This document summarizes a study that mapped street signs in Lemon Grove, California using Google tools. The study area was 3.94 square miles with a perimeter of 11.8 miles. Google Drive, My Maps, Street View, and Fusion Tables were used to collect, store, and visualize 584 street sign locations. Most signs were from 2014-2015 Street View imagery. Analysis found most signs were standalone poles or attached to stop signs and streetlights. Applications of the data include emergency response and transportation planning. Suggestions for improving the study include updating older imagery and defining missing intersections.
This session provides an overview of functionality, techniques, and tips for managing, analyzing, and mapping space-time data with ArcGIS. Guidelines are provided for taking advantage of the core support for space-time data in ArcGIS Pro. In particular, insights into the visualization of space-time data are provided.
Jerry Clough presents techniques for analyzing OpenStreetMap data using QGIS. He discusses using OSM data to simulate the European Urban Atlas project and mapping retail locations. Case studies include analyzing pub density in Britain, simulating land use classification, and tracking street light mapping. Challenges with OSM data like polygon overlaps and tagging variations are also covered.
This document summarizes an R package called SpherWave that analyzes scattered spherical data using spherical wavelets. The package implements spherical wavelets introduced by Li (1999) for representing multiscale structures on the globe. It allows users to estimate signals at arbitrary locations, decompose signals into different spatial scales, and apply thresholding techniques to denoise observations. The document demonstrates these capabilities of the package by analyzing temperature data from weather stations globally and decomposing the temperature field.
Working with space time data - esri uc 2018Aileen Buckley
This session provides an overview of functionality, techniques, and tips for managing, analyzing, and visualizing space-time data with ArcGIS. Guidelines are provided for taking advantage of the core support for space-time data in ArcGIS Pro. Methods for sharing temporal maps as web maps on ArcGIS Online and also discussed and demonstrated.
The team collected over 700 spatial data points using a GPS unit at the Soccer Complex. They created a data dictionary and classification system to efficiently organize the point, line, and polygon data during collection. The points were differentially corrected to increase their accuracy. Finally, the data was exported as shapefiles and added to an ArcGIS database to create detailed maps showing the spatial features and attributes collected.
This document summarizes a study that mapped street signs in Lemon Grove, California using Google tools. The study area was 3.94 square miles with a perimeter of 11.8 miles. Google Drive, My Maps, Street View, and Fusion Tables were used to collect, store, and visualize 584 street sign locations. Most signs were from 2014-2015 Street View imagery. Analysis found most signs were standalone poles or attached to stop signs and streetlights. Applications of the data include emergency response and transportation planning. Suggestions for improving the study include updating older imagery and defining missing intersections.
This session provides an overview of functionality, techniques, and tips for managing, analyzing, and mapping space-time data with ArcGIS. Guidelines are provided for taking advantage of the core support for space-time data in ArcGIS Pro. In particular, insights into the visualization of space-time data are provided.
Jerry Clough presents techniques for analyzing OpenStreetMap data using QGIS. He discusses using OSM data to simulate the European Urban Atlas project and mapping retail locations. Case studies include analyzing pub density in Britain, simulating land use classification, and tracking street light mapping. Challenges with OSM data like polygon overlaps and tagging variations are also covered.
This document summarizes an R package called SpherWave that analyzes scattered spherical data using spherical wavelets. The package implements spherical wavelets introduced by Li (1999) for representing multiscale structures on the globe. It allows users to estimate signals at arbitrary locations, decompose signals into different spatial scales, and apply thresholding techniques to denoise observations. The document demonstrates these capabilities of the package by analyzing temperature data from weather stations globally and decomposing the temperature field.
Working with space time data - esri uc 2018Aileen Buckley
This session provides an overview of functionality, techniques, and tips for managing, analyzing, and visualizing space-time data with ArcGIS. Guidelines are provided for taking advantage of the core support for space-time data in ArcGIS Pro. Methods for sharing temporal maps as web maps on ArcGIS Online and also discussed and demonstrated.
The team collected over 700 spatial data points using a GPS unit at the Soccer Complex. They created a data dictionary and classification system to efficiently organize the point, line, and polygon data during collection. The points were differentially corrected to increase their accuracy. Finally, the data was exported as shapefiles and added to an ArcGIS database to create detailed maps showing the spatial features and attributes collected.
This document discusses using Scala for geographic information systems (GIS) by leveraging Geotrellis and Spark to ingest, process, and analyze geospatial raster and vector data across distributed file systems and databases. It provides examples of computing zonal statistics, using space-filling curves like the Z-curve and Hilbert curve, and links to Geotrellis demonstrations for further information.
This document discusses how geographic information systems (GIS) can support various hydrologic modeling methods, including USGS regression equations, the NRCS Curve Number method, and two-dimensional hydrologic modeling. It provides examples of implementing these methods in GIS using data sources like LiDAR and NEXRAD rainfall data. The document aims to demonstrate how GIS can automate hydrologic analyses and improve understanding of watershed processes.
Making the Most of Raster Analysis with Living Atlas Data - Esri UC 2018Aileen Buckley
Content in the Living Atlas of the World isn’t just for making web maps—it includes ready-to-use data that can be used for analysis in the ArcGIS Platform. The Living Atlas includes hundreds of image services you can use in ArcGIS Desktop and Pro as inputs to geoprocessing tools. That means no downloading, no data preparation, and huge time savings. Image services work so well in ArcGIS, you will be challenged to find how they differ from the raster datasets on your hard drive. With a focus on Pro, this session teaches you about the keys to success when performing analysis with Living Atlas image services.
