1. FOSS4G 2015
14 Sep 2015
Hiroaki Sengoku, Ph.D
Satoshi Ueyama
Ritsu Sakuramachi
Mobmap: Introduction to
People Flow Analysis

2. 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).

3. 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

4. Team mobmap
三겹살、삼겹살、맛있다~!!

5. ADENDA
SESSION 1 INTRODUCTION
・Mobmap
・Hands-on data
SESSION 2 ANALYSIS
・Basic operation
・Attribute query
・Spatial query
・Other functions
SESSION 3 PRACTICE
・Practice
・Summary

6. SESSION 1
INTRODUCTION

7. 講習内容 商店街の来訪者はどこから来るのか？
Where do visitors come from?

8. Software used this Hands-on
Mobmap (Google Chrome)
QGIS（Free）
Google Earth (Free)

9. 2013–11–23 16:18:21
35.979504,139.827072
Visualising Moving Data, Interactively
Using Mobmap for Google Chrome
Satoshi Ueyama
EDITORIA,The University of Tokyo
http://shiba.iis.u-tokyo.ac.jp/member/ueyama/mm/

10. What s 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,,97
3700,1,1,1998/10/01 06:00:00,139.9123053021,35.753511987,1,10,4112107,10,97,33,,97
7300,1,1,1998/10/01 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,,97
9500,1,1,1998/10/01 06:00:00,139.9268670539,35.6868715236,1,2,4115011,12,97,26,,97
9700,1,1,1998/10/01 06:00:00,139.9238668934,35.6892555155,2,6,4115016,14,97,32,,97
11400,1,1,1998/10/01 06:00:00,139.9293917865,35.6808909812,1,6,4115107,9 ,97,36,,97
11800,1,1,1998/10/01 06:00:00,139.9077829215,35.6792209637,2,6,4115202,14,97,21,,97
10100,1,1,1998/10/01 06:00:00,139.9298447577,35.684551261,1,1,4115014,12,97,26,,97

11. What s mobmap?
Input Output
time-series data(CSV)
Route data(KML)
Movie(MP4)
Mesh data(CSV)
Polygon data(KML)

12. Install
• Install Google Chrome (if you don't install it )
• Search "mobmap2" in Chrome Web store

13. Application start
•Open a new tab of Google Chrome
•Open Google Chrome launcher
•Open Mobmap

14. Target area
Whole Tokyo (except for an isolated island）

15. Simulated
People Flow
data
Kansai, Nakagyo
version also have
been released.

16. Data used this hands-on
①Simulated People Flow data（as time-series data）
2013-07-01.csv 2013-07-07.csv
2013-10-07.csv 2013-10-13.csv
2013-12-16.csv 2013-12-22.csv
2013-07-22.csv 2013-07-28.csv
2013-09-16.csv 2013-09-22.csv
2013-12-24.csv 2013-12-29.csv
2013-08-08.csv 2013-08-11.csv
2013-09-16.csv 2013-09-22.csv
2013-12-24.csv 2013-12-29.csv
Metropolitan
Chukyo
Kansai

17. Data used this hands-on
②Commercial accumulation statistics（Polygon data）
ca_2011_13.kml
ca_2011_23.kml
ca_2011_27.kml
Tokyo
Nagoya
Osaka
③Stay population data(pstay)
pstay_sample.csv

18. Practice data① Simulated People Flow data

19. Practice data① Simulated 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)

20. Practice data① Simulated 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)

21. Raw geo tagged tweet data on a map (without the interpolations)
Step1

22. 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

23. 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,

24. Practice data① Simulated People Flow data
id sex date lat lon category1 category2 mode
categor
y
105 male
2013-07-01
22:10:39 35.71899231 139.31707368 MOVE
105 male
2013-07-01
22:15:39 35.71513008 139.31903984 MOVE
105 male
2013-07-01
22:20:39 35.71300252 139.31492206home arrival MOVE 8
105 male
2013-07-01
22:25:39 35.71483377 139.31029481
arts_enter
tainment Art Gallery MOVE 4
105 male
2013-07-01
22:30:39 35.71591093 139.30722089home arrival STAY 8
1071 male
2013-07-01
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"

