Processing: a Python Framework for the Seamless Integration of Geoprocessing Tools in QGIS
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Assessmentof Open Source GIS Software for Water Resources
Assessment of Open Source GIS Software for Water Resources Management in Developing Countries Daoyi Chen, Department of Engineering, University of Liverpool César Carmona-Moreno, EU Joint Research Centre Andrea Leone, Department of Engineering, University of Liverpool Shahriar Shams, Department of Engineering, University of Liverpool EUR 23705 EN - 2008 The mission of the Institute for Environment and Sustainability is to provide scientific-technical support to the European Union’s Policies for the protection and sustainable development of the European and global environment. European Commission Joint Research Centre Institute for Environment and Sustainability Contact information Cesar Carmona-Moreno Address: via fermi, T440, I-21027 ISPRA (VA) ITALY E-mail: [email protected] Tel.: +39 0332 78 9654 Fax: +39 0332 78 9073 http://ies.jrc.ec.europa.eu/ http://www.jrc.ec.europa.eu/ Legal Notice Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. Europe Direct is a service to help you find answers to your questions about the European Union Freephone number (*): 00 800 6 7 8 9 10 11 (*) Certain mobile telephone operators do not allow access to 00 800 numbers or these calls may be billed. A great deal of additional information on the European Union is available on the Internet. It can be accessed through the Europa server http://europa.eu/ JRC [49291] EUR 23705 EN ISBN 978-92-79-11229-4 ISSN 1018-5593 DOI 10.2788/71249 Luxembourg: Office for Official Publications of the European Communities © European Communities, 2008 Reproduction is authorised provided the source is acknowledged Printed in Italy Table of Content Introduction............................................................................................................................4 1. -
The Use of GIS and Remote Sensing for Environment Statistics Contents
UNITED NATIONS STATISTICS DIVISION (UNSD) Workshop on Environment Statistics in support of the implementation of the Framework for the Development of Environment Statistics, Arusha, Tanzania, 6-10 July 2015 The use of GIS and remote sensing for environment statistics Contents • Temporal and spatial considerations • GIS data • Geospatial information • Data collections • Remote sensing • GIS Tools • Demos Temporal and spatial considerations • From FDES 2013 – Environment statistics require the simultaneous consideration of a number of temporal and spatial issues. – Important to align the temporal aggregations of environmental data with those used in economic and social statistics to ensure their proper integration, often a uniform calendar or fiscal year do not fit the diversity of natural phenomena; – therefore the use of different time scales, longer or shorter time periods is also necessary for the aggregation of environmental data over time. Spatial and Temporal • Spatial - Anything/event that occur in space has a spatial dimension – e.g environmental phenomena like air pollution, biodiversity conservation… • Temporal – anything/event relating to or limited by time, e.g disasters in 2014, hourly PM10 concentration at a specific site… Temporal and spatial considerations • For example, in fluid environmental phenomena, the temporal dimension is needed since there can be ebbs and flows, droughts and floods, snow and runoffs which all influence measurements. • Sometimes daily variations and at other times seasonal variations depending on what is being measured. Seasonal variations can be seen in the fluctuations in certain types of fish biomass, surface water levels, ice cap surface or the incidence of fires – monitoring focused more during some months • Temporal statistics often point out the maximum, the minimum and/or other ways of describing the relevant phenomenon and its levels below or above certain benchmarks, and are not restricted to a sum or an average over a longer period. -
System for Automated Geoscientific Analyses (SAGA) V. 2.1.4
