mobmap hands-on @ foss4g seoul 2015
TRANSCRIPT
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FOSS4G 2015 14 Sep 2015
Hiroaki Sengoku, Ph.D Satoshi Ueyama
Ritsu Sakuramachi
Mobmap: Introduction to People Flow Analysis
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Purpose and Summary
This course covers how to visualise and analysis time-series data such as trajectory data using Mobmap for beginners. In this course we will use simulated people flow data developed by The University of Tokyo, Center for Spatial Information Science(CSIS).
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About microbase inc.microbase Inc. is the company which creates micro demographic data in Japan. This company has created simulated urban data such as people flow or people life style using open data. The member of microbase Inc. aim to create micro demographic data all over the world and simulation platform such as Sim City using these data.
Real-estate values Building Age
Future PopulationMicro CensusPersonal LifeStyle
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Team mobmap
~!!
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ADENDA
SESSION 1 INTRODUCTION Mobmap Hands-on data
SESSION 2 ANALYSIS Basic operation Attribute query Spatial query Other functions
SESSION 3 PRACTICE Practice Summary
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SESSION 1 INTRODUCTION
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Where do visitors come from?
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Software used this Hands-on
Mobmap (Google Chrome)
QGISFree
Google Earth (Free)
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20131123 16:18:21 35.979504,139.827072
Visualising Moving Data, InteractivelyUsing Mobmap for Google Chrome
Satoshi Ueyama EDITORIA, The University of Tokyo
http://shiba.iis.u-tokyo.ac.jp/member/ueyama/mm/
http://shiba.iis.u-tokyo.ac.jp/member/ueyama/mm/
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Whats mobmap?
Tool for visualising and analyzing time-series data (ex.GPS log) Show animation for time-series change on Google Maps Windows, Mac, Linux supports
1500,1,1,1998/10/01 06:00:00,139.9249985549,35.7318406842,2,7,4110309,14,97,33,,973700,1,1,1998/10/01 06:00:00,139.9123053021,35.753511987,1,10,4112107,10,97,33,,977300,1,1,1998/10/01 06:00:00,139.9132597066,35.7134959947,1,7,4114009,8 ,97,40,,97
5500,1,1,1998/10/01 06:00:00,139.9374260851,35.7387718937,2,12,4113004,14,97,32,,979500,1,1,1998/10/01 06:00:00,139.9268670539,35.6868715236,1,2,4115011,12,97,26,,979700,1,1,1998/10/01 06:00:00,139.9238668934,35.6892555155,2,6,4115016,14,97,32,,9711400,1,1,1998/10/01 06:00:00,139.9293917865,35.6808909812,1,6,4115107,9 ,97,36,,9711800,1,1,1998/10/01 06:00:00,139.9077829215,35.6792209637,2,6,4115202,14,97,21,,9710100,1,1,1998/10/01 06:00:00,139.9298447577,35.684551261,1,1,4115014,12,97,26,,97
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Whats mobmap?
Input Output
time-series data(CSV)Route data(KML)
Movie(MP4)Mesh data(CSV)
Polygon data(KML)
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Install
Install Google Chrome (if you don't install it ) Search "mobmap2" in Chrome Web store
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Application start
Open a new tab of Google Chrome Open Google Chrome launcher
Open Mobmap
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Target area
Whole Tokyo (except for an isolated island
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Simulated People Flow data
Kansai, Nakagyo version also have been released.
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Data used this hands-on
Simulated People Flow dataas time-series data
2013-07-01.csv 2013-07-07.csv2013-10-07.csv 2013-10-13.csv2013-12-16.csv 2013-12-22.csv
2013-07-22.csv 2013-07-28.csv2013-09-16.csv 2013-09-22.csv2013-12-24.csv 2013-12-29.csv
2013-08-08.csv 2013-08-11.csv2013-09-16.csv 2013-09-22.csv2013-12-24.csv 2013-12-29.csv
Metropolitan
Chukyo
Kansai
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Data used this hands-onCommercial accumulation statisticsPolygon data
ca_2011_13.kmlca_2011_23.kmlca_2011_27.kml
TokyoNagoyaOsaka
Stay population data(pstay)
pstay_sample.csv
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Practice dataSimulated People Flow data
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Practice dataSimulated People Flow data
Simulated People Flow data is made from geo-tagged Tweet data(presented by Nightlei Co., Ltd.)
I'm at Ramen Jiro Meguro shop (Meguro-ku)
139.70714271068635.6341373645078
ex)
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Practice dataSimulated People Flow data
This data is created as following estimation and interpolation methods from geo-tagged Tweet data.
Home estimate
Stay time estimation
Path interpolation
Home place is defined as a city and district which users have frequently checked in on morning and a holiday. Finally, the place is determined at random in the city.
