagged , , | Leave a comment

Wi-Fi Names: Neighbourhood Watch

This week’s Wi-Fi name blogpost looks at the concerningly passive-aggressive world of public communication using your Wi-Fi  name (yeah, grow up people) and all I have to say is bloody hell, can’t we all just love one another? As ever, these are real Wi-Fi network names detected in the last few weeks by the OpenSignal app.

1) DoNotStealFromNeighbors

Man – preachy, or what? Stop dressing this up like the Wi-Fi version of a subtweet, man up and throw out a proper accusation. This time next week I want this Wi-Fi network to be called ‘HEY. 22B. RETURN MY GODDAMN COPY OF THE NEW YORK TIMES YOU STOLE FROM MY FRONT DOORSTEP ON TUESDAY’. Stop dressing it up like you’re wireless Moses, down from the Internet with a weirdly specific commandment that could relate to ‘oh just about anyone’.

2) We Can Hear You Having Sex

This is crazy popular. In the most recent thousand Wi-Fi networks that vaguely relate to this topic there are many variations. It’s also weirdly ambiguous – why are you letting these people know this in this way? Why don’t you know them well enough to just address it with actual normal mouth-words? The creepiest one is probably ‘We can hear you having sex :)’ – why is there a smiley there? Is it ‘oh, good for you, you have a healthy, if experimental, sex life as a loving couple and I endorse this thoroughly in the context of marriage – you go guys!’ or is it ‘boy am I aroused by the muffled noises that echo through the walls when I’m trying to sleep’.

Both options give me the creeps, pick none of the above.

3) My Neighbors Suck 5G

This is an interesting one. Was there previously a ‘My Neighbors Suck 2.4G’? Could this be ‘My Neighbors Suck MY 5G’ as though the internet is penetrating through their wall in a surprisingly violating outward expression of domestic dominance? Or is it simply, yes my neighbors suck and, yes, this network is 5G so get ready for the faster speeds that implies. Ironically, of course, 5Ghz as a frequency is less good at wall penetration than 2.4Ghz. Oh how we laugh – get it together homeowner.

4) ShutUpYourKids

Won’t someone PLEASE think of the children? Hopefully they’re the kind of socially occluded children who aren’t allowed access to Wi-Fi enabled devices (I’m still traumatised from not being allowed an N64 as a kid – you know when my dad finally bought me one? LITERALLY THE DAY AFTER THEY STOPPED MAKING GAMES FOR IT. The resentment burns). Look, children are noisy, and yes, we all agree they should probably be illegal but for now they aren’t so you’re going to have to deal with it. And parents, c’mon, books are over let your kids play candy crush and snapchat with strange older men. This is the FUTURE. Learn to live in it.

5) Terry n pals- neighborhood watch

Worst paul blart mall cop sequel ever. Listen Terry, you’re not the sherriff in this town, and no, you don’t have a posse. Stop legitimizing vigilantism. No one who walks past and spots this Wi-Fi thinks you’re a badass, they think you probably spend your nights in your mancave with your ‘pals’ dressed up as batman and watching reruns of NCIS.

TERRY YOU’RE NOT 25 ANY MORE, YOU HAVE A FAMILY. YOU HAVE RESPONSIBILITIES. ACCEPT THE BALD PATCH AND WELCOME TO THE MIDLIFE CRISIS YOU’VE ALWAYS FEARED.

This goes for your ‘pals’ too.

 

Posted in Wi-FI Names | Tagged , , , , , , , , | Leave a comment

8 Crowdsourcing apps (besides OpenSignal) we love

If you’re reading this blog post, chances are you already have the OpenSignal app running on your smartphone. If so, you’re part of a community millions-strong sharing information about their mobile network’s connections. All of that information goes into databases that anyone can access on OpenSignal’s website to find the best performing networks in their area.