Geosci. Model Dev., 8, 1991–2007, 2015 www.geosci-model-dev.net/8/1991/2015/ doi:10.5194/gmd-8-1991-2015 © Author(s) 2015. CC Attribution 3.0 License. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4 O. Conrad1, B. Bechtel1, M. Bock1,2, H. Dietrich1, E. Fischer1, L. Gerlitz1, J. Wehberg1, V. Wichmann3,4, and J. Böhner1 1Institute of Geography, University of Hamburg, Bundesstr. 55, 20146 Hamburg, Germany 2scilands GmbH, Goetheallee 11, 37073 Göttingen, Germany 3LASERDATA GmbH, Technikerstr. 21a, 6020 Innsbruck, Austria 4alpS – Center for Climate Change Adaptation, Grabenweg 68, 6020 Innsbruck, Austria Correspondence to: B. Bechtel ([email protected]) Received: 12 December 2014 – Published in Geosci. Model Dev. Discuss.: 27 February 2015 Revised: 05 June 2015 – Accepted: 08 June 2015 – Published: 07 July 2015 Abstract. The System for Automated Geoscientific Analy- 1 Introduction ses (SAGA) is an open source geographic information sys- tem (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly During the last 10 to 15 years, free and open source software developed from a specialized tool for digital terrain analy- (FOSS) became a recognized counterpart to commercial so- sis to a comprehensive and globally established GIS plat- lutions in the field of geographic information systems and form for scientific analysis and modeling. SAGA is coded science. Steiniger and Bocher (2009) give an overview of in C CC in an object oriented design and runs under sev- free and open source geographic information system (GIS) eral operating systems including Windows and Linux. -
JULIO GABRIEL CHILELA +1 (832) 212-4501 ~ [email protected]
JULIO GABRIEL CHILELA +1 (832) 212-4501 ~ [email protected] EDUCATION: UNIVERSITY OF HOUSTON, College of Technology United States Of America, TX Internship: WEB Geographic Information Systems (WebGIS) for Smart Campus and Facility Management. Development of a cross-platform application to integrate smart door, smart HVAC and Volunteer Geographic Information. September 2015 – September 2016 UNIVERSITY OF COIMBRA Portugal, Coimbra Master of Geographic Information Technology October 2014 – June 2016 INSTITUTO SUPERIOR DE CIENCIAS DA EDUCACAO, ISCED Angola, Lubango Bachelors Degree in Computer Science for Education March 2008 – November 2012 LANGUAGES (Scale 0-5): Portuguese: spoken and written(5), English: spoken and written(3.5), French: spoken and written(2). EXPERIENCE: UNIVERSITIY MANDUME YA NDEMUFAIO, Department of Informatics and Computer Engeneering Angola, Lubango Teacher July 2013 § Subjects: Computation Technology, Introduction to Computer and Software Development, Introduction to Software Development and Problem Solving. HIGH-SCHOOL POLYTECHNICAL INSTITUTE OF NAMIBE, Department of Computer Science Angola, Namibe Teacher 2011 - 2014 § Subject: Technical of Software Development TECHNICAL PROFICIENCY: § Technical Certificate at Microsoft Certified Partner CompuClass, Sao Paulo, Brazil May, 2013 o Windows Server o SQL Server Level I (User) o SQL Server Level II (Admin) o PHP+MYSQL+JavaScript+CSS+HTML § Co-author of the School Software Management named as OSIKOLA. On behalf of the Angolan Television, Semba Communications held a report on its functions and advantages: Youtube: https://www.youtube.com/watch?v=d2fnhE6nB-Q § Last websites developed: o www.gestaodeigrejas.info (Created with Django Framework) o www.siblubango.com (Created with ASP.NET) § Amazon Web Services: o EC2, Route53. § Technical Knowledge: o Computer Networks; Computer maintenance and Repair; Software development, website development. -
A Survey of Open Source Geospatial Software Bob Bruce, P.Eng Mapping and Lands Branch, Manitoba Conservation [email protected]
Open Source Geospatial Software Bob Bruce, P.Eng. A Survey of Open Source Geospatial Software Bob Bruce, P.Eng Mapping and Lands Branch, Manitoba Conservation WWW.HWPS.CA [email protected] APEGM PD Breakfast Tuesday, 12 June 2007 access this presentation here: http://www.apegm.mb.ca/pdnet/papers.html There are many web addresses given in this presentation. In order to save you from writing them all down, I have provided you with the address to download this presentation. 1 Open Source Geospatial Software Bob Bruce, P.Eng. A Survey of Open Source Geospatial Software Outline of Presentation z What open source software is and how it is licensed z The organizations supporting open source geospatial software and some Canadian companies and consultants working in this field z WWW standards for accessing geospatial data z Some interesting and well known open source geospatial software applications z The use of open source geospatial software to support the spatial infrastructure in a government organization z Where you can get more information 2 Open Source Geospatial Software Bob Bruce, P.Eng. A Survey of Open Source Geospatial Software Some popular, successful open-source projects OpenOffice.org – is a multi-platform office suite which is compatible with all other major office suites. Firefox – a web browser from the Mozilla Foundation – has second largest useage worldwide Thunderbird – is a full-featured email application The open-source software field has a multitude of applications that mirror nearly every successful commercial field. Here are some high-profile applications that are free and can be used for common functions needed in computers: OpenOffice can replace Microsoft Office, it comes with a document editor (like Word), a spreadsheet, a presentation manager and a database manager and other utilities. -
