Mobmap as a visualization platform for spatio-temporal data
Sengoku, Hiroaki
Mobmap is a visualization platform for spatio-temporal data easily and simply. This next generation GIS tools is released as a Google Chrome application for anyone. Recently, location data tend to be available to the public. This origin starts with the spread of iot devices including smartphone and open data such as aerial photos or satellite imageries. However, time series data analysis and visualisation on map tend to be unsupported by general gis software and libraries. Mobmap enables users to deal with time series location data. This presentation shows the summary and demos of Mobmap using several data examples such as simulated people flow data from geo tagged tweets and estimated building age transition data from multi temporal aerial photos, estimated future population.
 Hi, nice to meet you. I'm Hiro from Tokyo. Today, I'd like to talk about this map. Maybe you may not know this map. So far, I can hear to tell this application. Before introducing this map, let me introduce my own company. Last year, I had a PhD, then I started my own company. This company provides microdemographic data. It's like simulated personal data. Our goal is to create a real-time city. So far, I'm interested in this microdata. Today, the topic is about people flow. It's like GPS trajectory data. I mean, it's time series data. I guess, so, current post-poly doesn't support this time series trajectory data. So, we're developing this application. This is our team. So, here is the data developer for this project data. Then, here is the student. 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, then, so, visualize, and so, easily analysis people put trajectory data. Then, it works on Google Chrome, so, you can use it as a Chrome application. So, you can use it on any, sorry, Windows, Mac, and Linux. Then, let me show some demo. So, this clock shows, it's about 2 a.m. Then, people are now sleeping. So far, so, doesn't move. Then, so, in morning, so, people get up. Then, so, starts to move. Then, each color shows, so, a transportation mode. For example, this blue color shows train, then yellow color shows, so, bicycle, like that. Then, so, to make this movie, so, generally, you need to make so many data and analysis too, but using MoMap, so, you can just make it easily. This is MoMap. By the way, Kuchu, right? It's hard to see. She said she wanted to see your face. Oh, cool. I didn't notice. Thanks. Then, so, how to use it? 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 Rancher. Then, just click a button. You can open MoMap. Then, this MoMap supports, so, mainly, so, trajectory data. So, it's, MoMap is a CSV. And also, if you want to calculate, so, I mean, sum up into, so, mesh, so, you can also import the mesh and other polygon data. Then, after that, so, you can export, so, analyze CSV or the root data of the movie. Okay. Then, so, what MoMap? So, why are we, so, start to create MoMap? So, it's our awareness. Then, maybe you have, you may have an experience, like, when you create this time, this data. So, you need to, so, gather each, so, separate different years data. Then, you need them. Then, in my case, so, I gather data and, so, export using Glass. Then, export so many PNG, imagery. Then, after that, merge it as a, a, defanimation. It's so, 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, future population. Then, so, the year has, has gone. Then, people start to decrease. Then, you know, this is a defanimation 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, more, more time. Look. So, it's a, a moving object. Then, this is a, a, made from, so, a, PNG data. Then, this, all made per one minute. Then, so, merge, and merge, and merge. Then, we can get it. But, so, is it, do, do you get, do you think? So, will it be popular? No. It's so hard. So, why? So, we guess, we need, so, time series, analysis application. Then, so, time series data tends to, tends to, tends to increase, so, gradually. So, why? So, in the, in the near future, time series data is, so, common, so, standard data. Then, with us, this kind of application, we can survive. You know? So, we start this move up project. Then, so, you know, this move up support, 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 an interest, please download now, then play it. Then, this is a move up, so, interface. It's like a movie player. So, if you, so, 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, then, so, import the data. I'm just looking for, then, so, now, I choose CSV data. Then, if you choose, so, move up needs four attributes. So, pass on ID and time stamp, that's wrong. Then, so, if you need more data, you can add, so, so, attribute, freely. 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. 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. 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 the, so, people's trajectory, we can get a, so, more, a great, a great, so, avant-planning. Then, so, this is a one, so, one function. This is a, so, gate function. It's like a special selection. If the people go through, then, like this, we can just go along. 