Ease-of-use and Effectiveness of Participatory GIS in Empowering Rural Commun...MapWindow GIS
This document describes experiments conducted to test a participatory GIS software called villageQGIS for empowering rural communities. Village youth were trained to collect spatial data like wells and farm boundaries using GPS and questionnaires. Students then used villageQGIS and QGIS to create maps from the collected data. On average, villageQGIS took half the time of QGIS and produced maps with comparable spatial accuracy according to experiments conducted with village students. The easy to use interface of villageQGIS allows villagers to understand local problems and participate better in decision making.
Fitting probability distribution into data is very essential knowledge for the researchers of any discipline. I hope this presentation slides may contribute in scientific research.
Fire Proximity Awareness Monitoring with FMESafe Software
This document describes the evolution of Manitoba Hydro's fire proximity awareness system using geospatial data and FME. It summarizes how fire data was initially pulled from shapefiles then points to improve legibility. Weather and infrastructure data were also incorporated. FME was used to process the data, calculate proximity of fires to infrastructure, and track crew locations over time in response to fires. The system provides near real-time awareness of fire and weather conditions to help monitor risks to Manitoba Hydro assets and operations.
Geographic information system and remote sensingchala hailu
ArcMap is where you create maps and access most of the ArcGIS functionality. Remote sensing is an instrument based of observing an object at a far distance without direct contact.
Processing Landsat 8 Multi-Spectral Images with GRASS Tools & the potential o...Shaun Lewis
Paul Shapley gave a presentation on processing Landsat 8 multi-spectral images with GRASS tools and the potential of the QGIS-GRASS plugin. He discussed his background and work with QGIS and GRASS for mapping at Neath Port Talbot Borough Planning Department. He provided an overview of Landsat 8 data and demonstrated using GRASS modules to classify Landsat images and analyze changes over time. Shapley also discussed advantages and updates to the QGIS-GRASS plugin, and future projects using GRASS and 3D data for his local development plan monitoring and property mapping.
The document describes geo-referencing and digitizing a map of IIT Roorkee campus in QGIS. It involves geo-referencing the scanned map using 14 ground control points collected using GPS. Various layers like buildings, roads and landscapes are then digitized on the geo-referenced map and attributes are added. Finally, a road graph is generated to analyze the shortest path between two points on campus based on time.
LinuxFest NW - Using Postgis To Add Some Spatial Flavor To Your AppSteven Pousty
The document provides an introduction to using PostGIS to add spatial capabilities to PostgreSQL applications. It discusses installing PostGIS, importing spatial data, performing spatial queries and functions, and using the results in applications. Examples demonstrate basic PostGIS concepts like spatial queries, functions for area, distance and geometry operations.
Delineation and Comparison of Urban Heat Islands in TamilnaduVignesh Sekar
An urban heat island is a city or metropolitan area that is significantly warmer than its surrounded rural areas due to human activities. Whereas, the term heat island refers to any area, populated or not, which is consistently hotter than the surrounding area.
Project Objectives :
Conversion of thermal band data of LANDSAT 5 & 8 (Satellites) into Temperature contours in order to isolate and compare the Urban Heat Islands (UHI) of Tamil Nadu over a decade (i.e., 2005 & 2015) and over the different seasons of an year (2014
Identification of the factors responsible for UHI formation with reference to Land use
Intensity of the UHI formed
Suggestion of Mitigation Measures
This document describes the CERTH team's participation in the MediaEval 2014 Synchronization of Multi-User Event Media task. The team proposed a method using near duplicate image detection, graph construction and traversal to synchronize the timestamps of images across different users. They then clustered the images into events using either a single clustering approach across all images or pre-clustering within each user's gallery. The team submitted 5 runs and evaluated the results, finding their modified near duplicate detection worked best for timestamp alignment on one dataset while the standard method was better for another. Color histogram similarity also helped improve clustering performance on one dataset compared to concept detection scores.
This document contains summaries of several presentations about using FME (Feature Manipulation Engine) for various GIS, BIM, and 3D data projects:
1. The Arkansas GIS Office migrated their state GIS data to the cloud using FME.
2. Irish Water used FME to migrate architectural data during a cloud migration project.
3. Mount Vernon Ladies' Association used FME to integrate BIM and GIS data from a historic preservation project.
4. Several other projects are summarized relating to meteorology map tile generation with FME Cloud, 3D data processing, pipeline risk analysis, UAV data collection, 3D modeling, tile cache generation, and spatial data migration
This document contains summaries of several presentations about using FME (Feature Manipulation Engine) for various GIS, BIM, and 3D data projects:
1. The Arkansas GIS Office migrated their state GIS data to the cloud using FME.
2. Irish Water used FME to migrate architectural data from BIM to GIS formats for a project.
3. The Mount Vernon Ladies' Association used FME to connect Revit BIM data to Esri GIS formats.
4. Several other presentations described using FME for tile map generation, 3D data processing, building a pipeline risk analysis system, UAV data processing, 3D model translation, and multi-
This document discusses how geographic information systems (GIS) can be used to support site remediation projects (SRP). It describes a three step iterative process for GIS analysis: 1) data assembly, 2) data analysis, and 3) data presentation. For data assembly, GIS is used to organize disparate data sources by relating data to geographic coordinates. For data analysis, GIS enables simple observations and modeling to build an understanding of site conditions. For data presentation, GIS creates maps, graphs and other visualizations to communicate spatial relationships and trends in the data. The document provides examples of how GIS can be applied throughout the different phases of an SRP.
