Android maps utils is an open-source library, which provides advanced features for our maps. We'll demonstrate the most important set of features on live examples, like heatmaps, marker clustering and spherical geometry algorithms.
LIDAR-derived DTM for archaeology and landscape history research some recent ...Shaun Lewis
This document discusses using QGIS and GRASS software to analyze LIDAR data and digital terrain models (DTMs) for archaeology and landscape history research. It provides examples of merging DTM tiles, applying color styles, using GRASS tools like r.slope.aspect to exaggerate surface differentials and reveal archaeological features, and overlaying processed DTMs with historical maps. GDAL and Python scripts are also used to further process the DTM data.
This document outlines the steps taken to determine material loss in the Grasberg area of Papua caused by private company exploration using a 3D analysis technique called cut and fill. The analysis involved generating elevation data points from SRTM data, converting the points to a vector file, creating a TIN surface, and executing a cut and fill between two TINs to calculate the volume of material loss in cubic meters.
Analyzing Larger RasterData in a Jupyter Notebook with GeoPySpark on AWS - FO...Rob Emanuele
This document outlines a presentation on analyzing large raster data in a Jupyter notebook with GeoPySpark on AWS. The presentation covers introductory material, exercises on working with land cover and Landsat imagery data, combining data layers to detect crop cycles, and combining different data types to create maps. It discusses where the notebooks are running, data sources, and GeoPySpark capabilities like working with space-time raster data. Attendees are encouraged to tweet maps created during the exercises.
This document describes a Python project that uses Google Cloud Platform services to analyze satellite images and estimate photovoltaic potential. It uses the Maps Static API to obtain satellite images for given addresses. Computer vision models like Mask R-CNN are used to segment rooftops and determine their sizes. AutoML is used to train classifiers to identify roof types and orientations. Together, this information is used to simulate and estimate a photovoltaic system's size and expected energy yield.
This document presents MATLAB programs to calculate the energy of simple graphs including cycles, wheels, and cyclic cubic graphs. The energy of a graph is defined as the sum of the absolute values of its eigenvalues. Algorithms are provided to generate MATLAB functions to calculate the energy for these graph types by generating the adjacency matrix and computing its eigenvalues. Examples applying the functions to cycles and wheels with n=40 and n=23 are shown. The programs make calculating graph energy for large values of n straightforward.
GeoGebra was created in 2001 as dynamic geometry software combining geometry, algebra, and calculus. The first prototype was developed for a master's thesis in 2002. Between 2004-2006, further development was supported through a PhD project. GeoGebra grew a large worldwide user community with over 300,000 monthly visitors by 2008. The International GeoGebra Institute was founded in 2007 to provide teacher training, support, and research to promote effective use of GeoGebra in classrooms.
2021 Dask Summit - Using STAC to catalog SpatioTemporal datasetsRob Emanuele
The document discusses using the SpatioTemporal Asset Catalog (STAC) to catalog geospatial datasets. STAC defines JSON schemas to encode metadata about spatiotemporal data like remote sensing imagery. This allows datasets like the European Space Agency's Sentinel-2 satellite data, containing petabytes of images, to be more easily searched. The STAC API also defines standards for searching and discovering STAC metadata. Tools like PySTAC and pystac-client make it easier to work with STAC catalogs and APIs in Python. Open questions remain around best representing multi-dimensional datasets like Zarr in STAC.
LIDAR-derived DTM for archaeology and landscape history research some recent ...Shaun Lewis
This document discusses using QGIS and GRASS software to analyze LIDAR data and digital terrain models (DTMs) for archaeology and landscape history research. It provides examples of merging DTM tiles, applying color styles, using GRASS tools like r.slope.aspect to exaggerate surface differentials and reveal archaeological features, and overlaying processed DTMs with historical maps. GDAL and Python scripts are also used to further process the DTM data.
