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5.2 Point Containment Problem No of Points Within Table 1. Points within a polygon vs Time for all implementation. Mongo Non-Index Mongo Index PostGIS Non-Index PostGIS Index 5002 6.75 7.75 52.95 54.97 5471 12 12 85.422 77.508 17004 20.8 24 142.53 158.76 45999 27.66 27.33 160.02 168.23 46008 50 51 675.33 631.556 122006 51.667 53.33 1339.23 1110.42 122016 96 99 2783.66 2609.47 204024 99.667 106 3991.564 3121.11223 298.23 125 112 - - Figure 1. Graph of Point within Vs Time 6. RESULTS Firstly the performance was analyzed without indexing any geometry and time was observed which suggests that MongoDB performs better as the datasize increases whereas PostGIS fails at very large datasets and time complexity increases exponentially. But after indexing the geometries performance of both the database engine improved by a substantial factor. A comparative study of performance analysis of MongoDB and PostGIS/PostGreSQL database systems 42
If we observe the results above we will notice that indexed datasets performs better than non indexed dataset and PostGIS time increases exponentially as size of dataset increases whereas MongoDB still performs within some bounds. 7. DISCUSSIONS AND CONCLUSIONS The above analysis was necessary to evaluate the performance of NoSQL with respect to spatial databases and to tell whether NoSQL performs better than SQL in our current scenario. These results suggest that MongoDB performs better by an average factor of 25x which increases exponentially as the data size increases in both indexed and non-indexed operations for both LineIntersection and Point Containment problem. Given these results NoSQL databases may be better stated for simultaneous multiple-user query systems including Web-GIS and mobile-GIS. This implies that non-relational databases are more suited to the multi-user query systems and has the potential to be implemented in servers with limited computational power. Further studies are required to identify its appropriateness and incorporate a range of spatial algorithms within non-relational databases. In future we are planning on expanding our study to other spatial query functions as well as spatial algorithms such as shortest problem to evaluate the performance of NoSQL on such platforms. 8. ACKNOWLEDGMENT We would like to acknowledge the responses and interactions with MongoDB developers through emails which helped during this research project. 9. REFERENCES References in the text should follow the standard format (Brown, 1997). As Brown (1997) suggested, they should be in the format shown immediately below. References are in 11 pt type. Journal titles should be in full. Journal and book titles should be italicised. Multiple authors should all be listed. Baas, B. Quak, W. Van Oosterom, P. De By, NoSQL spatial: Neo4j versus PostGIS Cláudio de Souza Baptista, Maxwell Guimarães de Oliveira ,Tiago Eduardo da Silva. Using OGC Services to Interoperate Spatial Data Stored in SQL and NoSQL Databases Dr. Johannes Scholz, TU Vienna, Gusshausstr. Coping with Dynamic, Unstructured Data Sets – NoSQL: a buzzword or a savior? Zhifeng Xiao, Yimin Liu. Remote sensing image database based on NOSQL database. http://nosql.mypopescu.com/post/300199706/geo-nosql-couchdb-mongodb-tokyo-cabinet FOSS4G Seoul, South Korea | September 14 th – 19 th , 2015 43
Page 1 and 2: PROCEEDINGS Honil Gangni Yeokdae Gu
Page 3 and 4: C O N T E N T S Welcome Messages Lo
Page 5 and 6: A Week of Sharing, Learning, and Fu
Page 7 and 8: ACADEMIC TRACKS 7
Page 9 and 10: 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: 4.2 Point Containment Problem Like
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 and 62: Figure 5: Map classification of blo
Page 63 and 64: 4.3 Point data Figure 8: Diverging
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
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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
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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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