It’s the perfect example of crowdsourcing technology – collecting bits of information that on their own aren’t that useful, but when combined with millions of other data points paint a much more meaningful picture. OpenSignal isn’t the only app that makes use of crowdsourcing techniques, though. Here are some favorites of my own as well as those of the OpenSignal team.

  • Waze: Now owned by Google, Waze is still the king of crowdsourced traffic apps. You use it like any other navigation app, but the traffic data you see on screen is sourced in real-time from other nearby Waze drivers reporting on their traffic speeds, road congestion and even obstacles on the road.
  • Moovit: The Waze of transit, Moovit collects real-time information on trains, buses, trams or any other form of public transportation. You can use Moovit to find the closest metro station, track how close your bus is to your local stop and receive alerts warning you of system congestions and even line closures.
  • FlightRadar24: While Moovit looks to ground transportation, FlightRadar24 scans the skies. FlightRadar’s community of global volunteers collect transponder data transmitted directly by aircraft overhead, creating a database that can track the location of most of the world’s flights in real time.
  • FireChat: Developed by Open Garden, FireChat isn’t crowdsourcing information so much as it’s crowdsourcing connections. FireChat-loaded phones automatically link up through Wi-Fi and Bluetooth connections creating hyperlocal chat networks. Very useful if you’re at a music festival and want to tap into the local buzz or are a political dissident trying to communicate with your peers at a protest rally.
  • Food52 Hotline: One of my personal favorites, Hotline is a mobile and web app developed by recipe site Food52 to provide immediate answers to cooking questions. Type your question (for instance “To what internal temperature should I cook a whole duck?”) into the interface, and it will be sent off to Food52’s community of cooks. It’s not unusual to get a response within minutes.
  • OpenStreetMap: The granddaddy of open-source, crowd collaboration projects, OpenStreetMap is still going strong after 11 years, and hundreds of thousands of amateur cartographers have contributed map points to its database. You can use OSM on its own, but chances are you’ve encountered its maps in other apps and websites such as Foursquare and Craigslist.
  • Mapillary: Google’s Street View may be a handy way to pick out landmarks on a map, but its drive-by photography isn’t exactly pretty. Crowdsourced street photo app Mapillary wants to change that, asking its users to map the streets, parks and points of interest in their cities through compelling photos.
  • WeatherSignal: We couldn’t really do a blog post about crowdsourcing without mentioning OpenSignal’s own climate-mapping app WeatherSignal. By measuring temperature changes in the phone’s battery, WeatherSignal can infer local temperature even on the most basic smartphones, but more sophisticated phones with larger sensor arrays can measure barometric pressure, humidity and even magnetic fields.

Have a favorite crowdsourcing app of your own? Be sure and tell us about it in the comments section!

Editor’s note: This is a guest post by Kevin Fitchard who is a journalist covering the mobile industry and wireless technology. He most recently wrote for Gigaom.

Posted in Crowdsourcing, Sensors, WeatherSignal | Leave a comment

Wi-Fi Names: Summer Loving

In honour of the fact that I got drunk in a park and told my girlfriend that I loved her when I actually meant to say ‘pass the pimms’, this edition of our favourite Wi-Fi names will be dedicated to the fickle summer goddess of Love. There is also a reasonable chance that the heat causes my fingers to stick to the keys resulting in a line of random letters but either way you’ll read it and you’ll enjoy it. Understood? As ever, these are real Wi-Fi hotspots spotted by the OpenSignal app over the past few months.

1) LOVE IS OBEDIENCE

Oh god, what a place to start. Who let Christian Grey have Wi-Fi admin privileges? Why would anyone want this as their Wi-Fi name? Here’s the thing about obedience, doesn’t it kind of remove the equivalence that should probably be present in all NON-ABUSIVE loving relationships? Hey, Wi-Fi owner, newsflash – you know when the Episcopal church removed ‘to obey’ from their wedding vows? Yep, that’s right – 1922. NINETEEN TWENTY-TWO.

