2018년 4월 6일 서울대학교 병원에서 개최된 한국역학회 봄 학술대외에서 특강한 자료입니다. 공간정보의 동향과 시사점을 중심으로 보건분야와의 융복합 가능성을 살펴봤습니다.

1. 최근의 공간정보 분야 동향과 시사점
2018년 4월 6일
가이아쓰리디㈜ 대표이사
신상희(shshin@gaia3d.com)
2018년 봄 학술대회

2. Trends?

3. Convergence of ICT and Spatial IT
<Source: Sakong, Hosang(2016), Policy Directions of Spatial Information for Hyper-connected Society>

4. Location Technology Evolution
<Source: Steve Liang(2016)>

5. Location Technology Evolution
Source: 이기준, 2012, “공간정보관련 현안 이슈와 대응전략” 발표 자료를 재구성
Size of Space
Number of Users
인구수
도시계획 국토건설네이게이션
인터넷 지도
스마트폰
LBS
Person -> Things
(IoT)
1980
1990
2000
2010 ~
2020

6. Location Technology Evolution
<Source: Steve Liang(2016)>

7. Location Technology Evolution
<Source: Steve Liang(2016)>

8. Location Technology Evolution
<Source: Steve Liang(2016)>
Location is the first class
citizen for IoT!

9. Location Technology Evolution
Size of Space
Update Cycle
Small
1980
1990
2000
2010 ~
2020
Large
Static
Dynamic
<Source: Sakong, Hosang(2016), Policy Directions of Spatial Information for Hyper-connected Society>

10. Location Technology Evolution
<Source: Thomas W. Oestreich(2015), “Location, the Next Champion in Analytics”, Gartner Group>

11. IoT, Big Data and A.I.
감각 기관
기억
지능
IoT(Internet of Things)
Big Data
A. I.
Smartizen(10Bn) Smart Devices(100Bn) Smart Sensor(100Tn)
<Source: 이상훈(2016), ‘ICT 기술 파도와 4차 산업혁명’을 수정, 재편집>

12. <Image Source: http://www.inhapress.com/news/photo/201706/7226_2328_957.png>

13. GeoSpatial Paradigm Shift
Current Future
Concept Object Information Context Awareness
Data Consumer Prosumer/DIY
User Person Things
Visualization Real World Digital Twins
Application Base Map Key Factor for Fusion
Driving Entity Public Sector Private Sector
Space Outdoor Indoor + Outdoor +
Update Cycle Static Dynamic
<Source: Hosang Sagong(2016), ‘Policy Directions of Spatial Information for Hyper-connected Society’ modified>

14. Any Problems? Bottleneck!!
BIM/AEC
Point
Cloud
Drone
IoT
Lidar
Self-Driving
Car
Crowd
Sourced

15. Implications?

16. Key Words
Live(Real-Time)
Geospatial
Indoor + Outdoor +
Underground
Mapping
Analytics
&
Simulation
Digital
Twin

17. Digital Twins
Digital Twins
A digital twin is a virtual representation of a physical
object or system across its lifecycle, using real-time
data to enable understanding, learning and
reasoning.

18. Digital Twins

19. Digital Twins

20. Integration of Indoor and Outdoor Space
GIS
CityGML
GeospatialInformation
GISStandards
IFC
BIM
OutdoorModel
IndoorModel
BIMStandards
Construction
DrawingsInfo.
In/Outdoor
GIModel
(GeospatialInformation
Model)

21. Integration of Indoor and Outdoor Space

22. Facility Management
GeoBIM Platform
Integration
Model

24. Underground Mapping

25. Live Data GIS

26. Live Data GIS
“CFD(Computational Fluid Dynamics): Modeling and predicting air quality, pollution modeling”

27. Analytics & Simulation

28. Analytics & Simulation

29. Analytics & Simulation

30. Real Cases

31. Supporting Elderly People
<Source: www.nively.com >

32. Supporting Elderly People
nursing home
General pratictioners
Medical helpdesk or other service provider
Private home
MentorAge® cloud
Private home
MentorAge®
MentorAge®
MentorAge®
<Source: www.nively.com >

