What is Geoaptial Data or Spatial Data or Geographic Information? What is GIS (Geographical Information System)
Views: 5121 Anuj Tiwari
What is GEOSPATIAL ANALYSIS? What does GEOSPATIAL ANALYSIS mean? GEOSPATIAL ANALYSIS meaning - GEOSPATIAL ANALYSIS definition - GEOSPATIAL ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Geospatial analysis, or just spatial analysis, is an approach to applying statistical analysis and other analytic techniques to data which has a geographical or spatial aspect. Such analysis would typically employ software capable of rendering maps processing spatial data, and applying analytical methods to terrestrial or geographic datasets, including the use of geographic information systems and geomatics. Geographic information systems (GIS), which is a large domain that provides a variety of capabilities designed to capture, store, manipulate, analyze, manage, and present all types of geographical data, and utilizes geospatial analysis in a variety of contexts, operations and applications. Geospatial analysis, using GIS, was developed for problems in the environmental and life sciences, in particular ecology, geology and epidemiology. It has extended to almost all industries including defense, intelligence, utilities, Natural Resources (i.e. Oil and Gas, Forestry ... etc.), social sciences, medicine and Public Safety (i.e. emergency management and criminology), disaster risk reduction and management (DRRM), and climate change adaptation (CCA). Spatial statistics typically result primarily from observation rather than experimentation. Vector-based GIS is typically related to operations such as map overlay (combining two or more maps or map layers according to predefined rules), simple buffering (identifying regions of a map within a specified distance of one or more features, such as towns, roads or rivers) and similar basic operations. This reflects (and is reflected in) the use of the term spatial analysis within the Open Geospatial Consortium (OGC) “simple feature specifications”. For raster-based GIS, widely used in the environmental sciences and remote sensing, this typically means a range of actions applied to the grid cells of one or more maps (or images) often involving filtering and/or algebraic operations (map algebra). These techniques involve processing one or more raster layers according to simple rules resulting in a new map layer, for example replacing each cell value with some combination of its neighbours’ values, or computing the sum or difference of specific attribute values for each grid cell in two matching raster datasets. Descriptive statistics, such as cell counts, means, variances, maxima, minima, cumulative values, frequencies and a number of other measures and distance computations are also often included in this generic term spatial analysis. Spatial analysis includes a large variety of statistical techniques (descriptive, exploratory, and explanatory statistics) that apply to data that vary spatially and which can vary over time. Some more advanced statistical techniques include Getis-ord Gi* or Anselin Local Moran's I which are used to determine clustering patterns of spatially referenced data. Geospatial analysis goes beyond 2D and 3D mapping operations and spatial statistics. It includes: Surface analysis —in particular analysing the properties of physical surfaces, such as gradient, aspect and visibility, and analysing surface-like data “fields”; Network analysis — examining the properties of natural and man-made networks in order to understand the behaviour of flows within and around such networks; and locational analysis. GIS-based network analysis may be used to address a wide range of practical problems such as route selection and facility location (core topics in the field of operations research, and problems involving flows such as those found in hydrology and transportation research. In many instances location problems relate to networks and as such are addressed with tools designed for this purpose, but in others existing networks may have little or no relevance or may be impractical to incorporate within the modeling process....
Views: 2232 The Audiopedia
data mining for bca|cluster analysis,web mining|bhavacharanam
Views: 44 BHAVA CHARANAM
This Video is specially for bsc it student Which is doing by mumbai university .. this video contains practical 3,4,5
Views: 2485 Mumbiker Suraj
Recorded for a class at Columbia University's Graduate School of Architecture, Planning, and Preservation
Views: 583 Danil Nagy
Summary: This tutorial explains how to use Random Forest to generate spatial and spatiotemporal predictions (i.e. to make maps from point observations using Random Forest). Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is sub-optimal. To account for this, we use Random Forest (as implemented in the ranger package) in combination with geographical distances to sampling locations to fit models and predict values. Tutorials: RFsp — Random Forest for spatial data (https://github.com/thengl/GeoMLA) Reference: Hengl T, Nussbaum M, Wright MN, Heuvelink GBM, Gräler B. (2018) Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ 6:e5518 https://doi.org/10.7717/peerj.5518 Requirements: RStudio, preinstalled packages based on the tutorial above.