How far we’ve come.

2) I love my cats meow meow

There are only two ways to read this. 1) What kind of Wi-Fi enabled freak cats is this person breeding? 2) We have a feline-on-human hostage situation underway (I am reliably informed that this is referred to by the FBI as a ‘code mew’). If you were a cat and wanted your hostage-human to tell their fellow non-hostage humans that everything is fine, wouldn’t you hold a claw to the jugular and say “type after me: I love my cats meow meow”? I would. Send help.

3) tell my WiFi love her

HEY. THIS ONE IS ACTUALLY PRETTY GOO… I’m sorry, I temporarily thought I was in 2011 and reading a Buzzfeed list of Wi-Fi names that are also puns on the word Wi-Fi. Seriously though, get over it, no one finds these funny anymore.

4) EricaLovesMike

Actually no, I’m tired of this. It’s really bloody hot in this armchair and guess what people I DON’T CARE ABOUT YOUR RELATIONSHIPS. Stop broadcasting them over Wi-Fi, literally no one cares about your happiness or your instagram posts of Sunday morning brunch tagged with heart-shaped Emojis. I’LL CARVE HEARTS INTO YOUR GODDAMN EYES. Monogamous relationships are a historical accident – smash the system.

I don’t know where that came from, sorry guys, apparently I’m dealing with some things right now.

5) Ilovepotatose

You know what you should love? Spellcheck. Potatoes shouldn’t rhyme with comatose. Show some bloody respect if you claim to love potatoes so much. I am now torn between making loads of jokes about our Irish CEO or bad puns based on chips, mash etc.

Instead I’m going to settle for neither.

Posted in Wi-FI Names | Tagged , , | Leave a comment

The (slight) rise of _nomap

In 2010, Google streetview cars were found to be collecting data from unsecured Wi-Fi networks in 30 countries worldwide. The German privacy regulator described the issue as ‘one of the biggest known data protection violations in history’, and the scandal became worldwide news – the latest in a long line of new privacy concerns raised by developments in technology.

Google initially claimed that they were merely collecting SSIDs and Mac addresses (harmless Wi-Fi names and identifiers) – but further examination revealed that they were in fact also collecting Wi-Fi payload data (information sent over the network) from unsecured hotspots. The idea that Google was ‘listening in’ on Wi-Fi hotspot owners naturally caused a big stir and yet again served to emphasise the importance of securing domestic hotspots.

So what did Google do about this? After apologising, bringing in security consulting firm Stroz Friedberg to conduct a review, and being investigated by regulatory authorities in several countries (with the subsequent levying of fines) – they introduced ‘_nomap’. By appending ‘_nomap’ to the end of your Wi-Fi hotspots you could opt out of all Wi-Fi network tracking and means your hotspot will not be used for improving location fixes on mobile devices. Google announced ‘_nomap’ in October 2011 to surprisingly little press attention – so how widespread actually was the take-up of this solution to privacy invasion?

At OpenSignal our app records the location of mobile hotspots along with their SSIDs, meaning that we have a database of several hundred million Wi-Fi names worldwide and so are able to see the actual real-world response to Google’s solution. In total, since the publication of Google’s blog post , we have seen 23,547 distinct Wi-Fi hotspots since October 2011 (up until September 2014) with ‘_nomap’ appended. To visualize this we looked at the proportion of Wi-Fi networks seen by our app each month that have ‘_nomap’ attached, which clearly shows the response immediately after Google published their blog post – despite the fact that coverage was limited.

Screen Shot 2015-07-02 at 17.39.11

The proportion of observed global Wi-Fi hotspots that have had ‘_nomap’ appended

This graph also shows a rise beginning at the end of 2013 and continuing into 2014. Edward Snowden’s revelations about the NSA’s privacy incursions occurred during the summer of 2013 – and so it is possible that the heightened awareness about privacy issues could have led to more people taking care that Google was not recording their Wi-Fi hotspot. However, compared to the number of global Wi-Fi networks detected by OpenSignal, it is clear that the number that adopted Google’s solution is very small.

