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GIS Lecture 1 : What is Geospatial or Spatial Data and GIS (Geographical Information System)
 
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What is Geoaptial Data or Spatial Data or Geographic Information? What is GIS (Geographical Information System)
Views: 9265 Anuj Tiwari
What is Spatial Data - An Introduction to Spatial Data and its Applications
 
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Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com Spatial Data, also referred to as geospatial data, is the information that identifies the geographic location of physical objects on Earth. It’s data that can be mapped, as it is stored as coordinates and topology. In this video, we introduce the concept of Spatial Data and break down the fundamentals of interacting with Spatial Data using common development tools. We then explore how these basics can be expanded upon in modern applications to assist in daily tasks, perform detailed analyses, or create interactive user experiences. Watch this video to learn: - What is Spatial Data - How and when to use Spatial Data - Spatial Data Examples and real-world applications
Views: 11665 Fullstack Academy
Spatial Data Mining I: Essentials of Cluster Analysis
 
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Whenever we look at a map, it is natural for us to organize, group, differentiate, and cluster what we see to help us make better sense of it. This session will explore the powerful Spatial Statistics techniques designed to do just that: Hot Spot Analysis and Cluster and Outlier Analysis. We will demonstrate how these techniques work and how they can be used to identify significant patterns in our data. We will explore the different questions that each tool can answer, best practices for running the tools, and strategies for interpreting and sharing results. This comprehensive introduction to cluster analysis will prepare you with the knowledge necessary to turn your spatial data into useful information for better decision making.
Views: 30194 Esri Events
Spatial Data Mining
 
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Topics Described: 1. Data Mining 2. Spatial Data Mining 3. Spatial Data Mining Architecture 4. Heterogeneity of Data Mining 5. Autocorrelation 6. Relations 7. Future Scopes 8. Summary Done by M.Karthikeyan
Views: 3584 kaka karthi
Week 1: Spatial Data, Spatial Analysis, Spatial Data Science
 
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Recorded lecture by Luc Anselin at the University of Chicago (September 2017).
Views: 7533 GeoDa Software
"Spatial vs Non-Spatial Data".. G.I.S. A brief Lecture  by   Gaurav Gauri... Incredible Geographica
 
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the detailed Lecture about spatial and non spatial data will uploaded soon.. Subscribe our channel... Stay Connected.. Mail us at :- [email protected]
Views: 10425 Incredible Geographica
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: 2737 The Audiopedia
What is special about mining spatial and spatio-temporal datasets?
 
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Presented by Shashi Shekhar
Views: 5240 UCF CRCV
Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
 
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Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 61713 Well Academy
Spatial Data Science Overview
 
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Recorded lecture by Luc Anselin at the University of Chicago (October 2016). Version with fixed sound: https://www.youtube.com/watch?v=VlgJsoImmlo
Views: 5836 GeoDa Software
Exploring GIS: Spatial analysis and decision making
 
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An introduction to GIS and spatial analysis, measurements in GIS, proximity analysis, vector overlay analysis, geocoding, network analysis, and decision making workflows.
Views: 21560 GIS VideosTV
Geospatial data and services
 
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Presented by Carsten Roensdorf, Spatial Data Expert & Adviser, Ordnance Survey Carsten explores the evolving need for location data in smart environments and how data sharing, standards and interoperability are key. At the centre of this is how foundation datasets can enable fundamental services which harness the power of geospatial and can be used in the creation of a smart geospatial location platform.
Views: 618 Ordnance Survey
GIS and R - Importing spatial data
 
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A demonstration of how to import spatial data into R.
Views: 130 Geography Lectures
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: 2623 Cloud Hound
What is SPATIAL ANALYSIS? What does SPATIAL ANALYSIS mean? SPATIAL ANALYSIS meaning & explanation
 
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What is SPATIAL ANALYSIS? What does SPATIAL ANALYSIS mean? SPATIAL ANALYSIS meaning - SPATIAL ANALYSIS definition - SPATIAL ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
Views: 8212 The Audiopedia
Esri 2014 UC Tech Session: Spatial Data Mining: A Deep Dive into Cluster Analysis
 
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Technical workshop conducted by Lauren Bennett and Flora Vale at the 2014 user conference in San Diego.
Views: 750 Esri Events
Spatial data in R: new directions
 
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by Edzer Pebesma
Views: 1049 MIT Education
Spatial Data Mining II: A Deep Dive into Space-Time Analysis
 