Views: 202 Tomislav Hengl (OpenGeoHub Foundation)
Processing Geodata using Python and Open Source Modules [EuroPython 2018 - Talk - 2018-07-27 - PyCharm [PyData]] [Edinburgh, UK] By Martin Christen The need for processing small-scale to large-scale spatial data is huge. In this talk, it is shown how to analyze, manipulate and visualize geospatial data by using Python and various open source modules. The following modules will be covered: Shapely: Manipulation and analysis of geometric objects Fiona - The pythonic way to handle vector data rasterio - The pythonic way to handle raster data pyproj - transforming spatial reference systems Vector File Formats (Shapefiles, GeoJSON, KML, GeoPackage) Geospatial analysis with GeoPandas Creating maps using Folium License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2018.europython.eu/en/speaker-release-agreement/
Views: 392 EuroPython Conference
Working tutorial about MS SQL and Spatial Databases More: http://mapsys.info
Views: 5068 ivche01
Here I read in some longitude and latitudes, and create a K nearest neighbor weights file. Then we visualize with a plot, and export the weights matrix as a CSV file. Link to R Commands: http://spatial.burkeyacademy.com/home/files/knn%20in%20R.txt Link to Spatial Econometrics Cheat Sheet: http://spatial.burkeyacademy.com/home/files/BurkeyAcademy%20Spatial%20Regression%20CheatSheet%200.6.pdf Link to Census Site: https://www.census.gov/geo/reference/centersofpop.html Great Circle Distances: https://youtu.be/qi9KIKDpHKY My Website: spatial.burkeyacademy.com or www.burkeyacademy.com Support me on Patreon! https://www.patreon.com/burkeyacademy Talk to me on my SubReddit: https://www.reddit.com/r/BurkeyAcademy/
Views: 2057 BurkeyAcademy
A temporal database is a database with built-in support for handling data involving time, being related to the slowly changing dimension concept, for example a temporal data model and a temporal version of Structured Query Language (SQL). More specifically the temporal aspects usually include valid time and transaction time. These attributes can be combined to form bitemporal data. Valid time is the time period during which a fact is true in the real world. Transaction time is the time period during which a fact stored in the database was known. Bitemporal data combines both Valid and Transaction Time. It is possible to have timelines other than Valid Time and Transaction Time, such as Decision Time, in the database. In that case the database is called a multitemporal database as opposed to a bitemporal database. However, this approach introduces additional complexities such as dealing with the validity of (foreign) keys. Temporal databases are in contrast to current databases (at term that doesn't mean, currently available databases, some do have temporal features, see also below), which store only facts which are believed to be true at the current time. Temporal databases supports System-maintained transaction time. With the development of SQL and its attendant use in real-life applications, database users realized that when they added date columns to key fields, some issues arose. For example, if a table has a primary key and some attributes, adding a date to the primary key to track historical changes can lead to creation of more rows than intended. Deletes must also be handled differently when rows are tracked in this way. In 1992, this issue was recognized but standard database theory was not yet up to resolving this issue, and neither was the then-newly formalized SQL-92 standard. Richard Snodgrass proposed in 1992 that temporal extensions to SQL be developed by the temporal database community. In response to this proposal, a committee was formed to design extensions to the 1992 edition of the SQL standard (ANSI X3.135.-1992 and ISO/IEC 9075:1992); those extensions, known as TSQL2, were developed during 1993 by this committee. In late 1993, Snodgrass presented this work to the group responsible for the American National Standard for Database Language SQL, ANSI Technical Committee X3H2 (now known as NCITS H2). The preliminary language specification appeared in the March 1994 ACM SIGMOD Record. Based on responses to that specification, changes were made to the language, and the definitive version of the TSQL2 Language Specification was published in September, 1994 An attempt was made to incorporate parts of TSQL2 into the new SQL standard SQL:1999, called SQL3. Parts of TSQL2 were included in a new substandard of SQL3, ISO/IEC 9075-7, called SQL/Temporal. The TSQL2 