Virtual stay time is set in advance per category of the check-in (movie, amusement, etc)
Paths are interpolated based on the places between check-in places using road data (cooperation: Hiroshi kanasugi, People Flow Team at Tokyo University CSIS)
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Raw geo tagged tweet data on a map (without the interpolations)
Step1
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Home place and stay time are given to geo tagged tweet data according to the check-in on map (night-time).
Step2
Virtual stay time per about 250 check-in place category
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Paths are interpolated (only in the road) for creating Simulated People Flow data per 5 min using INFORMATION PLATFORM FOR PEOPLE FLOW ANALYSIS by the university of Tokyo CSIS.
Step3
"STUDY OF INFORMATION PLATFORM FOR PEOPLE FLOW ANALYSIS IN URBAN AREA", the 36th Japan Society of Civil Engineering information use technology symposium, pp.111-114, 2011 about Yoshihide Sekimoto, your Satoshi Usui Hiroshi kanasugi, Yusuke Masuda,
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Practice dataSimulated People Flow data
id sex date lat lon category1 category2 mode category
105 male2013-07-01 22:10:39 35.71899231 139.31707368 MOVE
105 male2013-07-01 22:15:39 35.71513008 139.31903984 MOVE
105 male2013-07-01 22:20:39 35.71300252 139.31492206home arrival MOVE 8
105 male2013-07-01 22:25:39 35.71483377 139.31029481
arts_entertainment Art Gallery MOVE 4
105 male2013-07-01 22:30:39 35.71591093 139.30722089home arrival STAY 8
1071male2013-07-01 00:00:00 35.72355807 139.73582609home departure STAY 8
Following four attributes are necessary for using Mobmap. "id" (user ID), "date" (time information), "lat", "lon"
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Practice dataSimulated People Flow Path interpolation of the practice data is given only data of "MOVE" (during movement), and it is interpolated for every 5 minutes. The railroad network is not reflected by course interpolation.
Other user
Time yyyy-mm-dd HH:MM:SS
Category of the stay spot Detailed category of the stay spot
The information such as twitter id deleted it from the viewpoint of privacy protection
id sex date lat lon category1 category2 mode category
105 male2013-07-01 22:10:39 35.71899231 139.31707368 MOVE
105 male2013-07-01 22:15:39 35.71513008 139.31903984 MOVE
105 male2013-07-01 22:20:39 35.71300252 139.31492206home arrival MOVE 8
105 male2013-07-01 22:25:39 35.71483377 139.31029481
arts_entertainment Art Gallery MOVE 4
105 male2013-07-01 22:30:39 35.71591093 139.30722089home arrival STAY 8
1071male2013-07-01 00:00:00 35.72355807 139.73582609home departure STAY 8
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For people to want to play with more Simulated People Flow data
http://www.cs.uic.edu/~wolfson/html/p2p.html
http://research.microsoft.com/apps/pubs/?id=152883
University of Illinois Chicago school (around Illinois)
Microsoft Research(around Beijing)
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Practice Commercial accumulation dataPolygon data
Estimated commercial area such as downtown from yellow page which Zenrin Co., Ltd. offers by Yuki Akiyama, a researcher at the university of Tokyo CSIS.
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Practice Commercial accumulation dataPolygon data
Researchers can use it under collaborative research with the university of Tokyo from (JORAS)
Unit: Prefecture unit (all over Japan)
Time: 2010 2011
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Practice dataTransient population data
Transient population data of the stores around Yoyogi-Uehara Station (Japan) is created using crowdsourcing applications by the PStay project , a crowd souring project at micro geo data workshop.
The PStay project collects the transient population of a place and quantity of traffic, the parking number by crowdsourcing.
http://geodata.csis.u-tokyo.ac.jp/mgd/?page_id=926
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SESSION2 WORKSHOP
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MobmapBasic operation
Attribute query
Spatial query
Read data Several visualisation
Multi conditions
Line gate Polygon gata
Animation GoogleEarth
Export
Other visualisation Mesh visualisation 3D view
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Basic Operation
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Read data 1
Choose "Moving Objects" among a button forming a line in the welcome page and open the CSV file
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Read data 2
Before loading data, mobmap shows a preview of data When the data include lonlat located in Japan, the lonlat columns are
automatically selected.
You click and change a column as necessary column.
You can change a column when clicking
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Read data 3
Click "Start loading" of the lower part. Without any errors, Mobmap starts reading all data.
Start reading in
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Practice
Read Simulated People Flow data, Modify!
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Practice
Well read on map?
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PracticeDate changes
There are "Play", "Stop", "forwarding" button like a movies player. Each object begins to move when the Play button is clicked.