So why is this? Obviously it was deeply concerning that Google were tracking payload data – but it is not in itself concerning that they are collecting Wi-Fi SSIDs (after all, this is what we at OpenSignal do). Those technologically savvy enough to have followed the story (and continued to do so months after the initial outburst of outrage) will know that Google had publicly pledged to stop tracking Wi-Fi payload data, and so any appending _nomap to their Wi-Fi hotspots would not make any difference to that. Furthermore there is nothing private about a Wi-Fi name, it is a handle that is broadcast publicly and can be seen by anyone in range with a device capable of detecting the network. There is a great deal of evidence to suggest that individuals use their Wi-Fi network name (their SSID) as a way of broadcasting a message (often with a specific neighbour in mind) and we have collected many of these in our occasional blogs on Wi-Fi names that amuse us. Our research into the use of Wi-Fi names to declare political allegiance in Buenos Aires also supports this hypothesis.

Posted in Mobile Trends | Tagged , , , , | 1 Comment

MVNOs: Taking your mobile service virtual

If you walk into a wireless store, you’re not going to be at a loss for choice of mobile operators – far more choice than would seem possible. Whether you’re in the U.S., U.K., or Spain, there are going to be dozens of different options for your mobile phone service, even though in each of those countries there are only a handful of operators that actually run cellular networks.

How can you have more operators than networks? The answer is that most of the operators are virtual. They don’t actually own any network infrastructure. Instead they buy time on the major operators’ networks. So if you signed up for Lycamobile’s popular service to escape the tyranny of O2 in the U.K., I’ve got news for you: you’re still using O2’s minutes, text messages and data. You’re just sending your monthly checks to a middleman.

The reason why these so-called Mobile Virtual Network Operators, or MVNOs, are so prolific, is they often offer value or services that their bigger network-owning counterparts can’t or won’t offer. Let’s take a look at the ways an MVNO can differ from a major operator.

  • Many MVNOs are cheap compared to their big players, and that’s because they have relatively low overhead. They don’t do much marketing and advertising. Their customer acquisition costs are low and they certainly don’t have to maintain a capital expense budget since they have no networks to speak of. Many of them don’t even keep phone inventory, simply selling SIM cards, and those who do sell devices, often buy inexpensive Androids or refurbished phones. An MVNO’s biggest expense is paying their monthly capacity bill to their network providers, but since they’re buying their voice and data at wholesale rates, they can pass those savings onto their customers.
  • Everyone wants to be in the mobile business, whether you’re Virgin, Google or Tesco, and the MVNO route is an easy way to become an operator without burying enormous expense into buying spectrum and building a network. These vanity MVNOs also tend to be inexpensive, targeting budget minded consumers that an operator might not focus on in their main service plans.
  • You can’t make a one-size-fits-all service plan nor create a brand that encompasses all interests. One of the reasons why the major operators support MVNOs is to go after customer segments they normally wouldn’t be exposed to. For instance Virgin Mobile targets a younger demographic. Many other MVNOs like Lycamobile in Europe or Telcel America in the U.S. focus on expatriates or consumers who have a lot of friends and families overseas by offering cheap international plans.
  • New Business Models. Some of the newest MVNOs aren’t just competing on price; they’re trying to shake up the mobile industry by packaging traditional mobile service in new ways. For instance, FreedomPop in the U.S. offers a bare-bones voice and data plan every month at no charge. Google is using its MVNO Project Fi to explore metered data pricing – in the U.S. you pay $1 for every 100 MBs consumed – and a Wi-Fi first business model, which moves the majority of data traffic onto cheaper unlicensed networks. Truphone is trying to create an international data plan that allows you to cross borders without paying roaming fees.

