Eliza Shrestha
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
A Review of Openstreetmap Data Peter Mooney* and Marco Minghini† *Department of Computer Science, Maynooth University, Maynooth, Co
CHAPTER 3 A Review of OpenStreetMap Data Peter Mooney* and Marco Minghini† *Department of Computer Science, Maynooth University, Maynooth, Co. Kildare, Ireland, [email protected] †Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy Abstract While there is now a considerable variety of sources of Volunteered Geo- graphic Information (VGI) available, discussion of this domain is often exem- plified by and focused around OpenStreetMap (OSM). In a little over a decade OSM has become the leading example of VGI on the Internet. OSM is not just a crowdsourced spatial database of VGI; rather, it has grown to become a vast ecosystem of data, software systems and applications, tools, and Web-based information stores such as wikis. An increasing number of developers, indus- try actors, researchers and other end users are making use of OSM in their applications. OSM has been shown to compare favourably with other sources of spatial data in terms of data quality. In addition to this, a very large OSM community updates data within OSM on a regular basis. This chapter provides an introduction to and review of OSM and the ecosystem which has grown to support the mission of creating a free, editable map of the whole world. The chapter is especially meant for readers who have no or little knowledge about the range, maturity and complexity of the tools, services, applications and organisations working with OSM data. We provide examples of tools and services to access, edit, visualise and make quality assessments of OSM data. We also provide a number of examples of applications, such as some of those How to cite this book chapter: Mooney, P and Minghini, M. -
Inferring and Improving Street Maps with Data-Driven Automation
Inferring and Improving Street Maps with Data-Driven Automation Favyen Bastani1, Songtao He1, Satvat Jagwani1, Edward Park1, Sofiane Abbar2, Mohammad Alizadeh1, Hari Balakrishnan1, Sanjay Chawla2, Sam Madden1, Mohammad Amin Sadeghi, 1MIT CSAIL, ffavyen,songtao,satvat,parke,alizadeh,hari,[email protected], 2Qatar Computer Research Institute, fsabbar, [email protected] September 21, 2019 1 Introduction sulting the data. These data sources include satellite imagery, aerial imagery, and GPS trajectories (which Street maps help to inform a wide range of decisions. consist of sequences of GPS positions captured from Drivers, cyclists, and pedestrians rely on street maps moving vehicles). Although the data presented by for search and navigation. Rescue workers respond- these tools help users to update a map dataset, the ing to disasters like hurricanes, tsunamis, and earth- manual tracing and annotation process is cumber- quakes rely on street maps to understand where peo- some and a major bottleneck in map maintenance. ple are, and to locate individual buildings [23]. Trans- Over the past decade, many automatic map in- portation researchers rely on street maps to conduct ference systems have been proposed to automati- transportation studies, such as analyzing pedestrian cally extract information from these data sources at accessibility to public transport [25]. Indeed, with scale. Several approaches develop unsupervised clus- the need for accurate street maps growing in impor- tering and density thresholding algorithms to con- tance, companies are spending hundreds of millions struct road networks from GPS trajectory datasets [1, 1 of dollars to map roads globally . 4, 7, 16, 21]. Others apply machine learning meth- However, street maps are incomplete or lag behind ods to process satellite imagery and extract road net- new construction in many parts of the world. -
2. Lab of the Month 1. Activities of the Network