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Layer list
Add the layer that was formed by Read data to a list of layers of the left pane
The movable thing can replace order
Additional layer
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Layer setting
You can select detailed setting including the indication method of the layer in a list of layers.
Change order of layers Display layer Delete layer
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Read Polygon KML
Add the layer from the drop-down menu The polygon supports only KML and WGS84
sample data commercialDistricts.kml
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Read Polygon KML
Display the polygon data
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Read data 4 (application)
When you want to load an another attribute excepting basic attributes, input "a field name : data type" in the additional line.
Enter category:int
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Read data 5 (application)
To change Marker option, choose "By attribute" and change a field name in Vary by attribute.
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Labeling function
Labeling attribute data
Turn on a check and appoint Attribute
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Tail function
Add a tail depending on movement speed
Attribute
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Path Visualisation 1
Choose Target from drop-down menu of Settings
Click Settings
Click Settings
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Path Visualisation 2
A movement course is visualised by each user
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Path Visualisation 3
Change the color
Change Dark color
Check
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Path Visualisation 4
visualise people flow at a specified time
2.Drag to choose time span
1.Click
Tips: when you choose time span, press-and-hold Shift and drag, and you can get regular time.
Time span selection
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Path Visualisation 5
People flow from 10:00 a.m. to 12:00 a.m
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Attribute tableClick Data tab
Display an attribute of each person
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Attribute table
Display an attribute of each person
Change a table to display
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Practice
Read Transient population data (pstay_sample.csv) and Display it separating by color. A line called "est_pop" shows population per minute.
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Reading time series data
Were you able to display it when you changed time so that the color of the marker changed?
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Symbol size emphasis
A marker can change its color and size depending on the attribute per minute
Change Markers presets to "Large Scaling Marker"
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Attribute query
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Attribute query
enables mobmap to choose data under a specific condition
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Attribute query
Enter field name = level
category=4 Enter
1retail store (various) 2traffic 3restaurant 4entertainment, leisure 5retail store (food) 6Education 7Other 8Home
Category
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Attribute query
Choose an object in accord with a condition
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Deselecting
Deselect from an button in the screen of the top left
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High property search
||(OR)&&(AND)
In the case of plural conditions
category=4 || category = 8
OR sentenseor
AND sentenseand
category 1
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Spatial Query
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About spatial query
Deselecting
select of the polygon
select of the rectangle
select of the line gate
Choose a movement object from the select button of the upper part menu
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Basic selection
Choose data drawing a rectangle on map
Drug
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Basic selection
Only selected area are colored.
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Multiple selection
and / or can be used after selecting one objects.
or andnew
Selection mode Already selected
select other areas
andor
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Multiple selection
can choose multi area or only overlapped area
OR AND
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Invert SelectionClick
Selected area can be inverted.
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Gate function
Choose a person, a thing via a certain spot Line gate (appoint it in a segment of a line) Polygon gate (appoint it in a domain)
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Line gate application example
Extract a user of Soubu Line
Mount a gate in Shinkoiwa
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Line gate application 1
Apply to the expressway along Haneda Airport
click a line gate button
it can pull a line when drag it over a map.
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Line gate application 2
The details are coordinated by a line choice option
After pull a line, a menu is displayed by the line upper part
OK button Direction choice (up, down, both)
Bookmark of lineCancel
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Line gate application 3
35 objects was chosen
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Line gate application 4
Path visualisation also changes under the condition.
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Practice
Analyse passing traffic with a line gate function at any place.
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Polygon gate Function
Only a point inside polygons
include the trace inside polygons
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Polygon gate application 1
Choose the polygon data of the commerce accumulation data that weve read it before.
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Polygon gate application 2
The attribute of the chosen polygon is shown
choose a polygon layer in a combo box
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Polygon gate application 3
A detail menu of polygon can be shown when a line of the polygon ID is clicked
Indication of the choice polygon (in a map)
Single choiceDeselecting
Polygon gate function
After having developed the line of the table, click a button
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Polygon gate application 4
Choose only the movement object which passes a polygon by choosing a button "point + edge" or points only"
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Polygon gate application 5
Similarly, only the selected path can be visible
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Spatial-Attribute query
Possible to combine spatial query and attribute query together
Input an form
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Buffer based spatial query 11.Click
create a buffer polygon from an annotation point
2.Put a marker on map
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Buffer based spatial query 2
create a buffer polygon from an annotation point
3.Input radiuse
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Buffer based spatial query 3
Similarly, the moving objects can be caught with a buffer polygon
A buffer layer is added
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Practice
Analyse the visitor of any commerce accumulation place.
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Export
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ExportThese data can export this data from here.