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Space and time are inseparable, and integrating the temporal aspect of your data into your spatial analysis leads to powerful discoveries. This workshop will build on the cluster analysis methods discussed in Spatial Data Mining I by presenting advanced techniques for analyzing your data in the context of both space and time. We will cover space-time pattern mining techniques including aggregating your temporal data into a space-time cube, emerging hot spot analysis, local outlier analysis, best practices for visualizing your space-time cube, and strategies for interpreting and sharing your results. Come learn how to use these new techniques to get the most out of your spatiotemporal data.
Views: 10361 Esri Events
Introduction to Data Mining: Data Aggregation
 
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In this Data Mining Fundamentals tutorial, we discuss our first data cleaning strategy, data aggregation. Aggregation is combining two or more attributes (or objects) into a single attribute (or object). -- Learn more about Data Science Dojo here: https://hubs.ly/H0hCnj10 Watch the latest video tutorials here: https://hubs.ly/H0hCnHV0 See what our past attendees are saying here: https://hubs.ly/H0hCnj40 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 10946 Data Science Dojo
Spatial Regession in R 1: The Four Simplest Models
 
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We run OLS (with spatial diagnostics), SLX, Spatial Error and Spatial Lag Models. We also run the spatial Hausman test. Along the way, we discover a bug in the R SLX code in the spdep package, and get it fixed. Very exciting! Download the file with the data and commands here: http://spatial.burkeyacademy.com/home/files/R%20Spatial%20Regression1.zip Link to the entire Spatial Statistics Playlist: https://www.youtube.com/playlist?list=PLlnEW8MeJ4z6Du_cbY6o08KsU6hNDkt4k See more at http://spatial.burkeyacademy.com My Website: http://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: 12482 BurkeyAcademy
Introduction to Geospatial Data Analysis with Python | SciPy 2018 Tutorial | Serge Rey
 
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This tutorial is an introduction to geospatial data analysis in Python, with a focus on tabular vector data. It is the first part in a series of two tutorials; this part focuses on introducing the participants to the different libraries to work with geospatial data and will cover munging geo-data and exploring relations over space. This includes importing data in different formats (e.g. shapefile, GeoJSON), visualizing, combining and tidying them up for analysis, and will use libraries such as `pandas`, `geopandas`, `shapely`, `PySAL`, or `rasterio`. The second part will built upon this and focus on more more advanced geographic data science and statistical methods to gain insight from the data. No previous experience with those geospatial python libraries is needed, but basic familiarity with geospatial data and concepts (shapefiles, vector vs raster data) and pandas will be helpful. See tutorial materials here: https://scipy2018.scipy.org/ehome/299527/648136/ See the full SciPy 2018 playlist here: https://www.youtube.com/playlist?list=PLYx7XA2nY5Gd-tNhm79CNMe_qvi35PgUR
Views: 10652 Enthought
Introduction to data mining and architecture  in hindi
 
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#datamining #datawarehouse #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 257214 Last moment tuitions
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: 13578 Introtuts
Spatial data mining Project
 
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A novel spatial data mining project on dengue done by DR M N RAO & P.VEDAVATHI From scet engineering college, Narsapuram.
Views: 769 RAO & RAO
From Means to Medians to Machine Learning: Spatial Statistics Basics and Innovations
 
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This high-level overview will equip you with the basic knowledge necessary to get started exploring your data in new and meaningful ways. Stepping through each of the Spatial Statistics toolsets, we will discuss how the tools function and provide a variety of example applications to demonstrate the range of questions that can be answered. Concepts covered will include describing the shape and spatial distribution of your data; comparing datasets in meaningful, defensible ways; and mining for multivariate patterns. If you’re new to Spatial Statistics this is a great way to familiarize yourself with these powerful tools, methods, and workflows. If you’ve been using Spatial Statistics for a while, come discover the powerful new machine learning clustering methods introduced in ArcGIS Pro 2.1.
Views: 1220 Esri Events
Webinar:  Introduction to Geospatial Analysis in R
 
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Short Description: During this webinar we will provide an overview of common geospatial operations in R including: how to import data files into R, overlay layers, reduce spatial extent, select and reclassify values, and make a map. ------------------------------------------------------------------------- Detailed Description: The open-source software environment R is gaining popularity among many scientists, including geologists, biologists, and environmental scientists. R provides an alternative to traditional GIS software with numerous packages for geospatial analysis. This webinar will begin with a brief introduction to an example geospatial dataset from the ORNL DAAC and an overview of common geospatial operations in R. Next, we will demonstrate how to import files into R, overlay layers, reduce spatial extent, select and reclassify values, and make a map.
Views: 1587 NASA Earthdata
DBSCAN ( Density Based Spatial  Clustering of Application with Noise )  in Hindi | DWM | Data Mining
 
