FOSS4G Korea Smart City with Open Geospatial Technologies in Emerging Cities August 31, 2016

1. Yuki Akiyama（aki@csis.u-tokyo.ac.jp）
Assistant professor
Micro Geodata lab
Center for Spatial Information Science, University of Tokyo
Smart City and Spatial Big Data
-Studies and cases in Japan-
August 31, 2016
FOSS4G Korea
Smart City with Open Geospatial Technologies
in Emerging Cities

2. Self introduction
Name：Yuki Akiyama (秋山祐樹)
Birth place：Okayama city, Okayama pref. Japan
Affiliation：Assistant Professor
Micro Geodata laboratory
Center for Spatial Information Science, UT
Visiting Research Officer
Policy Research Institute for Land, Infrastructure,
Transport and Tourism (PRLIT)
Visiting Research Fellow
Korea Research Institute for Human Settlements
Main fields: Spatial information Science, Urban engineering
Geography
2
Urban heat island
GIS
Micro Geodata
Smart city
Tokyo
Seoul
Okayama

3. Background
Many problems in urban area
Reactivation of
city center
Aging
Traffic
management
Disaster
prevention
Facility placement
planning
Design urban planning to realize smart city
Many kinds of spatial data and census
data can support to solve them.
3

4. Recently, various geo data are available. 4

5. Building scale
Building data：Zmap-TOWNⅡ ©Zenrin CO. Ltd.
Detailed geo data are available.
Building scale → household scale → person scale…
5

6. Building scale
Household scale
Micro Population Census ©Yuki Akiyama
Detailed geo data are available.
Building scale → household scale → person scale…
6

7. People flow data ©CSIS
Person scale
Building scale
Household scale
Detailed geo data are available.
Building scale → household scale → person scale…
7

8. Person scale
People flow data ©CSIS
Big data with location and time or geo data
＝Micro Geo Data（MGD）
Building scale
Household scale
Detailed geo data are available.
Building scale → household scale → person scale…
8

9. Many conferences related with MGD!!
http://sigspatial2016.sigspatial.org/
http://giscience.geog.mcgill.ca/https://cupum2015.mit.edu/
http://www.isprs2016-prague.com/
9
Me

10. Japanese government is looking to the
utilization of MGD.
Japanese government begins to introduce legislation
toward promotion of utilization of big data.
NHK News Web（2014/06/09）
http://www3.nhk.or.jp/news/html/20140609/k10015066701000.html
Regional Economy Society Analyzing System (RESAS)
https://resas.go.jp/
10
Japanese government is developing
legislation to use big data enciphered
personal information without
owner’s consent.
Japanese Cabinet Office is
developing the “RESAS” which
visualize various census data and
big data for Japanese local
governments.

11. The widespread use of detailed digital map creates demands
for more detailed and reliable spatial data than before.
Not only researches but also private and government
sectors are also interested in how to utilize these data.
Micro Geodata (MGD)
is gathering attention now
how to develop, share and
apply for realization of smart city.
Coming of age to utilize MGD
Recently, computers and smart phones are becoming
widespread and internet environment are being
developed rapidly.
Anyone can access detailed digital map.
11

12. 1) Available Japanese MGD
for urban monitoring
2) Examples of studies & cases to utilize MGD
2.1 Utilize person MGD
2.2 Utilize disaster big data
2.3 Utilize public big data
3) Conclusions and future works
Today’s contents 12

13. Commercial accumulation
Statistics
Eric Fischer, “Eric Fischer’s photostream”,
http://www.flickr.com/photos/walkingsf/
Continuation
Change
Emergence
Demise
LegendTime-series changes 2003-2008
Time-series tenant data
Inter-enterprise
transaction big data
Residential map
Telephone directory
Web information (SNS and search results)
MGD about Buildings, shops and enterprises
1. Available Japanese MGD for urban monitoring

