. "Spatial data acquisition and positioning"@en . . "7.5" . "Reference System, Geodetic Datum.\nSpatial data sources. Spatial data collection techniques and analysis with:\nClassical and satellite Geodetic methods\nClassical and conventional methods, including classical and geodetic methods\nStatistical and photogrammetric methods; Statistical and photogrammetric methods; Remote sensing methods; Statistical and photogrammetric methods; Remote sensing methods.\nStatistical methods and methods of mapping" . . "Presential"@en . "TRUE" . . "Processing, analysis and display of spatial data"@en . . "7.5" . "ategories of spatial data [maps, photographs, remote sensing images].\nCartographic data: Interpretation - classification. Creation of structures based on selected spatial models. Transformations between different structures.\nPhotogrammetric data:\nInstruments, software, two-dimensional - three-dimensional rendering systems\nPhotogrammetric networks, software, instruments, software, tools, 3D and 3D data processing.\nOrthophotography\nRemote sensing images: \nDigital processing of multispectral images\nEnhancement of multispectral images with point and spatial processing\nClassification of multispectral images with supervised and unsupervised classifications\nCartographic data\nSynthesis - rendering of 3D models\nSynthesis of static dynamic maps - Visualisation elements\nErrors in spatial databases\nSpatial data analysis methodology\nDevelopment of applications for the processing, analysis and rendering of all categories of spatial data using existing databases." . . "Presential"@en . "FALSE" . . "Spatial databases"@en . . "7.5" . "Introduction to Knowledge Representation\nConceptual structures (Semantic Networks, Lattices)\nFormal Concept Analysis (FCA)\nInformation Flow (Channel theory - Information Flow)\nConceptual Graphs (Conceptual Graphs)\nOntologies - development, extension and integration\nSemantic Similarity Measures and mapping\nNatural Language Processing (NLP)" . . "Presential"@en . "FALSE" . . "Spatial data studio"@en . . "15" . "Combined theoretical (30%) and practical (70%) study.\nCompartments of the studio:\nAcquisition of environmental data.\nSpecifics and acquisition of spatial data.\nReliability and uncertainties of data, spatial data quality.\nData interoperability.\nGeoprocessing\n\nOutcome:\n- Understands and is able to apply methods of data acquisition in different fields\r\n- Is able to design and manage spatial database: knows the formats and software of important spatial data and databases\r\n- Knows spatial coordinate systems and is able to choose correct coordinate system according to region and putpose\r\n- Is able to use and create metadata based on suitable metadata standards, demonstrates the understating of the need for metadata\r\n- Knows main spatial data standards and is able to estimate the spatial data quality\r\n- Is able to independently pose and solve a problem by using spatial data from different geographic regions or global data\r\n- Knows and is able to consider the regional/cultural and sectoral differences in applying geospatial analysis\r\n- Understands the global challenges and is able to put them into local/regional context and provide solutions by using geospatial analysis\r\n- Demonstrates critical thinking and ability to work interdisciplinary teams when working with spatial data\r\n- Is able to communicate and visualise the spatial data in a meaningful way" . . "Hybrid"@en . "TRUE" . . "Spatial databases"@en . . "6" . "As described in the course objectives you will build on general database theory and how spatial data is incorporated. You will be introduced to standards for encoding geometry and spatial reference systems in the database realm. This course is about designing a database and working with geospatial data. You will learn spatial functions that form the building blocks of more sophisticated analytical models. Accessing your database with desktop and web applications will be important part of your practical exercises.\n\nOutcome:\nAfter successful passing the student will be able to:\r\n* design and create databases using PostgreSQL/PostGIS;\r\n* manage data and spatial data in PostgreSQL/PostGIS databases;\r\n* perform geospatial analysis in the spatial database." . . "Presential"@en . "FALSE" . . "Spatial data infrastructures"@en . . "3" . "SDI is the infrastructure behind the scenes of modern online geoportals. We will learn how to work with SDI web services and understand their specifications.\n\nOutcome:\nAfter passing the course student\r\nAfter successful passing the student will be able:\r\n- to orient her-/ himself in components and standards that comprise SDI\r\n- to find, access, use different types of data and services within an SDI and spatial data on the web in general\r\n- to understand data specifications according to INSPIRE rules\r\n- to publish data via SDI services such as Web Map Service (WMS), Web Feature Service (WFS) and Web Coverage Service (WCS) using GeoServer software platforms\r\n- understand the need for metadata and catalogues services" . . "Hybrid"@en . "FALSE" . . "Geospatial data infrastructures and land administration"@en . . "5" . "This course provides students with an understanding of geospatial data infrastructures and the principles of land registration. Practical aspects focus on the design and use of geodatabases for land information.