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Panda MPHC-Kachako Primary Health Center 3 Wudil 0.36 0.55 0.36 0.35 0.45 Sum Total 67.52 55.99 46.17 54.45 58.96 The error margins reported in table 7 and figure 3 were largely as a result of road quality in the rural area and traffic condition in the urban area, which affected drivers driving behaviors with respect to choice of routes. The cumulative margin of error ranges between 67.52 km to 46.17km. The maximum error margin was reported for QRG routing tool while the minimum was reported for GH both using OSM base map. In this case, choice of different routes must have being considered as a result of different routing algorithms used (a case for future investigation). When the cumulative error margin of a routing tool using different base map (GME) is considered with QRG, the difference is very close (QRG – 67.52, GME – 55.99), an indication that similar algorithm has been used. In view of the degree of error margin reported, it is not enough to draw conclusion on the best or otherwise routing tools based on this evident. Hence it is imperative to consider other valuable parameters which will be used as criteria for subsequent ranking of these routing tools. 288
Figure 3 - Comparing Discrepancy in Drive Test Survey and Desktop Routing Estimation 2.3. MultiCriteria Ranking 289
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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
Page 237 and 238: ANALYSIS OF SPATIAL DENSITY UTILIZI
Page 239 and 240: demographic data. He also states th
Page 241 and 242: Table 1. The combination of COUNTRY
Page 243 and 244: NUMBER OF SUBSCRIBERS 300,000 250,0
Page 245 and 246: Figure 9. The number of foreign tou
Page 247 and 248: Figure 16. Spatial call activity pa
Page 249 and 250: Recently, additional tourist inform
Page 251 and 252: country. It uses R-Studio, a statis
Page 253 and 254: Geosocial Big Data Analysis Using P
Page 255 and 256: AN ON-BOARD VISUAL-BASED ATTITUDE E
Page 257 and 258: 3.1 Keypoints detection and matchin
Page 259 and 260: complete the process. This has been
Page 261 and 262: Fleet, D., & Weiss, Y. (2006). Opti
Page 263 and 264: technologies in Geoinformation Scie
Page 265 and 266: technologies becomes irrelevant in
Page 267 and 268: Figure 2. Server (upper block) and
Page 269 and 270: Name Presence Description Table 1.
Page 271 and 272: SampleExtraApp sampleExtraApp annot
Page 273 and 274: 4. RESULTS Summing up, we should no
Page 275 and 276: Paradigms. John Wiley & Sons Inc.,
Page 277 and 278: FREEWAT: FREE and open source tools
Page 279 and 280: An Evaluation of Open Source Geogra
Page 281 and 282: 2. DATA AND METHOD The approach ado
Page 283 and 284: the beginning and end of each trip
Page 285 and 286: Table 4 - GIS Routing Tools S/N GIS
Page 287: State cold store- Hungu Primary Hea
Page 291 and 292: nations based on human per capita i
Page 293 and 294: investigating metropolitan traffic.
Page 295 and 296: A Cross National Comparison on the
Page 297 and 298: in 1980(Saaty, 1980). It assesses t
Page 299 and 300: ex or current government officials
Page 301 and 302: Herold, S., & Sawada, M. C. (2012).
Page 303 and 304: frameworkwhich covers an infrastruc
Page 305 and 306: The architecture of the application
Page 307 and 308: 5. CONCLUDING REMARKS Open source s
Page 309 and 310: cloud environment, so that end-user
Page 311 and 312: Figure 2 represents user interface
Page 313 and 314: geo-science application. Also the o
Page 315 and 316: PO-04 GeoDjango-Framwork-based Popu
Page 317 and 318: pH of 5.5 to 7.0; forest loam, rock
Page 319 and 320: Figure 4. Study Area (Davao Region)
Page 321 and 322: Figure 6. Topographic Map of Mindan
Page 323 and 324: Figure 8. Land Areas suitable for C
Page 325 and 326: It is respectfully recommended that
Page 327 and 328: ECDIS through introduction of Open
Page 329 and 330: to adopt development using open sou
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
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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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