This document outlines the steps taken to determine material loss in the Grasberg area of Papua caused by private company exploration using a 3D analysis technique called cut and fill. The analysis involved generating elevation data points from SRTM data, converting the points to a vector file, creating a TIN surface, and executing a cut and fill between two TINs to calculate the volume of material loss in cubic meters.
Analyzing Larger RasterData in a Jupyter Notebook with GeoPySpark on AWS - FO...Rob Emanuele
This document outlines a presentation on analyzing large raster data in a Jupyter notebook with GeoPySpark on AWS. The presentation covers introductory material, exercises on working with land cover and Landsat imagery data, combining data layers to detect crop cycles, and combining different data types to create maps. It discusses where the notebooks are running, data sources, and GeoPySpark capabilities like working with space-time raster data. Attendees are encouraged to tweet maps created during the exercises.
This document describes a Python project that uses Google Cloud Platform services to analyze satellite images and estimate photovoltaic potential. It uses the Maps Static API to obtain satellite images for given addresses. Computer vision models like Mask R-CNN are used to segment rooftops and determine their sizes. AutoML is used to train classifiers to identify roof types and orientations. Together, this information is used to simulate and estimate a photovoltaic system's size and expected energy yield.
This document presents MATLAB programs to calculate the energy of simple graphs including cycles, wheels, and cyclic cubic graphs. The energy of a graph is defined as the sum of the absolute values of its eigenvalues. Algorithms are provided to generate MATLAB functions to calculate the energy for these graph types by generating the adjacency matrix and computing its eigenvalues. Examples applying the functions to cycles and wheels with n=40 and n=23 are shown. The programs make calculating graph energy for large values of n straightforward.
GeoGebra was created in 2001 as dynamic geometry software combining geometry, algebra, and calculus. The first prototype was developed for a master's thesis in 2002. Between 2004-2006, further development was supported through a PhD project. GeoGebra grew a large worldwide user community with over 300,000 monthly visitors by 2008. The International GeoGebra Institute was founded in 2007 to provide teacher training, support, and research to promote effective use of GeoGebra in classrooms.
2021 Dask Summit - Using STAC to catalog SpatioTemporal datasetsRob Emanuele
The document discusses using the SpatioTemporal Asset Catalog (STAC) to catalog geospatial datasets. STAC defines JSON schemas to encode metadata about spatiotemporal data like remote sensing imagery. This allows datasets like the European Space Agency's Sentinel-2 satellite data, containing petabytes of images, to be more easily searched. The STAC API also defines standards for searching and discovering STAC metadata. Tools like PySTAC and pystac-client make it easier to work with STAC catalogs and APIs in Python. Open questions remain around best representing multi-dimensional datasets like Zarr in STAC.
Bayesian assimilation of rainfall sensors with fundamentally different integr...Andreas Scheidegger
Presents "CAIRS", a generic Bayesian method to assimilate signals from traditional and novel rain sensors. CAIRS is available for free as julia package: https://github.com/scheidan/CAIRS.jl
EGU 2012 ESSI: The FOSS GIS Workbench on the GFZ Load Sharing Facility compu...Peter Löwe
The document summarizes a FOSS GIS Workbench running on the GFZ Load Sharing Facility compute cluster. Some key points:
- The workbench allows users to run the open source GRASS GIS software on the HPC cluster for resource-intensive geospatial tasks. This provides benefits like parallel processing, long-running tasks, and secure/stable environment.
- The GFZ cluster has over 200 nodes with a total of over 3000 processor cores. The workbench provides modules to distribute GIS work across the cluster from within or outside of GRASS.
- Example applications discussed include tsunami mapping, long-term processing of large/complex datasets, generating globe maps and animations from
In this research, we propose a MapReduce al- gorithm for creating contiguity-based spatial weights. This algorithm provides the ability to create spatial weights from very large spatial datasets efficiently by using computing re- sources that are organized in the Hadoop framework. It works in the paradigm of MapReduce: mappers are dis- tributed in computing clusters to find contiguous neighbors in parallel, then reducers collect the results and generate the weights matrix. To test the performance of this al- gorithm, we design experiment to create contiguity-based weights matrix from artificial spatial data with up to 190 million polygons using Amazon’s Hadoop framework called Elastic MapReduce. The experiment demonstrates the scal- ability of this parallel algorithm which utilizes large com- puting clusters to solve the problem of creating contiguity weights on Big data.