approach was heavily criticized by Chris Date and Hugh Darwen. The ISO project responsible for temporal support was canceled near the end of 2001. As of December 2011, ISO/IEC 9075, Database Language SQL:2011 Part 2: SQL/Foundation included clauses in table definitions to define "application-time period tables" (valid time tables), "system-versioned tables" (transaction time tables) and "system-versioned application-time period tables" (bitemporal tables). A substantive difference between the TSQL2 proposal and what was adopted in SQL:2011 is that there are no hidden columns in the SQL:2011 treatment, nor does it have a new data type for intervals; instead two date or timestamp columns can be bound together using a PERIOD FOR declaration. Another difference is replacement of the controversial (prefix) statement modifiers from TSQL2 with a set of temporal predicates. For illustration, consider the following short biography of a fictional man, John Doe: John Doe was born on April 3, 1975 in the Kids Hospital of Medicine County, as son of Jack Doe and Jane Doe who lived in Smallville. Jack Doe proudly registered the birth of his first-born on April 4, 1975 at the Smallville City Hall. John grew up as a joyful boy, turned out to be a brilliant student and graduated with honors in 1993. After graduation he went to live on his own in Bigtown. Although he moved out on August 26, 1994, he forgot to register the change of address officially. It was only at the turn of the seasons that his mother reminded him that he had to register, which he did a few days later on December 27, 1994. Although John had a promising future, his story ends tragically. John Doe was accidentally hit by a truck on April 1, 2001. The coroner reported his date of death on the very same day.
Views: 11629 Introtuts
PyData NYC 2015 The democratization of GPS enabled devices has led to a surge of interest in the availability of high quality geocoded datasets. This data poses both opportunities and challenges for the study of social behavior. The goal of this tutorial is to introduce its attendants to the state-of-the-art in the mining and analysis in this new world of spatial data with a special focus on the real world. In this tutorial we will provide an overview of workflows for location rich data, from data collection to analysis and visualization using Python tools. In particular: Introduction to location rich data: In this part tutorial attendees will be provided with an overview perspective on location-based technologies, datasets, applications and services Online Data Collection: A brief introductions to the APIs of Twitter, Foursquare, Uber and AirBnB using Python (using urllib2, requests, BeautifulSoup). The focus will be on highlighting their similarities and differences and how they provide different perspectives on user behavior and urban activity. A special reference will be provided on the availability of Open Datasets with a notable example being the NYC Yellow Taxi dataset (NYC Taxy) Data analysis and Measurement: Using data collected using the APIs listed above we will perform several simple analyses to illustrate not only different techniques and libraries (geopy, shapely, data science toolkit, etc) but also the different kinds of insights that are possible to obtain using this kind of data, particularly on the study of population demographics, human mobility, urban activity and neighborhood modeling as well as spatial economics. Applied Data Mining and Machine Learning: In this part of the tutorial we will focus on exploiting the datasets collected in the previous part to solve interesting real world problems. After a brief introduction on python’s machine learning library, scikit-learn, we will formulate three optimization problems: i) predict the best area in New York City for opening a Starbucks using Foursquare check-in data, ii) predict the price of an Airbnb listing and iii) predict the average Uber surge multiplier of an area in New York City. Visualization: Finally, we introduce some simple techniques for mapping location data and placing it in a geographical context using matplotlib Basemap and py.processing. Slides available here: http://www.slideshare.net/bgoncalves/mining-georeferenced-data Code here: https://github.com/bmtgoncalves/Mining-Georeferenced-Data
Views: 1198 PyData