33. (2015.8. ~ 2017.8.)
• 구제역 발생 현황 및 축산시설 방문 차량을 이용한 역학 조사 분석 활용
• 망고시스템 수행
<Source: www.mangosystem.com>

34. (2015.8. ~ 2017.8.)
• 차량 방문 정보(약 1억건), 축산농가 및 시설(약 40만건)
• 구제역 발생현황 2014년(183건) 테이블로 구성
차량방문정보
차량번호
방문일자
농가번호
방문목적
…
…
축산농가정보
위치(Point)
농가번호
사육두수
….
FMD 발생현황
위치(Point)
농가번호
발생일
…
월별 차량
방문정보
view
시도, 시군구,
읍면동 행정경계
<Source: www.mangosystem.com>

35. (2015.8. ~ 2017.8.)
• 오픈소스GIS로 구현
<Source: www.mangosystem.com>
User Application Server
Apache Tomcat
HTTP
GET/POST Request
Response
HTTP
GET/POST Request
JSON, XML
UI Client
Web Browser
• Internet Explorer 8+
• Chrome 27+
• Firefox 19+
Web Service Client
• REST or SOAP
User Interface
UI
MAP
Service Framework
SpringMVC
Database Access
MyBatis
Database Server
PostgreSQL / PostGIS
GIS Server
OBJECT, XML
Service Type
WMS - Map, Legend
WFS - Vector, Spatial Query
WPS - Analysis Processing
DataStore
JDBC
HTTP
GET/POST Request
Image
XML
GML
GeoJSON
KML
Object-Relational
Mapping (ORM)
JDBC
: 적용기술
: 세부 적용기술
: 서비스 영역
: 데이터 및 처리방향
OBJECT
BASEMAP – V-World

36. (2015.8. ~ 2017.8.)
• FMD 발생자료를 이용한 Pin Map: 발생현황
• HeatMap/Kernel Density: WMS Rendering Transformation
<Source: www.mangosystem.com>

37. (2015.8. ~ 2017.8.)
• FMD 발생자료를 이용한 시도/시군구/읍면동별 단계구분도
• 실시간 Point in Polygon Overlay 분석 후 지도화
<Source: www.mangosystem.com>

38. (2015.8. ~ 2017.8.)
• FMD 발생자료를 이용한 Spatial Clump Map (5km)
• k-Nearest Neighbor Map(1 ~ n차)
<Source: www.mangosystem.com>

39. (2015.8. ~ 2017.8.)
• FMD 발생자료를 이용한 Wind Rose Map: 확산 Pattern 분석
• FMD 발생자료를 이용한 시계열 애니메이션
<Source: www.mangosystem.com>

40. (2015.8. ~ 2017.8.)
• FMD 발생자료와 차량방문정보를 이용한 사회연결망그래프
• 특정발생농가에서 최근 21일 내에 방문한 모든 차량과 차량이 방문한 모든 농가 확인
<Source: www.mangosystem.com>

41. (2015.8. ~ 2017.8.)
• 주, 월 단위 구제역 확산 특성 분석
<Source: www.mangosystem.com>

42. Ebola Predictive Model
<Source: UN Open GIS Initiative>
• UN Open GIS Initiative Spiral 3
 Vector Process
 Select, Merge, Clip …
 Count Point in Polygon
 Raster Process
 Line Density
 Mask
 Vector To Raster
 Zonal Statistics
 Spatial Statistics
 Ordinary Least Squares (OLS)
 Report
 Map

43. Ebola Predictive Model
<Source: UN Open GIS Initiative>
Cleaned Ebola Dataset
loaded via uDig
• Data Cleaning and Loading

44. Ebola Predictive Model
<Source: UN Open GIS Initiative>
• Line Density Analysis

45. Ebola Predictive Model
<Source: UN Open GIS Initiative>
• Zonal Statistics

46. Ebola Predictive Model
<Source: UN Open GIS Initiative>
• Ordinary Least Squares

47. Ebola Predictive Model
<Source: UN Open GIS Initiative>
• Ordinary Least Squares on WPS

48. Ebola Predictive Model
<Source: UN Open GIS Initiative>
• Heat Map Display on WPS

49. Ebola Predictive Model
<Source: UN Open GIS Initiative>
• Standard Deviational Eclipse on WPS

50. Thank you!