This document summarizes Kelin Li's portfolio, including three projects using parametric design, building energy modeling, and automated lighting control systems. The first project used Revit, Dynamo and Ladybug to create a conceptual, parametrically designed building with weather-responsive facades. The second project used Rhino, Diva, Daysim and EnergyPlus to analyze energy savings from different automated blind and lighting control systems. The third project used IES-VE and DesignBuilder to model and compare the energy use of two houses.
Widgets and astropy: accomplishing useful research with undergraduatesmwcraig
The document discusses packages for astronomical data analysis including Astropy and CCDproc. It introduces the reducer package, a widget-based interface for CCD data reduction created by Matt Craig. The presentation demonstrates reducer's notebook interface and widget architecture for guiding novice researchers through data analysis tasks. Future goals include adding photometry and time series analysis widgets.
Cogent3 d master slides (12 april 2009)Danny Bronson
Cogent3D is a small business based in Tucson, AZ that develops 3D visualization products and services. Their flagship product is Genesis, which generates 3D terrain scenes directly from geospatial data formats like DTED, imagery, and vector data. Genesis uses an XML-based system to map source data to 3D scenes in real-time. This allows for dynamic updates and modifications to the 3D environment. Cogent3D aims to provide correlation across multiple domains like ground, air, and space using a single worldwide terrain database.
This document discusses open source satellite imagery and provides details on the Landsat 8 and Sentinel 2 satellites. It describes the spectral and temporal resolution of each satellite's imagery as well as how to access and use the free imagery data in web applications. Resources are listed for working with Landsat 8 and Sentinel 2 data through open source tools and an imagery browser.
Altima: A KBB-like Reference Pricing SystemNeil Ryan
This document proposes using machine learning to build a reference pricing system for rental listings. It would integrate with online rental sites to provide renters with a reference price for negotiation. The business model would be to either build their own service scraping listings, partner with listing providers, or promote to similar sites. The approach uses a Seattle rental data set to explore models like KNN regression, decision trees, and ensembles. It finds KNN performs best and provides a demo of reference prices calculated from rental details. Areas for improvement are discussed.
Automatic Object Exploratiob based on Probabilitypeterson iit
This document summarizes a student presentation on automatic object exploration based on probability. It introduces the objectives of extracting features from partial objects, making an object feature database, computing probability from object features, and controlling a camera based on probability. It then outlines the experimental setup, database objects, feature extraction methods, probability calculation process, and system flow diagram. Finally, it discusses the scope, limitations, results, conclusions, and future recommendations of the project.
This document discusses using ArcView 3.3 to determine watersheds on Lombok Island, Indonesia. It describes Lombok's varied topography, especially in northern high mountain and valley areas. The document outlines steps to create a digital elevation model from SRTM data, calculate flow direction and accumulation, identify streams, label stream links, and determine watershed boundaries. It also mentions viewing the results in 3D using the 3Dscene extension.
The document discusses using ArcGIS spatial analyst tools to delineate watersheds in the Padang area of Indonesia. It involves creating a digital elevation model (DEM) from SRTM elevation data, then using hydrological modeling tools like Fill, Flow Direction, Flow Accumulation, and Watershed to generate vector layers representing streams, channels, and watershed boundaries. The final watershed delineation is viewed in 2D in ArcMap and 3D in ArcScene and Cosmo Player to understand the hydrological modeling process and watersheds in the Padang area.
1) The document presents a method for short-term forecasting of surface solar irradiance at night using satellite imagery, allowing forecasts before sunrise.
2) It defines cloud classes based on brightness temperature differences in infrared satellite images at night, and derives a cloud index for each class by mapping infrared values to historical daytime cloud index values.
