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Rice, M. T., Curtin, K. M., Paez, F. I., Seitz, C. R., & Qin, H. (2013a). Crowdsourcing to Support Navigation for the Disabled: A Report on the Motivations, Design, Creation and Assessment of a Testbed Environment for Accessibility (US Army Corps of Engineers, Engineer Research and Development Center, US Army Topographic Engineering Center Technical Report, Data Level Enterprise Tools Workgroup No. BAA: #AA10-4733, Contract: # W9132V-11-P-0011) (pp. 1– 62). Fairfax, VA: George Mason University. Retrieved from http://oai.dtic.mil/oai/oai? verb=getRecord&metadataPrefix=html&identifier=ADA588474 Rice, M. T., Hammill, W. C., Aburizaiza, A. O., Schwarz, S., & Jacobson, R. D. (2011). Integrating User-contributed Geospatial Data with assistive Geotechnology Using a localized Gazetteer. In A. Ruas (Ed.), Advances in Cartography and GIScience. Volume 1 (pp. 279–291). Springer Berlin Heidelberg. Retrieved from http://dx.doi.org/10.1007/978-3-642-19143-5_16 Rice, M. T., Jacobson, R. D., Caldwell, D. R., McDermott, S. D., Paez, F. I., Aburizaiza, A. O., … Qin, H. (2013b). Crowdsourcing techniques for augmenting traditional accessibility maps with transitory obstacle information. Cartography and Geographic Information Science, 40(3), 210– 219. http://doi.org/10.1080/15230406.2013.799737 Rice, M. T., Paez, F. I., Mulhollen, A. P., Shore, B. M., & Caldwell, D. R. (2012b). Crowdsourced Geospatial Data: A report on the emerging phenomena of crowdsourced and user-generated geospatial data (Annual No. AA10-4733). Fairfax, VA: George Mason University. Retrieved from http://www.dtic.mil/dtic/tr/fulltext/u2/a576607.pdf Rice, M. T., Paez, F. I., Rice, R. M., Ong, E. W., Qin, H., Seitz, C. R., … Medina, R. M. (2014). Quality Assessment and Accessibility Applications of Crowdsourced Geospatial Data: A report on the development and extension of the George Mason University Geocrowdsourcing Testbed (Annual No. BAA: #AA10-4733, Contract: # W9132V-11-P-0011) (p. 91). Fairfax, VA: George Mason University. Rice, Rebecca M. (2015).Validating VGI Data Quality in Local Crowdsourced Accessibility Mapping Applications: A George Mason University Case Study (Master of Science Thesis, July 2015). George Mason University, Fairfax, VA. Rice, R.M., A.O. Aburizaiza, M.T. Rice, and H. Qin. (2015) “Position Validation in Crowdsourced Accessibility Mapping”, Cartograpica (in press). Generating Footprints for Geoparsed Text from Crowdsourced Platial Data 36
Performance Analysis of MongoDB Vs. PostGIS/PostGreSQL Databases For Line Intersection and Point Containment Spatial Queries Sarthak Agarwal 1 and KS Rajan 2 1 Lab for Spatial Informatics, International Institute of Information Technology Gachibowli, Hyderabad 500032, India Email: sarthak.a@research.iiit.ac.in 2 Lab for Spatial Informatics, International Institute of Information Technology Gachibowli, Hyderabad 500032, India Email: rajan@iiit.ac.in ABSTRACT Relational databases have been around for a long time and Spatial databases have exploited this feature for close to two decades. The recent past has seen the development of NoSQL non-relational databases, which are now being adopted for spatial object storage and handling too. And this is gaining ground in the context of increased shift towards GeoSpatial Web Services on both the Web and mobile platforms especially in the usercentric services, where there is a need to improve the query response time. While SQL databases face scalability and agility challenges and fail to take the advantage of the cheap memory and processing power available these days, NoSQL databases can handle the rise in the data storage and frequency at which it is accessed and processed - which are essential features needed in geospatial scenarios, which do not deal with a fixed schema(geometry) and fixed data size. This paper attempts to evaluate the performance of an existing NoSQL database 'MongoDB' with its inbuilt spatial functions with that of a SQL database with spatial extension 'PostGIS' for two primitive spatial problems - LineIntersection and Point Containment problem, across a range of datasets, with varying features counts. For LineIntersection function, the dataset consisted of two independent layers of horizontal lines and vertical lines with incremental lengths and their size varied from ten lines to million lines in each layer and another dataset with two layers, one of random lines of variable size and shape and another layer of a single line which is intersecting many lines of layer1. For Point Containment problem, the dataset consists of two layers, one of polygons in space of different shape and size and another layer of random points in the space, some inside the polygons and some outside. All the data in the analysis was processed In-memory and no secondary memory was used. Initial results suggest that MongoDB performs better by an average factor of 25x for Line Intersection Problem and 10x for Point Containment Problem which increases exponentially as the data size increases in both indexed and nonindexed operations. Given these results NoSQL databases may be better suited for simultaneous multiple-user query systems including Web-GIS and mobile-GIS. Further studies are required to understand the full potential of NoSQL databases across various geometries and spatial query types. 1. INTRODUCTION Traditionally Databases were designed to structure and organize any form of data. But as the database size increased and to optimize databases for Geo Spatial domain we use Spatial Databases. Satellite images are one prominent example of spatial databases. To extract spatial information from a satellite image it has to be processed with respect to a spatial frame of reference. But satellites are not the only type of spatial databases. Maps are also stored in spatial database. Like other database systems Spatial databases have also relied on the Relational databases to handle and manage spatial objects and their associated attribute information. They have been of great value in cases where we have a defined structure of our schema, including geometrical characteristics of the spatial objects. While these have been able to FOSS4G Seoul, South Korea | September 14 th – 19 th , 2015 37
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
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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
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Page 35: 4. DISCUSSION AND CONCLUSIONS The G
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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 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
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Page 75 and 76: H 27 Male Student Maths / Geography
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Page 79 and 80: DEVELOPMENT OF DATA ARCHIVING AND D
Page 81 and 82: 2. RELATED WORK Recent developments
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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
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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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