2. Lab of the Month GeoDa Centre, Arizona State University, USA By Suchith Anand Table of Contents Suchith Anand, Editorial Nottingham Geospatial Institute, University of 1. Activities …………………..……… 1 Nottingham, UK 2. Lab of the month..…............. 1 Editorial Board ………………....…. 2 1. Activities of Dear Geo4All Colleagues, 3. Events …………………..…....……. 4 It is my great pleasure, to introduce 4. Conferences ……….…….…….… 4 the Network our colleagues at GeoDa Center for Geospatial Analysis and Computation 5. Webinars ………………………….. 6 at the Arizona State University as our 6. Courses ………….…….…………... 7 Ottawa, Ontario, OSGeo Meetup “Geo4All” lab of the month. Group meets on the third 7. Training programs ……………. 7 The GeoDa Center for Geospatial Thursday of each month. If you 8. Key research publications… 8 Analysis and Computation develops are located in the area, go to the 9. Funding opportunities state-of-the-art methods for link to sign up to the group and geospatial analysis, geovisualization, 10. Free and open software, geosimulation, and spatial process open data ………….….…..………… 9 get updates about future events. modeling, implements them through 11. Free Books open source software tools, applies OpenStreetMap webinar and 12. Articles …………………......... 11 them to policy-relevant research in Humanitarian Mapathons took the social and environmental sciences, 13. Scholarships for students th place in May 5 in GEOlab, and disseminates them through and staff …………….……………… 15 Politecnico di Milano. For those training and support to a growing 14. Exchange programs for students and staff interested in the slides and videos worldwide community. The GeoDa please go to Center deals with research questions 15. Awards ………….….………….. 12 http://www.geoforall.org/webina at different spatial scales, from the 16. -
Arxiv:2008.02653V2 [Cs.CY] 12 Oct 2020 P a Fteerpa Commission
OPENSTREETMAP DATA USE CASES DURING THE EARLY MONTHS OF THE COVID-19 PANDEMIC A PREPRINT SUBMITTED TO THE UN GGIM EDITED VOLUME: COVID-19: GEOSPATIAL INFORMATION AND COMMUNITY RESILIENCE Peter Mooney A. Yair Grinberger Department of Computer Science Department of Geography Maynooth University, Ireland Hebrew University of Jerusalem, Israel [email protected] [email protected] Marco Minghini∗ Digital Economy Unit European Commission, Joint Research Centre, Italy [email protected] Serena Coetzee Levente Juhasz Department of Geography, Geoinformatics and Meteorology GIS Center University of Pretoria, South Africa Florida International University, USA [email protected] [email protected] Godwin Yeboah Institute for Global Sustainable Development University of Warwick, UK [email protected] October 13, 2020 Abstract arXiv:2008.02653v2 [cs.CY] 12 Oct 2020 Created by volunteers since 2004, OpenStreetMap (OSM) is a global geographic database available under an open access license and currently used by a multitude of actors worldwide. This chapter describes the role played by OSM during the early months (from January to July 2020) of the ongoing COVID-19 pandemic, which - in contrast to past disasters and epidemics - is a global event impacting both developed and developing countries. A large number of COVID-19-related OSM use cases were collected and grouped into a number of research frameworks which are analyzed separately: dashboards and services simply using OSM as a basemap, applications using raw OSM data, initiatives to collect new OSM data, imports of authoritative data into OSM, and traditional academic research on OSM in the COVID-19 response. -
Generating Up-To-Date and Detailed Land Use and Land Cover Maps Using Openstreetmap and Globeland30
International Journal of Geo-Information Article Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30 Cidália Costa Fonte 1,2,*, Marco Minghini 3, Joaquim Patriarca 2, Vyron Antoniou 4,5, Linda See 6 and Andriani Skopeliti 7 1 Department of Mathematics, University of Coimbra, Largo D. Dinis, 3001-501 Coimbra, Portugal 2 INESC Coimbra, Rua Sílvio Lima, Pólo II, 3030-290 Coimbra, Portugal; [email protected] 3 Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; [email protected] 4 Hellenic Military Academy, Leof. Varis—Koropiou, 16673 Vari, Greece; [email protected] 5 Hellenic Military Geographical Service, 4, Evelpidon Str., 11362 Athens, Greece 6 International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A2361 Laxenburg, Austria; [email protected] 7 School of Rural and Surveying Engineering, National Technical University of Athens, 9 H. Polytechniou, 15780 Zografou, Greece; [email protected] * Correspondence: [email protected]; Tel.: +351-239-791-150 Academic Editor: Wolfgang Kainz Received: 4 March 2017; Accepted: 17 April 2017; Published: 22 April 2017 Abstract: With the opening up of the Landsat archive, global high resolution land cover maps have begun to appear. However, they often have only a small number of high level land cover classes and they are static products, corresponding to a particular period of time, e.g., the GlobeLand30 (GL30) map for 2010. The OpenStreetMap (OSM), in contrast, consists of a very detailed, dynamically updated, spatial database of mapped features from around the world, but it suffers from incomplete coverage, and layers of overlapping features that are tagged in a variety of ways. -
