Mobmap as a visualization platform for spatio-temporal data
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| Number of Parts | 183 | |
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| License | CC Attribution - NonCommercial - ShareAlike 3.0 Germany: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this | |
| Identifiers | 10.5446/32044 (DOI) | |
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| Production Year | 2015 | |
| Production Place | Seoul, South Korea |
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Computer animation
Transcript: English(auto-generated)
00:03
Hi, nice to meet you. I'm Hiro from Tokyo. Then today, so I'd like to talk about this mode map. Then maybe, so you may not know this mode map. So far I came here to tell this application.
00:23
Then, so before, so introducing this mode map. So let me introduce my own company. So right here, I had a PhD. Then I started my own company. Then this company provide a micro demographic data. It's like a simulated personal data.
00:44
Then, so our goal is to create a semi-city, you know? We have semi-city. So why? So I'm interested in this micro data. Then today, so today's topic is about people flow. Then, so it's a like a, so GPS.
01:04
Trajectory data. Then, I mean, it's a time series data. Then I guess, so post policy, current post policy doesn't support this time series trajectory data. So why? So we developing this application.
01:22
Then, so this is our team. Then, so obviously we are not, so now it's sitting here. So here is like a square of our map. Then, I'm a developer, a data developer for this trajectory data. Then, he is just a drinking fellow.
01:42
Oh, sorry, he's a student. Then, so we are creating this MoMap project. Then, so let me show what MoMap. Then, MoMap is, so I mentioned before, so loading this GPS and the trajectory data.
02:01
Then, so visualize and so Italy analysis, people put trajectory data. Then, it works on Google Chrome, so you can use it as a Chrome application. So why? So you can use it on any, oh sorry,
02:20
Windows, Mac, and Linux. Then, let me show some demo. So this clock shows, it's a, about 2 a.m. Then, people are now sleeping. So why, so doesn't move. Then, so in morning, so people get up.
02:43
Then, so start to move. Then, each color shows, so our transportation mode. For example, this blue color shows a train. Then, yellow color shows, so bicycle, like that.
03:01
Then, so to make this movie, so generally, you need to make so many data and so analysis too, but using MoMap, so you can just make it easily. This is the MoMap.
03:22
By the way, could you turn off the light? It's hard to see. She said she want to see your face. I don't think that's a thing. Oh, cool. I didn't notice. I think, thanks. Then, so how to use it?
03:44
So you can just Google MoMap too in Chrome store. You can find it. Then, just click. Then, after that, so you will install Google Chrome launcher then just click a button.
04:02
You can open MoMap. Then, this MoMap supports mainly, so trajectory data, so it's format is a CSV. And also, if you want to calculate, so I mean sum up into a sort of mesh,
04:21
so you can also import a mesh and the other polygon data. Then, after that, so you can export, so analyze it, so CSV, or load data, or the movie. Okay, then, so what MoMap?
04:40
So why we start to create MoMap? So it's our awareness. Then, maybe you will have a, you may have a experience, like when you create this time series data. So you need to gather each,
05:01
so separate different years data, then you need them. Then, in my case, so I gather a data and so export using grass, then export so many p-energy imagery. Then, after that, merge it as a definition.
05:22
It's so hard. I didn't, I don't like it. Then, this is a case, for example, so each of the Japanese, so huge population, then so the year has gone,
05:40
then people start to decrease. Then, you know, this is a definition to create it. So I spend so many times. Then, it's just a light case. In case of, so moving object, so it need more and more time. Then, look, so it's a moving object.
06:07
Then, these are a main form, so p-energy data. Then, these are all made per one minute.
06:20
Then, so merge and merge and merge, then we can get it. But, so is it, do you get, do you think, so will it be popular? No, it's so hard. So why? So we guess we need to time series analysis application.
06:46
Then, so time series data tend to increase so gradually. So why? So in the near future, time series data is a, so common, so standard data.