The State of Open Source GIS
The State of Open Source GIS Prepared By: Paul Ramsey, Director Refractions Research Inc. Suite 300 – 1207 Douglas Street Victoria, BC, V8W-2E7 [email protected] Phone: (250) 383-3022 Fax: (250) 383-2140 Last Revised: September 15, 2007 TABLE OF CONTENTS 1 SUMMARY ...................................................................................................4 1.1 OPEN SOURCE ........................................................................................... 4 1.2 OPEN SOURCE GIS.................................................................................... 6 2 IMPLEMENTATION LANGUAGES ........................................................7 2.1 SURVEY OF ‘C’ PROJECTS ......................................................................... 8 2.1.1 Shared Libraries ............................................................................... 9 2.1.1.1 GDAL/OGR ...................................................................................9 2.1.1.2 Proj4 .............................................................................................11 2.1.1.3 GEOS ...........................................................................................13 2.1.1.4 Mapnik .........................................................................................14 2.1.1.5 FDO..............................................................................................15 2.1.2 Applications .................................................................................... 16 2.1.2.1 MapGuide Open Source...............................................................16 -
Overview of GIS Applications Risk Assessment and Risk Management of Climate Change Hazards
Overview of GIS applications risk assessment and risk management of climate change hazards Fact irrigated lands Planed irrigated lands Watershed Water bodies Settlements River Soil types Overview of GIS applications risk assessment and risk management of climate change hazards © 2010 PREPARED The European Commission is funding the Collaborative project ‘PREPARED Enabling Change’ (PREPARED, project number 244232) within the context of the Seventh Framework Programme 'Environment'.All rights reserved. No part of this book may be reproduced, stored in a database or retrieval system, or published, in any form or in any way, electronically, mechanically, by print, photoprint, microfilm or any other means without prior written permission from the publisher COLOPHON Title Overview of GIS applications, risk assessment and risk management of climate change hazards Report number Prepared 2011.015 Deliverable number D2.5.1 Author(s) Ielizaveta Dunaieva (Crimean Scientific and Research Center) Victor Popovych (Crimean Scientific and Research Center) Elisa Traverso (Iren Acqua Gas) Quality Assurance Patrick Smeets (KWR Watercycle Research Institute) Document history Version Team member Status Date update Comments 01 Ielizaveta Dunaieva Draft 27-08-2010 Chapters 1-4, 6 02 Ielizaveta Dunaieva Draft 24-06-2011 Applications from cities Simferopol and Genoa added 03 Patrick Smeets Final 03-08-2011 QA by WA leader This report is: PU = Public Summary The first step to prepare for climate change effects on the water cycle is a risk assessment for the observed system to be prepared and, if it is necessary, protected. Risk assessment (RA) means the determination of qualitative and quantitative value of risks, related to a certain situation and the recognized hazards. -
Peter Petrik, Martin Dobias and Others