Quantifying Gendered Participation in Openstreetmap: Responding to Theories of Female (Under) Representation in Crowdsourced Mapping
GeoJournal https://doi.org/10.1007/s10708-019-10035-z (0123456789().,-volV)(0123456789().,-volV) Quantifying gendered participation in OpenStreetMap: responding to theories of female (under) representation in crowdsourced mapping Z. Gardner . P. Mooney . S. De Sabbata . L. Dowthwaite Ó The Author(s) 2019 Abstract This paper presents the results of an gender of 293 OSM users. Statistics relating to users’ exploratory quantitative analysis of gendered contri- editing and tagging behaviours openly accessible via butions to the online mapping project OpenStreetMap the ‘how did you contribute to OSM’ wiki page were (OSM), in which previous research has identified a subsequently analysed. The results reveal that vol- strong male participation bias. On these grounds, umes of overall activity as well editing and tagging theories of representation in volunteered geographic actions in OSM remain significantly dominated by information (VGI) have argued that this kind of men. They also indicate subtle but impactful differ- crowdsourced data fails to embody the geospatial ences in men’s and women’s preferences for modify- interests of the wider community. The observed ing and creating data, as well as the tagging categories effects of the bias however, remain conspicuously to which they contribute. Discourses of gender and absent from discourses of VGI and gender, which ICT, gender relations in online VGI environments and proceed with little sense of impact. This study competing motivational factors are implicated in these addresses this void by analysing OSM contributions observations. As well as updating estimates of the by gender and thus identifies differences in men’s and gender participation bias in OSM, this paper aims to women’s mapping practices. -
Master Thesis Understanding Mapswipe
Ruprecht-Karls-Universität Heidelberg Geographisches Institut Master Thesis Understanding MapSwipe: Analysing Data Quality of Crowdsourced Classifications on Human Settlements Benjamin Herfort Matrikel-Nr. 3040449 Supervisors: Prof. Dr. Alexander Zipf Prof. Dr. João Porto de Albuquerque 20.10.2017 Content Content Content ................................................................................................................................. I List of Figures .................................................................................................................... IV List of Tables ...................................................................................................................... V List of Abbreviations ......................................................................................................... VI Abstract ............................................................................................................................ VII 1 Introduction .................................................................................................................. 1 2 Related Works .............................................................................................................. 5 2.1 Crowdsourcing Geographic Information .............................................................. 5 2.1.1 Classification.................................................................................................. 5 2.1.2 Digitization ................................................................................................... -
2017-Annual-Report.Pdf
