R is a free software environment for statistical computing and graphics. It can be used for spatial data analysis and GIS tasks. Spatial data such as points, polygons, and raster files can be imported and analyzed in R using specialized packages. Two case studies demonstrated using R for spatial interpolation of temperature data, LiDAR data processing to create digital elevation models, and developing online viewers for spatial datasets. R allows for reproducible analysis through scripting and has numerous packages that implement statistical procedures, graphics, and interfaces with GIS software like GRASS and ArcGIS.
Slide show for the webinar on "Spatial Data Science with R" organized for the GeoDevelopers.org community. The video of the webinar and all the related materials 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
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.
Spatial analysis & interpolation in ARC GISKU Leuven
In ArcGIS, a data model describes the thematic layers used in the applications (for example, hamburger stands, roads, and counties); their spatial representation (for example, point, line, or polygon); their attributes; their integrity rules and relationships (for example, counties must nest within states).
Querying the data and selecting features in ARC GISKU Leuven
In ArcMap, the Selection > Select By Attributes and Selection > Select By Location tools let you interactively select features and view the highlighted selection as part of a feature layer. Their geoprocessing tool counterparts are Select Layer By Attribute and Select Layer By Location.
Visualizing Data with Geographic Information Systems (GIS)Kate Dougherty
Librarians in academic and research institutions are increasingly involved in the curation and visualization of data created by their organizations. This presentation, presented as part of a session on "The Data Librarian" at the Internet Librarian International 2013 conference, explored how information professionals can use open source GIS software to add value to data.
Slide show for the webinar on "Spatial Data Science with R" organized for the GeoDevelopers.org community. The video of the webinar and all the related materials 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
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.
Spatial analysis & interpolation in ARC GISKU Leuven
In ArcGIS, a data model describes the thematic layers used in the applications (for example, hamburger stands, roads, and counties); their spatial representation (for example, point, line, or polygon); their attributes; their integrity rules and relationships (for example, counties must nest within states).
Querying the data and selecting features in ARC GISKU Leuven
In ArcMap, the Selection > Select By Attributes and Selection > Select By Location tools let you interactively select features and view the highlighted selection as part of a feature layer. Their geoprocessing tool counterparts are Select Layer By Attribute and Select Layer By Location.
Visualizing Data with Geographic Information Systems (GIS)Kate Dougherty
Librarians in academic and research institutions are increasingly involved in the curation and visualization of data created by their organizations. This presentation, presented as part of a session on "The Data Librarian" at the Internet Librarian International 2013 conference, explored how information professionals can use open source GIS software to add value to data.
An introduction to GIS Data Types. Strengths and weaknesses of raster and vector data are discussed. Also covered is the importance of topology. Concludes with a discussion of the vector-based format of OpenStreetMap data.
An introduction to GIS Data Types. Strengths and weaknesses of raster and vector data are discussed. Also covered is the importance of topology. Concludes with a discussion of the vector-based format of OpenStreetMap data.
Learn how to get started making Leaflet maps through R statistical software. Sounds crazy? Maybe. But the R package leafletR allows people familiar with R, but maybe not so much with HTML and Javascript coding, to make a basic Leaflet map (interactive, slipping web map) quickly with minimal knowledge of other programming languages.
Example code posted here: https://github.com/MicheleTobias/RCode
Using R to Visualize Spatial Data: R as GIS - Guy LansleyGuy Lansley
This talk demonstrates some of the benefits of using R to visualize spatial data efficiently and clearly.
It was originally presented by Guy Lansley (UCL and the Consumer Data Research Centre) to the GIS for Social Data and Crisis Mapping Workshop at the University of Kent.
In this study various techniques for exploratory spatial data analysis are reviewed : spatial autocorrelation, Moran's I statistic, hot spots analysis, spatial lag and spatial error models.
Modeling Count-based Raster Data with ArcGIS and RAzavea
This presentation outlines the conceptual framework for building regression models of event counts where the unit of analysis is small. It explains how ArcGIS for Desktop can be used to build raster data sets that are modeled as generalized linear models within the open source R package.