\n\nOutcome:\nBy the end of this course students will be able to:\r\n\r\n■ Explain what geospatial data infrastructures are and their importance to good governance;\r\n\r\n■ Critically assess the development of topographic mapping and geospatial data infrastructures in the UK, selected countries and from a global perspective;\r\n\r\n■ Explain the nature and importance of land ownership, land tenure and associated rights;\r\n\r\n■ Explain the principles of cadastre and land registration;\r\n\r\n■ Discuss appropriate frameworks for land administration;\r\n\r\n■ Explain the role of the Geomatician in land administration;\r\n\r\n■ Explain the principles and practices of geospatial data modelling and geodatabase design\r\n\r\n■ Design a geospatial database" . . "Presential"@en . "TRUE" . . "Design of geospatial data models"@en . . "3" . "no data" . . "Presential"@en . "TRUE" . . "Spatial databases"@en . . "10" . "This module delivers an introduction to databases which culminates in a broader treatment of spatial databases. Spatial databases are databases specifically design to manage spatial or geographical data. The module provides an introduction to Structured Query Language (SQL) where no previous experience of SQL or databases is assumed. The module will provide students with a broad understanding of the challenges of working with spatial data and other types of spatially-related information. Using a relational database approach the module considers several key aspects of spatial data science or spatial analysis including:\r\n• relational database models for spatial data;\r\n• spatial data structures; spatial data integrity; spatial data manipulation;\r\n• spatial algorithms used in Geographic Information Systems (GIS);\r\n• spatial analysis techniques including statistical approaches for spatially-informed decision making;\r\n• spatial data visualisation using GIS;\r\n• spatial data formats (PostgreSQL PostGIS database, GeoJSON, ESRI Shapefiles, CSV);\r\n• alternative database models for geospatial data.\r\n\r\nThe course offers students an introduction into spatial and non-spatial SQL with treatments of modern topics in SQL such as Window Functions, query optimization, and so on included. Working with NoSQL and unstructured sources of geospatial data are also discussed.\r\n\r\nThe course offers a mixture of laboratory-based course work and an individual portfolio project which combines SQL and GIS.\n\nOutcome:\r\nOn successful completion of the module, students should be able to:\r\nDesign and implement spatial databases using standard models and spatial database management systems. Query spatial databases using standard query tools and languages\r\nUse Geographical Information Systems (GIS) to analyse and visualise spatial data\r\nCreate interfaces to view and customise, interact with spatial data\r\nDesign and implement spatial indices for efficient searching of data\r\nPerform spatial analysis on large spatial databases and datasets\r\nEnsure reliability, security, integrity and privacy in spatial databases\r\nUnderstand NoSQL model approaches such as key-value stores, document databases, etc\r\nUnderstand the challenges and most effective approaches to working with large databases of spatial data." . . "Presential"@en . "FALSE" . . "Opengis: standards, architectures and services"@en . . "3" . "no data" . . "Presential"@en . "TRUE" . . "Geospatial data infrastructures and land administration"@en . . "5" . "Short Description\nThis course provides students with an understanding of geospatial data infrastructures and the principles of land registration. Practical aspects focus on the design and use of geodatabases for land information.\nLearning Outcomes of Course\nBy the end of this course students will be able to:\n\n■ Explain what geospatial data infrastructures are and their importance to good governance;\n\n■ Critically assess the development of topographic mapping and geospatial data infrastructures in the UK, selected countries and from a global perspective;\n\n■ Explain the nature and importance of land ownership, land tenure and associated rights;\n\n■ Explain the principles of cadastre and land registration;\n\n■ Discuss appropriate frameworks for land administration;\n\n■ Explain the role of the Geomatician in land administration;\n\n■ Explain the principles and practices of geospatial data modelling and geodatabase design\n\n■ Design a geospatial database" . . "Presential"@en . "TRUE" . . "Geo-information governance"@en . . "5" . "In this course students will learn about the organisational and legal aspects relevant for developing a strategy for a geographic information infrastructure.