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. Then, look, currently, so, it's a 6am. People are getting, getting up. Oh, sorry. It doesn't work well. Then, so, each color shows, so, commuters line, mainly commuters line in Tokyo. Then, so, it's a rush hour, then, finish. Then, now, so, lunchtime, you can just movement. 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. You can, you can, you can get it. And also, this also shows a commuters train. Then, so, this blue line shows a train. Then, as a, this, red color shows other transposition mode. So, it's easy to get, so, people's transposition mode, too. Then, also, it has a pass visualization, too. Let me show. So, I've loaded, so, data. Then, so, when we'd like to show the network, just click this. Then, we can get this pass line. And so, let me show some sample. When, so, here is a Haneda airport, you know, international airport. Then, so, for example, if we'd like to know how many people and where, as people come from, just draw a line this. Then, you can know, so, the people's home place. Like this. People are mainly from, so, this area. Oh, it takes more time. Not completely. Like this. And, so, it has another, so, special query, too. So, you can select a moving object using a polygon data or other format. And, also, it has a attribute query. If you input some, so, sentence, you can also select it. Then, after that, so, if you'd like to export this line, so, you can just, just, so, click export button. You can create network data as a KML. Then, also, so, you can export, so, animation. So, for example, animation button is here. So, click this. So, the video menu appears. Then, choose a window. So, the video window appears. For example, if you'd like to name this movie, so, Tokyo, Tokyo, Torip. So, you can easily start to create. Then, now, so, rendering now. Very simple. Then, so, let me show. These are now, so, current, so, research team. It's a, so, visualize abnormal event. Then, general speaking, if you, so, detect abnormal event, so, I mean, abnormal detection, so, you mean, you may try some, so, data mining tools. Then, but, it's hard to, so, visualize. Then, in this case, if you want that, so, there are two ways. Then, let me show. So, now, I will, I will upload it, mesh data. Then, so, this color shows a night population. Then, from now, so, we, I, so, calculate, daytime population gain. So, counting those objects. Then, so, choose this moving object. Then, after that. Then, so, when I change. So, this mesh value changes, so, according to the hour. So, it has to see. Like this. Then, more detail. If you click more, so, you can see the, so, values. Then, so, if the, this value shows, it's compared with, so, night population. Then, if population goes high, it shows, it's a, so, very, different, so, place. So, with a, with night. So, like this. So, you can easily get, if the abnormal event has occurs, so, you can know. Because, that's place have a, so, high value. Like this. Then, if you click more, so, yeah, it shows like this. So, sorry, it's very small, but, so, night population is written here. Then, so, daytime population is here. This is one, two. And also, I'd like to show one more function. One more function is, so, 3D view function. Then, so, it doesn't stop. For example, so, now, I choose this line. So, here is, so, central area in Tokyo. Then, here is Hababon area. Then, now, I create a 3D map. Oh, too much. It's much, it's much. For example, like this. Yes. Now, we get a, oh, 3D map. So, it shows, so, population volume. So, per minute. Then, so, you can know. You see. Oh, it's difficult to see. So, here, this line is a ball. It's a hot-so-be-ball. It's a rush hour. Then, here, this is the big time. Now, what we can do on this? So, yeah, these are no events here. But, if you have, if you do this, some volume here, so, you can get, so, abnormal event here. This is one, too. Okay. So, this is the main, so, all map, too. Then, it can also visualize mesh data. It's also a future population. So, I showed you. So, before, the animation is very bad, but look. You can, so, easily look, so, beautiful map. Then, so, more map can import your own, so, GPS data from Android application. For example, so, this is my GPS log data. You just need this for what? Then, so, when loading, so, you can draw this trajectory and the map and export and create a movie. So, this is a, so, more map. So, please, use it. Then, the reason, so, why I came here is to tell the more map. And, so, I'm looking for, so, collaborators. So, that's it. Thank you so much. Thank you very much. Okay. We've got about three minutes for questions for Hito. That was very interesting talk. Sure. There's some questions out there. Anybody? Yes, please. Microphone. And then, you. Yeah, I see that you can detect the abnormal. I mean, the outlier. So, in my opinion, the abnormal or outlier should be with different confidence because sometimes it's noise, sometimes it's not. So, do we have