Keynote Lecture 6: Citizen Science, VGI, Geo- CrowdSourcing, Big Geo Data: how they matter to the FOSS4G Community
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| Serientitel | ||
| Anzahl der Teile | 183 | |
| Autor | ||
| Lizenz | CC-Namensnennung - keine kommerzielle Nutzung - Weitergabe unter gleichen Bedingungen 3.0 Deutschland: Sie dürfen das Werk bzw. den Inhalt zu jedem legalen und nicht-kommerziellen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen und das Werk bzw. diesen Inhalt auch in veränderter Form nur unter den Bedingungen dieser Lizenz weitergeben. | |
| Identifikatoren | 10.5446/32023 (DOI) | |
| Herausgeber | ||
| Erscheinungsjahr | ||
| Sprache | ||
| Produzent | ||
| Produktionsjahr | 2015 | |
| Produktionsort | Seoul, South Korea |
Inhaltliche Metadaten
| Fachgebiet | |
| Genre |
00:00
GeometrieBaumechanikBildschirmfensterKollaboration <Informatik>InformationGebäude <Mathematik>MereologieWort <Informatik>InstantiierungEreignishorizontProjektive EbeneGüte der AnpassungFolge <Mathematik>Data DictionaryGebäude <Mathematik>TermZahlenbereichFeuchteleitungNebenbedingungMetropolitan area networkInformationOffene MengeMapping <Computergraphik>BitComputeranimation
03:07
Offene MengeSchlüsselverwaltungCASE <Informatik>Wort <Informatik>EinsMapping <Computergraphik>MAPBitOffene MengeVorlesung/Konferenz
04:01
PaarvergleichRechnernetzInformationsmanagementTextur-MappingQuellcodeChi-Quadrat-VerteilungDigitale PhotographieKonvexe HülleWechselsprungTotal <Mathematik>Software Development KitSpezialrechnerExplosion <Stochastik>DoS-AttackeServerGoogolURLPasswortKonfiguration <Informatik>SystemplattformKonfigurationsraumOffene MengeLokales MinimumViewerCOMArchitektur <Informatik>SimulationSpeicherabzugKanal <Bildverarbeitung>BitfehlerhäufigkeitAbelsche KategoriePixelÜberlagerung <Mathematik>Kategorie <Mathematik>Überlagerung <Mathematik>Kartesische KoordinatenAlgorithmische ProgrammierspracheSoftwareentwicklerGraphAggregatzustandCASE <Informatik>Web SiteTypentheoriePunktInformationSystemplattformQuellcodeDimensionsanalyseGrundraumOrdnung <Mathematik>ServerComputerarchitekturPaarvergleichZellularer AutomatVideokonferenzAuflösung <Mathematik>SchlüsselverwaltungEchtzeitsystemComputerspielDreieckDifferenteMAPSpieltheorieMetropolitan area networkSymboltabelleMultiplikationsoperatorMeterMapping <Computergraphik>BenutzerbeteiligungBeobachtungsstudieElement <Gruppentheorie>Physikalisches SystemBildschirmmaskeFrequenzVirtuelle MaschineArithmetisches MittelCharakteristisches PolynomPlotterEnergiedichteData Miningt-TestInstantiierungSprachsyntheseInformatikp-BlockKollaboration <Informatik>Twitter <Softwareplattform>PixelMereologieCodeEin-AusgabeBitViewerOffene MengeGeometrieRelativitätstheoriePunktwolkePolygonComputeranimation
11:35
InformationInformationOrdnung <Mathematik>Zellularer AutomatMomentenproblemPunktDifferenteVorlesung/Konferenz
12:27
SatellitensystemTelekommunikationDiedergruppeServerMinkowski-MetrikQuellcodeOffene MengeStandardabweichungDivergente ReiheSpezialrechnerNASA World WindMini-DiscFinite-Elemente-MethodeKnotenmengePhysikalisches SystemComputerphysikGRASS <Programm>Bildgebendes VerfahrenAuflösung <Mathematik>Prozess <Informatik>PixelFlächentheorieWarteschlangeVideokonferenzYouTubeSoziale SoftwareInternetworkingE-MailGoogolSystemaufrufTemporale LogikZellularer AutomatInverser LimesDatensatzTwitter <Softwareplattform>Kerr-LösungSatellitensystemMomentenproblemHypermediaProzess <Informatik>ServerSystemaufrufMinkowski-MetrikPhysikalisches SystemProjektive EbeneStatistikCASE <Informatik>Lokales MinimumMultiplikationsoperatorKartesische KoordinatenWald <Graphentheorie>Quick-SortKollaboration <Informatik>t-TestZahlenbereichWort <Informatik>GeradeInzidenzalgebraDistributionenraumEnergiedichteBeobachtungsstudieTwitter <Softwareplattform>SprachsyntheseMAPTelekommunikationWeb SiteOffene MengePolstelleSoftwarePixelEchtzeitsystemBitWürfelInhalt <Mathematik>Digitale PhotographieE-MailInternetworkingRechenschieberSummierbarkeitMittelwertComputeranimation
19:02
DatenverwaltungHypermediaInformationsmanagementCASE <Informatik>Lokales MinimumStatistikProgrammierumgebungKlasse <Mathematik>MultiplikationsoperatorComputerunterstützte ÜbersetzungRhombus <Mathematik>ResultanteWort <Informatik>Elektronische PublikationShape <Informatik>MomentenproblemInformationVisualisierungProzess <Informatik>SystemaufrufTranslation <Mathematik>UmwandlungsenthalpieInstantiierungBitBenutzerbeteiligungDimensionsanalyseJSONXMLUML