3) Validation using over 100 German weather stations over 6 months showed the nighttime cloud index can accurately forecast global horizontal irradiance in the hours before sunrise.
Processing Geospatial Data At Scale @locationtechRob Emanuele
This document discusses processing large geospatial data at scale. It provides background on big data frameworks like Apache Hadoop, Apache Spark, and geospatial projects like GeoTrellis, GeoWave, and SpatialHadoop that enable processing geospatial data using these frameworks. The document outlines how these tools allow geospatial data from sources like satellite imagery, OpenStreetMap, and geotagged social media to be analyzed using distributed computing platforms and algorithms.
This document outlines the steps to derive watersheds in Central Celebes, Indonesia from SRTM data using 3D modeling software and ArcGIS tools:
1) Load and preprocess SRTM data to create a DEM raster, including cropping, filling blanks, converting projections, and saving as a GeoTIFF.
2) Perform terrain analysis in ArcGIS to calculate flow direction, flow accumulation, stream definition, stream segmentation, and catchment grid delineation using the ArcHydro tools.
3) View the resulting watersheds and hydrological networks in 3D in ArcScene by setting the appropriate layer properties and vertical exaggeration.
1) The document outlines the steps to delineate a catchment area using software like Global Mapper and WMS. It begins with preparing DEM and satellite imagery data and getting GPS coordinates for an outlet point.
2) The initial catchment area is delineated in WMS and streams are verified. The catchment area is then visualized. Relevant data like drainage networks are downloaded and imported.
3) Flow direction and accumulation is calculated using TOPAZ tools in Global Mapper. Contour lines and other data are displayed to finalize the delineated catchment area containing the outlet point.
Meteoio Introduction given by Mathias Bavey in BozenRiccardo Rigon
MeteoIO is a toolbox for working with meteorological data in C++. It acts as an interface between physical models and different data sources. MeteoIO reads data from various sources, preprocesses it, and provides the data to models in a standardized way regardless of the original data characteristics. It performs operations like unit conversions, interpolation, and filtering to prepare data for use in models. MeteoIO uses a plugin architecture so that each data source is handled by its own plugin, while exposing a common interface to models.
Big Linked Data Querying - ExtremeEarth Open WorkshopExtremeEarth
This document discusses querying large geospatial datasets using the Strabo2 system. Strabo2 performs GeoSPARQL query answering on massive RDF graphs containing geospatial data from Copernicus and other sources. It relies on Apache Sedona to perform distributed spatial analytics on Apache Spark. Strabo2 uses techniques like vertical data partitioning, caching of spatial relations and query results, and persistent spatial indexing to improve query performance on large datasets. It has been deployed on the CREODIAS platform to enable spatial analytics on datasets for polar and food security use cases.
The document discusses location services in Android and the Google Maps external library. It describes how to get location data from the LocationManager service, provide mock location data through DDMS or command line, and use the MapView class to display maps from the Google Maps API. The MapView is a subclass of ViewGroup that handles displaying maps tiles and user gestures to pan and zoom. It provides built-in functionality to work with Google Maps data.
A scene graph is a general data structure commonly used by vector-based graphics editing applications and modern computer games, which arranges the logical and often (but not necessarily) spatial representation of a graphical scene. Examples of such programs include Acrobat 3D, Adobe Illustrator, AutoCAD, CorelDRAW, OpenSceneGraph, OpenSG, VRML97, X3D, Hoops and Open Inventor.
This document provides an overview of geolocation and mapping using Google Maps services. It defines geolocation as identifying the real-world location of an object like a mobile phone or computer. Mapping refers to map-making and cartography. The document reviews the navigator.geolocation API for accessing location data, the Google Maps Javascript API for embedding maps, and common overlays like markers and polylines that can be added to maps. It also summarizes services for obtaining directions and distance matrix information between locations.