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Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 34753 Last moment tuitions
Geospatial Analysis with Python
 
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Data comes in all shapes and sizes and often government data is geospatial in nature. Often times data science programs & tutorials ignore how to work with this rich data to make room for more advanced topics. Our MinneMUDAC competition heavily utilized geospatial data but was processed to provide students a more familiar format. But as good scientists, we should use primary sources of information as often as possible. Come to this talk to get a basic understanding of how to read, write, query and perform simple geospatial calculations on Minnesota Tax shapefiles with Python. As always data & code will be provided. https://github.com/SocialDataSci/Geospatial_Data_with_Python @dreyco676 https://www.linkedin.com/in/johnhogue/
Views: 13302 Rogue Hogue
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: 2622 BurkeyAcademy
Exploring GIS: Spatial data representation
 
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An overview of how the real world is decomposed and stored digitally in the computer, what are spatial data models, specifying the vector data model, review of the raster data model, and map symbolizations.
Views: 21886 GIS VideosTV
Spatial database systems and their types
 
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Spatial database systems and their types
Getting Started with Spatial Data Analysis in R
 
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Spatial and spatial-temporal data have become pervasive nowadays. We are constantly generating spatial data from route planners, sensors, mobile devices, and computers in different fields like Transportation, Agriculture, Social Media. These data need to be analyzed to generate hidden insights that can improve business processes, help fight crime in cities, and much more. Simply creating static maps from these data is not enough. In this webinar we shall look at techniques of importing and exporting spatial data into R; understanding the foundation classes for spatial data; manipulation of spatial data; and techniques for spatial visualization. This webinar is meant to give you introductory knowledge of spatial data analysis in R needed to understand more complex spatial data modeling techniques. In this webinar, we will cover the following topics: -Why use R for spatial analysis -Packages for spatial data analysis -Types of spatial data -Classes and methods in R for spatial data analysis -Importing and exporting spatial data -Visualizing spatial data in R
Views: 49588 Domino Data Lab
Using Spatial Statistics to do More: Simple Approaches
 
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This high-level overview will equip you with the basic knowledge necessary to get started exploring your data in new and meaningful ways. Stepping through many of the Spatial Statistics tools, we will discuss how the tools function and provide a variety of example applications to demonstrate the range of questions that can be answered. Concepts covered will include describing the shape and spatial distribution of your data; comparing datasets in meaningful, defensible ways; and mining for multivariate patterns. If you’re new to Spatial Statistics this is a great way to familiarize yourself with these powerful tools, methods, and workflows. If you’ve been using Spatial Statistics for a while, come discover alternative applications and see how others are benefiting from the statistical analysis of their spatial data. Come learn how these tools really work and how to use them with your own data and applications.
Views: 9427 Esri Events
What Is DATA MINING? DATA MINING Definition & Meaning
 
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What is DATA MINING? What does DATA MINING mean? DATA MINING meaning - DATA MINING definition - DATA MINING explanation. Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] Data mining is an interdisciplinary subfield of computer science with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.[1][2][3][4] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[5] Aside from the raw analysis step, it also 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.[1] The term "data mining" is in fact a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.[6] It also is a buzzword[7] and is frequently 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 (e.g., machine learning) and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java[8] (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.[9] Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations. Source: Wikipedia.org
Views: 53 Audiopedia
What is SPATIOTEMPORAL DATABASE? What does SPATIOTEMPORAL DATABASE mean?
 
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What is SPATIOTEMPORAL DATABASE? What does SPATIOTEMPORAL DATABASE mean? SPATIOTEMPORAL DATABASE meaning - SPATIOTEMPORAL DATABASE definition - SPATIOTEMPORAL DATABASE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. A spatiotemporal database is a database that manages both space and time information. Common examples include: Tracking of moving objects, which typically can occupy only a single position at a given time. A database of wireless communication networks, which may exist only for a short timespan within a geographic region. An index of species in a given geographic region, where over time additional species may be introduced or existing species migrate or die out. Historical tracking of plate tectonic activity. At first glance, spatiotemporal databases are an extension of spatial databases. A spatiotemporal database embodies spatial, temporal, and spatiotemporal database concepts, and captures spatial and temporal aspects of data and deals with geometry changing over time and/or location of objects moving over invariant geometry (known variously as moving objects databases or real-time locating systems) However, although there exist numerous relational databases with spatial extensions, the spatiotemporal databases are not based on the relational model for practical reasons, chiefly among them that the data is multi-dimensional and capturing complex structures and behaviours. As of 2008, there are no RDBMS products with spatiotemporal extensions. There are some products such as the open-source TerraLib which use a middleware approach storing their data in a relational database. Unlike in the pure spatial domain, there are however no official or de facto standards for spatio-temporal data models and their querying. In general, the theory of this area is also less well-developed. Another approach is the constraint database system such as MLPQ (Management of Linear Programming Queries).
Views: 1611 The Audiopedia
Data Visualization for Spatial Analysis
 