This document discusses plugins available in QGIS and their uses. It describes several general plugins for tasks like flow estimation, 3D visualization, and background maps. It also outlines plugins for vector analysis, raster analysis, and visualization of TUFLOW hydraulic modeling results. Specific plugins covered provide functions like georeferencing, sampling raster values, digitizing shapes, plotting profiles, and more. The document provides examples of how many of the plugins in QGIS compare to analysis tools in other GIS software.
Mapping Toolbox provides tools for analyzing, visualizing, and mapping geographic data. It allows users to import vector and raster data formats, customize data through operations like subsetting and trimming, and perform geospatial analyses. The toolbox enables 2D and 3D map displays with imported data and base map layers. It offers functions for digital terrain analysis, geodesy calculations, map projections, and other geographic utilities.
This document provides an overview of geospatial technology and geographic information systems (GIS). It discusses how GIS integrates data from GPS and remote sensing to store, analyze and manage spatial data referenced to locations on Earth. The key aspects covered include GIS data models using vector and raster formats, representing terrain as digital terrain models (DTMs), performing analysis like overlay operations and neighborhood functions, and calculating slopes and aspects from elevation data. GIS is presented as a versatile system for solving real-world problems by linking thematic data layers based on their geographic coordinates.
This document discusses using Scala for geographic information systems (GIS) by leveraging Geotrellis and Spark to ingest, process, and analyze geospatial raster and vector data across distributed file systems and databases. It provides examples of computing zonal statistics, using space-filling curves like the Z-curve and Hilbert curve, and links to Geotrellis demonstrations for further information.
This document discusses how geographic information systems (GIS) can support various hydrologic modeling methods, including USGS regression equations, the NRCS Curve Number method, and two-dimensional hydrologic modeling. It provides examples of implementing these methods in GIS using data sources like LiDAR and NEXRAD rainfall data. The document aims to demonstrate how GIS can automate hydrologic analyses and improve understanding of watershed processes.
Making the Most of Raster Analysis with Living Atlas Data - Esri UC 2018Aileen Buckley
Content in the Living Atlas of the World isn’t just for making web maps—it includes ready-to-use data that can be used for analysis in the ArcGIS Platform. The Living Atlas includes hundreds of image services you can use in ArcGIS Desktop and Pro as inputs to geoprocessing tools. That means no downloading, no data preparation, and huge time savings. Image services work so well in ArcGIS, you will be challenged to find how they differ from the raster datasets on your hard drive. With a focus on Pro, this session teaches you about the keys to success when performing analysis with Living Atlas image services.
Ease-of-use and Effectiveness of Participatory GIS in Empowering Rural Commun...MapWindow GIS
This document describes experiments conducted to test a participatory GIS software called villageQGIS for empowering rural communities. Village youth were trained to collect spatial data like wells and farm boundaries using GPS and questionnaires. Students then used villageQGIS and QGIS to create maps from the collected data. On average, villageQGIS took half the time of QGIS and produced maps with comparable spatial accuracy according to experiments conducted with village students. The easy to use interface of villageQGIS allows villagers to understand local problems and participate better in decision making.
Fitting probability distribution into data is very essential knowledge for the researchers of any discipline. I hope this presentation slides may contribute in scientific research.
Fire Proximity Awareness Monitoring with FMESafe Software
This document describes the evolution of Manitoba Hydro's fire proximity awareness system using geospatial data and FME. It summarizes how fire data was initially pulled from shapefiles then points to improve legibility. Weather and infrastructure data were also incorporated. FME was used to process the data, calculate proximity of fires to infrastructure, and track crew locations over time in response to fires. The system provides near real-time awareness of fire and weather conditions to help monitor risks to Manitoba Hydro assets and operations.
Geographic information system and remote sensingchala hailu
ArcMap is where you create maps and access most of the ArcGIS functionality. Remote sensing is an instrument based of observing an object at a far distance without direct contact.
Processing Landsat 8 Multi-Spectral Images with GRASS Tools & the potential o...Shaun Lewis
Paul Shapley gave a presentation on processing Landsat 8 multi-spectral images with GRASS tools and the potential of the QGIS-GRASS plugin. He discussed his background and work with QGIS and GRASS for mapping at Neath Port Talbot Borough Planning Department. He provided an overview of Landsat 8 data and demonstrated using GRASS modules to classify Landsat images and analyze changes over time. Shapley also discussed advantages and updates to the QGIS-GRASS plugin, and future projects using GRASS and 3D data for his local development plan monitoring and property mapping.
The document describes geo-referencing and digitizing a map of IIT Roorkee campus in QGIS. It involves geo-referencing the scanned map using 14 ground control points collected using GPS. Various layers like buildings, roads and landscapes are then digitized on the geo-referenced map and attributes are added. Finally, a road graph is generated to analyze the shortest path between two points on campus based on time.
LinuxFest NW - Using Postgis To Add Some Spatial Flavor To Your AppSteven Pousty
The document provides an introduction to using PostGIS to add spatial capabilities to PostgreSQL applications. It discusses installing PostGIS, importing spatial data, performing spatial queries and functions, and using the results in applications. Examples demonstrate basic PostGIS concepts like spatial queries, functions for area, distance and geometry operations.
Delineation and Comparison of Urban Heat Islands in TamilnaduVignesh Sekar
An urban heat island is a city or metropolitan area that is significantly warmer than its surrounded rural areas due to human activities. Whereas, the term heat island refers to any area, populated or not, which is consistently hotter than the surrounding area.