20:45
WikiInformationsmanagementEndliche ModelltheorieTrennschärfe <Statistik>CASE <Informatik>SoftwareGrundraumProfil <Aerodynamik>Open SourceMultiplikationsoperatorOffice-PaketElektronische PublikationSichtenkonzeptDatenbankFreewareComputeranimation
22:38
Computeranimation
Transkript: Englisch(automatisch erzeugt)
00:05
Okay, so good morning to everybody. I'm a bit excited, obviously, a bit moved, a bit tired also. I know that all of you are tired, but okay, tomorrow there is the cosprint, but today is the last day for listening to presentation.
00:24
And I hope not to be too boring. I want to be very practical, presenting you some examples, some examples related to citizen science, geocount sourcing, and something related to big geodata.
00:46
I'm just presenting examples we made, mainly. I know that there are many other examples, very relevant, but I prefer to speak about things that I have done personally.
01:01
So first of all we start with this concept of citizen science that is growing in interest and importance, but the name, the term, as a matter of fact, entered in the Oxford dictionary only last year, you see June 2014, and citizen science is nothing but the scientific work undertaken by members of the general public.
01:25
So, a new word, but an old practice, because people have done that for many, many centuries, or if not centuries, years, going around, taking information and data about the world surrounding themselves.
01:47
And then there is a new name also, coined in 2007, that is volunteer geographic information. Also this one is not an old one, but now we have modernized it using new devices,
02:06
like, for instance, going around, taking data. This was a mapping party. I had the opportunity, the privilege, to be part of, last week in Osaka, about preparing people for hazard, for emergency.
02:31
What I have to do? Nothing, nothing. Okay, but probably the most relevant project of this kind was in this open street map,
02:44
and you can see how it grows, has grown in the years. These are two cities, just two cities, Milan and Seoul, starting from 2006 going to 2015,
03:02
the number of streets monitored and the number of buildings. I had the opportunity and the privilege of hosting the Phosphology Europe this year, and we decided also to practice a bit of citizen science and WGI and so on.
03:20
So, we had a mapping party, specifically related to open street map, was led by Peter Mooney, Luca Delucchi, who is here around, where is Luca? Yeah, thank you Luca for doing that. Your name is wrong. Yeah? Oh, your name is wrong.
03:44
Okay, but in any case, it's Luca Delucchi, okay, and Marco Mingini, and they teach people how to, they taught people how to work in an open street map way.
04:00
Apart from collecting data, we want also to start checking the quality of the collected data, because we are now interested also in accuracy, not only having data, but we want to have accurate data, and this is an example. We studied a procedure for comparing the open street map data with the authoritative ones,
04:27
and the procedure is all available, you can run yourself using grass, but the very good point is that the first step of the procedure is also available as WPS now,
04:43
so if you go on this website, you can run by yourself, at least the first step, and then we are going to implement the next one in the next period. But apart from the outdoor mapping, another very interesting point is the indoor mapping,
05:01
because we are going toward the personal navigation system, and therefore, in Como, we also organized an indoor mapping that was led by Nicola Doregati, the name is correct, and Ludovico Biaggi, also in this case, the only mistake is the mistake about the surname of Luca,
05:20
and if you are interested, there is a platform, an open platform, where you can collect data, helping you to collecting data related to indoor mapping. But this is not enough, speaking about voluntary geographic means also speaking of something different,
05:41
for instance the emotional maps, and so we added the opportunity of trying also this participatory sensing. What is participatory sensing? People going around and voting, scoring the quality of the life in the different parts of the city.