This practical session will base on the introductory lecture on machine-learning based modelling of spatial and spatio-temporal data held on Monday. Two examples will be provided to dive into machine learning for spatial and spatio-temporal data in R: The first example is a classic remote sensing example dealing with land cover classification at the example of the Banks Peninsula in New Zealand that suffers from spread of the invasive gorse. In this example we will use the random forest classifier via the caret package to learn the relationships between spectral satellite information and provided reference data on the land cover classes. Spatial predictions will then be made to create a map of land use/cover based on the trained model. As second example, the vignette "Introduction to CAST" is taken from the CAST package. In this example the aim is to model soil moisture in a spatio-temporal way for the cookfarm (http://gsif.r-forge.r-project.org/cookfarm.html). In this example we focus on the differences between different cross-validation strategies for error assessment of spatio-temporal prediction models as well as on the need of a careful selection of predictor variables to avoid overfitting. Slides: https://github.com/HannaMeyer/Geostat2018/tree/master/slides Exercise A: https://github.com/HannaMeyer/Geostat2018/tree/master/practice/LUCmodelling.html Exercise B: https://github.com/HannaMeyer/Geostat2018/tree/master/practice/CAST-intro.html Data for Exercise A: https://github.com/HannaMeyer/Geostat2018/tree/master/practice/data/
Views: 429 Tomislav Hengl (OpenGeoHub Foundation)
In this video I will quickly introduce some of the basic concepts associated with spatial data analysis, things such as coordinate reference systems. Then I will show how to carry out basic GIS tasks in the freely available software QGIS. By the time you finish watching this video you should get the basic know how of getting started with QGIS for spatial data analysis. Detailed instructional lectures can be found on my Udemy course on Core spatial data analysis. The discount coupon for the full course can be found at: https://www.udemy.com/core-spatial-data-analysis-with-r-and-qgis/?couponCode=COREGIS_15
Views: 586 Data Analysis and All
Watch more at http://www.lynda.com/AutoCAD-tutorials/AutoCAD-2014-New-Features/122439-2.html?utm_campaign=mVqIyhXpa_8&utm_medium=viral&utm_source=youtube. When designing a site plan in AutoCAD 2014, it's helpful to see your work in context. In this tutorial, Jeff Bartels shows how to reference a high-quality aerial photograph as a background. This tutorial is a single movie from the AutoCAD 2014 New Features course presented by lynda.com author Jeff Bartels. The complete course duration is 2 hours and 3 minutes long and shows how to start incorporating all the new features from AutoCAD 2014 into your CAD workflow, including command-line data access, georeferencing, and reality capture. Introduction 1. Connecting to Autodesk 360 2. Exploring New Interface Tools 3. Georeferencing Drawings 4. Incorporating Reality Capture 5. Documenting and Protecting Designs Conclusion
Views: 19662 LinkedIn Learning
Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1684 Audiopedia
Listen to Dr. Robert Vos as he provides insights into the Spatial Data Acquisition and Integration track in the USC M.S. in Geographic Information Science & Technology (GIST) program. During this recorded event, Dr. Vos was joined by Ken Schmidt and Mark Sarojak, who discuss the value of getting this degree and the career outlook for students. HOST Robert Vos, Ph.D., Adjunct Assistant Professor of the Practice of Spatial Sciences PRESENTERS: Mark Sarojak, Geospatial Executive, Pixia Corporation Ken Schmidt, GIS Administrator, City and County of Honolulu, HI
Views: 2267 Sean Lin
What is LOCATION INTELLIGENCE? What does LOCATION INTELLIGENCE mean? LOCATION INTELLIGENCE meaning - LOCATION INTELLIGENCE definition - LOCATION INTELLIGENCE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Location intelligence (LI), or spatial intelligence, is the process of deriving meaningful insight from geospatial data relationships to solve a particular problem. It involves layering multiple data sets spatially and/or chronologically, for easy reference on a map, and its applications span industries, categories and organizations It is generally agreed that more than 80% of all data has a location element to it and that location directly affects the kinds of insights that you might draw from many sets of information. Maps have been used to represent information throughout the ages, but what might be referenced as the first example of true location 'intelligence' was in London in 1854 when John Snow was able to debunk theories about the spread