FOSS4G North America 2016
Sometimes your map needs a little extra polish. In this talk, I will outline my workflow for bringing maps from QGIS into Inkscape and exporting to commonly used formats to produces a finished professional-quality map. It will include "do's" and "don'ts" to help you develop your own workflow and avoid common pitfalls, as well as some features unique to Inkscape such as filters.
Linking Socio-economic and Demographic Characteristics to Twitter Topics - Gu...Guy Lansley
Social media data is now widely considered a viable source for market and social research. Everyday Twitter’s users generate large quantities of data through Tweet messages which express the users’ thoughts and opinions, and may also describe their activity, plans and location. In its raw form, textual data at this volume is hard to process and understand, however, it is possible to model the Tweets into a small number of topics using generative probabilistic algorithms. This paper aims to research how the content of Tweets may vary by socio-economics and demographic characteristics using Tweets from Inner London sourced from the Twitter application programming interface.
Earlier research has successfully allocated over 1 million geo-located Tweets from Inner London in 2013 into a hierarchical classification of 20 groups and 100 subgroups created using a latent dirichlet allocation algorithm. The 20 groups consist of distinctive topics and uses of language, and they all demonstrate unique spatial and temporal patterns across Inner London. The next stage of the analysis explores how the Twitter classification varies across the residential geography of Inner London. Assuming that most Tweets sourced from residential buildings are likely to be sourced by residents, the classification can be compared to socio-economic and other demographic characteristics from open data sources. In addition, some characteristics such as gender and ethnicity can also be inferred from the names of Twitter users.
The present study evaluates the possibility of spatial heterogeneity in the effects on municipal-level crime rates of both demographic and socio-economic variables. Geoggraphically weighted regression (GWR) is used for exploring spatial heterogeneity and confirms that place matters.
Finding Meaning in Points, Areas and Surfaces: Spatial Analysis in RRevolution Analytics
Everything happens somewhere and spatial analysis attempts to use location as an explanatory variable. Such analysis is made complex by the very many ways we habitually record spatial location, the complexity of spatial data structures, and the wide variety of possible domain-driven questions we might ask. One option is to develop and use software for specific types of spatial data, another is to use a purpose-built geographical information system (GIS), but determined work by R enthusiasts has resulted in a multiplicity of packages in the R environment that can also be used.
The goal of this workshop is to introduce fundamental capabilities of R as a tool for performing data analysis. Here, we learn about the most comprehensive statistical analysis language R, to get a basic idea how to analyze real-word data, extract patterns from data and find causality.
Introduction to GIS - Basic spatial concepts - Coordinate Systems - GIS and Information Systems – Definitions – History of GIS - Components of a GIS – Hardware, Software, Data, People, Methods – Proprietary and open source Software - Types of data – Spatial, Attribute data- types of attributes – scales/ levels of measurements.
Process and steps that are followed in creation of successful visualization. Taking an example of Encyclopaedia of life data and tableu visualization prototype
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
2. CONTENTS
2
1. Introduction to R programming language.
2. Spatial analysis in ‘R’
3. R and GIS
4. Literature review
5. Case studies
6. Summary
7. References.
3. Introduction to R language
3
Environment for statistical computing and graphics
- Free software
Associated with simple ,interpreted programming language
Versions of R exist of Windows, MacOS, Linux other Unix
flavours
Easy to create your own functions in R
Simple GIS tasks like topological overlay, raster algebra
etc., can be carried
4. R language includes
4
an effective data handling and storage facility,
a suite of operators for calculations on arrays, in particular
matrices,
a large, coherent, integrated collection of intermediate tools
for data analysis,
graphical facilities for data analysis and display either on-
screen or on hardcopy, and
a well-developed, simple and effective programming
language which includes conditionals, loops, user-defined
recursive functions and input and output facilities.
5. R Function Libraries
5
Implement many common statistical procedures.
Provide excellent graphics functionality.
A convenient starting point for many data analysis projects.
Examples :
maps: allows you to make maps of the world, the US, and
smaller areas
mapproj: allows you to do cartographic projections
6. 6
Fig 1 R project for statistical computing
www. r-project.org
8. 8
R developers have written the R package ‘sp’ to extend R with
classes and methods for spatial data .