25. Practice data① Simulated 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
categor
y
105 male
2013-07-01
22:10:39 35.71899231 139.31707368 MOVE
105 male
2013-07-01
22:15:39 35.71513008 139.31903984 MOVE
105 male
2013-07-01
22:20:39 35.71300252 139.31492206home arrival MOVE 8
105 male
2013-07-01
22:25:39 35.71483377 139.31029481
arts_enter
tainment Art Gallery MOVE 4
105 male
2013-07-01
22:30:39 35.71591093 139.30722089home arrival STAY 8
1071 male
2013-07-01
00:00:00 35.72355807 139.73582609home departure STAY 8

26. 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)

27. Practice ② Commercial accumulation data（Polygon 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.

28. Practice ② Commercial accumulation data（Polygon data）
Researchers can use it
under collaborative
research with the university
of Tokyo from (JORAS)
Unit:
Prefecture unit
(all over Japan)
Time:
2010
2011

29. Practice data③ Transient 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

30. SESSION2 WORKSHOP

31. Mobmap
Basic operation
Attribute query
Spatial query
• Read data
• Several visualisation
• Multi conditions
• Line gate
• Polygon gata
• Animation
• GoogleEarth
Export
Other visualisation • Mesh visualisation
• 3D view

32. Basic Operation

33. Read data 1
•Choose "Moving Objects" among a button forming a
line in the welcome page and open the CSV file

34. 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

35. Read data 3
•Click "Start loading" of the lower part. Without any
errors, Mobmap starts reading all data.
Start reading in

36. Practice①
Read Simulated People Flow data,
Modify!

37. Practice①
Well read on map?

38. Practice①
Date changes
There are "Play", "Stop", "forwarding" button like a movies player.
Each object begins to move when the Play button is clicked.

39. 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

40. 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

41. Read Polygon KML
• Add the layer from the drop-down menu
• The polygon supports only KML and WGS84
sample data
commercialDistricts.kml

42. Read Polygon KML
Display the polygon data

43. 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

44. Read data 5 (application)
• To change Marker option, choose "By attribute" and
change a field name in Vary by attribute .

45. Labeling function
Labeling attribute data
Turn on a check and appoint Attribute

46. Tail function
Add a tail depending on
movement speed
チェックを入れ、Attributeを指定

47. Path Visualisation 1
•Choose Target from drop-down menu of Settings
Click Settings
Click Settings

48. Path Visualisation 2
A movement course is visualised by each user

49. Path Visualisation 3
Change the color
Change
Dark
color
Check

50. 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

51. Path Visualisation 5
People flow from 10:00 a.m. to 12:00 a.m

52. Attribute table
Click Data tab
Display an attribute of each person

53. Attribute table
Display an attribute of each person
Change a table to
display

54. Practice②
Read Transient population data
(pstay_sample.csv) and Display it
separating by color. A line called
"est_pop" shows population per minute.

55. Reading time series data
•Were you able to display it when you changed time
so that the color of the marker changed?

56. Symbol size emphasis
A marker can change its color and size
depending on the attribute per minute
Change Markers presets to
"Large Scaling Marker"

57. Attribute query

58. Attribute query
enables mobmap to choose data
under a specific condition

59. Attribute query
Enter field name = level
category=4
Enter
1：retail store (various)
2：traffic
3：restaurant
4：entertainment, leisure
5：retail store (food)
6：Education
7：Other
8：Home
Category

60. Attribute query
Choose an object in accord with a condition

61. Deselecting
Deselect from an button in the
screen of the top left

62. High property search
¦¦(OR)、&&(AND)
In the case of plural conditions
category=4 || category = 8
OR sentense（or）
AND sentense（and）
category<4 && category > 1

63. Spatial Query

64. 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

65. Basic selection
•Choose data drawing a rectangle on map
Drug

66. Basic selection
•Only selected area are colored.

67. Multiple selection
•and / or can be used after selecting one objects.
or andnew
Selection
mode Already selected
select other areas
and
or

68. Multiple selection
•can choose multi area or only overlapped area
OR AND

69. Invert Selection
Click
•Selected area can be inverted.

70. 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)

71. Line gate application example
Extract a user of Soubu Line
Mount a gate in
Shinkoiwa

72. Line gate application 1
Apply to the expressway along Haneda Airport
①click a line gate button
②it can pull a line when drag
it over a map.