07:06
Then, without this kind of application, we can't survive, you know? So we start this move up project. Then, so, you know, this move up support
07:20
this trajectory data. Then, let me show, so yeah, basic application. Then, of course, so you can download this move up to now. So if you have interest, please download now, then play it. Then, this is a move up, so interface. It's like a movie player.
07:42
So if you import the CSV data, so you can load from this, the play button. If you click this, so you can play like a movie. So let me show sample demo. Then, when you run the move up,
08:07
then, so import the data. I'm just looking for, then, so now I choose CSV data.
08:22
Then, if you choose, so move up needs four attributes. So password ID and the time stamp, later on. Then, so if you need more data, you can add so attribute, really.
08:46
Then, so now loading, but it takes so many times because this sample file has about 700 megabytes. Then, while loading, I'll show you next, okay?
09:07
Then, so what is our theme? Our theme is very simple. So where and how do people come from? And also, how many people go past the road? So you know, Japan has a big earthquake in 2011.
09:22
At that time, so we can't count how many people are damaged or not. Then, so if we can get a, so these, so people's volume, and so people's trajectory, we can get a, so more accurate, a great, so up and planning.
09:42
Then, so this is a, one, so move up function. This is a, so get function. It's like a special selection. If the people go through, then like this,
10:01
we can just draw a line. We can count the people's. Then, so we select the people's movement, and also visualize like this, so more details. So now, I just show the people movement.
10:21
Then, look, currently, so it's 6 a.m. People are getting up. Oh, sorry, it doesn't work so well.
10:47
Then, so each color shows, so commuter's line, being the commuter's line in Tokyo. Then, so it's the last hour, then finish. Then now, so last time, if that's movement.
11:02
Then, this time, so you can know. When you go to Tokyo Disneyland or Tokyo Station, so you can get, so which direction or place so people come from. Then, you can know, oh, this color, so it means he come from, so this western area.
11:22
You can easily get it. And also, this also shows a commuter train. Then, so this blue line shows a train. The other, this red color shows other transportation mode. Like this, so easily get a, so people's transportation
11:47
mode, too. Then also, it has a transportation station, too. Let me show. Now, I brought it, so data.
12:04
Then, so when we direct to show the network, just click this, then we can get this path line.
12:24
And so, let me show some sample. When, so here is a Haneda Airport, you know, international airport. Then, so for example, if we direct to know how many people under, so where does the people
12:41
come from, just draw a line, this. Then, you can know, so the people's home place, like this, people are made from, so this area.
13:03
Oh, it takes more time, not completely, like this. And so, it has another, so special query, too.
13:24
So, you can select a moving object using polygon data or other format. And also, it has a attribute query. If you input some, so sentence,
13:40
you can also select it. Then, after that, so if you'd like to expose this line, so you can just, so click export button, you can create network data as a KML. Then also, so you can export, so animation.
14:04
So, for example, animation button is here, so click this. So, the video menu appears. Then, choose a window, so the video window appear.
14:21
For example, if you direct to name this movie, so Tokyo, Tokyo Torip, so you can easily start to create. Then now, so rendering now, pretty simple.
14:46
Then, so let me show, these are now, so current, so research theme. It's a, so visualize abnormal event. Then, general speaking, if you, so detect abnormal event,
15:05
so I mean abnormal detection, so you may try some, so data mining tools. Then, but it's hard to visualize. In this case, if you want that, so there are two ways.
15:23
Then, let me show. So now, I will upload it mesh data. Then, so this color shows night population.
15:42
Then, from now, so we, I, so calculate a daytime population gain, so counting those object. Then, so choose this moving object.
16:00
Then, after that, then, so when I change, so this mesh value changes, so according to the hour.
16:21
So it's hard to see, like this. Then, more detail, if you click more, so you can see the, so values.
16:41
Then, so if the, this value shows, it compare with, so night population, then population goes high, it shows, it's a very different, so place, so with night.
17:03
So like this, so you can easily get, if the abnormal event has occurred, so we can know, because that place have a high value. Like this.