\n\nAfter this course the student is able to:\n- recognize and anticipate upon relevant legal and organisational issues related to the acquisition, processing, dissemination and use of (open) geo-information\n- apply the concepts, processes and main components of geo-information infrastructures to support geo-information sharing between organisations\n- critically assess geo-information management strategies for organisations\n- assess the performance of an geo-information infrastructure from a user perspective\n- (co-)author a scientific paper on a selected SDI topic" . . "Presential"@en . "TRUE" . . "Geospatial data collection and management"@en . . "15" . "The aim of this module is to introduce students to the basics of\nAcquisition of primary data with the help of surveying and photogrammetric\nTeaching methods in theory and practice. Furthermore, methods and procedures of\nData storage and management (geodatabases) and program-based modification\nand processing mediated. After completing this module, the students will be able to\nTo describe methods of primary data acquisition and to use them practically, geodatabases\ndesign and use as well as to create corresponding software applications" . . "Presential"@en . "TRUE" . . "Data assimilation for geosciences"@en . . "5" . "Appplications of data assimilation in oceanography, meteorology, hydrology, seismology, reservoir engineering and/or\ngeotechnics;\nOpen and FAIR science, open interfaces\nStudy Goals 1. To explain the Bayesian principles of data assimilation\n2. To relate selected data assimilation methods to these principles (Strong-constraint\n4Dvar/Weak-constraint 4Dvar/EnRML, ESMDA/EnKF/Particle Filter)\n3. To evaluate existing data-assimilation methods and select the most appropriate method for\na given data-assimilation problem\n4. To design a data-assimilation workflow that connects a selected model of a particular\nearth system (oceanographic, meteorologic, hydrologic, reservoir engineering, seismology,\ngeotechnical) to a data assimilation method, or of another estimation problem (traffic,\nmedical sciences, finance)\n5. To apply open interfaces\n6. To apply a data-assimilation method to a proposed data-assimilation problem" . . "Presential"@en . "FALSE" . . "Location based services/ cadaster"@en . . "no data" . "no data" . . "Presential"@en . "TRUE" . . "spatial data management & visualization"@en . . "5" . "no data" . . "Presential"@en . "TRUE" . . "Spatial reference systems l"@en . . "no data" . "no data" . . "Presential"@en . "TRUE" . . "Spatial databases"@en . . "no data" . "no data" . . "Presential"@en . "TRUE" . . "Spatial database management"@en . . "2" . "The aim of the study course is to provide knowledge in the theoretical and practical aspects of spatial database management. The goals of course are to introduce students to the storage of spatial data in databases, the specifics of the creation of spatial databases, geospatial analysis; to familiarize with data integrity and database performance issues; to gain an insight into linear referencing systems and routing methods; to consolidate the acquired theoretical knowledge by solving practical geospatial problems with the aid of PostGIS and SpatiaLite database management systems. Course languages: English, Latvian\r\nCourse responsible lecturer\tZaiga Krišjāne\r\nResults\tKnowledge 1. knows the most popular spatial database management systems; 2. is familiar with the representation of geographical objects in databases; 3. knows the methods of analysis of geographical object spatial relations, DE9IM; 4. describes tools for ensuring the quality of geospatial data; 5. explains potential causes of performance problems of geospatial data analysis queries and proposes possible solutions; 6. is familiar with the principles of linear referencing systems; 7. describes methods of solving routing problems. Skills 8. constructs object geometries in OGC WKT format; 9. works with PostGIS and SpatiaLite databases in QGIS program; 10. imports and exports data to PostGIS using command line or graphical tools; 11. analyses object geometry spatial relationships with PostGIS queries; 12. performs routing tasks with PostGIS tools; 13. analyses causes of geospatial SQL query performance problems. Competence 14. provides justified geospatial data integration into a database structure; 15. recommends solutions to improve performance of geospatial data analysis queries; 16. justifies the chosen solution for storage and analysis of geospatial data." . . "Presential"@en . "FALSE" . . "Publishing geospatial data"@en . . "4" . "Legal and technical aspects of publishing spatial data.\r\nThe INSPIRE Initiative and Directive, benefits of its im\u0002plementation and main components of spatial infor\u0002mation infrastructure. Spatial data services from the \r\npoint of view of their providers and users on various \r\nlevels of administration." . . "Presential"@en . "TRUE" . . "Facultative class 4 - spatial data infrastructure"@en . . "3" . "Spatial data infrastructure (SDI), Spatial information infrastructure (IIP), Spatial knowledge infrastructure, INSPIRE, standards and norms for collecting and sharing spatial information and metadata. The role and functions of geoportals. Spatial data themes implemented within IIP in Poland" . . "Presential"@en . "FALSE" . . "Acquisition and exploration of geospatial data"@en . . "7" . "no data" . . "Presential"@en . "TRUE" . . "Geospatial data: concepts, acquisition and management"@en . . "5" . "no data" . . "Presential"@en . "TRUE" . . "Big geodata processing"@en . . "5" . "no data" . . "Presential"@en . "FALSE" . . "Spatial data infrastructure"@en . . "12" . "Be able to describe the main components of SDIs and know key objectives, benefits and current state-of-the-art of such initiatives.