73. 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 line
Cancel

74. Line gate application 3
商業集積地ポリゴンデータを
読み込んでみましょう！
35 objects was chosen

75. Line gate application 4
Path visualisation also changes under the condition.

76. Practice④
Analyse passing traffic with a line gate
function at any place.

77. Polygon gate Function
Only a point
inside polygons
include the trace
inside polygons

78. Polygon gate application 1
Choose the polygon data of the commerce
accumulation data that we ve read it before.

79. Polygon gate application 2
The attribute of the chosen polygon is shown
choose a polygon layer in a combo box

80. 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 choice
Deselecting
Polygon gate function
After having developed the line of the
table, click a button

81. Polygon gate application 4
Choose only the movement object which passes a
polygon by choosing a button "point + edge" or points
only"

82. Polygon gate application 5
Similarly, only the selected path can be visible

83. Spatial-Attribute query
Possible to combine spatial query and attribute
query together
Input an form

84. Buffer based spatial query 1
1.Click
create a buffer polygon from an annotation point
2.Put a marker on map

85. Buffer based spatial query 2
create a buffer polygon from an annotation point
3.Input radiuse

86. Buffer based spatial query 3
Similarly, the moving objects can be caught with a buffer
polygon
A buffer layer is added

87. Practice⑤
Analyse the visitor of any commerce
accumulation place.

88. Export

89. Export
These data can export this data from here.
Without selection, all data be exported

90. KMLexport
•An export button displays it on an attribute table if an
object was chosen with a line gate or a polygon gateさ
Export button

91. KMLexport
•Export the movement course of a chosen movement
object in KML form as a polyline

92. KMLexport
• It is saved by default by a name "mobmap-exported.kml"

93. 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

94. Cooperation with QGIS
Display in QGIS. Read KML form.

95. Cooperation with QGIS
It is compatible with other layers

96. 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

97. Cooperation with GoogleEarth
Of course the data is available in Google Earth

98. Animation Export function① Adjust 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.

99. Animation export function② Choose an output screen
choose Window of "Mobmap for Chrome"

100. Animation export function③ input 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

101. Animation export function③ input output information
Title
Display a clock
Detailed setting for Movie Export

102. Animation export function③ Entry the output data
Output range can be coordinated by adjustment of the screen
size

103. Animation export function④ Start output
After setting up, start exporting movies
with "Record" button

104. Animation export function⑤ Preserve animation
Save it with "Save" button

105. Check the animation
• the sample movie can be seen on YouTube
• URL http://youtu.be/iAqJR-cy73M

106. Other visualisation

107. Visualising Mesh data
2.Choose Mesh CSV and open the CSV file
NationalCensus__3JTokyo-2010.csv or
NationalCensus_4JTokyo-2010.csv .
1.Click
•Ex. National Population Census in Tokyo

108. Visualising Mesh data
Click Cell appearance
•Mobmap data change Mesh data

109. Visualising Mesh data
1.Click Choose preset
2.Choose Heat (transparent)
•Mesh data can separate by color

110. Visualising Mesh data
•Much population is red, and Little is blue

111. Visualising night-time population
2.Click 3.Choose
CSV File
1.Click Statiscal operation

112. Visualising night-time population
Check boxes
Choose AVG
•AVG function calculates average of attribute values

113. 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.

114. Visualising night-time population
•With Person Trip data, Mesh data becomes like this

115. Visualising night-time population
• COUNT function just counts points inside the cell
Chose COUNT

116. Visualising night-time population
• SUM function sums attribute values up
Chose SUM

117. 3D View
Click
Watch 3D map of this data
To use this function, you have to
select data in advance.

118. 3D View
A new window will be shown

119. 3D View
You can check time series variation and an abnormal movement
easily.

120. SESSION3 PRACTICE

121. 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.

122. Presentation
Please upload a movie which you tried in practice as an
animation in YouTube. After creating the movie, tell us the
movie URL.

123. Summary
Using 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.

124. Request of the
questionnaire
http://goo.gl/bwgCcl
Please answer following questionnaire to improve
Mobmap and hands on in future.

125. 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.

126. Reference

127. 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
-> 3
After the third line
Value
(population)）
Mesh code

128. Reading in typhoon data
• It reads in the behavior of the typhoon from the website
Source: degital typhoon data

129. Reading in typhoon data
• Enter the URL of the typhoon page of the digital typhoon
http://agora.ex.nii.ac.jp/digital-typhoon/summary/wnp/s/201115.html.ja

130. Reading in typhoon data
• Display the movement trace of the typhoon with an
animation