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What is GEOSPATIAL ANALYSIS? What does GEOSPATIAL ANALYSIS mean? GEOSPATIAL ANALYSIS meaning
 
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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: 2734 The Audiopedia
Spatial database systems and their types
 
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Spatial database systems and their types
Spatial database
 
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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: 9928 Audiopedia
Data mining
 
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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: 1712 Audiopedia
Temporal Database in Hindi
 
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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.[3] 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[4] 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.[3] The TSQL2 approach was heavily criticized by Chris Date and Hugh Darwen.[5] 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: 13528 Introtuts
Spatial Data
 
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Views: 6350 UWFGISMOOC
Introduction Spatial Data
 
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Excel Maps, applied to MS Power BI Course curriculum - http://bitly.com/Agenda-BI Cloud Hound website http://www.cloudhound.co.uk
Views: 2619 Cloud Hound
Introduction to Spatial Information (2011)
 
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This video will introduce you to: - Spatial Information and Geographic Information Systems (GIS) - How GIS is used at the Department of Conservation - Examples of how GIS is used in our day-to-day business
Views: 227 DOCskillable
R Spatial Data 2: KNN from Longitude and Latitude
 
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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: 2618 BurkeyAcademy
Spatial Vision Solutions
 
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Today, the power of spatial information to assist people and businesses to make informed decisions is well recognised. In fact, its benefits have become so clear that an increasing number of enterprises are relying on Geographic Information Systems (GIS) and broader spatial technologies to support their key operations and interactions. Spatial Vision is a leading specialist in information and spatial technologies. Integrating geographic and organisational data, we provide business systems, advanced spatial analyses, reliable planning systems and practical mapping applications to address some of the country's most pressing environmental, economic and resource issues. Combining the latest tools and techniques with years of innovation and experience, the company has implemented some of Australia's landmark spatial technology projects. Our award-winning solutions assist our clients to better manage their natural assets, respond to emergencies, understand markets, target customers and deliver high-quality products and services. Established in 1999, Spatial Vision has proudly created and built tailored solutions for all levels of government and industry. And will continue to deliver best practice solutions that stand the test of time. For more information please visit: www.spatialvision.com.au
Views: 2120 Spatial Vision
What is LOCATION INTELLIGENCE? What does LOCATION INTELLIGENCE mean? LOCATION INTELLIGENCE meaning
 
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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: 1682 The Audiopedia
Tom Hengl: "Machine Learning as a generic framework for spatial prediction"
 
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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.
Bruno Goncalves, Anastasios Noulas: Mining Georeferenced Data
 
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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: 1204 PyData
data mining for bca|cluster analysis,web mining|bhavacharanam
 
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data mining for bca|cluster analysis,web mining|bhavacharanam
Views: 50 BHAVA CHARANAM
Martin Christen - Processing Geodata using Python and Open Source Modules
 
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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/
Continuous Operating Reference Station (CORS) Networks: SAGE Research
 
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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: 1709 AboutUNSW
INSPIRE: Common geospatial data standards for more informed and timely decisions
 
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You would like to exchange data cross-border and cross-sector using reference codes? Find out more on this action: http://europa.eu/!Qt44yc
Views: 609 ISA2 programme
USC GIST Presents | Spatial Data Acquisition
 
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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
Geovisualization of coastal areas
 
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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 .
Hanna Meyer: "Machine-learning based modelling of spatial and spatio-temporal data" (practical)
 
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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/
R tutorial: Working with Geospatial Data in R
 
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Learn more about Geospatial Data in R with DataCamp: https://www.datacamp.com/courses/working-with-geospatial-data-in-r
Views: 5633 DataCamp
7 Techniques to Visualize Geospatial Data | SocialCops Academy
 
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Here are 7 types of geospatial visualizations for your data! Learn more: http://bit.ly/2nIjb5O The way we see the world today is largely shaped by the images of physical and political maps that we pored over in our school atlases. Visualizing geospatial data helps us communicate how different variables correlate to geographical locations by layering these variables over maps. Find more resources, tips, and tools for visualizing all kinds of data on blog.socialcops.com Follow us on Twitter: http://www.twitter.com/Social_Cops Follow us on Facebook: https://www.facebook.com/socialcops Subscribe to our Channel: https://www.youtube.com/channel/UCUn-jzOyfg_6_C8z12baKqA
Views: 1067 SocialCops
Geospatial Solutions - Highly accurate Earth data for professionals
 
