An Overview of Geotagging Geospatial Location
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Metadata and GIS
Metadata and GIS ® An ESRI White Paper • October 2002 ESRI 380 New York St., Redlands, CA 92373-8100, USA • TEL 909-793-2853 • FAX 909-793-5953 • E-MAIL [email protected] • WEB www.esri.com Copyright © 2002 ESRI All rights reserved. Printed in the United States of America. The information contained in this document is the exclusive property of ESRI. This work is protected under United States copyright law and other international copyright treaties and conventions. No part of this work may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording, or by any information storage or retrieval system, except as expressly permitted in writing by ESRI. All requests should be sent to Attention: Contracts Manager, ESRI, 380 New York Street, Redlands, CA 92373-8100, USA. The information contained in this document is subject to change without notice. U.S. GOVERNMENT RESTRICTED/LIMITED RIGHTS Any software, documentation, and/or data delivered hereunder is subject to the terms of the License Agreement. In no event shall the U.S. Government acquire greater than RESTRICTED/LIMITED RIGHTS. At a minimum, use, duplication, or disclosure by the U.S. Government is subject to restrictions as set forth in FAR §52.227-14 Alternates I, II, and III (JUN 1987); FAR §52.227-19 (JUN 1987) and/or FAR §12.211/12.212 (Commercial Technical Data/Computer Software); and DFARS §252.227-7015 (NOV 1995) (Technical Data) and/or DFARS §227.7202 (Computer Software), as applicable. Contractor/Manufacturer is ESRI, 380 New York Street, Redlands, CA 92373- 8100, USA. -
Creating Permissionless Blockchains of Metadata Records Dejah Rubel
Articles No Need to Ask: Creating Permissionless Blockchains of Metadata Records Dejah Rubel ABSTRACT This article will describe how permissionless metadata blockchains could be created to overcome two significant limitations in current cataloging practices: centralization and a lack of traceability. The process would start by creating public and private keys, which could be managed using digital wallet software. After creating a genesis block, nodes would submit either a new record or modifications to a single record for validation. Validation would rely on a Federated Byzantine Agreement consensus algorithm because it offers the most flexibility for institutions to select authoritative peers. Only the top tier nodes would be required to store a copy of the entire blockchain thereby allowing other institutions to decide whether they prefer to use the abridged version or the full version. INTRODUCTION Several libraries and library vendors are investigating how blockchain could improve activities such as scholarly publishing, content dissemination, and copyright enforcement. A few organizations, such as Katalysis, are creating prototypes or alpha versions of blockchain platforms and products.1 Although there has been some discussion about using blockchains for metadata creation and management, only one company appears to be designing such a product. Therefore, this article will describe how permissionless blockchains of metadata records could be created, managed, and stored to overcome current challenges with metadata creation and management. LIMITATIONS OF CURRENT PRACTICES Metadata standards, processes, and systems are changing to meet twenty-first century information needs and expectations. There are two significant limitations, however, to our current metadata creation and modification practices that have not been addressed: centralization and traceability. -
Mapping Spatiotemporal Data to RDF: a SPARQL Endpoint for Brussels
International Journal of Geo-Information Article Mapping Spatiotemporal Data to RDF: A SPARQL Endpoint for Brussels Alejandro Vaisman 1, * and Kevin Chentout 2 1 Instituto Tecnológico de Buenos Aires, Buenos Aires 1424, Argentina 2 Sopra Banking Software, Avenue de Tevuren 226, B-1150 Brussels, Belgium * Correspondence: [email protected]; Tel.: +54-11-3457-4864 Received: 20 June 2019; Accepted: 7 August 2019; Published: 10 August 2019 Abstract: This paper describes how a platform for publishing and querying linked open data for the Brussels Capital region in Belgium is built. Data are provided as relational tables or XML documents and are mapped into the RDF data model using R2RML, a standard language that allows defining customized mappings from relational databases to RDF datasets. In this work, data are spatiotemporal in nature; therefore, R2RML must be adapted to allow producing spatiotemporal Linked Open Data.Data generated in this way are used to populate a SPARQL endpoint, where queries are submitted and the result can be displayed on a map. This endpoint is implemented using Strabon, a spatiotemporal RDF triple store built by extending the RDF store Sesame. The first part of the paper describes how R2RML is adapted to allow producing spatial RDF data and to support XML data sources. These techniques are then used to map data about cultural events and public transport in Brussels into RDF. Spatial data are stored in the form of stRDF triples, the format required by Strabon. In addition, the endpoint is enriched with external data obtained from the Linked Open Data Cloud, from sites like DBpedia, Geonames, and LinkedGeoData, to provide context for analysis. -