MDAL Peter Petrik, Martin Dobias and others Sep 22, 2021 CONTENTS 1 What is MDAL? 1 2 Download 3 2.1 Current Releases.............................................3 2.2 Past Releases...............................................3 2.3 Conda Installation............................................3 2.3.1 UPM Installation........................................3 2.4 Development Source...........................................4 3 Programs 5 3.1 MDAL programs.............................................5 3.1.1 mdalinfo.............................................5 3.1.1.1 Synopsis........................................5 3.1.1.2 Description......................................5 3.1.1.3 Example........................................5 4 MDAL drivers 7 4.1 GRIB – WMO General Regularly-distributed Information in Binary form...............8 4.2 NetCDF – Network Common Data Form................................8 4.3 2dm – SMS mesh format.........................................8 4.4 XDMF – eXtensible Data Model and Format..............................9 4.5 XMDF – eXtensible Model Data Format................................9 4.6 DAT – ASCII or Binary Dataset Files..................................9 4.7 SWW – NetCDF format for AnuGA................................... 10 4.8 HDF – HEC-RAS outputs format.................................... 10 4.9 3Di – NetCDF with Climate and Forecast (CF) metadata........................ 10 4.10 PLY – ASCII Stanford Polygon Format................................. 11 4.11 UGRID – NetCDF with Climate and Forecast (CF) -
Architecting Location Intelligence Platforms Using Open-Source Components
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B4-2021 XXIV ISPRS Congress (2021 edition) ARCHITECTING LOCATION INTELLIGENCE PLATFORMS USING OPEN-SOURCE COMPONENTS A. BENAHMED DAHO 1, H. BEKHELIFI 1 1 TransformaTek, SARL (LLC), Ain Temouchent, Algeria - (ali.benahmeddaho, hatem.bekhelifi)@transformatek.dz Commission IV, WG IV/6 KEY WORDS: GIS, Cloud Architecture, Location intelligence, Agile approach, GeoNode, Source control. ABSTRACT: Location Intelligence is an emerging application of geospatial industry. It allows professionals from different business domains (finance, disaster management, retail, health) and with minimum expertise in GIS technologies to use efficiently many spatial analysis tools and algorithms to solve their day-to-day problems. One of the major characteristics of Location Intelligence is the use of multisource and alternative data either user ingested or directly available in the platform (by geo-enrichment). In this contribution we investigate the architectural challenges raised by the development of this kind of platforms, particularly in term of functionalities, use of open-source components, management practices, deployment on the Cloud and source code control. To validate our assumptions, we built a new platform, named “Geoweba”, based on the GeoNode Project and having as objective to disrupt the Algerian consulting market. The resulting platform is deployed on the internet and freely available for users. In this contribution we demonstrate that our development approach based on Agile principles and open-source components gives a good result, mainly in the early stages when just a Minimum Viable Product (MVP) is needed for market validation of the idea. 1. INTRODUCTION Location intelligence software, also called spatial intelligence Functioning software, is a business intelligence solution that provides location Sprint Deliverables analytics to identify the relationship between certain objects TODO 2-4 Weeks based on their physical locations. -
Postgis Case Studies
PostGIS Introduction and Case Studies W W W . R E F R A C T I O N S . N E T PostGIS Case Studies What is it, who is using it, and why? What is a spatial database? Why would you want one? PostGIS extends PostgreSQL to make it a spatial database. Why would I want PostgreSQL Scalable in two ways: technically and economically. Economic scalability gives PostgreSQL / PostGIS great price/performance. So if you transition to PostGIS, you can either lower your costs for the same performance, improve your performance for the same price, or even do both! 1 PostGIS Introduction and Case Studies W W W . R E F R A C T I O N S . N E T The Beginning • PostGIS is an open source geospatial extension for PostgreSQL • PostgreSQL is an open source relational database management system (RDBMS) • A relational database management system is… ? Let’s start from first principles… 2 PostGIS Introduction and Case Studies W W W . R E F R A C T I O N S . N E T Databases The relational database is the big yellow cylinder that sits in the middle of all our systems architecture diagrams. The reason it is always there, usually in the middle, is because it performs a critical role in systems architecture, providing a safe, random access way of storing large amounts of data. Geographic information systems, manipulate INFORMATION, and that information has to reside somewhere. It should be EASY TO ACCESS the data, with a STANDARD QUERY SYSTEM, and MULTIPLE USERS should be able to read and write that data over the network. -
Species Distribution Modelling and Open Source GIS: Why Are They Still So Loosely Connected?