some attribute to description confidence? I mean, maybe it's a probability to detect its abnormal or something? Oh, it doesn't support probability. It just shows the map. So, I mean, so. So, you mean we can make a judgment by ourselves? Yes, yes, by my, our eye. Okay. Good question. I think we've got. First of all, this is very interesting. Thank you so much. And I think I missed one. What is the abnormal, abnormal event in that case? For example, so if the typhoon and the earthquake and other so as more fetchable as occur, then so people get us then so. So, sometimes we just know we would like just know so. So, abnormal events so easily but so generally abnormal detection need more data and more analysis tools but we can just know or it has one note by our eyes. And you show the very big popularities of movement. The thing is how you can track those data, 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 power five years or 10 years. Then we are estimating pass interpolation using PZ routing then so creating that kind of moving object. So, is it there? The thing is, I'm not sure about this 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? Okay, so there are several ways. So one way is so collaborate with a different company. Then they, you know, they had so many data. This is one case and one more case is from government so we just collaborate with so big data holders. So just we didn't together. Yeah. Yeah, that was my question too about the source of the data but that mostly is cell phone. Yeah. So, how? Gotten, right? Okay, we are right at the break time. I think there's going to be some parties and things tonight. So I think we can stop right here. A big thanks to all three speakers. I think 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. Thank you. Sorry, let me tell one more. So if you have an interest, so two days ago we had a hands on then so we upload that document on here. So if you have an interest, please access to here. Thank you.
 Hi, nice to meet you. I'm Hiro from Tokyo. Today, I'd like to talk about this map.  Maybe you may not know this map. So far, I can hear to tell this application.  Before introducing this map, let me introduce my own company. Last year, I had a PhD, then I started my own company.  This company provides microdemographic data. It's like simulated personal data.  Our goal is to create a real-time city. So far, I'm interested in this microdata.  Today, the topic is about people flow. It's like GPS trajectory data.  I mean, it's time series data. I guess, so, current post-poly doesn't support this time series trajectory data.  So, we're developing this application. This is our team.  So, here is the data developer for this project data.  Then, here is the student. 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, then, so, visualize, and so, easily analysis people put trajectory data.  Then, it works on Google Chrome, so, you can use it as a Chrome application. So, you can use it on any, sorry, Windows, Mac, and Linux.  Then, let me show some demo.  So, this clock shows, it's about 2 a.m. Then, people are now sleeping. So far, so, doesn't move.  Then, so, in morning, so, people get up. Then, so, starts to move.  Then, each color shows, so, a transportation mode.  For example, this blue color shows train, then yellow color shows, so, bicycle, like that.  Then, so, to make this movie, so, generally, you need to make so many data and analysis too, but using MoMap, so, you can just make it easily.  This is MoMap.  By the way, Kuchu, right? It's hard to see.  She said she wanted to see your face.  Oh, cool. I didn't notice.  Thanks. Then, so, how to use it? 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 Rancher.  Then, just click a button. You can open MoMap.  Then, this MoMap supports, so, mainly, so, trajectory data. So, it's, MoMap is a CSV.  And also, if you want to calculate, so, I mean, sum up into, so, mesh, so, you can also import the mesh and other polygon data.  Then, after that, so, you can export, so, analyze CSV or the root data of the movie.  Okay. Then, so, what MoMap? So, why are we, so, start to create MoMap?  So, it's our awareness. Then, maybe you have, you may have an experience, like, when you create this time, this data.  So, you need to, so, gather each, so, separate different years data. Then, you need them.  Then, in my case, so, I gather data and, so, export using Glass. Then, export so many PNG, imagery.  Then, after that, merge it as a, a, defanimation. It's so, 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, future population. Then, so, the year has, has gone.  Then, people start to decrease. Then, you know, this is a defanimation 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, more, more time.  Look. So, it's a, a moving object. Then, this is a, a, made from, so, a, PNG data.  Then, this, all made per one minute. Then, so, merge, and merge, and merge. Then, we can get it.  But, so, is it, do, do you get, do you think? So, will it be popular? No. It's so hard. So, why?  