Project Objectives :
Conversion of thermal band data of LANDSAT 5 & 8 (Satellites) into Temperature contours in order to isolate and compare the Urban Heat Islands (UHI) of Tamil Nadu over a decade (i.e., 2005 & 2015) and over the different seasons of an year (2014
Identification of the factors responsible for UHI formation with reference to Land use
Intensity of the UHI formed
Suggestion of Mitigation Measures
This document describes the CERTH team's participation in the MediaEval 2014 Synchronization of Multi-User Event Media task. The team proposed a method using near duplicate image detection, graph construction and traversal to synchronize the timestamps of images across different users. They then clustered the images into events using either a single clustering approach across all images or pre-clustering within each user's gallery. The team submitted 5 runs and evaluated the results, finding their modified near duplicate detection worked best for timestamp alignment on one dataset while the standard method was better for another. Color histogram similarity also helped improve clustering performance on one dataset compared to concept detection scores.
This document contains summaries of several presentations about using FME (Feature Manipulation Engine) for various GIS, BIM, and 3D data projects:
1. The Arkansas GIS Office migrated their state GIS data to the cloud using FME.
2. Irish Water used FME to migrate architectural data during a cloud migration project.
3. Mount Vernon Ladies' Association used FME to integrate BIM and GIS data from a historic preservation project.
4. Several other projects are summarized relating to meteorology map tile generation with FME Cloud, 3D data processing, pipeline risk analysis, UAV data collection, 3D modeling, tile cache generation, and spatial data migration
This document contains summaries of several presentations about using FME (Feature Manipulation Engine) for various GIS, BIM, and 3D data projects:
1. The Arkansas GIS Office migrated their state GIS data to the cloud using FME.
2. Irish Water used FME to migrate architectural data from BIM to GIS formats for a project.
3. The Mount Vernon Ladies' Association used FME to connect Revit BIM data to Esri GIS formats.
4. Several other presentations described using FME for tile map generation, 3D data processing, building a pipeline risk analysis system, UAV data processing, 3D model translation, and multi-
This document discusses how geographic information systems (GIS) can be used to support site remediation projects (SRP). It describes a three step iterative process for GIS analysis: 1) data assembly, 2) data analysis, and 3) data presentation. For data assembly, GIS is used to organize disparate data sources by relating data to geographic coordinates. For data analysis, GIS enables simple observations and modeling to build an understanding of site conditions. For data presentation, GIS creates maps, graphs and other visualizations to communicate spatial relationships and trends in the data. The document provides examples of how GIS can be applied throughout the different phases of an SRP.
This document discusses plugins available in QGIS and their uses. It describes several general plugins for tasks like flow estimation, 3D visualization, and background maps. It also outlines plugins for vector analysis, raster analysis, and visualization of TUFLOW hydraulic modeling results. Specific plugins covered provide functions like georeferencing, sampling raster values, digitizing shapes, plotting profiles, and more. The document provides examples of how many of the plugins in QGIS compare to analysis tools in other GIS software.
Mapping Toolbox provides tools for analyzing, visualizing, and mapping geographic data. It allows users to import vector and raster data formats, customize data through operations like subsetting and trimming, and perform geospatial analyses. The toolbox enables 2D and 3D map displays with imported data and base map layers. It offers functions for digital terrain analysis, geodesy calculations, map projections, and other geographic utilities.
This document provides an overview of geospatial technology and geographic information systems (GIS). It discusses how GIS integrates data from GPS and remote sensing to store, analyze and manage spatial data referenced to locations on Earth. The key aspects covered include GIS data models using vector and raster formats, representing terrain as digital terrain models (DTMs), performing analysis like overlay operations and neighborhood functions, and calculating slopes and aspects from elevation data. GIS is presented as a versatile system for solving real-world problems by linking thematic data layers based on their geographic coordinates.
Learning to organize, analyze, and visualize space-time data can be daunting. Get up to speed in this workshop by learning how to manage your space-time data aptly, analyze it appropriately, and map it artfully. A variety of examples are used demonstrate how ArcGIS helps you gain greater understanding of space-time data using powerful analytical tools for aggregating your data into a space-time cube; performing hot spot, cluster, and outlier analyses; and visualizing your data and analysis results. Techniques are also presented for creating captivating and informative displays to share your insights with others.
2018 AAG Annual Meeting
Wednesday, April 11, 3:20 p.m. – 5:00 p.m.
This document provides an introduction to Geographic Information Systems (GIS). It defines GIS as a system designed to store, manipulate, analyze and display spatially referenced data. The key components of a GIS are hardware, software and data. Common GIS software includes desktop programs like ArcGIS and open-source options like QGIS. GIS can incorporate different types of spatial data like raster, vector and remote sensing data along with associated attribute tables. Example applications discussed are in hydrology, including watershed analysis and flood modeling.
This document outlines a 5-day training program on GIS and remote sensing for overlay analysis using QGIS. Day 1 focuses on an introduction to overlay analysis, QGIS, and processing input data layers on lithology and land use. The morning will cover introductions to GIS concepts, overlay analysis principles, and the QGIS interface. The afternoon involves preparing lithology and land use layers for analysis.
This document outlines a 5-day training program on GIS and remote sensing for overlay analysis using QGIS. Day 1 focuses on an introduction to overlay analysis, QGIS, and processing input layers on lithology and land use. The morning will cover introductions to GIS concepts, overlay analysis principles, and the QGIS interface. The afternoon involves preparing lithology and land use layers for analysis.