14. Person flow data Various mobile census
Pseudo person flow data based on
SNS tweets with geo tags
Micro population census
Person flow project
http://pflow.csis.u-tokyo.ac.jp/index-j.html
©Nightley Inc. ©Shibasaki & Sekimoto lab. Univ. of Tokyo
©Micro Geodata forum ©Person flow project
©Center for Spatial Information Science, The Univ. of Tokyo
Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of
Micropopulation Census through Disaggregation of National
Population Census", CUPUM2013 conference papers, 110.
1. Available Japanese MGD for urban monitoring
MGD about person distributions and flows

15. 1) Available Japanese MGD
for urban monitoring
2) Examples of studies & cases to utilize MGD
2.1 Utilize person MGD
2.2 Develop disaster big data
2.3 Utilize public big data
3) Conclusions and future works
Today’s contents 15

16. Recently, various census data are being digitalized and we can get
them easily from websites of national and local governments.
Especially, the population census is used as base data to understand
distribution and movement of population for following urban problems.
2.1 Utilize person MGD
Urban
planning
Disaster damage
estimation and
prevention
Traffic
planning
Marketing
support
Epidemic
prevention
More detailed and reliable population data are required than before
to resolve them.
How to monitor
detailed population distribution ?
16

17. 2.1 Utilize person MGD
How to monitor
detailed population distribution ?
1. Residential population
> Micro population census (MPC)
2. Dynamic population
> Mobile phone data (CDR, GPS log etc.)
17

18. 18
Micro population census (MPC)
(Population census + residential map + housing statistics)
MPC can monitor detailed residential population.
It was realized to disaggregate population census. (Disaggregate: data processing to
reallocate data statistically based on other statistics and spatial features.)
東松原駅
LEGEND
Households
1 person
3 persons
4 persons
5 persons
≧6 persons
2 persons
Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of
Micropopulation Census through Disaggregation of National
Population Census", CUPUM2013 conference papers, 110.
18

19. 19
東松原駅
Attribute table
Building type house
Longitute 139.65633
Latitude 35.663664
Area[m2
] 105.34
Family type 8
Household size 5
Householder [age-gender] 45 - 1
Spouse [age - gender] 40 - 2
Number of child 2
Information of children 5-1 | 10 - 2
Number of parent 1
Information of parent 75-2
Number of others 0
Information of others None
LEGEND
Households
1 person
3 persons
4 persons
5 persons
≧6 persons
2 persons
Micro population census
(Population census + residential map + housing statistics)
Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of
Micropopulation Census through Disaggregation of National
Population Census", CUPUM2013 conference papers, 110.
MPC can monitor detailed residential population.
It was realized to disaggregate population census. (Disaggregate: data processing to
reallocate data statistically based on other statistics and spatial features.)
19

20. 20
LEGEND
Households
1 person
3 persons
4 persons
5 persons
≧6 persons
2 persons
Micro population census
(Population census + residential map + housing statistics)
Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of
Micropopulation Census through Disaggregation of National
Population Census", CUPUM2013 conference papers, 110.
20

21. 21
LEGEND
Households
1 person
3 persons
4 persons
5 persons
≧6 persons
2 persons
Micro population census
(Population census + residential map + housing statistics)
Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of
Micropopulation Census through Disaggregation of National
Population Census", CUPUM2013 conference papers, 110.
21

22. LEGEND
Households
1 person
3 persons
4 persons
5 persons
≧6 persons
2 persons
Micro population census
(Population census + residential map + housing statistics)
Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of
Micropopulation Census through Disaggregation of National
Population Census", CUPUM2013 conference papers, 110.
22

23. LEGEND
Households
1 person
3 persons
4 persons
5 persons
≧6 persons
2 persons
The Important thing to understand this data
This data is estimated data.
Values of each point are necessarily match as actual states.
Micro population census
(Population census + residential map + housing statistics)
Akiyama, Y., Takada, T. and Shibasaki, R., 2013, "Development of
Micropopulation Census through Disaggregation of National
Population Census", CUPUM2013 conference papers, 110.
23

24. Aggregation by grid (250m square meter)
Micro population census
(Population census + residential map + housing statistics)
24