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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: 607 Leica Geosystems AG
Alexander Müller - Spatial Range Queries Using Python In-Memory Indices
 
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When you’re working with a spatial dataset a common use case is that you need to get points of interests that are within a certain radius of a reference point, also know as spatial range queries. A standard solution for this problem is to use databases like MongoDB or Postgres which provided advanced spatial indexing capabilities. However, if you don’t have those capabilities available or you need to perform millions of queries and don’t want to add load to your production database, you need to explore other alternatives. Thus, this talk will discuss a potential remedy for this problem by showing how to use python together with some available libraries (numpy, sklearn, rtree, geohash) to enable in-memory radius searches. We will dive into some implementation details and show which methods to use for which use cases, by benchmarking them against each other. **** www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1973 PyData
Introduction to DHS Datasets
 
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This video provides a brief introduction on The DHS Program datasets. 0:16 What is a Dataset? 0:33 Units of Analysis 0:58 Cases and Variables 1:54 Data Collection and Questionnaires 2:40 DHS Data Files
Views: 1695 The DHS Program
Spatial query , Query Builder , Analysis Tool ,Vector data Classification In Hindi /  हिंदी
 
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This Video is specially for bsc it student Which is doing by mumbai university .. this video contains practical 3,4,5
Views: 2776 Mumbiker Suraj
Spatial Data Infrastructure
 
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A description of what spatial data is and the importance or different mapping applications.
Getting Started With Spatial Data Analysis
 
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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
GEOG 485L/585L Module 3 - GIS Concepts and Geospatial Services Oriented Architectures
 
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This week's lecture covers some basic concepts related to spatial data types and coordinate reference systems, with reference to some command line tools that can provide useful information about geospatial data (GDAL/OGR) and perform coordinate transformations (PROJ4, and the proj and cs2cs utilities). This lecture also introduces the concept of a tiered architectural model for geospatial applications and outlines the components and interoperability standards that enable interaction between those components.
Views: 333 Karl Benedict
Georeferencing lecture (NCSU Geospatial Modeling and Analysis)
 
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Lecture: Georeferencing Lecturer: Helena Mitasova Course: NCSU GIS/MEA582: Geospatial Modeling and Analysis Materials: http://ncsu-geoforall-lab.github.io/geospatial-modeling-course
Views: 733 NCSU GeoForAll Lab
Food for Thought: Introduction to Ontology
 
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CGIAR started developing and adopting existing ontologies to propose semantically organized reference lists of documented concepts (traits, agronomic parameters, units, etc.) used by scientists for inclusion into breeding and agronomic databases, as well as a nomenclature for naming the observed or measured variables that could easily be uploaded into electronic field books or Microsoft Excel forms for data collection. The Community of Practice (CoP) of the CGIAR Big Data Platform recommended adopting or developing ontologies for Socio-Economics Data, for Livestock Data, for Fish Data and for Water Management Data. This Food for Thought webinar features two distinguished speakers from CGIAR Big Data Platform's Ontologies Data CoP. Dr. Marie-Angélique Laporte will introduce what is an ontology and its purpose in knowledge modelling. Ms. Elizabeth Arnaud will then explain how ontologies are currently applied in CGIAR for multidisciplinary data, and the role of both the CGIAR Ontology Working Group and the Ontology CoP of Big Data Platform. To support the discussion for FISH CRP, this talk will introduce examples of the Crop Ontology, Agronomy Ontology, the new Ontology for Agricultural Household surveys that are all products of the CoP. --------------------------------------------------------------------------------------------------------------------------------------------------------------- Dr. Marie-Angélique Laporte joined Bioversity International in 2015 as a postdoc to work on the NSF-funded project Planteome, which aimed at developing a set of reference ontologies for plants. She is now an associate scientist working on various data related issues, including developing ontologies and metadata standards, and applying text-mining techniques to extract relevant agricultural information. She is involved with the Big Data Platform of the CGIAR and the Crop Ontology and Agronomy Ontology projects. Marie-Angélique holds a Ph.D. in ecology during which she worked on the integration of functional plant traits data. After her Ph.D., she worked as a postdoc at IRD in Montpellier on automatic classification of remote sensing images using ontologies and at the Max Planck Institute for Biogeochemistry in Jena on developing a semantic system to harmonise functional plant traits data. Ms. Elizabeth Arnaud holds a M.Sc. in Biology and a Master in Scientific data management techniques and communication and is based at Bioversity International’s Montpellier office, France. She coordinated the Musa Germplasm Information System (MGIS) and the CGIAR System-wide Information System on Genetic Resources (SINGER), the development of the Bioversity geospatial database for collected crop samples and since 2008, has led the Crop Ontology project developed with other CGIAR Research Centers and external partners. She is co-principal investigator for the NSF-awarded ontology project of Planteome. She co-led the first phase of the Agronomy Ontology development. Since 2017, she leads the Ontology Community of Practice of the CGIAR Platform for Big Data in Agriculture. Elizabeth is the head of Bioversity's delegation in the Global Biodiversity Information Facility (GBIF) Governing Body and she was member of the Scientific Committee from 2012 to 2018. In 2016, she chaired the GBIF/Bioversity Task group on GBIF Data fitness for use in Agrobiodiversity. For 2 years, she was member of the Advisory Committee of DivSeek Initiative and is now member of the Advisory Group of Excellence in Breeding Platform.
Views: 97 WorldFish
Introducing SAP HANA spatial services application
 