There’s a lot to like about MVNOs, but there are some drawbacks to them as well. To keep prices low, MVNOs have to keep their costs down, which means they often skimp on customer service. Also many mobile operators put restrictions on their virtual partners. In some cases, MVNOs don’t get access to the fastest 4G networks or they’re prohibited from selling the newest devices.

And just because your MVNO is alive and kicking today doesn’t mean it will be around tomorrow. The mobile industry is littered with failed MVNOs. Some are shut down by their parent companies (Disney Mobile and ESPN Mobile), others get bought out by bigger carriers (Boost Mobile) while still others are startups that run out of money (Amp’d Mobile).

There’s definitely a lot of innovation going on in the world of virtual operators, but not all innovative ideas find a market.

Editor’s note: This is a guest post by Kevin Fitchard who is a journalist covering the mobile industry and wireless technology. He most recently wrote for Gigaom.

Posted in Mobile Trends, Understanding signal | Leave a comment

What smartphone batteries know about São Paulo’s weather

A couple of years ago, our ginger CTO (yes, that’s how he likes to introduce himself!) was having a look at battery temperature data collected by OpenSignal users when he was struck by an unexpected discovery…insert suspense music here. What James realised was that air temperature could be retrieved from smartphones’ battery temperature by means of a simple mathematical transformation. The idea was further developed in collaboration with a team of researchers including Aart Overeem from Wageningen University and the Royal Netherlands Meteorological Institute (KNMI), and Berthold Horn from MIT. Their findings can be read in the Geophysical Research Letters.

Two years on, Jan Jaap Pape, one of Aart’s students, has picked up the torch and produced an awesome bachelor’s thesis, where he put the heat transformation model to the test. For this he studied battery temperature readings from OpenSignal users in São Paulo and Buenos Aires. Although his research hasn’t yet been published, Jan Jaap has kindly allowed us to share with our readers his promising results. So here they are, in a nutshell:

  • For a city with the climatic characteristics of São Paulo or Buenos Aires, it takes around 250 battery temperature readings to get an accurate estimate of daily-averaged temperatures.
  • The model was also tried for the retrieval of hourly-averaged temperatures. Although these estimates are not as reliable as the daily ones, Jan Jaap notes that a “signal” or correspondence with the weather station data “is nonetheless present”.
  • A first attempt was made to detect spatial variability for daily-averaged temperatures, which would allow to address phenomena such as the urban heat island effect (UHI). It was observed that as the city of São Paulo was further divided in smaller parts, the more the correlation between daily-averaged temperatures from batteries and weather station data decreased. However, more weather station data would be needed to reach reliable conclusions.
  • Jan Jaap tried as well new ways of calibrating and validating the smartphone data. In fact, for daily-averaged temperature estimates in São Paulo, better results were obtained when calibration was done using data for the entire year of 2013 and validation using data for the whole of 2014 (as opposed to taking the odd days for calibration and the even ones for validation).
  • Last but not least, Jan Jaap was able to determine that estimating air temperatures using one specific type of smartphone model can be significantly more accurate compared to using data from all smartphone models. The graph below shows that the smartphone temperature curve is closely correlated to that of weather station datas when a unique series of smartphones is used (in this particular case, the Samsung GTI-series).
São Paulo temperature curves in 2014 based on the Samsung GTI-series

São Paulo temperature curves in 2014

As the team working to bring OpenSignal to your smartphone or tablet, we glow with pride when we read a paper or thesis like Jan Jaap’s. But really, it’s you as our user who should give yourself a hearty pat on the back. Thanks to you, our crowdsourced database on wireless networks grows every day: your contribution has helped improve OpenSignal and made possible WeatherSignal, CrisisSignal and WiFiMapper; and it is thanks to you that we can collaborate with researchers such as Aart, Berthold and Jan Jaap. So here here to you!

Posted in Academic, Crowdsourcing, Sensors, WeatherSignal | Tagged | Leave a comment