25. Aggregation by grid (250m square meter)
LEGEND
Population
0 - 250
501 - 1000
1001 - 2000
2001 -
251 - 500
Aim of the Micropopulation census
=Development the new population census which can be aggregated
into arbitrary spatial unit.
Micro population census
(Population census + residential map + housing statistics)
25

26. Aggregation by city blocks
LEGEND
Population
0 - 50
101 - 250
251 - 500
501 -
51 - 100
Micro population census
(Population census + residential map + housing statistics)
26

27. LEGEND
Aging rate
(Over 65 yrs. old)[%]
0 - 10
20 - 30
30 - 50
50 -
10 - 20
27
Monitoring of aging rate
(rate of population over 65years old in 250m square grid)
27

28. 凡例
高齢化率
(65歳以上割合)[%]
0 - 10
20 - 30
30 - 50
50 -
10 - 20
28
Monitoring of aging rate
(rate of population over 65years old in 250m square grid)
LEGEND
Aging rate
(Over 65 yrs. old)[%]
0 - 10
20 - 30
30 - 50
50 -
10 - 20
28

29. 29
Monitoring of aging rate
(rate of population over 65years old in 250m square grid)
29

30. 4.3 billion people use mobile phones in 2014 *1.
Many mobile phones have GPS function.
Studies to monitor dynamic population using mass mobile
phone GPS data attract considerable attention for resolve
various urban problems *2.
*1: DESIGNBAM, “5 billion people will use mobile phones by 2017”,
http://designbam.com/2013/10/04/5-billion-people-will-use-mobile-phones-by-2017/
*2: Sekimoto, Y., Horanont T. and Shibasaki, R., 2011. Trend of People Flow Analysis
Technology Using Mobile Phone. IPSJ Magazine,. 52(12): 1522-1530. (In Japanese)

31. 4
Application example of mobile phone GPS data
Example in USA
This map shows characteristics of
each road based on dynamic
population calculated from mass
mobile phone GPS data in San
Francisco. This result is actually
used for traffic planning in the city.
PHYS.ORG, “Cellphone, GPS data suggest new
strategy for alleviating traffic tie-ups”,
http://phys.org/news/2012-12-cellphone-gps-
strategy-alleviating-traffic.html
bc: How popular they are as connectors
between other roads
Kroad : Number of geographic areas that
contribute to traffic on a particular road
31

32. 5
Example in Kenya
Three quarters of Kenyan
use mobile phones.
Mobile phone GPS data
are used for animal
quarantine.
When livestock disease
occurs, Kenyan farmer
deliver and share
information of them with
location information.
http://www.un.org/apps/news/sto
ry.asp?NewsID=44259&Cr=livestoc
k&Cr1=#.VFLsW_l_u51
Application example of mobile phone GPS data 32

33. Japanese mobile phone GPS data
We can monitor estimate populations of every
hours by 250m square grids (updated everyday).
・Real time data based on GPS logs by mobile phones
・It covers throughout Japan.
Congestion analysis (Zenrin Data Com Co., Ltd.)
http://lab.its-mo.com/densitymap/
33

34. Estimated numbers of visitor in each commercial area
Shinjuku
Shibuya
Legend
Estimated number
of visitors
Application 1: Estimation of Dynamic population
in commercial areas
34
秋山祐樹 ・Teerayut Horanont・柴崎亮介，2013年，「大規模
人流データを用いた商業地域における来訪者数の時系列分析」，
第22回地理情報システム学会講演論文集（CD-ROM, C-5-4）

35. Time-series numbers of visitor in each commercial area
Shinjuku
Shibuya
Legend
Estimated number
of visitors
Application 1: Estimation of Dynamic population
in commercial areas
Number of Shops：198
Average number of daily visitors：3,039
Average Hourly number of visitors in weekdays
0
500
1,000
1,500
2,000
2,500
3,000
3,500
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
35
秋山祐樹 ・Teerayut Horanont・柴崎亮介，2013年，「大規模
人流データを用いた商業地域における来訪者数の時系列分析」，
第22回地理情報システム学会講演論文集（CD-ROM, C-5-4）