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Introducing SAP HANA spatial services application. In this video, Marcus Dorfmeyer, Product Manager at SAP, will show you an end to end demo of our new service offering: SAP HANA spatial service. For more information: Solution Brief: http://spr.ly/6053DoDDv Reference Guide: http://spr.ly/6055DoDDx
Views: 2093 SAP Technology
KotlinConf 2017 - kscript - Scripting Enhancements for Kotlin by Holger Brandl
 
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Kotlin has some support for scripting already but it is not yet feature-rich enough to be a viable alternative in the shell. Over the last year we have evolved a small utility called kscript which complements the existing scripting support provided by kotlinc with several new features. First, it caches compiled scripts and tracks changes by using md5 checksums. Second, it allows to declare dependencies with gradle-style resource locators and resolves those with maven. Third, it allows for different modes to provide the scripts including URLs, reading from stdin, local files, direct script arguments, or by using it as interpreter in the shebang line of a script. Finally, kscript is complemented by a support library to ease the writing of Kotlin scriptlets. The latter includes solutions to common use-cases like argument parsing, data streaming, IO utilities, and various iterators to streamline the development of kscript applications. Take all these features together, kscript provides an easy-to-use, very flexible, and almost zero-overhead solution to write self-contained mini-applications with Kotlin. To ensure long-term stability, kscript is developed against a continuously integrated suite of tests. Over the course of the last year it has a growing but active community.It is documented well with an extensive reference manual including many examples and recipes. Holger Brandl works as a data scientist at the Max Planck Institute of Molecular Cell Biology and Genetics (Dresden, Germany). He holds a Ph.D. degree in machine learning, and has developed new concepts in the field of computational linguistics. More recently he has co-authored publications in high-ranking journals such as Nature and Science. He is a main developer of the "R Language Integration" for Intellij IDEA, which is increasingly written in Kotlin, and has published several Kotlin artifacts for bioinformatics, high-performance computing and data mining.
Views: 2281 JetBrainsTV
Spatial Data Infrastructure Concepts and Components
 
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Defines spatial data infrastructure (SDI) and describes key components that any SDI implementation should incorporate.
Views: 5552 gsdivideos
Hadoop for Data Warehousing professionals
 
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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: 2876 TechGig
Spatial Processing with SAP HANA
 
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This whiteboard session provides an excellent and comprehensive overview of the SAP HANA native spatial capabilities, ecosystem, use cases, and key benefits
Views: 4414 SAP Technology
Spatial Data Science con R (English subtitles)
 
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Todos los materiales de la presentación incluyendo el código fuente y los datos de muestra se pueden descargar desde este link: http://amsantac.co/blog/es/2016/08/07/spatial-data-science-r-es.html All the English-translated materials for this webinar, including source code and sample data, can be downloaded from this link: http://amsantac.co/blog/en/2016/08/07/spatial-data-science-r.html En este webinar se explican algunos conceptos y herramientas de Spatial Data Science, con un énfasis en el uso del lenguaje R durante las diferentes fases de trabajo, desde la importación y procesamiento de datos espaciales hasta la visualización y publicación de resultados. No olvides suscribirte a mi canal de Youtube para más videos! In this webinar I talked about Data Science in the context of its application to spatial data and explained how we can use the R language for the analysis of geographic information within the different stages of a data science workflow, from the import and processing of spatial data to visualization and publication of results. Subscribe to my channel on Youtube for more videos!
Views: 2827 Alí Santacruz
Creating High Availability Spatial Databases- Part 1
 