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This workshop with cover how data visualization techniques within ArcGIS can help you explore your data, interpret the results of analysis, and communicate findings. Learn how different data visualization methods, from maps to charts to 3D scenes, can help you compare categories and amounts, visualize distributions and frequency, explore relationships and correlations, and understand change over time or distance. This workshop will focus on charting in ArcGIS Pro, spatial statistical techniques, and communication tools like layouts and Story Maps.
Views: 2340 Esri Events
Introduction to Datawarehouse in hindi | Data warehouse and data mining Lectures
 
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#datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 325646 Last moment tuitions
Spatial Data Science with ArcGIS: A Tour
 
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ArcGIS is a comprehensive analytics platform for data scientists. It enables you to unlock your data's full potential by integrating data exploration, statistical and machine learning algorithms, and advanced modeling techniques. Whether your analysis requires a powerful spatial data science workstation or a dynamic scripting environment hosted on a distributed analytics platform, ArcGIS has you covered. In this tour, we demonstrate the rich analytical capabilities of the ArcGIS platform, its APIs in Python & R and its ability to interoperate with open source data science libraries. -------------------------------------------------------------------------------------------------------------------------- Follow us on Social Media! Twitter: https://twitter.com/Esri Facebook: https://facebook.com/EsriGIS LinkedIn: https://www.linkedin.com/company/esri Instagram: https://www.instagram.com/esrigram The Science of Where: http://www.esri.com
Views: 134 Esri Events
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
Geospatial Data Scientist
 
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The Geospatial Data Scientist career video will assist in building your career path in the world of science and geography!
Views: 1194 Esri South Africa
Mining Spatial Data using FDL Query
 
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A Project on Data Mining
Views: 144 Aniel Ronald
Spatial Data Type :- Geometry
 
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This is a video explaining the concept of Geometry data type in PostGIS. Twitter Handle https://twitter.com/RahulSoshte
Views: 84 Rahul Soshte
R Spatial Data 1: Read in SHP File
 
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Here we use R and RStudio to read in a spatial data file (as a SHP file), read in a contiguity (GAL) file created in GeoDa, create the same queen contiguity matrix in R and check that the two are the same, and compute a Moran's I. Link to Data File: https://sites.google.com/a/burkeyacademy.com/spatial/home/files/R%20Spatial%201%20Data.zip Link to Playlist of all Spatial Videos: https://www.youtube.com/playlist?list=PLlnEW8MeJ4z6Du_cbY6o08KsU6hNDkt4k My Website: http://www.burkeyacademy.com/ Support me on Patreon! https://www.patreon.com/burkeyacademy Talk to me on my SubReddit: https://www.reddit.com/r/BurkeyAcademy/ Link to download R: https://cloud.r-project.org/ Link to Download RStudio: https://www.rstudio.com/products/rstudio/download/ Here are the commands we used: library(spdep) library(rgdal) NCVACO = readOGR(dsn = ".", layer = "NCVACO") queen.nb=read.gal("queen.gal", region.id=NCVACO$FIPS) summary(queen.nb) queen.R.nb=poly2nb(NCVACO, row.names=NCVACO$FIPS) #Rook would be rook.R.nb=poly2nb(NCVACO,queen=FALSE) summary(queen.R.nb) isTRUE(all.equal(queen.nb,queen.R.nb,check.attributes=FALSE)) #moran(variable, listw, no. regions, sum of weights) moran(NCVACO$SALESPC,nb2listw(queen.nb), length(NCVACO$SALESPC), Szero(nb2listw(queen.nb))) moran.test(NCVACO$SALESPC,nb2listw(queen.nb))
Views: 8196 BurkeyAcademy
K Means Clustering Data Mining Example | Machine Learning part 1
 
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K-means clustering algorithm is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. The problem is computationally difficult (NP-hard); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both k-means and Gaussian mixture modeling. Additionally, they both use cluster centers to model the data; however, kmeans clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes. ====================================================== watch part 2 here: https://www.youtube.com/watch?v=AukQSbtZ1NQ book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar
Views: 21747 fun 2 code
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