36. Time-series numbers of visitor in each commercial area
Shinjuku
Legend
Estimated number
of visitors
Application 1: Estimation of Dynamic population
in commercial areas
Number of Shops：198
Average number of daily visitors：3,039
Average Hourly number of visitors in weekdays
0
500
1,000
1,500
2,000
2,500
3,000
3,500
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
36
秋山祐樹 ・Teerayut Horanont・柴崎亮介，2013年，「大規模
人流データを用いた商業地域における来訪者数の時系列分析」，
第22回地理情報システム学会講演論文集（CD-ROM, C-5-4）
Shibuya渋谷
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Number of Shops：1,580
Average number of daily visitors：92,619
Average Hourly number of visitors in weekdays

37. 37
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
22,000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Averagenumberofvisitorsinweekday
O’clock
浅草 歌舞伎町 原宿 八重洲1丁目 下北沢南口
Asakusa Kabuki-cho Harajuku Yaesu-1 Shimo-kitazawa
It is realized to integrate the Commercial Accumulation Statistics with the mobile
census data by ZDC in the city center of Tokyo. (365days in 2012)
Application 1: Estimation of Dynamic population
in commercial areas
!
37

38. 25
Regional characteristics in each grids were calculated as follows.
Numbers of
each kind of
stay points can
be calculated in
all grids.
Regional
characteristics
are defined by
this method.
Akiyama, Y. and Shibasaki, R., 2014, "Time-series Monitoring of Area Characteristics Using Mass Person Flow Data by Mobile Phone GPS Data -
Case study in Greater Tokyo Region-",The International Symposium on City Planning 2014,SS03,S03-12.
Application 2: Estimation of regional characteristics 38

39. Time-series estimation of regional characteristics in Tokyo using
mobile census data in 2012
39
Akiyama, Y. and Shibasaki, R., 2014, “Time-series Monitoring of Area Characteristics Using Mass Person Flow Data
by Mobile Phone GPS Data“, The International Symposium on City Planning – Vietnam 2014, SS03,S03-12.
Application 2: Estimation of regional characteristics 39

40. Akiyama, Y. and Shibasaki, R., 2014, “Time-series Monitoring of Area Characteristics Using Mass Person Flow Data
by Mobile Phone GPS Data“, The International Symposium on City Planning – Vietnam 2014, SS03,S03-12.
Number of grids throughout Kanto region by 500m square grid
Population of each types throughout Kanto region by 500m square grid
0
10,000
20,000
30,000
40,000
50,000
0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00
Numberofgrid
Time
Commercial/Sightseeing Residential Business Transport Mixed
0
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00
Population
Time
Commercial/Sightseeing Residential Business Transport Mixed
Application 2: Estimation of regional characteristics 40

41. Application 3: Event detection
Akiyama, Y., Ueyama, S., Shibasaki, R. and Adachi, R., 2016, “Event Detection Using Mobile Phone Mass GPS Data and Their Reliability Verification
by MDSP/OLS Night light Image”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-2, 77-84.
We defined sudden population
concentration spatio-temporally as the
“Event”.
Events contains positive events
(festivals, concerts, ceremonies etc.) and
negatives (traffic accidents, disasters
etc.).
41

42. 15
4. Result
Akiyama, Y., Ueyama, S., Shibasaki, R. and Adachi, R., 2016, “Event Detection Using Mobile Phone Mass GPS Data and Their Reliability Verification
by DMSP/OLS Night light Image”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-2, 77-84.
42

43. 15
4. Result
A: Open event on the Iwakuni
base of US air force(May 5)
C: Omagari firework display
(August 26)
Akiyama, Y., Ueyama, S., Shibasaki, R. and Adachi, R., 2016, “Event Detection Using Mobile Phone Mass GPS Data and Their Reliability Verification
by DMSP/OLS Night light Image”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-2, 77-84.
43