17:23
Then, if you click more, the, yeah, it shows like this. So, sorry, it's very small, but, so night population is written here, then, so daytime population is here, and here.
17:43
This is one, two. And also, I'd like to show one more function. One more function is, so 3D view function. Then, so, oh, it doesn't stop, stop.
18:14
For example, so, now I choose this line,
18:20
so here is, so central area, in Tokyo, and here is the Suburban area. Then I create a 3D map. Also, it's too much, it's too much, it's too much. For example, like this, yes.
18:46
Now, we get a, oh, this 3D map. So, it shows, so population volume, so per minute.
19:03
Then, so you can know, you see, it's difficult to see. So here, this line are bold, this line also be bold.
19:21
So, it's a nice hour, then here, this is the daytime. Then, what we can know on this? So, yeah, these are no event here, but if you have, if you do this with some volume here, so you can get abnormal event here.
19:41
This is one, two. So this is a main, so OMAP, two. Then, it can also visualize, so mesh data, it's also a future population. So, I showed you, so before,
20:00
this animation is very bad, but look, you can easily look, it's a beautiful map. Then, so OMAP can import your own GPS data from an Android application. For example, so this is my GPS log data.
20:23
You just need these four attributes. Then, so when loading, so you can add this trajectory and the map, and export, and create a movie.
20:40
So this is a, so OMAP, so please use it. Then, the reason, so why I came here is to tell the OMAP, and so I'm looking for collaborators. So that's it. Thank you so much. Thank you very much.
21:02
Okay, we've got about three minutes for questions for Hido. That was a very interesting talk. Sure there's some questions out there. Anybody? Yes, please, microphone, and then you. Yes, really, really amazing demo, yeah. I see that you can't detect the abnormal,
21:23
I mean, the outlier. So, in my opinion, the abnormal or outlier should be with different confidence, because sometimes it's not, right? So, do we have some attribute or description confidence?
21:44
I mean, maybe it's a probability to detect it's abnormal, or something? Oh, it doesn't support probability. It just shows a map, so I mean, so.
22:03
So you mean that we can make a judgment by ourself? Yes, yes, by our eye, with our eye. Okay, good question. I think we've got a. First of all, it is very interesting. Thanks so much.
22:20
And I think I missed one. What is the abnormal event in that case? For example, so if the typhoon and the earthquake and other, so a small festival has occurred, then, so people get us, then, so,
22:42
so sometimes we just know, we just know, so the abnormal event, so easily, but so generally, abnormal detection need more data and more analysis tools, but we can just know,
23:04
or it has one note by our eyes. And you showed the very big popularities of movement, the thing is, how you can track those data,
23:21
how you can get those data? In this case, this is a questionnaire, questionnaire basis data, then, so some countries, you know, investigate, so people's transportation, so five years or 10 years,
23:41
then we are estimating path interpolation using PZ routing, then, so creating that kind of moving object. So is it, you mean? The thing is, I'm not sure about this,
24:02
but maybe without people donating their own GPS information, you may difficult in getting those data from a lot of people, right? And how did you get those data, I mean, the moving data?
24:23
Okay, so there are several ways, so one way is, so collaborate with a telephone company, then they, you know, they have so many data, this is one case, and the one more case is from a government, so we just collaborate with,
24:42
so our big data holders, so just, we didn't forget that, yeah. Yeah, that was my question, too, about the source of the data, but that mostly is cellphone data somehow gotten, right?
25:03
Okay, we are right at the break time, I think there's gonna be some parties and things tonight, so I think we can stop right here. A big thanks to all three speakers. I think if there's any other questions, maybe everybody can hang around for a little bit, maybe have some question time, but otherwise we'll officially end right now, so thank you very much.
25:21
Thank you. Sorry, let me tell one more. So if you have interest, so two days ago, we had a hands-on, so we upload that document here,
25:40
on here, so if you have interest, please access to here. Thank you.
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