This document provides a whirlwind tour of GIS concepts in 25 slides. It defines GIS as geographical information science and discusses data capture methods like surveys and remote sensing. It explores analysis and visualization techniques, different GIS platforms, common spatial phenomena modeled in GIS, and modeling approaches. The document also covers GIS history, software, data types, attributes, overlay operations, coordinate reference systems, common file formats, data storage, open source GIS, web GIS, and potential future directions for GIS including location-based services and cloud computing.
This document provides a whirlwind tour of GIS concepts in 25 slides. It defines GIS as geographical information science and discusses data capture techniques including remote sensing and sensor networks. It explores analysis and visualization of spatial data in 2D and 3D maps and how visualization can enable further analysis. The document also briefly outlines the history of GIS software and formats, as well as concepts like spatial data types, attributes, modeling frameworks, coordinate reference systems, and industry standard and open source GIS tools. It concludes with discussions of future directions for GIS including location-based services, sensors, cloud computing, and social implications.
This document provides a whirlwind tour of GIS concepts in 25 slides. It defines GIS as geographical information science and discusses data capture methods like surveys and remote sensing. It explains how GIS allows for analysis and visualization of spatial data in 2D and 3D maps. Key aspects of GIS covered include its history, common data types of vector and raster, attributes, modeling frameworks, data storage, open source options, and future directions such as location-based services and cloud computing. The document aims to quickly introduce fundamental GIS concepts.
This document provides a whirlwind tour of GIS concepts in 25 slides. It defines GIS as geographical information science and discusses data capture methods like remote sensing and GPS. It explains how spatial data can be analyzed and visualized in 2D and 3D maps. Common data types in GIS like vector and raster data are introduced along with concepts like attributes, overlay operations, and coordinate reference systems. Popular GIS software like ArcGIS and open source options are overviewed. The document concludes by discussing emerging areas in GIS like web mapping, mobile apps, sensor networks, and cloud computing.
State of GeoServer provides an update on our community and reviews the new and noteworthy features for 2018. GeoServer is a web service for publishing your geospatial data. using industry standards for vector, raster and mapping.
We have an active community and a lot to cover for 2.12 and 2.13 release, as well what is cooking in September’s 2.14 release.
Each release provides exciting new features, this talk covers diverse improvements across GeoServer:
* OGC compliance work for WFS 2.0 and WMTS 1.0, WFS 3.0 support
* improvements for cloud deployments
* cascade WMTS services
* progress in NetCDF support
* getting ready for the Java 18.9 roadmap
* And much more…
Attend this talk for a cheerful update on what is happening with this popular OSGeo project. Whether you are an expert user, a developer, or simply curious what GeoServer can do for you.
This document provides guidelines for creating geographic information system (GIS) datasets under a hydrology project in India. It discusses the types of spatial data to be created (points, lines, polygons), the themes to be mapped (land use, soils, geology, etc.), and the methodology for generating the datasets from satellite imagery and existing maps. Standardizing the data collection process across multiple vendors is emphasized. The goal is to integrate the spatial data into surface water and groundwater databases to improve understanding of water resources.
This document provides guidelines for creating geographic information system (GIS) datasets under a hydrology project in India. It describes the types of spatial data to be created (points, lines, polygons), the themes (land use, soils, geology, etc.), and the methodology for generating the data. Standardized processes are outlined for procuring data services, database organization, attribute coding, and delivering final data products. The goal is to create consistent GIS datasets across states and scales to support analysis of surface water and groundwater resources.
This document provides an outline for a presentation on geospatial technologies including remote sensing, GPS, mapping, surveying, and GIS. It begins with an introduction to the geomatic umbrella and defines key geospatial concepts. It then discusses remote sensing platforms and sensors, and provides examples of agricultural and forestry applications. It also summarizes GPS systems and applications. The document defines mapping and surveying and provides examples. It concludes with an overview of GIS hardware, software, data, and functions and discusses example applications in emergency management, petroleum management, and utilities.
This document is the master's thesis of Georgios Stamoulis submitted to the National and Kapodistrian University of Athens. The thesis explores visualizing and exploring time-evolving linked geospatial data. It aims to improve upon the original version of Sextant, a tool for exploring linked geospatial data, by adding new features to create a more user-friendly application. The enhanced application allows users to explore and visualize linked geospatial and temporal data, produce statistical charts, and create, share, and search thematic maps combining geospatial information from different sources.
Accumulo Summit 2016: GeoMesa: Using Accumulo for Optimized Spatio-Temporal P...Accumulo Summit
LocationTech GeoMesa is a project that builds on open-source, distributed databases like Accumulo, HBase, and Cassandra to scale up indexing, querying, and analyzing billions of spatio-temporal data points. GeoMesa uses space-filling curves to index multi-dimensional data in Accumulo, and we'll discuss recent improvements for non-point geometries. Over the two and a half years GeoMesa has been an open-source project, GeoMesa's Accumulo schemas have evolved and our team has had a chance to work through creating and optimizing custom Accumulo iterators. These custom iterators allow for better query performance and interesting aggregations. GeoMesa provides support for distributed processing in Spark via MapReduce input and output formats that extend their Accumulo counterparts. We will discuss the performance benefit gained by reducing the number of default map/Spark tasks created for complex query patterns. The talk will conclude with updates about GeoMesa's integration with Jupyter notebook and improvements to GeoMesa's Spark integration.