A Message from Our Executive Director Looking back on 2017, one of the things I’m most proud of is our community’s ability to adapt and respond to some of the most complex humanitarian and development challenges. This year, HOT pioneered new approaches for working directly with Syrian refugees in Turkey and South Sudanese refugees in Uganda. By teaching use of OpenStreetMap to document services (and gaps) in their new countries, refugees and host community members took an active role in humanitarian response, pushing Caption goes here. the system to become more accountable to the populations it seeks to serve. Leserum voloria HOT also pioneered new methods of open data collection to make cities spelia dolo minctas more resilient to natural hazards. These approaches were field tested in some CONTENTS nus aceaquam of the world’s largest and fastest growing cities: Jakarta and Dar es Salaam, cullace pedigendem respectively. ipsam quae A Message from our Executive Director 3 volupta eossum Where we did not have staff on the ground, HOT donors came together to sundest otaquatqui fund “Microgrants” for seven small-scale, big-impact open mapping projects. What we do 4 velibernati dolor Each grantee’s project focused on increasing resilience to or responding a volorporro to local hazards and was designed and executed entirely by local leaders. Our Impact blaboreperum The HOT global volunteer community activated a total of 11 times for coreicidel disaster responses, delivering critical base map data to humanitarian partner maionsequis et qua. 11. Refugee Response 6 organizations. We proudly launched a new version of our Tasking Manager and “bridged the gap” between citizen generated data and the humanitarian 22. -
Abstract Utilization of Crowdsourcing And
ABSTRACT UTILIZATION OF CROWDSOURCING AND VOLUNTEERED GEOGRAPHIC INFORMATION IN INTERNATIONAL DISASTER MANAGEMENT by Julaiti Nilupaer Large-scale disasters result in enormous impacts on vulnerable communities worldwide, and data acquisition has become a major concern in this time-critical situation: the limitations of geospatial technologies impede the real-time data collection, also the absent or poor data collection in some regions. With the current advances of Web 2.0, crowdsourcing and Volunteered Geographic Information (VGI) have become commonly used. As a potential solution to fill the gap of real-time geographic data, crowdsourcing and VGI enable timely information exchange through a voluntary approach and enhance amateur citizen participation. Importantly, such geographic information can substantially facilitate emergency coordination by fulfilling the needs of impacted communities and appropriately allocating relief supplies and funds. My research interest centers on the utilization of crowdsourcing and VGI for disaster management. Particularly, I work to explore their potential value and contributions by reviewing two notable and destructive disaster events as case studies: the 2011 Tohoku Earthquake and Tsunami, and the 2013 Typhoon Haiyan. In addition, I examine the challenges of this information and seek potential solutions. This research aims to contribute a comprehensive qualitative analysis of how Volunteer and Technical Communities (V&TCs) have used crowdsourced data and VGI to enhance the coordination of disaster management. -
Corporate Editors in the Evolving Landscape of Openstreetmap
International Journal of Geo-Information Article Corporate Editors in the Evolving Landscape of OpenStreetMap Jennings Anderson 1,* , Dipto Sarkar 2 and Leysia Palen 1,3 1 Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309, USA; [email protected] 2 Department of Geography, National University of Singapore, Singapore 119077, Singapore; [email protected] 3 Department of Information Science, University of Colorado Boulder, Boulder, CO 80309, USA * Correspondence: [email protected] Received: 18 April 2019; Accepted: 14 May 2019; Published: 18 May 2019 Abstract: OpenStreetMap (OSM), the largest Volunteered Geographic Information project in the world, is characterized both by its map as well as the active community of the millions of mappers who produce it. The discourse about participation in the OSM community largely focuses on the motivations for why members contribute map data and the resulting data quality. Recently, large corporations including Apple, Microsoft, and Facebook have been hiring editors to contribute to the OSM database. In this article, we explore the influence these corporate editors are having on the map by first considering the history of corporate involvement in the community and then analyzing historical quarterly-snapshot OSM-QA-Tiles to show where and what these corporate editors are mapping. Cumulatively, millions of corporate edits have a global footprint, but corporations vary in geographic reach, edit types, and quantity. While corporations currently have a major impact on road networks, non-corporate mappers edit more buildings and points-of-interest: representing the majority of all edits, on average. Since corporate editing represents the latest stage in the evolution of corporate involvement, we raise questions about how the OSM community—and researchers—might proceed as corporate editing grows and evolves as a mechanism for expanding the map for multiple uses. -