So, we guess, we need, so, time series, analysis application. Then, so, time series data tends to, tends to, tends to increase, so, gradually.  So, why? So, in the, in the near future, time series data is, so, common, so, standard data.  Then, with us, this kind of application, we can survive. You know? So, we start this move up project.  Then, so, you know, this move up support, 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 an interest, please download now, then play it.  Then, this is a move up, so, interface. It's like a movie player. So, if you, so, 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, then, so, import the data.  I'm just looking for, then, so, now, I choose CSV data. Then, if you choose, so, move up needs four attributes.  So, pass on ID and time stamp, that's wrong. Then, so, if you need more data, you can add, so, so, attribute, freely.  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. 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. 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 the, so, people's trajectory, we can get a, so, more, a great, a great, so, avant-planning.  Then, so, this is a one, so, one function. This is a, so, gate function. It's like a special selection. If the people go through, then, like this, we can just go along.  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. Then, look, currently, so, it's a 6am. People are getting, getting up.  Oh, sorry.  It doesn't work well. Then, so, each color shows, so, commuters line, mainly commuters line in Tokyo. Then, so, it's a rush hour, then, finish.  Then, now, so, lunchtime, you can just movement. 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. You can, you can, you can get it.  And also, this also shows a commuters train. Then, so, this blue line shows a train. Then, as a, this, red color shows other transposition mode.  So, it's easy to get, so, people's transposition mode, too. Then, also, it has a pass visualization, too. Let me show.  So, I've loaded, so, data. Then, so, when we'd like to show the network, just click this. Then, we can get this pass line.  And so, let me show some sample. When, so, here is a Haneda airport, you know, international airport. Then, so, for example, if we'd like to know how many people and where, as people come from, just draw a line this.  Then, you can know, so, the people's home place. Like this. People are mainly from, so, this area. Oh, it takes more time. Not completely. Like this.  And, so, it has another, so, special query, too. So, you can select a moving object using a polygon data or other format.  And, also, it has a attribute query. If you input some, so, sentence, you can also select it. Then, after that, so, if you'd like to export this line, so, you can just, just, so, click export button.  You can create network data as a KML. Then, also, so, you can export, so, animation. So, for example, animation button is here. So, click this. So, the video menu appears. Then, choose a window.  So, the video window appears. For example, if you'd like to name this movie, so, Tokyo, Tokyo, Torip. So, you can easily start to create.  Then, now, so, rendering now. Very simple. Then, so, let me show. These are now, so, current, so, research team. It's a, so, visualize abnormal event.  Then, general speaking, if you, so, detect abnormal event, so, I mean, abnormal detection, so, you mean, you may try some, so, data mining tools.  Then, but, it's hard to, so, visualize. Then, in this case, if you want that, so, there are two ways. Then, let me show. So, now, I will, I will upload it, mesh data. Then, so, this color shows a night population.  Then, from now, so, we, I, so, calculate, daytime population gain. So, counting those objects. Then, so, choose this moving object. Then, after that.  Then, so, when I change. So, this mesh value changes, so, according to the hour. So, it has to see. Like this. Then, more detail.  If you click more, so, you can see the, so, values. Then, so, if the, this value shows, it's compared with, so, night population. Then, if population goes high, it shows, it's a, so, very, different, so, place.  So, with a, with night. So, like this. So, you can easily get, if the abnormal event has occurs, so, you can know. Because, that's place have a, so, high value. Like this.  Then, if you click more, so, yeah, it shows like this. So, sorry, it's very small, but, so, night population is written here. Then, so, daytime population is here.  This is one, two. And also, I'd like to show one more function. One more function is, so, 3D view function. Then, so, it doesn't stop.  For example, so, now, I choose this line. So, here is, so, central area in Tokyo. Then, here is Hababon area. Then, now, I create a 3D map.  Oh, too much. It's much, it's much. For example, like this. Yes. Now, we get a, oh, 3D map. So, it shows, so, population volume.  So, per minute. Then, so, you can know. You see. Oh, it's difficult to see. So, here, this line is a ball. It's a hot-so-be-ball. It's a rush hour.  