06:01
We were very lucky because Aotian 1 provided us such a kind of application, so we added also this emotional mapping, and you are also lucky because you can download this application both for iOS and Android,
06:24
or as you are developers, or many of you are developers, you can also download the code if you want to improve it, edit something and so on and so on. But obviously, there are many other participatory sensing applications, I just want to show you some of these applications that we developed,
06:46
an application for architectural barriers, another for cultural elements, street furniture, biodiversity, and very recently for the Osaka France, we developed this bike parking surveying, and this is the way for collecting the data,
07:04
we proposed for collecting the data is build up using ODK, and then you can download it if you want, now obviously it is focused on Osaka, but you can change it as you want, and once the data are collected, we can immediately, so in real time,
07:24
we can view the data on a participatory map on the web, and this is possible both in two dimension, or is it possible, so in three dimension, using what we call the polycrowd, poly means the polytechnic of Milan, that is my university,
07:43
the polycrowd application, this application is an application built using NASA whirlwind, and it is not only a viewer of geo cloud source data using ODK, it is really a collaborative platform, because if you login into the platform,
08:05
you can add other pieces of information or other data, to the data that other people collected before, every kind of data, so text data, or video, audio, and sound, everything you want,
08:25
and if you are interested, this is the website where you find the application with the first data collected by, where is Irofumi, Irofumi is not here, collected by him, and then if you are interested as a developer,
08:47
you can find here the platform itself in such a way that you can try to play, to play with it, add something and so on. Okay, this is the general architecture for the people that are developer,
09:01
you see it is a reader of both WMS server and ODK server, and it is based on NASA whirlwind, these are the main characteristics. But then we develop also something related to, I think that is a bit different, that is volunteer thinking, volunteer thinking means taking advantage
09:24
of the people for processing data that machines are not able to process, like for instance making classifications, VGI, Charan Venkata Chalam, who is a student of mine,
09:40
and Irene Celina, who is a collaborator of mine, they develop this game, this one that you see, that is a particular game helping us to compare two different land coverages. One land coverage is global land 30, global land 30 is worldwide land coverage
10:03
with 30 meters of resolution, which was donated last year by the Chinese government as open data to the UN, and we compare it with DUSAF, which is the high resolution land coverage in Italy, but there are some pixels in which the comparison didn't agree,
10:26
and so we asked people of voting about that. It is very simple, this game, you simply enter, then you are presented a pixel, and you look at the pixel and you decide the most suitable land cover category,
10:43
you have to watch the time, because you have to quick, the time is also involved in this game, because it was studied as game, as serious game, and then at this point you win score and badges and beat your friends, this was the idea.
11:00
So, people are looking around and they want to contribute in collecting data in order to better know the world where we live, but this is not enough, now we have something more, something more that are all the data that are running around us
11:23
and that we are producing, so like Twitter and all this kind of data that you see here, and what is important is that this data are produced by us, but as a matter of fact, we are not able to use it,
11:43
and I can assure you, they are used at the moment by companies for making money, and I say, do we agree with this? In my opinion, the answer is no, because I think that data and information belong to people,
12:04
we are the producer of this data, so we want to have this data, to study this data, not only to leave them studying this data for purpose of research about the market and so on, we want to use them in order to build up something different,
12:24
but the point is that going towards this data, we are going towards big data, and which kind of big data we know at the moment, we know very well satellites and we are able to process satellite data,
12:42
we know very well that there are sensors everywhere, and we are able, or we are trying to be able to process also in real time all this kind of data, just two very quick examples that I asked to some people that are more expert than me,
13:05
the first one is Razdaman, Razdaman is, or this Project Earth server that is, whose principal main software is Razdaman, and they are able to treat, you can see more than one petabyte of space time data cubes that are multidimensional pixels,
13:26
or another example provided me kindly by Marcus Nettler is this one, always related to satellite imagery, and you see that they were able to process 17,000 map
13:41
of more than 4 million pixels each, so a huge amount of data, but speaking about social media and telecommunication data, we are in the same trouble, you see here 10,000 tweets sent in one second, or 2,000 Instagram photos uploaded in one second,
14:06