Bayesian assimilation of rainfall sensors with fundamentally different integr...Andreas Scheidegger
Presents "CAIRS", a generic Bayesian method to assimilate signals from traditional and novel rain sensors. CAIRS is available for free as julia package: https://github.com/scheidan/CAIRS.jl
EGU 2012 ESSI: The FOSS GIS Workbench on the GFZ Load Sharing Facility compu...Peter Löwe
The document summarizes a FOSS GIS Workbench running on the GFZ Load Sharing Facility compute cluster. Some key points:
- The workbench allows users to run the open source GRASS GIS software on the HPC cluster for resource-intensive geospatial tasks. This provides benefits like parallel processing, long-running tasks, and secure/stable environment.
- The GFZ cluster has over 200 nodes with a total of over 3000 processor cores. The workbench provides modules to distribute GIS work across the cluster from within or outside of GRASS.
- Example applications discussed include tsunami mapping, long-term processing of large/complex datasets, generating globe maps and animations from
In this research, we propose a MapReduce al- gorithm for creating contiguity-based spatial weights. This algorithm provides the ability to create spatial weights from very large spatial datasets efficiently by using computing re- sources that are organized in the Hadoop framework. It works in the paradigm of MapReduce: mappers are dis- tributed in computing clusters to find contiguous neighbors in parallel, then reducers collect the results and generate the weights matrix. To test the performance of this al- gorithm, we design experiment to create contiguity-based weights matrix from artificial spatial data with up to 190 million polygons using Amazon’s Hadoop framework called Elastic MapReduce. The experiment demonstrates the scal- ability of this parallel algorithm which utilizes large com- puting clusters to solve the problem of creating contiguity weights on Big data.
This document summarizes Kelin Li's portfolio, including three projects using parametric design, building energy modeling, and automated lighting control systems. The first project used Revit, Dynamo and Ladybug to create a conceptual, parametrically designed building with weather-responsive facades. The second project used Rhino, Diva, Daysim and EnergyPlus to analyze energy savings from different automated blind and lighting control systems. The third project used IES-VE and DesignBuilder to model and compare the energy use of two houses.
Widgets and astropy: accomplishing useful research with undergraduatesmwcraig
The document discusses packages for astronomical data analysis including Astropy and CCDproc. It introduces the reducer package, a widget-based interface for CCD data reduction created by Matt Craig. The presentation demonstrates reducer's notebook interface and widget architecture for guiding novice researchers through data analysis tasks. Future goals include adding photometry and time series analysis widgets.
Cogent3 d master slides (12 april 2009)Danny Bronson
Cogent3D is a small business based in Tucson, AZ that develops 3D visualization products and services. Their flagship product is Genesis, which generates 3D terrain scenes directly from geospatial data formats like DTED, imagery, and vector data. Genesis uses an XML-based system to map source data to 3D scenes in real-time. This allows for dynamic updates and modifications to the 3D environment. Cogent3D aims to provide correlation across multiple domains like ground, air, and space using a single worldwide terrain database.
This document discusses open source satellite imagery and provides details on the Landsat 8 and Sentinel 2 satellites. It describes the spectral and temporal resolution of each satellite's imagery as well as how to access and use the free imagery data in web applications. Resources are listed for working with Landsat 8 and Sentinel 2 data through open source tools and an imagery browser.
Altima: A KBB-like Reference Pricing SystemNeil Ryan
This document proposes using machine learning to build a reference pricing system for rental listings. It would integrate with online rental sites to provide renters with a reference price for negotiation. The business model would be to either build their own service scraping listings, partner with listing providers, or promote to similar sites. The approach uses a Seattle rental data set to explore models like KNN regression, decision trees, and ensembles. It finds KNN performs best and provides a demo of reference prices calculated from rental details. Areas for improvement are discussed.
Automatic Object Exploratiob based on Probabilitypeterson iit
This document summarizes a student presentation on automatic object exploration based on probability. It introduces the objectives of extracting features from partial objects, making an object feature database, computing probability from object features, and controlling a camera based on probability. It then outlines the experimental setup, database objects, feature extraction methods, probability calculation process, and system flow diagram. Finally, it discusses the scope, limitations, results, conclusions, and future recommendations of the project.