Without selection, all data be exported
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KMLexport
An export button displays it on an attribute table if an object was chosen with a line gate or a polygon gate
Export button
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KMLexport
Export the movement course of a chosen movement object in KML form as a polyline
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KMLexport
It is saved by default by a name "mobmap-exported.kml"
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Coopration with other software
Be careful the attribute because only the first record is reflected If it is sex not to change in time series, there is no problem
Read QGIS
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Cooperation with QGIS
Display in QGIS. Read KML form.
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Cooperation with QGIS
It is compatible with other layers
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Cooperation with QGIS
Because Mobmap is specialized in the visualising and analyzing moving trace data, the operation of the general GIS is carried out on QGIS
Example Coordinate transformation (cases of the rectangular coordinates system plane a file)
File conversion to KML form, CSV form Space analysis, operation such as the buffering
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Cooperation with GoogleEarth
Of course the data is available in Google Earth
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Animation Export functionAdjust screen
adjust a screen for the animation export.
Please put time bars together at the time when you want to start an animation.
If animation export preparations are possible, and then click this button.
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Animation export functionChoose an output screen
choose Window of "Mobmap for Chrome"
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Animation export functioninput output information
Detailed setting of the animation to output
Output size
set it from here to raise flame
By the default setting, output a share for ten minutes in animation reproduction one second. eg: In the case of 15sec, it is 150min
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Animation export functioninput output information
Title
Display a clock
Detailed setting for Movie Export
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Animation export functionEntry the output data
Output range can be coordinated by adjustment of the screen size
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Animation export functionStart output
After setting up, start exporting movies
with "Record" button
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Animation export functionPreserve animation
Save it with "Save" button
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Check the animation
the sample movie can be seen on YouTube URLhttp://youtu.be/iAqJR-cy73M
http://youtu.be/iAqJR-cy73M
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Other visualisation
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Visualising Mesh data
2.Choose Mesh CSV and open the CSV fileNationalCensus__3JTokyo-2010.csv or NationalCensus_4JTokyo-2010.csv.
1.Click
Ex. National Population Census in Tokyo
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Visualising Mesh data
Click Cell appearance
Mobmap data change Mesh data
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Visualising Mesh data
1.Click Choose preset
2.Choose Heat (transparent)
Mesh data can separate by color
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Visualising Mesh data
Much population is red, and Little is blue
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Visualising night-time population
2.Click 3.Choose CSV File
1.Click Statiscal operation
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Visualising night-time population
Check boxes
Choose AVG
AVG function calculates average of attribute values
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Visualising night-time population
The ratio of each local night-time population are shown. The data in this hands-on doesn have the magnification factor and
completeness of parameter so the ratio is 0% largely.
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Visualising night-time population
With Person Trip data, Mesh data becomes like this
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Visualising night-time population
COUNT function just counts points inside the cell
Chose COUNT
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Visualising night-time population
SUM function sums attribute values up
Chose SUM
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3D View
Click
Watch 3D map of this data
To use this function, you have to select data in advance.
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3D View
A new window will be shown
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3D View
You can check time series variation and an abnormal movement easily.
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SESSION3 PRACTICE
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Practice
Using Simulated People Flow data, decide the target area and find the characteristic trend of the place and consider the reason. Finally, have an effective presentation using mobmap movie function.
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PresentationPlease upload a movie which you tried in practice as an animation in YouTube. After creating the movie, tell us the movie URL.
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SummaryUsing Mobmap, We learned the method to visualise and analyze GIS data with the time-series data.
This exercise provide for simulated people flow data as sample data. Also, you can handle your own data as well.
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Request of the questionnaire
http://goo.gl/bwgCcl
Please answer following questionnaire to improve Mobmap and hands on in future.
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Thanks
For creating data and this exercise, Hiroshi Kanasugi helped us to interpolate and create the Simulated People Flow data. The Simulated data is made from the geo tagged tweet data by Ishikawa, Nightlei Co., Ltd.. We appreciate them.
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Reference
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Reading mesh CSV
Only CSV is the correspondence in the current version. It is not for analysis but for drawing.
Sample data Census-MeshTest2005_3.csv
@static-mesh
@use-mesh-code 3
36533748 0
49395673 0
51394139 0
53393642 0
53393653 0
process a format as follows to display it in mobmap.The first line describe it in the first row with "@static-mesh"The second line describe "@ use-mesh-code" in the first row describe a scale of the mesh in the second row ex) In the case of the third mesh -> 3After the third line
Value (population)Mesh code
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Reading in typhoon data
It reads in the behavior of the typhoon from the website
Source: degital typhoon data
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Reading in typhoon data
Enter the URL of the typhoon page of the digital typhoonhttp://agora.ex.nii.ac.jp/digital-typhoon/summary/wnp/s/201115.html.ja
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Reading in typhoon data
Display the movement trace of the typhoon with an animation