Then, here, this is the big time. Now, what we can do on this? So, yeah, these are no events here. But, if you have, if you do this, some volume here, so, you can get, so, abnormal event here.  This is one, too. Okay. So, this is the main, so, all map, too. Then, it can also visualize mesh data. It's also a future population. So, I showed you. So, before, the animation is very bad, but look. You can, so, easily look, so, beautiful map.  Then, so, more map can import your own, so, GPS data from Android application. For example, so, this is my GPS log data. You just need this for what?  Then, so, when loading, so, you can draw this trajectory and the map and export and create a movie. So, this is a, so, more map. So, please, use it. Then, the reason, so, why I came here is to tell the more map.  And, so, I'm looking for, so, collaborators. So, that's it. Thank you so much.  Thank you very much. Okay. We've got about three minutes for questions for Hito. That was very interesting talk. Sure. There's some questions out there. Anybody? Yes, please. Microphone. And then, you.  Yeah, I see that you can detect the abnormal. I mean, the outlier. So, in my opinion, the abnormal or outlier should be with different confidence because sometimes it's noise, sometimes it's not.  So, do we have some attribute to description confidence? I mean, maybe it's a probability to detect its abnormal or something?  Oh, it doesn't support probability. It just shows the map. So, I mean, so. So, you mean we can make a judgment by ourselves? Yes, yes, by my, our eye.  Okay. Good question. I think we've got. First of all, this is very interesting. Thank you so much. And I think I missed one. What is the abnormal, abnormal event in that case?  For example, so if the typhoon and the earthquake and other so as more fetchable as occur, then so people get us then so. So, sometimes we just know we would like just know so.  So, abnormal events so easily but so generally abnormal detection need more data and more analysis tools but we can just know or it has one note by our eyes.  And you show the very big popularities of movement. The thing is how you can track those data, 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 power five years or 10 years.  Then we are estimating pass interpolation using PZ routing then so creating that kind of moving object. So, is it there?  The thing is, I'm not sure about this 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?  Okay, so there are several ways. So one way is so collaborate with a different company. Then they, you know, they had so many data.  This is one case and one more case is from government so we just collaborate with so big data holders. So just we didn't together.  Yeah.  Yeah, that was my question too about the source of the data but that mostly is cell phone.  Yeah.  So, how? Gotten, right? Okay, we are right at the break time. I think there's going to be some parties and things tonight.  So I think we can stop right here. A big thanks to all three speakers. I think 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.  Thank you.  Sorry, let me tell one more. So if you have an interest, so two days ago we had a hands on then so we upload that document on here.  So if you have an interest, please access to here. Thank you.
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Hi, nice to meet you. I'm Hiro from Tokyo. Today, I'd like to talk about this map. Maybe you may not know this map. So far, I can hear to tell this application. Before introducing this map, let me introduce my own company. Last year, I had a PhD, then I started my own company. This company provides microdemographic data. It's like simulated personal data. Our goal is to create a real-time city. So far, I'm interested in this microdata. Today, the topic is about people flow. It's like GPS trajectory data. I mean, it's time series data. I guess, so, current post-poly doesn't support this time series trajectory data. So, we're developing this application. This is our team. So, here is the data developer for this project data. Then, here is the student. 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, then, so, visualize, and so, easily analysis people put trajectory data. Then, it works on Google Chrome, so, you can use it as a Chrome application. So, you can use it on any, sorry, Windows, Mac, and Linux. Then, let me show some demo. So, this clock shows, it's about 2 a.m. Then, people are now sleeping. So far, so, doesn't move. Then, so, in morning, so, people get up. Then, so, starts to move. Then, each color shows, so, a transportation mode. For example, this blue color shows train, then yellow color shows, so, bicycle, like that. Then, so, to make this movie, so, generally, you need to make so many data and analysis too, but using MoMap, so, you can just make it easily. This is MoMap. By the way, Kuchu, right? It's hard to see. She said she wanted to see your face. Oh, cool. I didn't notice. Thanks. Then, so, how to use it? 