of cholera by overlaying a map of the area with the location of water pumps and was able to narrow the source to a single water pump. This layering of information over a map was able to identify relationships, and in turn insights that might otherwise never have been understood. This is the core of location intelligence today. Deploying location intelligence by analyzing data using a geographical information system (GIS) within business is becoming a critical core strategy for success in an increasingly competitive global economy. Location or GIS tools enable spatial experts to collect, store, analyze and visualize data. Location intelligence experts are defined by their advanced education in spatial technology and applied use of spatial methodologies. Location intelligence experts can use a variety of spatial and business analytical tools to measure optimal locations for operating a business or providing a service. Location intelligence experts begin with defining the business ecosystem which has many interconnected economic influences. Such economic influences include but are not limited to culture, lifestyle, labor, healthcare, cost of living, crime, economic climate and education. The term "location intelligence" is often used to describe the people, data and technology employed to geographically "map" information. These mapping applications can transform large amounts of data into color-coded visual representations that make it easy to see trends and generate meaningful intelligence. The creation of location intelligence is directed by domain knowledge, formal frameworks, and a focus on decision support. Location cuts across through everything i.e. devices, platforms, software and apps, and is one of the most important ingredient of understanding context in sync with social data, mobile data, user data, sensor data, using platforms as CARTO (former CartoDB) where data as a service and the analytical and visualisation tools blend together to create a business friendly environment. Location intelligence is also used to describe the integration of a geographical component into business intelligence processes and tools, often incorporating spatial database and spatial OLAP tools. In 2012, Dr. Wayne Gearey from the commercial real estate industry was selected to offer the first applied course on location intelligence at the University of Texas at Dallas. In this course, Dr. Gearey defines location intelligence as the process for selecting the optimal location that will support workplace success and address a variety of business and financial objectives. Geoblink defines location intelligence as the capability to understand and optimize a physical network of points of sale in the process of making business decisions. Pitney Bowes MapInfo Corporation describes location intelligence as follows: "Spatial information, commonly known as "Location", relates to involving, or having the nature of where. Spatial is not constrained to a geographic location however most common business uses o spatial information deal with how spatial information is tied to a location on the earth. Miriam-Webster® defines Intelligence as "The ability to learn or understand, or the ability to apply knowledge to manipulate one`s environment." Combining these terms alludes to how you achieve an understanding of the spatial aspect of information and apply it to achieve a significant competitive advantage."
Views: 1519 The Audiopedia
Produced in collaboration between NOAA’s National Geodetic Survey (NGS) and The COMET Program, this video explains the role of topo-bathy lidar products in NOAA’s mapping and charting program, and how these products provide a critical dataset for coastal resilience, coastal intelligence, and place-based conservation. Federal, state and local decision-makers, coastal zone managers, community planners as well as general and scientific users of mapping products will find this 4-minute video helpful for understanding the benefits of coastal elevation data produced by NGS. For more information on geospatial infrastructure, visit http://www.geodesy.noaa.gov/. For more information and a gallery of reusable resources from this video see https://www.meted.ucar.edu/training_module.php?id=1218 See COMET's MetEd website for hundreds of other geo-science training resources: http://www.meted.ucar.edu.
Views: 4953 The COMET Program/MetEd
Professor Chris Rizos talks with Dr Craig Roberts about using GPS signals and CORS networks in earth science research to measure movements in the Earth's surface.