This document discusses using ArcView 3.3 to determine watersheds on Lombok Island, Indonesia. It describes Lombok's varied topography, especially in northern high mountain and valley areas. The document outlines steps to create a digital elevation model from SRTM data, calculate flow direction and accumulation, identify streams, label stream links, and determine watershed boundaries. It also mentions viewing the results in 3D using the 3Dscene extension.
The document discusses using ArcGIS spatial analyst tools to delineate watersheds in the Padang area of Indonesia. It involves creating a digital elevation model (DEM) from SRTM elevation data, then using hydrological modeling tools like Fill, Flow Direction, Flow Accumulation, and Watershed to generate vector layers representing streams, channels, and watershed boundaries. The final watershed delineation is viewed in 2D in ArcMap and 3D in ArcScene and Cosmo Player to understand the hydrological modeling process and watersheds in the Padang area.
1) The document presents a method for short-term forecasting of surface solar irradiance at night using satellite imagery, allowing forecasts before sunrise.
2) It defines cloud classes based on brightness temperature differences in infrared satellite images at night, and derives a cloud index for each class by mapping infrared values to historical daytime cloud index values.
3) Validation using over 100 German weather stations over 6 months showed the nighttime cloud index can accurately forecast global horizontal irradiance in the hours before sunrise.
Processing Geospatial Data At Scale @locationtechRob Emanuele
This document discusses processing large geospatial data at scale. It provides background on big data frameworks like Apache Hadoop, Apache Spark, and geospatial projects like GeoTrellis, GeoWave, and SpatialHadoop that enable processing geospatial data using these frameworks. The document outlines how these tools allow geospatial data from sources like satellite imagery, OpenStreetMap, and geotagged social media to be analyzed using distributed computing platforms and algorithms.
This document outlines the steps to derive watersheds in Central Celebes, Indonesia from SRTM data using 3D modeling software and ArcGIS tools:
1) Load and preprocess SRTM data to create a DEM raster, including cropping, filling blanks, converting projections, and saving as a GeoTIFF.
2) Perform terrain analysis in ArcGIS to calculate flow direction, flow accumulation, stream definition, stream segmentation, and catchment grid delineation using the ArcHydro tools.
3) View the resulting watersheds and hydrological networks in 3D in ArcScene by setting the appropriate layer properties and vertical exaggeration.
1) The document outlines the steps to delineate a catchment area using software like Global Mapper and WMS. It begins with preparing DEM and satellite imagery data and getting GPS coordinates for an outlet point.
2) The initial catchment area is delineated in WMS and streams are verified. The catchment area is then visualized. Relevant data like drainage networks are downloaded and imported.
3) Flow direction and accumulation is calculated using TOPAZ tools in Global Mapper. Contour lines and other data are displayed to finalize the delineated catchment area containing the outlet point.
Meteoio Introduction given by Mathias Bavey in BozenRiccardo Rigon
MeteoIO is a toolbox for working with meteorological data in C++. It acts as an interface between physical models and different data sources. MeteoIO reads data from various sources, preprocesses it, and provides the data to models in a standardized way regardless of the original data characteristics. It performs operations like unit conversions, interpolation, and filtering to prepare data for use in models. MeteoIO uses a plugin architecture so that each data source is handled by its own plugin, while exposing a common interface to models.
Big Linked Data Querying - ExtremeEarth Open WorkshopExtremeEarth
This document discusses querying large geospatial datasets using the Strabo2 system. Strabo2 performs GeoSPARQL query answering on massive RDF graphs containing geospatial data from Copernicus and other sources. It relies on Apache Sedona to perform distributed spatial analytics on Apache Spark. Strabo2 uses techniques like vertical data partitioning, caching of spatial relations and query results, and persistent spatial indexing to improve query performance on large datasets. It has been deployed on the CREODIAS platform to enable spatial analytics on datasets for polar and food security use cases.