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Working tutorial about MS SQL and Spatial Databases More: http://mapsys.info
Views: 5080 ivche01
Big Data and ArcGIS: Large Scale Batch Analytics for Feature and Tabular Data
 
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We will discuss what is being developed to complete batch analysis of feature and tabular data, with a description of the new analytic capabilities, as well as an in-depth discussion on how the solutions were implemented. We will present real world examples and briefly discuss integrated technology components such as underlying storage technology.
Views: 1865 Esri Events
Configuring and Installing Spatial Warehouse (MS SQL)
 
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Learn how to install and configure GeoSpatial Analysis Spatial Warehouse based on a simple example
Continuous Operating Reference Station (CORS) Networks: Surveying and Geospatial Engineering (SAGE)
 
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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: 1716 AboutUNSW
Geographic information system | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Geographic_information_system 00:01:57 1 History of development 00:09:50 2 Techniques and technology 00:10:54 2.1 Relating information from different sources 00:13:12 2.2 GIS uncertainties 00:15:21 2.3 Data representation 00:16:50 2.4 Data capture 00:22:06 2.5 Raster-to-vector translation 00:23:35 2.6 Projections, coordinate systems, and registration 00:24:30 3 Spatial analysis with geographical information system (GIS) 00:26:06 3.1 Slope and aspect 00:30:00 3.2 Data analysis 00:32:03 3.3 Topological modeling 00:32:43 3.4 Geometric networks 00:33:45 3.5 Hydrological modeling 00:35:26 3.6 Cartographic modeling 00:36:20 3.7 Map overlay 00:38:12 3.8 Geostatistics 00:41:04 3.9 Address geocoding 00:42:32 3.10 Reverse geocoding 00:43:26 3.11 Multi-criteria decision analysis 00:44:17 3.12 Data output and cartography 00:45:54 3.13 Graphic display techniques 00:48:03 3.14 Spatial ETL 00:48:48 3.15 GIS data mining 00:49:35 4 Applications 00:51:48 4.1 Open Geospatial Consortium standards 00:53:49 4.2 Web mapping 00:55:10 4.3 Adding the dimension of time 00:57:52 5 Semantics 01:00:27 6 Implications of GIS in society 01:01:27 6.1 GIS in education 01:02:30 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: 0.8502004685025949 Voice name: en-US-Wavenet-F "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.
Views: 6 wikipedia tts
MicroStrategy 2 min #33 - Sentiment Analysis on Social Media with R
 
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Please contact MicroStrategy Professional Services to get additional details on how to create R scripts
Views: 2103 HF Chadeisson
Real-Time Detection of Traffic From Twitter Stream Analysis
 
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Real-Time Detection of Traffic From Twitter Stream Analysis TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: www.jpinfotech.org, Blog: www.jpinfotech.blogspot.com Social networks have been recently employed as a source of information for event detection, with particular reference to road traffic congestion and car accidents. In this paper, we present a real-time monitoring system for traffic event detection from Twitter stream analysis. The system fetches tweets from Twitter according to several search criteria; processes tweets, by applying text mining techniques; and finally performs the classification of tweets. The aim is to assign the appropriate class label to each tweet, as related to a traffic event or not. The traffic detection system was employed for real-time monitoring of several areas of the Italian road network, allowing for detection of traffic events almost in real time, often before online traffic news web sites. We employed the support vector machine as a classification model, and we achieved an accuracy value of 95.75% by solving a binary classification problem (traffic versus non-traffic tweets). We were also able to discriminate if traffic is caused by an external event or not, by solving a multiclass classification problem and obtaining an accuracy value of 88.89%.
Views: 1499 jpinfotechprojects
Bob Gleichauf, Lab41, In-Q-Tel (DG'13)
 
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Bob Gleichauf is the Director of Lab41 and Chief Scientist at In-Q-Tel (IQT), an independent, strategic investor that delivers innovative technology solutions to the U.S. Intelligence Community. Prior to joining IQT in 2009 Bob worked at Cisco, WheelGroup, IQ Software, and Datapoint. Prior to that, Bob spent time in Africa and the University of Michigan pursuing a PhD (which he did not complete) in early human prehistory. What he did not fully appreciate at the time was how much time and effort he was expending on signal to noise processing of field data.
Views: 2531 Data Gotham