Views: 1663 AboutUNSW
A spatial database, or geodatabase is a database that is optimized to store and query data that represents objects defined in a geometric space. Most spatial databases allow representing simple geometric objects such as points, lines and polygons. Some spatial databases handle more complex structures such as 3D objects, topological coverages, linear networks, and TINs. While typical databases are designed to manage various numeric and character types of data, additional functionality needs to be added for databases to process spatial data types efficiently. These are typically called geometry or feature. The Open Geospatial Consortium created the Simple Features specification and sets standards for adding spatial functionality to database systems. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 9354 Audiopedia
You all know Google Earth and Google Street View – these beautiful services for standard users. Would you like to know how highly accurate 3D data for professional users and applications are produced?
Views: 580 Leica Geosystems AG
A tutorial on how to get started using LISTdata
Views: 329 Land Information System Tasmania
Learn how to install and configure GeoSpatial Analysis Spatial Warehouse based on a simple example
Views: 67 SpatialEye Business Development
Professor Chris Rizos talks with Dr Craig Roberts about using GPS signals and CORS networks in earth science research to measure movements in the Earth's surface. Professor Rizos talks about how CORS networks around the world take GPS signals from positioning satellites to deliver centimetre accurate coordinates which can be used to help scientists to measure and understand earthquakes and other movements on the surface of the Earth. He also talks about how CORS networks are also increasingly used in other applications including precision agriculture, mining and infrastructure development.
Views: 1678 AboutUNSW
The Geospatial Underground Tracking System (GUTS) was developed to track robots or people in tunnels underground. The system utilizes low frequencies that travel through the earth and are received above ground. The above ground Locator uses the signal strength and NFER® technology to determine the location of the underground robot or person. Further, by integrating GPS into the system, the underground location has a real-world reference. For more information on this break through technology visit: www.Q-Track.com Direct Link: http://q-track.com/index.php/video-demonstrations/geospatial-underground-tracking-system-guts
Views: 373 QTDemos
Lecture: Georeferencing Lecturer: Helena Mitasova Course: NCSU GIS/MEA582: Geospatial Modeling and Analysis Materials: http://ncsu-geoforall-lab.github.io/geospatial-modeling-course
Views: 709 NCSU GeoForAll Lab
Buy GIS books (affiliate): Remote Sensing and GIS https://amzn.to/2Ce41NL Advanced Surveying: Total Station, GPS, GIS & Remote Sensing by Pearson https://amzn.to/2wEAXcj An Introduction to Geographic Information Technology https://amzn.to/2Q2XuID Mastering QGIS https://amzn.to/2oFi717 QGIS Python Programming Cookbook https://amzn.to/2wHUkSu QGIS: Becoming a GIS Power User https://amzn.to/2PYCz9D Remote Sensing: Principles and Applications https://amzn.to/2Q4Wi7x Gis: Fundamentals, Applications and Implementations https://amzn.to/2Q5iFK6 Remote Sensing and Geographical Information Systems: Basics and Applications https://amzn.to/2Q2dI4y Textbook of Remote Sensing and Geographical Information Systems https://amzn.to/2Q748h9 Remote Sensing and GIS in Environment Resource Management https://amzn.to/2Q2fpPs ------------------------------------- Notes of Geographical Information Systems Fundamentals on this link - https://viden.io/knowledge/geographical-information-systems-fundamentals?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=ajaze-khan-1
Views: 85671 LearnEveryone
Organizations across all industries are growing extremely fast, resulting in high volume of complex and unstructured data. The huge data generated is limiting the traditional Data Warehouse system, making it tougher for IT and data management professionals to handle the growing scale of data and analytical workload. The flow of data is so much more than what the existing Data Warehousing platforms can absorb and analyse. Looking at the expenses, the cost to scale traditional Data Warehousing technologies are high and insufficient to accommodate today's huge variety and volume of data. Therefore, the main reason behind organizations adopting Hadoop is that, it is a complete open-source data management system. Not only does it organize, store and process data (whether structured, semi-structured or unstructured), it is cost effective as well. Hadoop's role in Data Warehousing is evolving rapidly. Initially, Hadoop was used as a transitory platform for extract, transform, and load (ETL) processing. In this role, Hadoop is used to offload processing and transformations performed in the data warehouse. You can visit the site edureka.in for more details on Big Data & Hadoop. Hadoop simplifies your job as a Data Warehousing professional. With Hadoop, you can manage any volume, variety and velocity of data, flawlessly and comparably in less time. As a Data Warehousing professional, you will undoubtedly have troubleshooting and data processing skills. These skills are sufficient for you to be a proficient Hadoop-er.