– Speaker –
Dr. James Hughes
Mathematician, Commonwealth Computer Research, Inc (CCRi)
Dr. James Hughes is a mathematician at Commonwealth Computer Research, Inc. in Charlottesville, Virginia. He is a core committer for GeoMesa which leverages Accumulo and other distributed database systems to provide distributed computation and query engines. He is a LocationTech committer for GeoMesa, SFCurve, and GeoBench. He serves on the LocationTech Project Management Committee and Steering Committee. Through work with LocationTech and OSGeo projects like GeoTools and GeoServer, he works to build end-to-end solutions for big spatio-temporal problems. He holds a PhD in algebraic topology from the University of Virginia.
— More Information —
For more information see http://www.accumulosummit.com/
This document provides an introduction to spatial data analysis using open source software R. It discusses spatial data concepts like spatial reference systems and coordinate reference systems. It describes how to create, load and visualize spatial point, line and polygon data in R. It also covers digital image processing and classification in QGIS. Methods discussed include spatial point pattern analysis, interpolation, geostatistics, spatial modeling and accuracy assessment. The document uses data from Kilimanjaro region as an example to demonstrate these spatial analysis techniques in R and QGIS.
Precision Farming (PF) is introduced and history in short is reviewed. Essential activities of GPS locating, soil mapping, GIS dataprocessing and presentation and VRT application are described. Basic principles of PF are shown to be:
• Precision Farming is the management process of within-field variability.
• This management must bring profit or at least reduce the risk of loss
• This management must reduce the impact of farming on environment.
Techniques used in Precision Farming are described. Economics of Precision Farming is discussed. A general cost/benefit analysis and profitability of PF are reviewed. The price of PF adoption facing a farmer is discussed. Methods of process analysis and activity based costing are shown as useful instruments for PF process analysis and model building. PF process is analysed and process graph is developed.
Methods for analyzing and mapping temporal dataAileen Buckley
This presentation focuses on the multiple methods you can use to effectively analyze your temporal data using ArcGIS and how to communicate the temporal nature of your data through maps designed to be shared in print, on-screen, and online. A variety of examples are used to demonstrate how ArcGIS can help you to provide greater understanding of your data through appropriate statistical analyses and share that understanding with aesthetically pleasing and effectively communicative visualizations.
Similar to GRASS as a Temporal GIS - Sören Gebbert (20)
PyWPS is an open source Python implementation of the OGC Web Processing Service standard. It allows users to publish and discover geospatial processes that can be invoked remotely through a RESTful API. Some key points about PyWPS include that it supports all geospatial tools available in Python, uses standards like WFS and WCS, and allows processes to be run asynchronously and in isolated containers. The current version, PyWPS 4.0.0, features improvements like enhanced data validation, multiprocessing support, and an updated codebase to work with newer Python and geospatial technologies.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3Data Hops
Free A4 downloadable and printable Cyber Security, Social Engineering Safety and security Training Posters . Promote security awareness in the home or workplace. Lock them Out From training providers datahops.com
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
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Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
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HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
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TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
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See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
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1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
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The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
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Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Programming Foundation Models with DSPy - Meetup SlidesZilliz
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GRASS as a Temporal GIS - Sören Gebbert
1. Introduction
Time in GRASS GIS
Temporal Modules
GRASS as Temporal GIS
Sören Gebbert
THÜNEN Institute of Climate-Smart Agriculture
November 23, 2013
Sören Gebbert 2013
GRASS as Temporal GIS
2. Introduction
Time in GRASS GIS
Temporal Modules
Topics
1
Introduction
2
Time in GRASS GIS
3
Temporal Modules
Sören Gebbert 2013
GRASS as Temporal GIS
3. Introduction
Time in GRASS GIS
Temporal Modules
GRASS GIS
GRASS Geographic Resources Analysis Support System
Multipurpose raster, 3D raster and vector based GIS
Developed 1982 - 1995 at CERL in Illinois/USA
Since 1999 open source under GPL license with active
international development community
Very modular, provides GUI, command line and batch
processing support
About 500 modules for management, processing, analysis
and visualization of geographical data are available
(GRASS version 7 2013 Aug.)
Sören Gebbert 2013
GRASS as Temporal GIS
4. Introduction
Time in GRASS GIS
Temporal Modules
Raster processing
Slope, Aspect
Exposition
spatial aggregation
Map algebra
Spline Interpolation
Principal components
...
Sören Gebbert 2013
GRASS as Temporal GIS
5. Introduction
Time in GRASS GIS
Temporal Modules
Vector processing
Generalize, Buffer
Patch,Overlay,
Select,Extract
Shortest Path
Traveling salesman
problem
Delaunay, Voronoi
triangulation
...
Sören Gebbert 2013
GRASS as Temporal GIS
6. Introduction
Time in GRASS GIS
Temporal Modules
Why a temporal GIS?
We need to model and assess GHG emissions, Soil Organic
Carbon (SOC) change and Land Use Change (LUC) at
continental scale with different spatial and temporal resolutions
Sören Gebbert 2013
GRASS as Temporal GIS
7. Introduction
Time in GRASS GIS
Temporal Modules
Why reinventing the wheel?
Available temporal GIS and related multi purpose
environmental modeling solutions do not fit our needs
Sören Gebbert 2013
GRASS as Temporal GIS
8. Introduction
Time in GRASS GIS
Temporal Modules
Topics
1
Introduction
2
Time in GRASS GIS
3
Temporal Modules
Sören Gebbert 2013
GRASS as Temporal GIS
9. Introduction
Time in GRASS GIS
Temporal Modules
Temporal GRASS GIS goal
Development of the first comprehensive field based
temporal GIS
Efficient management, processing, analysis and
visualization of large spatio-temporal fields and their
interactions
Providing interoperability between sophisticated
spatio-temporal analyzing tools like CDO, R and ParaView
Creating an intuitive object oriented spatio-temporal
framework
Sören Gebbert 2013
GRASS as Temporal GIS
10. Introduction
Time in GRASS GIS
Temporal Modules
What is a field based temporal GIS?
