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Figure 11. GHI for March Figure 12. GHI for April Figure 13. GHI for May Figure 14. GHI for June Figure 15. GHI for July Figure 16. GHI for August Figure 17. GHI for September Figure 18. GHI for October FOSS4G Seoul, South Korea | September 14 th – 19 th , 2015 370
Figure 19. GHI for November Figure 20. GHI for December Figure 21. Annual Average GHI for Region 3 In terms of spatial variation, the western and northern parts of the region typically receive more solar radiation. From the results, the monthly average GHI in the region computed by r.sun ranged from 3706.8 Wh/m 2 -day in December to 6021.0 Wh/m 2 -day in May with an annual average GHI of 4727.12 Wh/m 2 -day indicating a good amount of resource potential. High GHI values were observed for the summer months of March to May (Mean: 5640.26 Wh/m 2 -day) while the cold and rainy season ranging from July to December showed relatively lower values (Mean: 4298.98 Wh/m 2 -day). The Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) between the measured and modelled GHI were 352.88 Wh/m 2 -day and 8.53%, respectively, with the lowest error in March (73.94 Wh/m 2 -day, 1.44%) and the highest in August (844.01 Wh/m 2 -day, 21.65%). Solar Energy Resource Assessment using r.sun 371
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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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2.3. Object events 2.4. Display rad
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instructional videos, and basic exa
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application for addressing, the pre
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Environments, 6(4), 355-385. Carmig
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1. INTRODUCTION Use of landmarks in
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the junctions which made with major
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4. IMPLEMENTATION 4.1 Technology Se
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Figure 3: Linear map output with si
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AN OPEN SOURCE WEB SERVICE FOR REGI
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2. IGSN OVERVIEW Figure 1 shows the
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types (e.g., dateType and relationT
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REST API(Fielding, 2000). The servi
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An Open Source Web Service for Regi
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Research Centre (ARRC) and the Nati
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Developing a land use database of t
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Figure 3. Land use input applicatio
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Paddy 19.4 23.7 Upland field, orcha
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Figure 6. Overlay point based land
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Integrating Open Source GIS Softwar
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Advanced GIS course, we recognized
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for undergraduates. Research take t
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Figure 8 The USDA Foodshed project
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4. Data is sent to the web pages ei
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Advances in Civic Co-management wit
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2. JAKARTA'S DISASTER RISK MANAGEME
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The aim of the system is to improve
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8. REFERENCES Baker, J. L., 2012. (
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EVALUATION OF AN OPEN-SOURCE COLLAB
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therelevant stakeholderswith differ
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at each stage of the exercise: · l
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differentmitigationmeasuresaccordin
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students before the exercise. 5 4 3
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the third stage of the exercise in
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doi:10.1016/j.envsoft.2003.12.019.
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e integrated directly in the Web-GI
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3. Implementation The main features
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Phase 1: Earthquake data Input Data
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Figure 4 . Magnitude 7.8 Earthquake
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After adding all the layer maps, th
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EVALUATING FLOOD HAZARD POTENTIAL I
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σ 0 = 10*log 10 (DN 2 ) + CF (1) W
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Figure 4. The geomorphological feat
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from water class in land cover map
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ANALYSIS OF SPATIAL DENSITY UTILIZI
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demographic data. He also states th
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Table 1. The combination of COUNTRY
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NUMBER OF SUBSCRIBERS 300,000 250,0
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Figure 9. The number of foreign tou
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Figure 16. Spatial call activity pa
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Recently, additional tourist inform
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country. It uses R-Studio, a statis
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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
Page 319 and 320: Figure 4. Study Area (Davao Region)
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Page 327 and 328: ECDIS through introduction of Open
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Page 331 and 332: 3. Conclusion ECDIS is navigation e
Page 333 and 334: PO-08 Development of an Agent Based
Page 335 and 336: 1. INTRODUCTION 1.1 Background of t
Page 337 and 338: Figure 2. Workflow of the semi-auto
Page 339 and 340: threshold set. GRASS 7.0 is used in
Page 341 and 342: Figure 7. Result of the application
Page 343 and 344: Ming, D. M. D., Luo, J. L. J., Shen
Page 345 and 346: PO-10 3D Visualization of City GML
Page 347 and 348: As A Service (PAAS) and Infrastruct
Page 349 and 350: Figure 2: System Architecture and D
Page 351 and 352: The resulted crowd source data cont
Page 353 and 354: Table 1: Confusion Matrix Crowd sou
Page 355 and 356: PO-12 House Number Interpolation Fo
Page 357 and 358: (DOE) has awarded a total of 82 Gri
Page 359 and 360: Most solar radiation models compute
Page 361 and 362: 2. OBJECTIVES, SCOPE, AND LIMITATIO
Page 363 and 364: Figure 4. BSWM Solar Sensors for Va
Page 365 and 366: 4.2 Site Suitability Analysis Inter
Page 367 and 368: 5. RESULTS AND DISCUSSION 5.1 Month
Page 369: Figure 8. Monthly Average Real-sky
Page 373 and 374: Figure 26. Non-Resource Criteria Su
Page 375 and 376: Kryza, M. et al. 2010. “Spatial i
Page 377 and 378: example coordinates agents, based o
Page 379 and 380: A benefit of using multiple agents
Page 381 and 382: A local GeoServer instance was set
Page 383 and 384: Figure 3. Screen captures of search
Page 385 and 386: In combination with ranking of resu
Page 387 and 388: INTRODUCTION TO A NEW GEO-REFERENCE
Page 389 and 390: Figure 2. ‘Click-to-go’ functio
Page 391 and 392: Figure 5. The conceptual structure
Page 393 and 394: Figure 6. Software architecture of
Page 395 and 396: GIS ORIENTED SERVICE OPTIMIZATION T
Page 397 and 398: Vehicle Routing Problem (VRP) refer
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Page 405 and 406: Analyzing the number of customers,
Page 407 and 408: PO-17 A Study of the Development an
Page 409 and 410: MODELING OF TERMINOLOGY DATABASE IN
Page 411 and 412: 3.2 Interpretation of the terms 4 I
Page 413 and 414: Figure 4 shows the result of SWOT a
Page 415 and 416: Add terms in the database: Super us
Page 417 and 418: 6. REFERENCES Damdinsuren A., 2013.
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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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