How to Find the Best Hashtags for Your Business Hashtags Are a Simple Way to Boost Your Traffic and Target Specific Online Communities
CHECKLIST How to find the best hashtags for your business Hashtags are a simple way to boost your traffic and target specific online communities. This checklist will show you everything you need to know— from the best research tools to tactics for each social media network. What is a hashtag? A hashtag is keyword or phrase (without spaces) that contains the # symbol. Marketers tend to use hashtags to either join a conversation around a particular topic (such as #veganhealthchat) or create a branded community (such as Herschel’s #WellTravelled). HOW TO FIND THE BEST HASHTAGS FOR YOUR BUSINESS 1 WAYS TO USE 3 HASHTAGS 1. Find a specific audience Need to reach lawyers interested in tech? Or music lovers chatting about their favorite stereo gear? Hashtags are a simple way to find and reach niche audiences. 2. Ride a trend From discovering soon-to-be viral videos to inspiring social movements, hashtags can quickly connect your brand to new customers. Use hashtags to discover trending cultural moments. 3. Track results It’s easy to monitor hashtags across multiple social channels. From live events to new brand campaigns, hashtags both boost engagement and simplify your reporting. HOW TO FIND THE BEST HASHTAGS FOR YOUR BUSINESS 2 HOW HASHTAGS WORK ON EACH SOCIAL NETWORK Twitter Hashtags are an essential way to categorize content on Twitter. Users will often follow and discover new brands via hashtags. Try to limit to two or three. Instagram Hashtags are used to build communities and help users find topics they care about. For example, the popular NYC designer Jessica Walsh hosts a weekly Q&A session tagged #jessicasamamondays. -
Text and Data Mining: Technologies Under Construction
Text and Data Mining: Technologies Under Construction WHO’S INSIDE Accenture Elsevier Science Europe American Institute of Figshare SciTech Strategies Biological Sciences General Electric SPARC Battelle IBM Sparrho Bristol-Myers Squibb Komatsu Spotfire Clinerion Linguamatics Limited Talix Columbia Pipeline Group MedAware UnitedHealth Group Copyright Clearance Mercedes-Benz Verisk Center, Inc. Meta VisTrails CrossRef Novartis Wellcome Trust Docear OMICtools Copyright Clearance Center, Inc. (CCC) has licensed this report from Outsell, Inc., with the right to distribute it for marketing and market education purposes. CCC did not commission this report, nor is this report a fee-for-hire white paper. Outsell’s fact-based research, analysis and rankings, and all aspects of our opinion were independently derived and CCC had no influence or involvement on the design or findings within this report. For questions, please contact Outsell at [email protected]. Market Advancing the Business of Information Performance January 22, 2016 Table of Contents Why This Topic 3 Methodology 3 How It Works 3 Applications 7 Scientific Research 7 Healthcare 10 Engineering 13 Challenges 15 Implications 17 10 to Watch 18 Essential Actions 21 Imperatives for Information Managers 22 Related Research 24 Table of Contents for Figures & Tables Table 1. Providers of TDM-Related Functions 5 © 2016 Outsell, Inc. 2 Why This Topic Text and data mining (TDM), also referred to as content mining, is a major focus for academia, governments, healthcare, and industry as a way to unleash the potential for previously undiscovered connections among people, places, things, and, for the purpose of this report, scientific, technical, and healthcare information. Although there have been continuous advances in methodologies and technologies, the power of TDM has not yet been fully realized, and technology struggles to keep up with the ever-increasing flow of content. -
Adaptive Geoparsing Method for Toponym Recognition and Resolution in Unstructured Text
remote sensing Article Adaptive Geoparsing Method for Toponym Recognition and Resolution in Unstructured Text Edwin Aldana-Bobadilla 1,∗ , Alejandro Molina-Villegas 2 , Ivan Lopez-Arevalo 3 , Shanel Reyes-Palacios 3, Victor Muñiz-Sanchez 4 and Jean Arreola-Trapala 4 1 Conacyt-Centro de Investigación y de Estudios Avanzados del I.P.N. (Cinvestav), Victoria 87130, Mexico; [email protected] 2 Conacyt-Centro de Investigación en Ciencias de Información Geoespacial (Centrogeo), Mérida 97302, Mexico; [email protected] 3 Centro de Investigación y de Estudios Avanzados del I.P.N. Unidad Tamaulipas (Cinvestav Tamaulipas), Victoria 87130, Mexico; [email protected] (I.L.); [email protected] (S.R.) 4 Centro de Investigación en Matemáticas (Cimat), Monterrey 66628, Mexico; [email protected] (V.M.); [email protected] (J.A.) * Correspondence: [email protected] Received: 10 July 2020; Accepted: 13 September 2020; Published: 17 September 2020 Abstract: The automatic extraction of geospatial information is an important aspect of data mining. Computer systems capable of discovering geographic information from natural language involve a complex process called geoparsing, which includes two important tasks: geographic entity recognition and toponym resolution. The first task could be approached through a machine learning approach, in which case a model is trained to recognize a sequence of characters (words) corresponding to geographic entities. The second task consists of assigning such entities to their most likely coordinates. Frequently, the latter process involves solving referential ambiguities. In this paper, we propose an extensible geoparsing approach including geographic entity recognition based on a neural network model and disambiguation based on what we have called dynamic context disambiguation. -
Studying Social Tagging and Folksonomy: a Review and Framework
Studying Social Tagging and Folksonomy: A Review and Framework Item Type Journal Article (On-line/Unpaginated) Authors Trant, Jennifer Citation Studying Social Tagging and Folksonomy: A Review and Framework 2009-01, 10(1) Journal of Digital Information Journal Journal of Digital Information Download date 02/10/2021 03:25:18 Link to Item http://hdl.handle.net/10150/105375 Trant, Jennifer (2009) Studying Social Tagging and Folksonomy: A Review and Framework. Journal of Digital Information 10(1). Studying Social Tagging and Folksonomy: A Review and Framework J. Trant, University of Toronto / Archives & Museum Informatics 158 Lee Ave, Toronto, ON Canada M4E 2P3 jtrant [at] archimuse.com Abstract This paper reviews research into social tagging and folksonomy (as reflected in about 180 sources published through December 2007). Methods of researching the contribution of social tagging and folksonomy are described, and outstanding research questions are presented. This is a new area of research, where theoretical perspectives and relevant research methods are only now being defined. This paper provides a framework for the study of folksonomy, tagging and social tagging systems. Three broad approaches are identified, focusing first, on the folksonomy itself (and the role of tags in indexing and retrieval); secondly, on tagging (and the behaviour of users); and thirdly, on the nature of social tagging systems (as socio-technical frameworks). Keywords: Social tagging, folksonomy, tagging, literature review, research review 1. Introduction User-generated keywords – tags – have been suggested as a lightweight way of enhancing descriptions of on-line information resources, and improving their access through broader indexing. “Social Tagging” refers to the practice of publicly labeling or categorizing resources in a shared, on-line environment. -
Georeferencing Accuracy of Geoeye-1 Stereo Imagery: Experiences in a Japanese Test Field
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Volume XXXVIII, Part 8, Kyoto Japan 2010 GEOREFERENCING ACCURACY OF GEOEYE-1 STEREO IMAGERY: EXPERIENCES IN A JAPANESE TEST FIELD Y. Meguro a, *, C.S.Fraser b a Japan Space Imaging Corp., 8-1Yaesu 2-Chome, Chuo-Ku, Tokyo 104-0028, Japan - [email protected] b Department of Geomatics, University of Melbourne VIC 3010, Australia - [email protected] Commission VIII, Working Group ICWG IV/VIII KEY WORDS: GeoEye-1, Stereo, RPCs, geopositioning, accuracy evaluation, bias compensation, satellite imagery ABSTRACT: High-resolution satellite imagery (HRSI) is being increasingly employed for large-scale topographic mapping, and especially for geodatabase updating. As the spatial resolution of HRSI sensors increases, so the potential georeferencing accuracy also improves. However, accuracy is not a function of spatial resolution alone, as it is also dependent upon radiometric image quality, the dynamics of the image scanning, and the fidelity of the sensor orientation model, both directly from orbit and attitude observations and indirectly from ground control points (GCPs). Users might anticipate accuracies of, say, 1 pixel in planimetry and 1-3 pixels in height when using GCPs. However, there are practical and in some cases administrative/legal imperatives for the georeferencing accuracy of HRSI systems to be quantified through well controlled tests. This paper discusses an investigation into the georeferencing accuracy attainable from the GeoEye-1 satellite, and specifically the 3D accuracy achievable from stereo imagery. Both direct georeferencing via supplied RPCs and indirect georeferencing via ground control and bias-corrected RPCs were examined for a stereo pair of pansharpened GeoEye-1 Basic images covering the Tsukuba Test Field in Japan, which contains more than 100 precisely surveyed and image identifiable GCPs. -
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. -
Knowledge-Driven Geospatial Location Resolution for Phylogeographic