Two important Temporal GIS classifications (Goodchild 1989,
Heuvelink 1998)
a) Temporal GIS dealing with objects like points, lines, arcs,
areas that represent geometrical or topological features in
space and time
b) Temporal GIS dealing with fields, natural, social or
epidemiological. Their attribute data representing the
distribution of temperature, precipitation, hydrological or
ecological patterns in space and time
We use the field based approach in GRASS GIS.
Sören Gebbert 2013
GRASS as Temporal GIS
11. Introduction
Time in GRASS GIS
Temporal Modules
Temporal GRASS GIS concept
Do not break the existing GRASS GIS functionalities and
assure backward compatibility
Follow the UNIX paradigm, create small modules for a
specific purpose and combine them to manage complex
tasks
Design time support for the existing datatypes: raster, 3D
raster and vector map layers
Design new spatio-temporal datatypes, space time
datasets, that manage time series data
Reuse existing raster, 3D raster and vector modules to
process space time datasets
Sören Gebbert 2013
GRASS as Temporal GIS
12. Introduction
Time in GRASS GIS
Temporal Modules
Temporal GRASS GIS concept
Sören Gebbert 2013
GRASS as Temporal GIS
13. Introduction
Time in GRASS GIS
Temporal Modules
Temporal relationships
Supported temporal relationships (Claramunt and Jiang 2001).
Sören Gebbert 2013
GRASS as Temporal GIS
14. Introduction
Time in GRASS GIS
Temporal Modules
Temporal GRASS GIS concept
Use interval time and time instances of absolute
(Gregorian calendar) and relative time (years to seconds)
Use a SQL database to store the temporal and spatial
extent of space time datasets, map layers and their
metadata
Use the SQL database to store relations between maps
and space time datasets
Implement a comprehensive object oriented temporal
framework
Implement new GRASS modules based on the temporal
framework for managing, processing and analyzing of
space time datasets
Sören Gebbert 2013
GRASS as Temporal GIS
15. Introduction
Time in GRASS GIS
Temporal Modules
Space time datasets
Sören Gebbert 2013
GRASS as Temporal GIS
16. Introduction
Time in GRASS GIS
Temporal Modules
Space time datasets
Sören Gebbert 2013
GRASS as Temporal GIS
17. Introduction
Time in GRASS GIS
Temporal Modules
Topics
1
Introduction
2
Time in GRASS GIS
3
Temporal Modules
Sören Gebbert 2013
GRASS as Temporal GIS
18. Introduction
Time in GRASS GIS
Temporal Modules
Modules to manage space time datasets
t.create
Create a space time dataset
t.register
Assign time stamps and register raster, vector or voxel map
layers in a space time dataset
t.unregister
Unregister map layers from space time datasets
t.remove
Remove space time datasets
Sören Gebbert 2013
GRASS as Temporal GIS
20. Introduction
Time in GRASS GIS
Temporal Modules
Modules to manage space time datasets
t.support
Modify the metadata of a space time dataset
t.topology
Shows and checks the temporal topology of a space time
dataset
t.shift
Temporally shift a space time dataset
t.snap
Create a valid temporal topology of a space time dataset
Sören Gebbert 2013
GRASS as Temporal GIS
21. Introduction
Time in GRASS GIS
Temporal Modules
Modules to process space time raster datasets
t.rast.list
List registered raster map layers. Support SQL WHERE
statements as well as methods like: list by time order or list by
granularity
t.rast.series
Performs different aggregation algorithms on all or a subset of
raster map layers in a space time raster dataset
t.rast.extract
Extract space time raster datasets from an existing STRDS
using SQL WHERE statements and map-calculation
expressions.
Sören Gebbert 2013
GRASS as Temporal GIS
22. Introduction
Time in GRASS GIS
Temporal Modules
Modules to process space time raster datasets
t.rast.mapcalc
Spatio-temporal raster algebra
t.rast.aggregate
Temporally aggregate a space time raster dataset using
different statistical aggregation methods
t.rast.univar
Calculates univariate statistics for each registered raster map
layer of a space time raster dataset
Sören Gebbert 2013
GRASS as Temporal GIS
23. Introduction
Time in GRASS GIS
Temporal Modules
t.rast.aggregate
Sören Gebbert 2013
GRASS as Temporal GIS
24. Introduction
Time in GRASS GIS
Temporal Modules
t.info precipitation_1950_2011_yearly
+-------------------- Space Time Raster Dataset -----------------------------+
|
|
+-------------------- Basic information -------------------------------------+
| Id: ........................ precipitation_1950_2011_yearly@PERMANENT
| Name: ...................... precipitation_1950_2011_yearly
| Mapset: .................... PERMANENT
| Creator: ................... soeren
| Creation time: ............. 2013-09-18 13:35:16.243647
| Temporal type: ............. absolute
| Semantic type:.............. mean
+-------------------- Absolute time -----------------------------------------+
| Start time:................. 1950-01-01 00:00:00
| End time:................... 2012-01-01 00:00:00
| Granularity:................ 1 year
| Temporal type of maps:...... interval
+-------------------- Spatial extent ----------------------------------------+