Openstreetmap (OSM)
OpenStreetMap (OSM) https://www.openstreetmap.org ● OpenStreetMap (OSM) è il più noto progetto diInformazione Geografica Volontaria (VGI) ○ iniziato da Steve Coast in UK nel 2004 OpenStreetMap (OSM) https://www.openstreetmap.org ● OpenStreetMap (OSM) è il più noto progetto diInformazione Geografica Volontaria (VGI) ○ iniziato da Steve Coast in UK nel 2004 ○ finalizzato alla creazione di un database geografico ‘crowdsourced’ libero del mondo OpenStreetMap (OSM) https://www.openstreetmap.org ● OpenStreetMap (OSM) è il più noto progetto diInformazione Geografica Volontaria (VGI) ○ iniziato da Steve Coast in UK nel 2004 ○ finalizzato alla creazione di un database geografico ‘crowdsourced’ libero del mondo OpenStreetMap (OSM) https://www.openstreetmap.org ● OpenStreetMap (OSM) è il più noto progetto diInformazione Geografica Volontaria (VGI) ○ iniziato da Steve Coast in UK nel 2004 ○ finalizzato alla creazione di un database geografico ‘crowdsourced’ libero del mondo ■ il database geografico più grande, vario, completo ed aggiornato al mondo ■ la Wikipedia delle mappe ~ OpenStreetMap (OSM) https://www.openstreetmap.org ● OpenStreetMap (OSM) è il più noto progetto diInformazione Geografica Volontaria (VGI) ○ iniziato da Steve Coast in UK nel 2004 ○ finalizzato alla creazione di un database geografico ‘crowdsourced’ libero del mondo ■ il database geografico più grande, vario, completo ed aggiornato al mondo ■ la Wikipedia delle mappe ■ OpenStreetMap non è un’alternativa a Google Maps! Un database, tante mappe... https://www.openstreetmap.org Perché OpenStreetMap? ● La crescita e il successo del progetto OSM sono dovuti a un insieme di fattori: ○ societari & economici: ■ mancanza di dati geografici ufficiali in molte parti del mondo ■ presenza di dati geografici a pagamento e/o con licenze di utilizzo restrittive ○ tecnologici: ■ diffusione di Internet eWeb 2.0 ■ disponibilità di dispositivi GPS a basso costo ■ disponibilità di immagini satellitari ad alta risoluzione ● 10 anni di OSM (video): https://www.youtube.com/watch?v=7sC83j6vzjo OpenStreetMap vs. -
Motivation for Contribution to Volunteered Geographic Information in Nepal
Motivation for Contribution to Volunteered Geographic Information in Nepal By Yoshiatsu Tanaka Bachelor of Engineering (Keio University, Japan) 2014 Thesis Submitted in partial satisfaction of the requirements for the degree of Master of Science in Community Development in the Office of Graduate Studies of the University of California, Davis Approved: Jonathan London, Chair Heidi Ballard Sheryl-Ann Simpson Committee in Charge 2017 i Acknowledgements This thesis would not have been possible without the support of many people. First, my academic advisor, Dr. Jonathan London, has been fundamental to the development of my research from the beginning. Second, my thesis committee, Dr. Heidi Ballard and Dr. Sheryl- Ann Simpson have supported the research process, method, analysis, and recommendation. Third, Dr. Nama Budhathoki has supported the research process and given me academic advice. Additionally, in Nepal, I am so grateful to the staff members, interns, and former staff members at Kathmandu Living Labs for their contribution to this research. In the United States, I would like to express my deepest gratitude to Isao Fujimoto, Karen Watson-Gegeo, Patsy Eubanks Owens, Stephen Wheeler, Janet Lane, and Nancy Erbstein for their support and teachings. Special thank you to Carrie Armstrong-Ruport, Ai Namiki, Brandon Louie, Jeffrey Lee, Mohnish Sen, Yohei Kato, Yuichiro Hattori, and Yuko Nakamura. Lastly, I would like to thank my cohort, family, and friends. ii Abstract Volunteered Geographic Information (VGI) is a geographic information provided by individuals and has drawn increasing attention in various fields. This study presents an examination of motivation for contribution to OpenStreetMap, a site for volunteered geographic information.