The document discusses location services in Android and the Google Maps external library. It describes how to get location data from the LocationManager service, provide mock location data through DDMS or command line, and use the MapView class to display maps from the Google Maps API. The MapView is a subclass of ViewGroup that handles displaying maps tiles and user gestures to pan and zoom. It provides built-in functionality to work with Google Maps data.
A scene graph is a general data structure commonly used by vector-based graphics editing applications and modern computer games, which arranges the logical and often (but not necessarily) spatial representation of a graphical scene. Examples of such programs include Acrobat 3D, Adobe Illustrator, AutoCAD, CorelDRAW, OpenSceneGraph, OpenSG, VRML97, X3D, Hoops and Open Inventor.
This document provides an overview of geolocation and mapping using Google Maps services. It defines geolocation as identifying the real-world location of an object like a mobile phone or computer. Mapping refers to map-making and cartography. The document reviews the navigator.geolocation API for accessing location data, the Google Maps Javascript API for embedding maps, and common overlays like markers and polylines that can be added to maps. It also summarizes services for obtaining directions and distance matrix information between locations.
The document discusses the Eclipse Layout Kernel (ELK), which provides algorithms and infrastructure for automatically laying out diagrams. It summarizes that ELK originated from the KIELER project and contains high-quality layout algorithms with numerous options. It also describes how applications can integrate ELK by building graphs from diagrams and applying computed layouts.
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 mapping in Drupal using the OpenLayers module. It begins by introducing OpenLayers and describing its functionality as a JavaScript framework for building map applications. It then provides an overview of Drupal and relevant mapping modules. The document outlines a three step process for creating maps: 1) adding layers, 2) setting map presets, and 3) displaying maps through Views. It provides examples and screenshots for configuring geofield data, layers, styles, and behaviors. Finally, it discusses extensions, improvements, and the status of OpenLayers for Drupal 7.
This document provides an introduction and overview of Google Charts, a JavaScript library for creating interactive charts and graphs. It discusses the key features of Google Charts, including that it is free to use, compatible with different browsers and platforms, and has a variety of chart types supported. It also provides information on setting up Google Charts, including importing the library, populating a data table, customizing chart options, and drawing the chart.
Google Developer Days Brazil 2009 - Google Social WebPatrick Chanezon
The document discusses how Google is leveraging social features to enhance its products and platform. It provides examples of how Google products like Gmail, Calendar, and iGoogle integrate social features through OpenSocial, allowing sharing, communication, and collaboration. It also discusses Google's implicit social graph and how developers can build applications that take advantage of social data through APIs. Finally, it walks through an example application called Quarter Mile that allows users to track exercise data socially across different Google products.
GeoDataspace: Simplifying Data Management Tasks with GlobusTanu Malik
This document describes GeoDataspace, a framework for enabling data and model sharing in computational geosciences. GeoDataspace uses geounits, which package code, data, and environment, to capture scientific activities and research outputs. The Globus Catalog provides a flexible metadata catalog for hosting and querying geounits. GeoDataspace aims to improve reproducibility and enable validation of shared models and data through re-executing geounits. Several geoscience applications including plate tectonics, hydrology, and space science are discussed as potential adopters.
Hadoop/MapReduce is an open source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce, a programming model where input data is processed by "map" functions in parallel, and results are combined by "reduce" functions, to process and generate outputs from large amounts of data and nodes. The core components are the Hadoop Distributed File System for data storage, and the MapReduce programming model and framework. MapReduce jobs involve mapping data to intermediate key-value pairs, shuffling and sorting the data, and reducing to output results.
Google Devfest 2009 Argentina - Google and the Social WebPatrick Chanezon
This document discusses Google's approach to building social features into its products and enabling developers. It covers Google's social graph and data sharing through OpenSocial, Portable Contacts, and Google Friend Connect. An example gadget called Quartermile is used to demonstrate how to build social features like leveraging a user's social graph, quick data entry, dashboards, and container-specific extensions across Google products like iGoogle, Gmail, Calendar, and out to third-party sites. The document recommends developers take advantage of Google's social infrastructure and common social data to enrich their own applications and integrations.