| North:...................... 75.5
| South:...................... -0.5
| East:.. .................... 75.5
| West:....................... -40.5
| Top:........................ 0.0
| Bottom:..................... 0.0
+-------------------- Metadata information ----------------------------------+
Sören Gebbert 2013
GRASS as Temporal GIS
25. Introduction
Time in GRASS GIS
Temporal Modules
t.info precipitation_1950_2011_yearly
+-------------------- Metadata information ----------------------------------+
| Raster register table:...... precipitation_1950_2011_yearly_PERMANENT_raster_register
| North-South resolution min:. 1.0
| North-South resolution max:. 1.0
| East-west resolution min:... 1.0
| East-west resolution max:... 1.0
| Minimum value min:.......... 0.0
| Minimum value max:.......... 2059.0
| Maximum value min:.......... 16881.0
| Maximum value max:.......... 35116.0
| Number of registered maps:.. 62
|
| Title:
| Yearly precipitation 1950 - 2011
| Description:
| Yearly precipitation 1950 - 2011 in [0.1 mm]
| Command history:
| # 2013-09-18 13:35:16
| t.rast.aggregate
|
input="precipitation_1950_2011_monthly"
|
output="precipitation_1950_2011_yearly" base="precip_yearly"
|
granularity="1 year" method="sum"
| # 2013-09-18 14:28:35
| t.support in="precipitation_1950_2011_yearly"
|
title="Yearly precipitation 1950 - 2011"
|
descr="Yearly precipitation 1950 - 2011 in [0.1 mm]"
|
+----------------------------------------------------------------------------+
Sören Gebbert 2013
GRASS as Temporal GIS
26. Introduction
Time in GRASS GIS
Temporal Modules
t.rast.aggregate + t.rast.extract + t.rast.univar + R
Seasonal mean temperature trend of the
temperate European climate Zone from 1950 − 2010
20
q
15
0
5
10
q
q
q
qq
q qq q q qq
q
q
q
q
q
qq
q
q qq q q
q q qq q q qq q
q q
q
q qqqq
q qq
q
q
q
q
q qq
q
qq q
q
q
q
q
−5
Temperature in degree Celsius
q
Summer temperature, linear regression slope 0.019
Fall temperature, linear regression slope 0.006
Spring temperature, linear regression slope 0.023
Winter temperature, linear regression slope 0.019
1950
1960
1970
1980
1990
2000
Years
Sören Gebbert 2013
GRASS as Temporal GIS
2010
27. Introduction
Time in GRASS GIS
Temporal Modules
Import and Export of space time raster datasets
t.rast.export and t.rast.import
Sören Gebbert 2013
GRASS as Temporal GIS
28. Introduction
Time in GRASS GIS
Temporal Modules
Modules to process space time vector datasets
t.vect.list
Lists registered vector map layers of a space time vector
dataset
t.vect.extract
Extracts a subset of a space time vector dataset. Supports
SQL WHERE queries for metadata and attributes.
t.vect.univar
Calculates univariate statistics of attributes for each registered
vector map layer of a space time vector dataset
Sören Gebbert 2013
GRASS as Temporal GIS
29. Introduction
Time in GRASS GIS
Temporal Modules
Modules to process space time vector datasets
t.vect.db.select
Prints attribute data of vector map layers of a specific space
time vector dataset
t.vect.observe.strds
Observe specific locations in a space time raster dataset over a
period of time using vector points
t.vect.what.strds
Store values of raster map layers at spatial and temporal
positions of vector points as vector attributes
Sören Gebbert 2013
GRASS as Temporal GIS
30. Introduction
Time in GRASS GIS
Temporal Modules
t.vect.observe.strds + t.vect.db.select + R
q
20
25
Seasonal mean temperature trend of Berlin from 1950 − 2010
0
5
10
15
q
q
q
q
q
qq q
q
qq
q
q
q q
q
qq
q q
q
q
qq qq q
qq
qq
q
q qqq
q
q q
q
q q
qq
q
q
q
q q
q q
q
q
q q
q
q
q
q
−5
Temperature in degree Celsius
q
Summer temperature, linear regression slope 0.023
Fall temperature, linear regression slope 0.011
Spring temperature, linear regression slope 0.034
Winter temperature, linear regression slope 0.029
q
1950
1960
1970
1980
1990
2000
Years
Sören Gebbert 2013
GRASS as Temporal GIS
2010
31. Introduction
Time in GRASS GIS
Temporal Modules
Import and Export of space time vector datasets
t.vect.export and t.vect.import
Sören Gebbert 2013
GRASS as Temporal GIS
32. Introduction
Time in GRASS GIS
Temporal Modules
Modules to process space time raster 3D datasets
t.rast3d.list
List registered 3D raster map layers.
t.rast3d.univar
Calculates univariate statistics from the non-null cells for each
registered 3D raster map layer of a space time raster 3D
dataset
t.rasedt.mapcalc
Spatio-temporal raster 3D algebra
t.rast3d.extract
Extract space time raster 3D datasets from an existing
STR3DS using SQL where and r.mapcalc queries.
Sören Gebbert 2013
GRASS as Temporal GIS
33. Introduction
Time in GRASS GIS
Temporal Modules
Animation with g.gui.animate
Sören Gebbert 2013
GRASS as Temporal GIS
34. Introduction
Time in GRASS GIS
Temporal Modules
Comparison of two maps with g.gui.mapswipe
Sören Gebbert 2013
GRASS as Temporal GIS
35. Introduction
Time in GRASS GIS
Temporal Modules
Time-line visualization with g.gui.timeline
Sören Gebbert 2013
GRASS as Temporal GIS
36. Introduction
Time in GRASS GIS
Temporal Modules
The End
Thank you
Sören Gebbert 2013
GRASS as Temporal GIS