Bioinformatics, 31, 2015, i348–i356 doi: 10.1093/bioinformatics/btv259 ISMB/ECCB 2015 Knowledge-driven geospatial location resolution for phylogeographic models of virus migration Davy Weissenbacher1,*, Tasnia Tahsin1, Rachel Beard1,2, Mari Figaro1,2, Robert Rivera1, Matthew Scotch1,2 and Graciela Gonzalez1 1Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and 2Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA *To whom correspondence should be addressed. Abstract Summary: Diseases caused by zoonotic viruses (viruses transmittable between humans and ani- mals) are a major threat to public health throughout the world. By studying virus migration and mutation patterns, the field of phylogeography provides a valuable tool for improving their surveil- lance. A key component in phylogeographic analysis of zoonotic viruses involves identifying the specific locations of relevant viral sequences. This is usually accomplished by querying public data- bases such as GenBank and examining the geospatial metadata in the record. When sufficient detail is not available, a logical next step is for the researcher to conduct a manual survey of the corresponding published articles. Motivation: In this article, we present a system for detection and disambiguation of locations (topo- nym resolution) in full-text articles to automate the retrieval of sufficient metadata. Our system has been tested on a manually annotated corpus of journal articles related to phylogeography using inte- grated heuristics for location disambiguation including a distance heuristic, a population heuristic and a novel heuristic utilizing knowledge obtained from GenBank metadata (i.e. a ‘metadata heuristic’). Results: For detecting and disambiguating locations, our system performed best using the meta- data heuristic (0.54 Precision, 0.89 Recall and 0.68 F-score). -
Geotagging: an Innovative Tool to Enhance Transparency and Supervision
Geotagging: An Innovative Tool To Enhance Transparency and Supervision Transparency through Geotagging Noel Sta. Ines Geotagging Geotagging • Process of assigning a geographical reference, i.e, geographical coordinates (latitude and longitude) + elevation - to an object. • This could be done by taking photos, nodes and tracks with recorded GPS coordinates. • This allows geo-tagged object or SP data to be easily and accurately located on a map. WhatThe is Use Geotagging and Implementation application in of the Geo Philippines?-Tagging • A revolutionary and inexpensive approach of using ICT + GPS applications for accurate visualization of sub-projects • Device required is only a GPS enabled android cellphone, and access to freely available apps • Easily replicable for mainstreaming to Government institutions & CSOs • Will help answer the question: Is the right activity implemented in the right place? – (asset verification tool) Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geotagging Example No. 1: Visualization of a farm-to-market road in a conflict area: showing specific location, ground distance, track / alignment, elevation profile, ground photos (with coordinates, date and time taken) of Farm-to-market Road, i.e. baseline information + Progress photos + 3D visualization Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geotagging Example No. 2: Location and Visualization of rehabilitation of a city road in Earthquake damaged-area in Tagbilaran, Bohol, Philippines by the auditors and the citizen volunteers Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geo-tagging Example No. 3 Visualization of a water supply project showing specific location, elevation profile from the source , distribution lines and faucets, and ground photos of community faucets Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geo-tagging Example No. -
Introduction to Web 2.0 Technologies
Introduction to Web 2.0 Joshua Stern, Ph.D. Introduction to Web 2.0 Technologies What is Web 2.0? Æ A simple explanation of Web 2.0 (3 minute video): http://www.youtube.com/watch?v=0LzQIUANnHc&feature=related Æ A complex explanation of Web 2.0 (5 minute video): http://www.youtube.com/watch?v=nsa5ZTRJQ5w&feature=related Æ An interesting, fast-paced video about Web.2.0 (4:30 minute video): http://www.youtube.com/watch?v=NLlGopyXT_g Web 2.0 is a term that describes the changing trends in the use of World Wide Web technology and Web design that aim to enhance creativity, secure information sharing, increase collaboration, and improve the functionality of the Web as we know it (Web 1.0). These have led to the development and evolution of Web-based communities and hosted services, such as social-networking sites (i.e. Facebook, MySpace), video sharing sites (i.e. YouTube), wikis, blogs, etc. Although the term suggests a new version of the World Wide Web, it does not refer to any actual change in technical specifications, but rather to changes in the ways software developers and end- users utilize the Web. Web 2.0 is a catch-all term used to describe a variety of developments on the Web and a perceived shift in the way it is used. This shift can be characterized as the evolution of Web use from passive consumption of content to more active participation, creation and sharing. Web 2.0 Websites allow users to do more than just retrieve information.