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chilli peppers rock cafe c = coffee_shop eis-cafe da vinci c = ice_cream steakhouse cheyenne c = steak_house eiscafé dolce vita c = ice_cream fischkombüse c = seafood baguetterie filou c = sandwich classic western steakhouse c = steak_house shaki sushi c = sushi cafe kamps c = coffee_shop trattoria la grappa c = pizza sakura sushi & grill c = sushi speisekammer c = ice_cream piccola italia c = pizza döner haus c = kebab sushi for friends c = japanese il capriccio c = italian deutscher hof c = german delphi c = greek zum bembelsche c = german mykonos c = greek rhodos c = greek zum neuen schwanen c = german sausalitos c = mexican my thai c = thai mr. kebab c = turkish dschingis khan c = chinese el paso c = mexican café mallorca c = spanish 4.2 Other Amenity and Shop Tags In total, the data set contained 938 different amenity and 1,853 shop tags. The five most frequent amenity tags not related to eating and drinking are bank (18,765 times), pharmacy (16,256), place_of_worship (14,309), parking (10,853) and kindergarten (10,174). The most prominent shop tags are bakery (22,634 times), supermarket (17,655), clothes (14,440), hairdresser (13,310) and butcher (6,862). Overall, we identified 67 reasonable amenity and 73 reasonable shop classes. The cross-validation revealed a classification accuracy of 84%. Applied to real data, we got 4,212 new tags for previously unclassified POIs. We manually checked for each of the 140 considered classes two extrapolated POIs with that class for correctness. The accuracy was about 76%. Considering only the ten most frequent classes listed above, and 10 examples each, the accuracy was 88%, though. Table 3 lists the most frequent k-grams for the main shop tags. Reconsidering our example shop=haidresser, the main k-grams extracted by our program are close to what one would select manually. Interestingly, for supermarket, the k-grams almost exclusively equal supermarket chains. We observed a similar result for gas station chains. Nevertheless, for almost all classes we identified k-grams that occurred in over ten percent of the respective class names. This fact, and the overall good classification accuracy, shows that indeed many names contain classification information. Table 3. K-grams and their for selected shops. bakery supermarket clothes hairdresser butcher 38.75 bäcker 38.71 rei 33.27 bäckerei 11.36 back 11.20 sch 5.62 ste 4.60 mann 2.80 konditorei 2.13 backstube 12.01edeka 11.72 netto 11.42 markt 10.67 rewe 10.03 aldi 6.69 lidl 6.51 penny 5.72 kauf 3.81 norma 10.87 mode 7.94 haus 7.13 kik 5.61 textil 4.41 family 2.91 s.oliver 2.85 jeans 2.31 peek 2.20 kleid 25.45 fris 19.91 friseur 15.91 haar 15.10 salon 13.84 hair 8.96 studio 8.20 friseur 5.00 haarstudio 4.62 cut 54.84 erei 51.62 erei 42.67 ger 35.08 metzger 34.99 metzgerei 24.50 fleisch 16.13 fleischere 16.13 leischerei 4.83 land Automatic Improvement of Points-of-Interest Tags in OSM 22
4.3 Tourism and Leisure Tags We identified 168 different tourism tags of which 16 occurred more than 200 times. Information (45,879) and hotel (12,228) and attraction (9,404) had the highest counts. The others are viewpoint, artwork, hostel, museum, alpine_hut, picnic_site, camp_site, guest_house, caravan_site, chalet, theme_park, apartment, and zoo. For leisure, 153 different tags were contained in the data. Only 9 of them exhibit a high frequency: sports_centre (4,622), playground (3,108), marina (1,734), as well as park, water_park, pitch, stadium, slipway and nature_reserve. We excluded artwork, as due to its nature where is little hope for consistent indicator phrases. Furthermore, we excluded attraction as this class is too diverse and the extracted k-grams were too general. The remaining 23 classes were fed in our classifier. The first cross-validation indicated too much mix-up between information and hotel. Therefore, we first created a hotel classifier in order to prune the information data. After this step, the overall accuracy improved from 62% to 73%. For the real data, we newly augmented 3,452 POIs with a tourism or leisure tag. Computing the precision by manually looking up samples was not so easy in this case, as information tagged entities are often simply signs next to hiking trails. Moreover, most classes were not assigned at all. Therefore, we restricted ourselves in the precision calculation to hotel, playground, marina, and sports_centre. We checked 50 examples for each class. The overall accuracy was 92%. For sports_centre, we even achieved 98% (e.g. ‘tennishalle görner’, ‘willy-lemkens-sportpark’, ‘eissporthalle’, ‘the strike bowlingcenter’, ‘tanzsportzentrum’, ‘turnhalle herringhausen’ are correct examples). 5. CONCLUSIONS AND FUTURE WORK We showed the potential of OSM name tags to serve as basis for extrapolating tags that indicate the class of a POI. Our machine learning approach for automatic tag extrapolation was proven to work well on real data. The accuracy was significantly over 80% for most considered tags. And especially for cuisine, a significant fraction of missing tags was identified with our approach. In future work, other tags beside name tags could be considered to improve the results further. For example, the opening_hour tag could help to distinguish between restaurants and pubs. The brand tag could be helpful when it comes to supermarkets, gas stations, dealerships, clothing stores and so on. Also the free text tags note and description could be parsed for that purpose. Maybe it is possible to learn also the set of indicator tags for each class in an automated way. Furthermore, other countries besides Germany should be investigated. Some tags only occur in certain parts of the world, and the indicator phrases as well as their frequencies for certain tags are expected to change significantly for other countries. 6. REFERENCES Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E., 2011, Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830 Jilani, M., Corcoran, P., and Bertolotto, M., 2014, Automated Highway Tag Assessment of OpenStreetMap Road Networks. Proc. 22nd ACM SIGSPATIAL Int. Conference on Advances in Geographic Information Systems FOSS4G Seoul, South Korea | September 14 th – 19 th , 2015 23
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: 81%. For cuisine=german, the precis
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 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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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
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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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