Views: 2875 TechGig
Geographic Information Systems (GIS) are computer-based tools for the entry, maintenance, and analysis of spatial data. GIS are critical for effective resource management, and have been applied across a wide range of science, business, and government endeavours. This book provides an introduction to the theory and application of GIS. It is written for use in an introductory GIS class and as a reference for the GIS practitioner. This fifth edition balances theoretical and applied material, so that students may apply knowledge of GIS in the solution of real-world problems. Improvements over the previous editions are included in each chapter. Topics treated include an introduction to GIS, spatial data models, map projections, data entry, image data, GPS, digital data, database systems in GIS, general spatial analysis, raster analysis, terrain modeling, metadate, standards, and accuracy assessments. Visit Link http://bookarea.download
Views: 9 Willypd
PilotGaea GisDK Integrate GIS vector data and UAV Real-time images on the fly processing can not only assist disaster relief but also provide real-time surveillance and reconnaissance in defense and intelligence. This system allows the overlaying of UAV Full Motion Video onto GIS vector data, image data and in turn making it possible to perform instant attribute query, spatial analysis, and strategic decisions during emergency situations.
Views: 2545 Li Jessie
Join Wenco subject matter experts Simran Walia, Eric Winsborrow, and Jason Clarke, and Murray O'Keefe of Maules Creek mine, as they discuss Unlocking Hidden Value in Mine Operations Data.
Views: 320 Wencomine
Abstract: Geographic Information Systems (GIS) are a cornerstone of any science where broad-scale geographic patterns matter. Ecologists, geologists, and biologists all turn to software like ArcGIS to create maps and to deal with their spatial data for problems like land use classification, ore reserve estimation and species distribution modelling. However, not all scientists or workers in these fields have access to proprietary software or, despite preprocessing their data in a GIS, still call upon other software to perform the final analysis. The open-source community has responded to this problem by extending existing open-source software like the statistical programming language R to allow spatial data analysis and visualisation to be performed in the same, free but well-documented computing environment that is used to do the modelling. This presentation explores R as an unusual but highly functional, open-source GIS with the potential to rival more traditional GIS software. The presentation will be appropriate for both budding and experienced users of GIS.