more than 2 million emails sent in one second, so if we want to deal with this data, because our data is produced by us, we need also to be able to manage them,
14:21
because if we will be given, and we are not able to manage, this is the worst case, so I tried to do something related to that, I want to show you just one example of what we did, or one data set, the very good news is that somewhere there are some,
14:47
this kind of data available, so if you are interested, please mark this website, because here you can find some relevant data about social media and telecommunication,
15:02
this is one example, it's a Milano grid, so it means that there are data from Milan, you have two months of data, with a temporal step of 10 minutes, for every 10 minutes you have the number of incoming calls, incoming SMS,
15:20
and so on, from every province in Italy to Milan, and from Milan to every province in Italy, from every foreign country in the world to Milan, and from Milan to every foreign country, so it's a very rich data set, obviously you don't have the content of the SMS,
15:44
and we don't need, but we have at least the number, so you see what are the data, so received SMS, sent SMS, incoming calls, outgoing calls, internet connection, and geolocalized tweets, a big amount of data, and so adding this data set,
16:05
I try to do something with my students and collaborators of mine, I show you three examples, the first one is this example, in which that was built up using MongoDB Apache and OpenLayers,
16:22
and you have the example, if you are interested you can go there, I will put my slides on the slideshares, no trouble at all, as soon as I finish I put them on the slideshares, they will be available, so you don't have to write anything, so I want just to show you this example,
16:48
okay, so you can select the province, you can select the province, I don't remember this was SMS in, you can select the day you are interested,
17:01
in the two months, in this case I choose the 25th of December, which is Christmas, special day in Italy, and then you can ask about the average of telephone calls, the sum or the minimum in that day,
17:20
so you see this is the distribution of the incoming SMS, the average, or if you want to have the maximum, the maximum is also computed, these are the incoming calls from Rome to Milan, Rome and Milan are the two biggest cities in Italy,
17:42
you see there are many, many incoming calls, but I can choose another province that is a smaller one, and in this case we are expecting to have less, in fact we have less incoming calls, and obviously you can decide which kind of statistics
18:04
you want for this data, but you can also play with the time during the day, verifying how many calls or SMS there are in a certain interval during the day.
18:21
Okay, and a second example, in the second example we use something, some technology a bit different, specifically we decided to use Rastaman, Rastaman is so suitable for imagery, satellite imagery, in this case the idea was a bit different, the idea was to verify the social data
18:48
with respect to the land coverage, because in this way we have a sort of footprint of the different usage of the territory, and so here you have the second application,
19:04
with the second demo, and you can select also in this case the kind of data you want, for instance this is the call out, you can select the data, there are only data of one month at the moment, you can select the specific land coverage
19:24
class you are interested in, then you can compute some relevant statistics, maximum, minimum, average, and so on, in that class using WPCPS, and then you can also visualize the result of the processing of your data,
19:44
I go quick because you can try by yourself if you are interested, the last experiment was a bit different, it was a multidimensional visualization, I mean three-dimensional visualization, I will explain better what I mean with this
20:00
three-dimensional visualization in this special case, and then the fourth dimension that is moving in time, considering all the ten minutes, I told you that the data are collected every ten minutes, so first of all we are able to read the data from MongoDB to NetCDF, NetCDF is a scientific format, an environmental format,
20:26
like I don't know if people know it, but probably you are used in using Shapefile, NetCDF is very important in the scientific environmental world, first of all a translator from MongoDB to NetCDF, and then the web visualizer
20:45
using NASA whirlwind, so in this case is a selection of six days, the first three days you see in the pile are day without rain, the last, the first data day with rain, obviously you can cut the model
21:03
as you want, and then you can select the profiles, you can add every WMS you want, obviously, so then you can select profile and you can see the value of the data along that specific profile for the every ten minutes.
21:25
Okay, so these are just examples, obviously, what is interesting is that the importance and interest in these themes is increasing, and therefore within Geo4All, I suppose that everybody of you knows what Geo4All is,
21:46
it's a network of universities, of labs, of universities and research centers who want to make geospatial education and opportunities accessible to all,
22:01
and within Geo4All we decided to start also a thematic network that is specifically a thematic network of these themes, it's geo crowdsourcing, citizen science, free and open source software for geospatial, and I ask all of you that is interested to join this initiative.
22:24
So, thank you for your attention, and I hope to be in time.