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4.2 Colour scheme Figures 7 and 8 represent the application of two different colour schemes to the same set of data. Figure 7 uses a red, yellow, green colour scheme, delimited using natural breaks. In contrast, Figure 8 uses a diverging scale around a pivot point for the same data set. In this case, 0.75 was selected as a significant value and therefore applied as the pivot point. Consequently the thematic map highlights the values above and below 0.75. This example shows why the method used to present the data should be tied to the purpose of the resulting visualisation. Figure 7: Red-green colour scheme over natural breaks. 62 Dynamic styling for thematic mapping
4.3 Point data Figure 8: Diverging colour scheme around a pivot point value of 0.75. Figures 9 and 10 show examples of thematic visual styling and contextual representation respectively for data set comprising multivariate point data, in this case the long and short term average of time series data, and the proportion of the two. Quintiles map classification was used in both cases, along with a red, yellow, green colour scheme. Figure 9 shows solely the proportion of the short term to long term averages. Figure 10 shows the same information, with the proportion being included as the label, and the radius determined by the short term average. While a static radius was used for both figures, the minimum radius can be tied to zoom level to dynamically adjust radii of points as a user zooms in. FOSS4G Seoul, South Korea | September 14 th – 19 th , 2015 63
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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
Page 11 and 12: September 16 (Wed) & 18 (Fri) 15:05
Page 13 and 14: AUTOMATIC IMPROVEMENT OF POINT-OF-I
Page 15 and 16: In the following, we will list
Page 17 and 18: and specify their relations manuall
Page 19 and 20: Conducting a cross-validation on th
Page 21 and 22: 81%. For cuisine=german, the precis
Page 23 and 24: 4.3 Tourism and Leisure Tags We ide
Page 25 and 26: GENERATING GEOSPATIAL FOOTPRINTS FO
Page 27 and 28: quality assessment of geocrowdsourc
Page 29 and 30: Figure 2: The first simple case of
Page 31 and 32: Figure 5: The footprint of walkways
Page 33 and 34: say instead “Between 1st Street a
Page 35 and 36: 4. DISCUSSION AND CONCLUSIONS The G
Page 37 and 38: Performance Analysis of MongoDB Vs.
Page 39 and 40: 2. REVIEW OF SPATIAL DATABASES Simi
Page 41 and 42: 4.2 Point Containment Problem Like
Page 43 and 44: If we observe the results above we
Page 45 and 46: esolution data (RapidEye and Landsa
Page 47 and 48: and used for calibration and coeffi
Page 49 and 50: scatter plot of RapidEye (Global (C
Page 51 and 52: is efficient to extrapolate depth f
Page 53 and 54: DYNAMIC STYLING FOR THEMATIC MAPPIN
Page 55 and 56: all) datasets on offer or applying
Page 57 and 58: 3.2.1 Data inputs The styler accept
Page 59 and 60: determine the radius (both in the x
Page 61: Figure 5: Map classification of blo
Page 65 and 66: 4.4 Non-spatial visualisations Figu
Page 67 and 68: 6 ACKNOWLEDGEMENTS The Cooperative
Page 69 and 70: educational resources (OER) and mod
Page 71 and 72: workshops have revealed that microc
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Page 75 and 76: H 27 Male Student Maths / Geography
Page 77 and 78: 5. CONCLUSIONS AND FUTURE WORK We h
Page 79 and 80: DEVELOPMENT OF DATA ARCHIVING AND D
Page 81 and 82: 2. RELATED WORK Recent developments
Page 83 and 84: 3. Significance and Objectives With
Page 85 and 86: Figure 3. Map Tiles Grid Selection
Page 87 and 88: The Ceph architecture comprises of
Page 89 and 90: Boundless, 2015. GeoExplorer — Ge
Page 91 and 92: Object-Based Building Boundary Extr
Page 93 and 94: 2.2. A Grid-Based Algorithm A grid-
Page 95 and 96: 3.1 Boundary Extraction The extract
Page 97 and 98: The Development of Web3D-based Open
Page 99 and 100: Singh et al.(2012) have utilized op
Page 101 and 102: Monitoring concept on operating asp
Page 103 and 104: (a) Mining area (Left:2007, Right:2
Page 105 and 106: geospatial information open platfor
Page 107 and 108: open platform utilized in open-pit
Page 109 and 110: Data Acquisition Methods Acquisitio
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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
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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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