\n Understand the conceptual strategies, organizational requirements and legal frameworks for leveraging the advantages of open geographic data infrastructures.\n Recognize the importance of standardized data models to store, analyse and manipulate geographic phenomena.\n Be able to explain the role of metadata for spatial data shar-ing across distributed networks.\n Be able to describe the existing spatial data sharing policies including intellectual property rights, security issues, privacy issues, Open Government data initiatives.\n Be able to explain the Service Oriented Architecture (SOA) concept together with its underlying publish-find-bind principle.\n Know internationally accepted geographic- and IT standards (OGC, OASIS & ISO) and apply these in practical projects.\n Be able to understand, design and implement geodata models according to standardised approaches.\n Be able to publish geodata and geoprocessing services over the web: map services, data services (editing, search, image service), and analytical services.\n Be able to define the interoperability needs beyond technical issues like direct access and industry standards on a legal, semantic and organizational level.\n Understand the principles and techniques of spatial data organization and apply these principles and techniques to design and build spatial databases.\n Based on these concepts, the students will learn how to utilize open, shared GIS resources to design and use Open GIS data structures, workflows and processes leveraging information repositories." . . "Presential"@en . "TRUE" . . "Spatial databases"@en . . "3" . "This class covers the most important concepts for setting up and \nmanaging database systems (relational databases), populating databases \nwith data and executing SQL queries.Basic topics about relational databases in general are covered in the first part.The second part focuses on spatial concepts, spatial databases and \ntouches the basics of standardisation, security, data integrity, and \noptimisation.Some more recent trends coming from big data analytics such as NoSQL, real-time- and in-memory systems will be touched." . . "Presential"@en . "FALSE" . . "Digital earth: big earth data concepts"@en . . "2" . "Understand current trends of big data in remote sensing and\n its background as well as\n applying new concepts and approaches." . . "no data"@en . "FALSE" . . "Geodata acquisition"@en . . "6" . "no data" . . "Presential"@en . "TRUE" . . "Spatial data management"@en . . "15.0" . "EGM712 - Spatial Data Management (15 credits) – this module runs in weeks 7-12 of semester 1 and is a compulsory module.\n\nThis module builds on the knowledge and practical skills gained in EGM711 to provide students with further experience in the acquisition, manipulation and analysis of spatial data. Methods for generating and collecting digital spatial data from primary and secondary sources are considered, and data processing, selection, integration and analysis extensively practiced. Lecture and practical sessions include digitising, geo-registration, GPS, accessing and using secondary sources, spatial join and overlay, network analysis and 3D modelling, and incorporate experience of a variety of large and small scale vector and raster datasets. The module also incorporates practice in statistical analysis and interpretation. Development of GIS software skills focuses on ArcGIS and extensions." . . "Presential"@en . "TRUE" . . "Location based services"@en . . "6" . "no data" . . "Presential"@en . "TRUE" . . "Geospatial data acquisition"@en . . "6" . "Introduction to advanced geospatial data acquisition via terrestrial, airborne, and space-based methodologies, referencing and management of geoinformation. Student understanding of geospatial data acquisition, coordinate reference systems and frames, and management of geoinformation via programming techniques. Upon completion of this course, it is expected that the learner will be able to: (1) recognise and discriminate terrestrial, airborne and satellite data acquisition methodologies, (2) classify and appraise national, regional and terrestrial coordinate reference systems (CRS) and transformation processes, (3) organise heterogeneous geospatial information by means of high-level programming languages, such as Python." . . "Presential"@en . "TRUE" . . "Geospatial data science"@en . . "6" . "Methods and techniques of geospatial data science and its role in Geoinformatics and Earth Observation. Upon completion of this course, it is expected that the learner will be able to: (1) identify core methods of different disciplines contributing to spatial data science, (2) select and employ the most appropriate analytical methods depending on the research question and the type of geospatial data required to address that question, (3) Synthesize and present high-quality analytical results involving spatial data." . . "Presential"@en . "TRUE" . . "Geospatial Data Architecture And Management"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .