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3.2 System Architecture The system is modeled on a web-based geographical information system architecture, comprised of four components. (1) A reports module to gather data from the input API streams. (2) A spatial database to cache incoming hazard reports, and store incidents of flooding as verified by the user interface. (3) A web-server to serve both the data for the risk matrix, and the API of resulting hazard layer. (4) A web-based user interface consisting of the Risk Evaluation Matrix for use by BPBD DKI. The system will be based on the aforementioned CogniCity framework, which provides a scaleable client-server architecture for handling real-time geospatial reports of flooding, powering the crowd-sourced flood map at Peta- Jakarta.org. For the Risk Evaluation Matrix, CogniCity will be extended to support the additional three API data sources. The reports module, created using the NodeJS platform will periodically poll each of the APIs for data updates, and send this data to the PostgreSQL/Post- GIS spatial database for caching. Once data is received by the database, the server module, also written in NodeJS, pushes this data to the client interface within the BPBD DKI control room (Figure 1). The use of NodeJS and the open source PostgreSQL/PostGIS is a proven technology stack in this context, effective at handling high-throughput of information and capable of scaling to meet demand of concurrent users. For example the PetaJakarta.org CogniCity instance deployed during the 2014-2015 was shown to be capable of handling 250 tweets per second, and more than 10,000 concurrent website users (Holderness & Turpin, 2015). The client interface will also extend the technology developed for PetaJakarta.org, using the LeafetJS library and HTML5 technology to provide a scaleable “slippy” map of the city of Jakarta, as well as embedded data table forming the Risk Evaluation Matrix. Once an area is marked as flooded in the interface, this information is pushed back to the database, whereupon it is available via the system API in the Common Alerting Protocol or GeoJSON formats for consumption, primarily by the InAWARE and InaSAFE software and BPBD website. The Risk Evaluation Matrix is open source software, and while the output hazard data will be freely accessible under an open license, due to sensitivity of incoming reports and potential malicious use, access to the operational system itself will be restricted to the BPBD control room. It is envisaged, however that by making the software open source and available online other organizations, such as other regional governments in Indonesia, could implement the system as part of their own disaster risk management systems. 4. CONCLUSIONS This paper presents an open source system, currently in development, which has the potential to significantly improve information management and resulting response to flooding in the city of Jakarta, Indonesia. This is an important evolution of the disaster risk management ecosystem in the region as the number of new and innovative hazard data, such as crowd-sourced information, comes online. This approach is applicable across a number of Southeast Asian coastal megacities, many of which will bear the brunt of future climate change and resulting extreme weather events. Further, it is important to recognize that the system has been designed in conjunction with, and based on the requirements of, the Jakarta Emergency Management Agency, using a detailed understanding of the organization's information flows and institutional ethnography carried out as part of the preceding PetaJakarta.org project. However, through the use of open source software and standards-compliant data APIs it is envisaged that the system will be readably transferable to other cities, hazards, and languages in the region. 6 Advances in Civic Co-management within the Geospatial Ecosystem Applied to Disaster Risk Management 202
8. REFERENCES Baker, J. L., 2012. (editor), Climate change, disaster risk, and the urban poor: cities building resilience for a changing world. The World Bank, Washington, D.C. Holderness, T., 2014. Geosocial Intelligence. Technology and Society Magazine, IEEE, 33, 17-18. Holderness, T., Turpin, E., 2015. White Paper – PetaJakarta.org: Assessing the Role of Social Media for Civic Co-Management During Monsoon Flooding in Jakarta, Indonesia. University of Wollongong, Wollongong. Jain, A., Adebayo, J., de Leon, E., Li, W.; Kagal, L., Meier, P., and Castillo, C., 2015. Mobile Application Development for Crisis Data. Procedia Engineering, 107, 255-262. Kobo Toolbox, 2015. Kobo Toolbox | Data Collection Tools for Challenging Enironments. Available online at http://www.kobotoolbox.org/. Accessed 24 th July 2015. Meier, P., 2015. Digital Humanitarians: How Big Data Is Changing the Face of Humanitarian Response. CRC Press, Boca Raton. Peters, G., Butsch, C., Krachten, F., Kraas, F., Namperumal, S. & Marfai, M. A., 2015. Analyzing Risk and Disaster in Megaurban Systems-Experiences from Mumbai and Jakarta. Planet@ Risk, 3. Semiocast, 2012. Twitter reaches half a billion accounts. Available online at http://semiocast.com/en/publications/2012_07_30_Twitter_reaches_half_a_billion_accounts_14 0m_in_the_US. Accessed 20 April 2015. Turpin, E., Bobbette, A., Miller, M., 2013. (editors) Jakarta: Architecture + Adaptation. Jakarta. Universitas Indonesia Press, Jakarta. Ward, P. J., Marfai, M. A., Yulianto, F., Hizbaron, D. R., Aerts, J. C. J. H., 2011. Coastal inundation and damage exposure estimation: a case study for Jakarta. Natural Hazards 56(3) 899- 916. Wickramasuriya, R., Ma, J., Berryman, M., Perez, P., 2013. Using geospatial business intelligence to support regional infrastructure governance. Knowledge-Based Systems, 53, 80-89. FOSS4G Seoul, South Korea | 14-19 September 2015 203
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PROCEEDINGS Honil Gangni Yeokdae Gu
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C O N T E N T S Welcome Messages Lo
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A Week of Sharing, Learning, and Fu
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ACADEMIC TRACKS 7
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September 17, Thursday 11:00-12:15
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September 16 (Wed) & 18 (Fri) 15:05
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AUTOMATIC IMPROVEMENT OF POINT-OF-I
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In the following, we will list
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and specify their relations manuall
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Conducting a cross-validation on th
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81%. For cuisine=german, the precis
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4.3 Tourism and Leisure Tags We ide
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GENERATING GEOSPATIAL FOOTPRINTS FO
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quality assessment of geocrowdsourc
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Figure 2: The first simple case of
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Figure 5: The footprint of walkways
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say instead “Between 1st Street a
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4. DISCUSSION AND CONCLUSIONS The G
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Performance Analysis of MongoDB Vs.
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2. REVIEW OF SPATIAL DATABASES Simi
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4.2 Point Containment Problem Like
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If we observe the results above we
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esolution data (RapidEye and Landsa
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and used for calibration and coeffi
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scatter plot of RapidEye (Global (C
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is efficient to extrapolate depth f
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DYNAMIC STYLING FOR THEMATIC MAPPIN
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all) datasets on offer or applying
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3.2.1 Data inputs The styler accept
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determine the radius (both in the x
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Figure 5: Map classification of blo
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4.3 Point data Figure 8: Diverging
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4.4 Non-spatial visualisations Figu
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6 ACKNOWLEDGEMENTS The Cooperative
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educational resources (OER) and mod
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workshops have revealed that microc
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300 250 200 150 100 50 0 Figure 4.
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H 27 Male Student Maths / Geography
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5. CONCLUSIONS AND FUTURE WORK We h
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DEVELOPMENT OF DATA ARCHIVING AND D
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2. RELATED WORK Recent developments
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3. Significance and Objectives With
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Figure 3. Map Tiles Grid Selection
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The Ceph architecture comprises of
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Boundless, 2015. GeoExplorer — Ge
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Object-Based Building Boundary Extr
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2.2. A Grid-Based Algorithm A grid-
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3.1 Boundary Extraction The extract
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The Development of Web3D-based Open
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Singh et al.(2012) have utilized op
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Monitoring concept on operating asp
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(a) Mining area (Left:2007, Right:2
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geospatial information open platfor
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open platform utilized in open-pit
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Data Acquisition Methods Acquisitio
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(a) Ecological Restoring Area 1 (Le
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possibility of development of open-
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2. DATA ACQUISITION 2.1 Image gathe
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Hugin Pix4Dmapper Pablo d'Angelo et
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utit may be improved using many ste
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Wójcicka, A., and Wróbel, Z., 201
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platforms, which are easy to use an
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SWE framework proposes the followin
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Steenkamp et al. (2009) state that
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technology and are published under
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Measurement value yes Measurement s
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4.3.1 Explore The explore view allo
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Figure 8. Registration of a generic
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participants completely agree with
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467 - 471. Sohraby, K., Minoli, D.,
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In augmented reality a live view of
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Figure 4. Dwellings in an informal
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1. Gen eral crite ria 1.1. Platform
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matching, 3D engine, image tracking
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1.4. Implemented standards OGC ARML
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Page 159 and 160: 1. INTRODUCTION Use of landmarks in
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Page 163 and 164: 4. IMPLEMENTATION 4.1 Technology Se
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Page 167 and 168: AN OPEN SOURCE WEB SERVICE FOR REGI
Page 169 and 170: 2. IGSN OVERVIEW Figure 1 shows the
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Page 179 and 180: Developing a land use database of t
Page 181 and 182: Figure 3. Land use input applicatio
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Page 219 and 220: e integrated directly in the Web-GI
Page 221 and 222: 3. Implementation The main features
Page 223 and 224: Phase 1: Earthquake data Input Data
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Geosocial Big Data Analysis Using P
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AN ON-BOARD VISUAL-BASED ATTITUDE E
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3.1 Keypoints detection and matchin
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complete the process. This has been
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Fleet, D., & Weiss, Y. (2006). Opti
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technologies in Geoinformation Scie
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technologies becomes irrelevant in
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Figure 2. Server (upper block) and
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Name Presence Description Table 1.
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SampleExtraApp sampleExtraApp annot
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4. RESULTS Summing up, we should no
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Paradigms. John Wiley & Sons Inc.,
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FREEWAT: FREE and open source tools
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An Evaluation of Open Source Geogra
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2. DATA AND METHOD The approach ado
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the beginning and end of each trip
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Table 4 - GIS Routing Tools S/N GIS
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State cold store- Hungu Primary Hea
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Figure 3 - Comparing Discrepancy in
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nations based on human per capita i
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investigating metropolitan traffic.
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A Cross National Comparison on the
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in 1980(Saaty, 1980). It assesses t
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ex or current government officials
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Herold, S., & Sawada, M. C. (2012).
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frameworkwhich covers an infrastruc
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The architecture of the application
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5. CONCLUDING REMARKS Open source s
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cloud environment, so that end-user
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Figure 2 represents user interface
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geo-science application. Also the o
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PO-04 GeoDjango-Framwork-based Popu
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pH of 5.5 to 7.0; forest loam, rock
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Figure 4. Study Area (Davao Region)
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Figure 6. Topographic Map of Mindan
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Figure 8. Land Areas suitable for C
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It is respectfully recommended that
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ECDIS through introduction of Open
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to adopt development using open sou
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3. Conclusion ECDIS is navigation e
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PO-08 Development of an Agent Based
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1. INTRODUCTION 1.1 Background of t
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Figure 2. Workflow of the semi-auto
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threshold set. GRASS 7.0 is used in
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Figure 7. Result of the application
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Ming, D. M. D., Luo, J. L. J., Shen
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PO-10 3D Visualization of City GML
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As A Service (PAAS) and Infrastruct
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Figure 2: System Architecture and D
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The resulted crowd source data cont
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Table 1: Confusion Matrix Crowd sou
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PO-12 House Number Interpolation Fo
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(DOE) has awarded a total of 82 Gri
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Most solar radiation models compute
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2. OBJECTIVES, SCOPE, AND LIMITATIO
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Figure 4. BSWM Solar Sensors for Va
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4.2 Site Suitability Analysis Inter
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5. RESULTS AND DISCUSSION 5.1 Month
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Figure 8. Monthly Average Real-sky
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Figure 19. GHI for November Figure
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Figure 26. Non-Resource Criteria Su
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Kryza, M. et al. 2010. “Spatial i
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example coordinates agents, based o
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A benefit of using multiple agents
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A local GeoServer instance was set
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Figure 3. Screen captures of search
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In combination with ranking of resu
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INTRODUCTION TO A NEW GEO-REFERENCE
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Figure 2. ‘Click-to-go’ functio
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Figure 5. The conceptual structure
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Figure 6. Software architecture of
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GIS ORIENTED SERVICE OPTIMIZATION T
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Vehicle Routing Problem (VRP) refer
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So, buffer of 2 meters was created
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log data which was the actual dista
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was being served by three vehicles.
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Analyzing the number of customers,
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PO-17 A Study of the Development an
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MODELING OF TERMINOLOGY DATABASE IN
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3.2 Interpretation of the terms 4 I
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Figure 4 shows the result of SWOT a
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Add terms in the database: Super us
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6. REFERENCES Damdinsuren A., 2013.
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PO-20 Vulnerability Assessment Usin
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LEIGHTWEIGHT URBAN COMPUTATION INTE
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where events are instantly propagat
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not be conceived as responsive by a
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3.5. Remote Services Among a few
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4. USE CASES 4.1. Transition Worksh
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to display the rank of the player i
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PO-23 Development of Opensource-bas
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Author Index Aburizaiza, Ahmad O. 2
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