International Environmental Modelling and Software Society (iEMSs) 2012 International Congress on Environmental Modelling and Software Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany R. Seppelt, A.A. Voinov, S. Lange, D. Bankamp (Eds.) http://www.iemss.org/society/index.php/iemss-2012-proceedings Species distribution modelling and open source GIS: why are they still so loosely connected? Anne Ghisla a, Duccio Rocchinia, Markus Netelera, Michael Forster¨ b, Birgit Kleinschmitb aFondazione Edmund Mach, Research and Innovation Centre, Department of Biodiversity and Molecular Ecology, GIS and Remote Sensing Unit, Via E. Mach 1, 38010, S. Michele all’Adige (TN), Italy ([email protected], [email protected], [email protected]) b Technische Universitat¨ Berlin - Fakultat¨ VI, Institut fur¨ Landschaftsarchitektur und Umweltplanung, Fachgebiet Geoinformation in der Umweltplanung, Sekr. EB 5 - Raum 235a, Straße des 17. Juni 145 , D-10623 Berlin ([email protected], [email protected]) Abstract: Species Distribution Models (SDMs) are correlative models that use envi- ronmental and/or geographical information to explain patterns of species occurrences. Those models are being used in various fields including climate change, invasive species research, evolutionary biology and epidemiology. Thanks to the availability of increasing computational resources, new methods continue to be developed. However, software packages that include the SDM algorithms usually focus on one or few methods, and have different degrees of integration with other geographical and statistical software. Specifically, SDM implementations are often standalone programs developed by univer- sity laboratories, either as extensions to statistical software. In few cases they are written as extensions for the most common proprietary Geographic Information System (GIS) products, despite the strong geographical component present in the data. -
RQGIS: Integrating R with QGIS for Statistical Geocomputing by Jannes Muenchow, Patrick Schratz, Alexander Brenning
CONTRIBUTED RESEARCH ARTICLE 409 RQGIS: Integrating R with QGIS for Statistical Geocomputing by Jannes Muenchow, Patrick Schratz, Alexander Brenning Abstract Integrating R with Geographic Information Systems (GIS) extends R’s statistical capabilities with numerous geoprocessing and data handling tools available in a GIS. QGIS is one of the most popular open-source GIS, and it furthermore integrates other GIS programs such as the System for Automated Geoscientific Analyses (SAGA) GIS and the Geographic Resources Analysis Support System (GRASS) GIS within a single software environment. This and its QGIS Python API makes it a perfect candidate for console-based geoprocessing. By establishing an interface, the R package RQGIS makes it possible to use QGIS as a geoprocessing workhorse from within R. Compared to other packages building a bridge to GIS (e.g., rgrass7, RSAGA, RPyGeo), RQGIS offers a wider range of geoalgorithms, and is often easier to use due to various convenience functions. Finally, RQGIS supports the seamless integration of Python code using reticulate from within R for improved extendability. Introduction Defining a GIS as a system for the analysis, manipulation and visualization of geographical data (Longley et al., 2011), one could argue that R has become a GIS (Bivand et al., 2013). In great part this is thanks to packages that provide spatial classes and algorithms coded in and for R (despite this these packages might also link to other software outside of R). These include maptools (Bivand and Lewin-Koh, 2017), raster (Hijmans, 2017), sp (Bivand et al., 2013) and sf (Pebesma, 2017). Further packages even extend R’s GIS capabilities through advanced mapping, e.g., mapview (Appelhans et al., 2017) and mapmisc (Brown, 2016), and routing, e.g., osmar (Eugster and Schlesinger, 2013) and dodgr (Padgham and Peutschnig, 2017), among others.