Learn more about TIMMS - http://www.applanix.com/timms The Trimble Indoor Mobile Mapping Solution (TIMMS) is the optimal fusion of technologies for capturing spatial data of indoor and other GNSS denied areas. It provides both LiDAR and spherical video, enabling the creation of accurate, real-life representations (maps, models) of interior spaces and all of its contents; every object in the interior space - including desks, chairs, stairs, and doors - appear in the plan. The maps are geo-located, meaning that the real world positions of each area of the building and its contents are known. Because of its tremendous efficiency and speed, TIMMS is very effective for facilities of all sizes, including very large areas extending over several city blocks. Users can obtain holistic 3D indoor geospatial views of all kinds of infrastructure including: - Plant and factory facilities - High-rise office, residential, and government buildings - Airports, train stations and other transportation facilities - Music halls, theatres, auditoriums and other public event spaces - Covered pedestrian concourses (above and below ground) with platforms, corridors, stair locations and ramps - Underground mines and tunnels Learn more about TIMMS - http://www.applanix.com/timms
Views: 9635 applanixcorporation
5th Annual Wolfram Data Summit 2014 Curt Aubley, Vice President, Data Center Group, Intel In this session, we discuss the state of IoT and Big Data technologies, then dive down into applying IoT and Big Data to help in the area of healthcare. For the latest information, please visit: http://www.wolfram.com
Views: 192 Wolfram
SLIDE: https://docs.google.com/presentation/d/1qLSAy2aOhLcfRGe210tmC56BLPc9COmeS7fka0icwcI/edit?usp=sharing Riepilogo: - importare una geometria - importare una tabella - creare un join dei 2 import - creare una view altri esempi di SQL
Views: 888 CityPlanner QGIS - GISTIPSTER
This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Geographic_information_system 00:01:44 1 History of development 00:08:57 2 Techniques and technology 00:09:57 2.1 Relating information from different sources 00:12:01 2.2 GIS uncertainties 00:14:00 2.3 Data representation 00:15:19 2.4 Data capture 00:20:09 2.5 Raster-to-vector translation 00:21:31 2.6 Projections, coordinate systems, and registration 00:22:21 3 Spatial analysis with geographical information system (GIS) 00:23:49 3.1 Slope and aspect 00:27:11 3.2 Data analysis 00:29:05 3.3 Topological modeling 00:29:42 3.4 Geometric networks 00:30:38 3.5 Hydrological modeling 00:32:10 3.6 Cartographic modeling 00:32:59 3.7 Map overlay 00:34:41 3.8 Geostatistics 00:37:16 3.9 Address geocoding 00:38:36 3.10 Reverse geocoding 00:39:26 3.11 Multi-criteria decision analysis 00:40:13 3.12 Data output and cartography 00:41:41 3.13 Graphic display techniques 00:43:40 3.14 Spatial ETL 00:44:21 3.15 GIS data mining 00:45:04 4 Applications 00:47:04 4.1 Open Geospatial Consortium standards 00:48:54 4.2 Web mapping 00:50:08 4.3 Adding the dimension of time 00:52:38 5 Semantics 00:55:00 6 Implications of GIS in society 00:55:55 6.1 GIS in education 00:56:54 6.2 GIS in local government Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. Listen on Google Assistant through Extra Audio: https://assistant.google.com/services/invoke/uid/0000001a130b3f91 Other Wikipedia audio articles at: https://www.youtube.com/results?search_query=wikipedia+tts Upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts Speaking Rate: 0.914666102936741 Voice name: en-US-Wavenet-E "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data. GIS applications are tools that allow users to create interactive queries (user-created searches), analyze spatial information, edit data in maps, and present the results of all these operations. GIS (more commonly GIScience) sometimes refers to geographic information science (GIScience), the science underlying geographic concepts, applications, and systems.GIS can refer to a number of different technologies, processes, techniques and methods. It is attached to many operations and has many applications related to engineering, planning, management, transport/logistics, insurance, telecommunications, and business. For that reason, GIS and location intelligence applications can be the foundation for many location-enabled services that rely on analysis and visualization. GIS can relate unrelated information by using location as the key index variable. Locations or extents in the Earth space–time may be recorded as dates/times of occurrence, and x, y, and z coordinates representing, longitude, latitude, and elevation, respectively. All Earth-based spatial–temporal location and extent references should be relatable to one another and ultimately to a "real" physical location or extent. This key characteristic of GIS has begun to open new avenues of scientific inquiry.
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Demonstration at the GeoVIS Workshop, ISPRS GeoSpatial Week. Reference : Masse A., Christophe S. (2015) Geovisualization of coastal areas from heterogeneous spatio-temporal data. ISPRS Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, GeoVIS'15, Vol. XL-3/W3 .
Views: 516 IGN LASTIG GI Sciences and Technologies Lab