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 Rancher. Then, just click a button. You can open MoMap. Then, this MoMap supports, so, mainly, so, trajectory data. So, it's, MoMap is a CSV. And also, if you want to calculate, so, I mean, sum up into, so, mesh, so, you can also import the mesh and other polygon data. Then, after that, so, you can export, so, analyze CSV or the root data of the movie. Okay. Then, so, what MoMap? So, why are we, so, start to create MoMap? So, it's our awareness. Then, maybe you have, you may have an experience, like, when you create this time, this data. So, you need to, so, gather each, so, separate different years data. Then, you need them. Then, in my case, so, I gather data and, so, export using Glass. Then, export so many PNG, imagery. Then, after that, merge it as a, a, defanimation. It's so, 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, future population. Then, so, the year has, has gone. Then, people start to decrease. Then, you know, this is a defanimation 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, more, more time. Look. So, it's a, a moving object. Then, this is a, a, made from, so, a, PNG data. Then, this, all made per one minute. Then, so, merge, and merge, and merge. Then, we can get it. But, so, is it, do, do you get, do you think? So, will it be popular? No. It's so hard. So, why? So, we guess, we need, so, time series, analysis application. Then, so, time series data tends to, tends to, tends to increase, so, gradually. So, why? So, in the, in the near future, time series data is, so, common, so, standard data. Then, with us, this kind of application, we can survive. You know? So, we start this move up project. Then, so, you know, this move up support, 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 an interest, please download now, then play it. Then, this is a move up, so, interface. It's like a movie player. So, if you, so, 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, then, so, import the data. I'm just looking for, then, so, now, I choose CSV data. Then, if you choose, so, move up needs four attributes. So, pass on ID and time stamp, that's wrong. Then, so, if you need more data, you can add, so, so, attribute, freely. 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. 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. 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 the, so, people's trajectory, we can get a, so, more, a great, a great, so, avant-planning. Then, so, this is a one, so, one function. This is a, so, gate function. It's like a special selection. If the people go through, then, like this, we can just go along. 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. Then, look, currently, so, it's a 6am. People are getting, getting up. Oh, sorry. It doesn't work well. Then, so, each color shows, so, commuters line, mainly commuters line in Tokyo. Then, so, it's a rush hour, then, finish. Then, now, so, lunchtime, you can just movement. 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. You can, you can, you can get it. And also, this also shows a commuters train. Then, so, this blue line shows a train. Then, as a, this, red color shows other transposition mode. So, it's easy to get, so, people's transposition mode, too. Then, also, it has a pass visualization, too. Let me show. So, I've loaded, so, data. Then, so, when we'd like to show the network, just click this. Then, we can get this pass line. And so, let me show some sample. When, so, here is a Haneda airport, you know, international airport. Then, so, for example, if we'd like to know how many people and where, as people come from, just draw a line this. Then, you can know, so, the people's home place. Like this. People are mainly from, so, this area. Oh, it takes more time. Not completely. Like this. And, so, it has another, so, special query, too. So, you can select a moving object using a polygon data or other format. And, also, it has a attribute query. If you input some, so, sentence, you can also select it. Then, after that, so, if you'd like to export this line, so, you can just, just, so, click export button. You can create network data as a KML. Then, also, so, you can export, so, animation. So, for example, animation button is here. So, click this. So, the video menu appears. Then, choose a window. So, the video window appears. For example, if you'd like to name this movie, so, Tokyo, Tokyo, Torip. So, you can easily start to create. Then, now, so, rendering now. Very simple. Then, so, let me show. These are now, so, current, so, research team. It's a, so, visualize abnormal event. Then, general speaking, if you, so, detect abnormal event, so, I mean, abnormal detection, so, you mean, you may try some, so, data mining tools. Then, but, it's hard to, so, visualize. Then, in this case, if you want that, so, there are two ways. Then, let me show. So, now, I will, I will upload it, mesh data. Then, so, this color shows a night population. Then, from now, so, we, I, so, calculate, daytime population gain. So, counting those objects. Then, so, choose this moving object. Then, after that. Then, so, when I change. So, this mesh value changes, so, according to the hour. So, it has to see. Like this. Then, more