44. 4. Result
Akiyama, Y., Ueyama, S., Shibasaki, R. and Adachi, R., 2016, “Event Detection Using Mobile Phone Mass GPS Data and Their Reliability Verification
by MDSP/OLS Night light Image”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-2, 77-84.
15
D: New Year events in Saijo
Inari shrine
holidays)
Many events were held in
New Year holidays
intensively
F: Typhoon effect (September 30 ~ October 1)
Event Meshes with a small number of Event
visitors. Many tourists were stuck due to the
effect of a severe typhoon.
44

45. 1) Available Japanese MGD
for urban monitoring
2) Examples of studies & cases to utilize MGD
2.1 Utilize person MGD
2.2 Develop disaster big data
2.3 Utilize public big data
3) Conclusions and future works
Today’s contents 45

46.  Japanese government estimates that victims by Tokai-
Tonankai earthquakes are over 300 thousands.
 Calculation and estimation of micro-scale regional
disaster risks throughout Japan is very important for
planning of earthquake disaster prevention in Japan.
2.2 Develop disaster big data
Y, Akiyama., Y, Ogawa., H, Sengoku., R, Shibasaki. And T, Kato., “Development of Micro Geo Data for Evaluation
of Disaster Risk and Readiness by Large-scale Earthquakes Throughout Japan”, Proceeding of Annual
conference on Infrastructure and Management, 2013, 392. (in Japanese)
Y, Ogawa., Y, Akiyama. and R, Shibasaki, “The Development of Method to Evaluate the damage of Earthquake
Disaster Considering Community-based Emergency Response Throughout Japan”, GI4DM2013, 2013, TS03-1.
Using various MGD and census data…
Estimation data about
earthquake damage risk and
death rate of each building
= Disaster big data
46

47. Estimation of disaster risk and first responder power of all buildings
Building collapse risk
①Est. structure
②Est. building age
Existing census data Micro Geo Data (MGD) Earthquake motion
Human damage risk
⑤Resident（age, gender etc.）
first responder power
⑥Expected number of rescuee
⑦Distance from fire stations
Realization of environment for estimation of damage situation in an
arbitrary spatial unit scale-seamlessly.
→Development micro scale base data throughout Japan
Building fire risk
③Est. fire resistance performance
④Est. fire occurrence ratio
Housing and land census
Population census
etc.
Residential map
Telephone directory
Commercial accumulation
etc.
Seismic activity map
2.2 Develop disaster big data 47

48. Seismic
input
Estimation of damage and first responder power in each building
Bldg.
structure
Bldg.
age
fire occurrence
ratio
Fire spread
cluster
Residents
Rescue power by
fire authorities
Mutual power by
neighborhood
Calculation of
collapse risk
Calculation of
fire risk
Calculation of mutual power
Calculation of rescue power
Number of recsuee
from collapsed
buildings rescued
by neighborhoods.
Fire extinguished possibility
by fire authorities
2.2 Develop disaster big data 48

49. Building unit →Aggregation data
＝Scale seamless damage estimation
Municipality District Grid City block
Collapse risk
Fire risk
Mutual power
Rescue power
Fire extinguished possibility
by fire authorities
－
－
Disaster
risks
First responder
powers
Damage
estimation
Estimated
number of
casualty
Number of recsuee
from collapsed
buildings rescued
by neighborhoods.
2.2 Develop disaster big data 49

50. Earthquake Disaster damage estimation by MGDEarthquake Disaster
damage estimation by MGD
(250m square grid)
Estimated death
rate [%]
In case of earthquake which will
occur 2% in 50years

51. Estimated death
rate [%]
In case of earthquake which will
occur 2% in 50years
Earthquake Disaster
damage estimation by MGD
(250m square grid)

52. Estimated death
rate [%]
In case of earthquake which will
occur 2% in 50years
Earthquake Disaster
damage estimation by MGD
(250m square grid)

53. Estimated death
rate [%]
In case of earthquake which will
occur 2% in 50years
Earthquake Disaster
damage estimation by MGD
(250m square grid)