OpenMapTiles: Vector tiles from OpenStreetMapPetr Pridal
OpenMapTiles provides open-source tools for generating and styling vector map tiles from OpenStreetMap data. It includes an open vector tile schema, tools for generating MBTiles containing vector tiles, and prepared styles. Users can choose from various servers and clients to host and use the tiles. The tools are open-source and customizable, allowing anyone to generate tiles for a specific area or modify existing styles and layers. Hosted tile services are also available from OpenMapTiles.com.
This document outlines a presentation about using the Drupal Migrate module to migrate content into Drupal from external sources. The presentation agenda covers an introduction to Migrate, implementation details like classes and handlers, and a potential demo. The presenter is introduced and disclaimers are provided. Migration options like custom scripts, Feeds, and Migrate are briefly compared. Key aspects of using Migrate like sources, destinations, mappings, and migrations are explained at a high level.
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
Second part of the Course "Java Open Source GIS Development - From the building blocks to extending an existing GIS application." held at the University of Potsdam in August 2011
Project Matsu aimed to provide persistent data resources and elastic computing for disaster relief by making imagery available for processing using large-scale cloud computing. It evaluated three approaches: 1) Using Hadoop and MapReduce to split images and process parts in parallel; 2) Using Hadoop streaming with Python to preprocess images into a single file and process line-by-line; and 3) Using the Sector distributed file system to keep images together on nodes and applying user-defined functions to process images without splitting. The goal was to enable change detection on images from different times to assist relief workers.
This document provides a tutorial for publishing a geoprocessing model as a service in ArcGIS Server. It describes building a simple model using the Buffer and Clip tools in ArcMap. The model buffers input points by a specified distance and clips the results to a coastline feature class. The document outlines setting the environment, building the model with variables, and publishing/running the model as a service to be accessed in web applications. It focuses on supported data types and using the server jobs directory to manage intermediate and output data.
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 presentation has been developed in the context of the Mobile Applications Development course, DISIM, University of L'Aquila (Italy), Spring 2016.
http://www.ivanomalavolta.com
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3. Introduction
• open-source library which contains classes that are
useful for a wide range of applications using the
Google Maps Android API.
• developed by Google
• current version is 0.3 (still in heavy development)
7. • Implement ClusterItem to represent a marker on the
map
• Add a new ClusterManager to group the cluster
items (markers) based on zoom level.
• Set the map's OnCameraChangeListener() to the
ClusterManager
• Feed the markers into the ClusterManager
Marker clustering
8.
9.
10.
11. Bubble icons
• Use custom markers a bit like
info windows - in that way
markers contains more
information
16. Heatmaps
• Add one or more heatmaps
to a Google map in your
application. Heatmaps
make it easy for viewers to
understand the distribution
and relative intensity of
data points on a map
17. Heatmaps
• Use HeatmapTileProvider.Builder(), passing it a
collection of LatLng objects, to add a new
HeatmapTileProvider.
• Create a new TileOverlayOptions object with the
relevant options, including the
HeatmapTileProvider.
• Call GoogleMap.addTileOverlay() to add the
overlay to the map.
18. Spherical geometry
• computeDistanceBetween() – Returns the distance, in
meters, between two latitude/longitude coordinates.
• computeHeading() – Returns the bearing, in degrees,
between two latitude/longitude coordinates.
• computeArea() – Returns the area, in square meters, of a
closed path on the Earth.
• interpolate() – Returns the latitude/longitude coordinates of
a point that lies a given fraction of the distance between
two given points. You can use this to animate a marker
between two points, for example.
19. Algorithms
• encode & decode polylines (i.e. obtained using
Google Directions API) with PolyUtil
20. Conclusion
• Android Maps Utils is a great lightweight library to
add that “extra” touch to Google maps interaction
• still in heavy development (missing few basic
functionalities)
• new utilities are already promised (new algorithms)