detail. If you click more, so, you can see the, so, values. Then, so, if the, this value shows, it's compared with, so, night population. Then, if population goes high, it shows, it's a, so, very, different, so, place. So, with a, with night. So, like this. So, you can easily get, if the abnormal event has occurs, so, you can know. Because, that's place have a, so, high value. Like this. Then, if you click more, so, yeah, it shows like this. So, sorry, it's very small, but, so, night population is written here. Then, so, daytime population is here. This is one, two. And also, I'd like to show one more function. One more function is, so, 3D view function. Then, so, it doesn't stop. For example, so, now, I choose this line. So, here is, so, central area in Tokyo. Then, here is Hababon area. Then, now, I create a 3D map. Oh, too much. It's much, it's much. For example, like this. Yes. Now, we get a, oh, 3D map. So, it shows, so, population volume. So, per minute. Then, so, you can know. You see. Oh, it's difficult to see. So, here, this line is a ball. It's a hot-so-be-ball. It's a rush hour. Then, here, this is the big time. Now, what we can do on this? So, yeah, these are no events here. But, if you have, if you do this, some volume here, so, you can get, so, abnormal event here. This is one, too. Okay. So, this is the main, so, all map, too. Then, it can also visualize mesh data. It's also a future population. So, I showed you. So, before, the animation is very bad, but look. You can, so, easily look, so, beautiful map. Then, so, more map can import your own, so, GPS data from Android application. For example, so, this is my GPS log data. You just need this for what? Then, so, when loading, so, you can draw this trajectory and the map and export and create a movie. So, this is a, so, more map. So, please, use it. Then, the reason, so, why I came here is to tell the more map. And, so, I'm looking for, so, collaborators. So, that's it. Thank you so much. Thank you very much. Okay. We've got about three minutes for questions for Hito. That was very interesting talk. Sure. There's some questions out there. Anybody? Yes, please. Microphone. And then, you. Yeah, I see that you can detect the abnormal. I mean, the outlier. So, in my opinion, the abnormal or outlier should be with different confidence because sometimes it's noise, sometimes it's not. So, do we have some attribute to description confidence? I mean, maybe it's a probability to detect its abnormal or something? Oh, it doesn't support probability. It just shows the map. So, I mean, so. So, you mean we can make a judgment by ourselves? Yes, yes, by my, our eye. Okay. Good question. I think we've got. First of all, this is very interesting. Thank you so much. And I think I missed one. What is the abnormal, abnormal event in that case? For example, so if the typhoon and the earthquake and other so as more fetchable as occur, then so people get us then so. So, sometimes we just know we would like just know so. So, abnormal events so easily but so generally abnormal detection need more data and more analysis tools but we can just know or it has one note by our eyes. And you show the very big popularities of movement. The thing is how you can track those data, 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 power five years or 10 years. Then we are estimating pass interpolation using PZ routing then so creating that kind of moving object. So, is it there? The thing is, I'm not sure about this 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? Okay, so there are several ways. So one way is so collaborate with a different company. Then they, you know, they had so many data. This is one case and one more case is from government so we just collaborate with so big data holders. So just we didn't together. Yeah. Yeah, that was my question too about the source of the data but that mostly is cell phone. Yeah. So, how? Gotten, right? Okay, we are right at the break time. I think there's going to be some parties and things tonight. So I think we can stop right here. A big thanks to all three speakers. I think 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. Thank you. Sorry, let me tell one more. So if you have an interest, so two days ago we had a hands on then so we upload that document on here. So if you have an interest, please access to here. Thank you.
microdata. Today, the topic is about people flow. works on Google Chrome, so, you can use it as a Chrome application. So, you can use it on any, so many data and analysis too, but using MoMap, so, you can just make it easily. This is MoMap. analysis application. Then, so, time series data standard data. Then, with us, this kind of this is a one, so, one function. This is a, so, know, international airport. Then, so, for so, visualize abnormal event. Then, general It's also a future population. So, I showed you. data from Android application. For example, so, It just shows the map. So, I mean, so. So, you more analysis tools but we can just know or it there's going to be some parties and things