54. Estimated death
rate [%]
In case of earthquake which will
occur 2% in 50years
Earthquake Disaster
damage estimation by MGD
(250m square grid)

55. Estimated death
rate [%]
In case of earthquake which will
occur 2% in 50years
Earthquake Disaster
damage estimation by MGD
(250m square grid)

56. Estimated death
rate [%]
In case of earthquake which will
occur 2% in 50years
Earthquake Disaster
damage estimation by MGD
(250m square grid)

57. Earthquake Disaster
damage estimation
by MGD
Estimated death
rate [%]
In case of earthquake which will
occur 2% in 50years

58. This result received a lot of attention in Japan.
Medias (NHK)
Magazines and books
Get research funds
(Ministry of Land, Infrastructure, Transport and Tourism etc.)
2.2 Develop disaster big data
Utilized disaster drill for citizens
58

59. 1) Available Japanese MGD
for urban monitoring
2) Examples of studies & cases to utilize MGD
2.1 Utilize person MGD
2.2 Develop disaster big data
2.3 Utilize public big data
3) Conclusions and future works
Today’s contents 59

60. 2.3 Utilize public data 60
Local governments have many precious public data.
Now we resolve a problem of local government using
various public data.
The problem of local government is increasing Vacant
house.
Local government want to monitor locations of vacant houses.

61. 2.3 Utilize public data 61
Collect some public data without private information
(citizen’s names)
Vacant house?
1) Resident Register information
of each house
2) Consumption data of city water
of each house
3) Property tax data of each house
> Building age and structure
We developed estimating model of vacant house to integrate public
data with results of field surveys in some sample areas.
e.g. No residents, no water consumption and no taxation on the house A,
> The house is estimated a vacant house.

62. 2.3 Utilize public data 62
City block unit
Result: Estimate Number of vacant house (Kagoshima city)
This research project is ongoing in some cities.
Tokyo
Seoul
Kagoshima
This result was provided for housing
and city planning sector of
Kagoshima city.
Our result shows there are about 1,700
vacant houses out of about 32,000 houses
(5.36%) in city center of Kagoshima city.

63. 1) Available Japanese MGD
for urban monitoring
2) Examples of studies & cases to utilize MGD
2.1 Utilize person MGD
2.2 Develop disaster big data
2.3 Utilize public big data
3) Conclusions and future works
Today’s contents 63

64. Applicative studies which were difficult before are
being realized.
・Today is data “abundant” and “accumulating” era.
・Unrealizable studies (only ideas) can be realized by MGD.
Anyone can develop an environment to handle and
analyze MGD.
・We can acquire high spec PC and high-capacity HDD at low prices.
Our approaches expect to support policy development
based on quantitative bases.
・It is expected that data-driven policies will have persuasive power
for citizens.
・Conduct of policies based on data will support to realize the
smart city.
Conclusions
3 Conclusions and future works 64

65. Challenges
Development of human resources who can handle and
analyze MGD is needed.
・We need to acquire skills to handle and understand MGD.
> Programming skills to handle big data
> Skills to handle GIS software for visualization
> Knowledge of statistics to analyze results
Integration of MGD with field data
・We need to understand MGD is not universal data.
・To integrate MGD with field data, MGD will be used more than now.
> Skill and sense to integrate field data with MGD
Collaboration with government sectors for smart cities
・To realize smart cities, we need to collaborate with national and
local governments and to share our researches and their
tasks.
・It is difficult to collect useful public data. However it is possible if
our suggestion is helpful for local government and citizens.
3 Conclusions and future works 65

66. Thank you for your kind attention
<Contact>
Yuki Akiyama（aki@csis.u-tokyo.ac.jp）
Assistant professor
Center for Spatial Information Science (CSIS)
The University of Tokyo
URL: http://akiyama-lab.jp/yuki/
（You can download some my papers and slides)
Web page of Micro Geo Data Forum
http://microgeodata.jp/
Search「秋山祐樹」or” akiyama.yuuki”!