- Classes specify a structure and define how spatial data
are organised and stored. - -
- Methods are instances of functions specialised for a
particular data class.
9. Analysis of spatial data in R using points:
9
Points are pairs of coordinates (x; y), representing events, observation posts,
individuals, cities or any other discrete object denned in space.
Let's take a look at the dataset crime, which is just a table of geographic
coordinates (decimal degrees) for crime locations in Baltimore, MD.
head(crime)
ID LONG LAT
1 -76.65159 39.23941
2 -76.47434 39.35274
3 -76.51726 39.25874
4 -76.52607 39.40707
5 -76.51001 39.33571
6 -76.70375 39.26605
11. Polygons and lines:
11
Polygons can be thought of as sequences of connected points, where the
first point is the same as the last.
- An open polygon, where the sequence of points does not result in a
closed shape with a denned area, is called a line.
In the R environment, line and polygon data are stored in objects of classes
SpatialPolygons and Spatial Lines")
Class Polygon [package "sp"]
Name: labpt area hole ringDir coords
Class: numeric numeric logical integer matrix
The data are stored as a SpatialPolygons dataframe, which is a subclass of
SpatialPolygons containing a dataframe of attributes.
12. Preparation of a simple map in R
12
Fig 4- showing a simple map
library(maps)
library(mapdata)
map("worldhires","canada",
xlim=c(-141,-53),
ylim=c(40,85),
col="gray90",
fill=TRUE)
http://www.r-bloggers.com/maps-with-r-and-polygon-
boundaries/
13. R and GIS
13
The aim of integrating R and ArcGIS is to provide
an automated way of offering R scripts as ArcGIS
Geoprocessing Tools.
In some cases the analysis is composed by several
steps, demanding different capabilities in such cases
this kind of interface is most suitable.
14. 14
Examples of R packages providing an interface to
GIS:
GRASS GIS can be connected through R package spgrass6.
R can access SAGAGIS modules through the R
package RSAGA (currently Windows, Linux, FreeBSD and probably
others); SAGA GIS is an open-source GIS with mainly raster processing
capabilities such as terrain analysis.
R can also run ArcGIS geoprocessing tools through the R
package RPyGeo (Windows only).
-RPyGeo uses Python scripts to communicate with ArcGIS.
RPyGeo/ArcGIS operates on files (raster and shapefiles).
15. 15
Figure 5- shows the workflow required to expose an R script as a Python toolbox and how the
toolbox communicate with the original R script in order to run the algorithm.
16. Applications:
16
Geosciences
Water resources
Environmental science
Agriculture and soil science
Mathematics and statistics
Ecology
Geodesy
The exploitation of fossil fuels, and
Meteorology
17. LITERATURE REVIEW
17
Bivand(2001) gives the sketching of key modes of spatial data
analysis (point pattern, continuous surface, areal/lattice), and
how they integrate into legacy GIS data models.
Roger(2007) gave a brief description of how to access data
and also covered how coordinate reference systems are
handled, because they are the foundation for spatial data
integration
Bajat(2012)presents possibilities of applying the
geographically weighted regression, method in mapping
population change index
18. 18
Bajat(2012) presents possibilities of applying the
geographically weighted regression, method in mapping
population change index in the spatial modelling of population
concentration
Shane(2013) described some statistical and mapping
techniques developed for handling and interrogating large-
scale multi-media geochemical datasets using the R with
Python scripting languages along with GIS
19. CASE STUDY 1
19
Kate(2013) Utilized open-source programming languages to
statistically and spatially analyse regional-scale
geoenvironmental datasets.
Objective
Making best use of open-source programming languages such
as R in analysing regional-scale geoenvironmental datasets and
developing a web mapping service and online viewer for the
datasets.
Study area
The border region of Northern Ireland and interior of
Northern Ireland.
20. 20
Fig 5: graphical plots produced in R after quality assurance and quality
control assessment of analytical data
Methodology
R–Statistical analyses:
-R is employed initially to output a range of graphical plots for quality
assurance and quality control assessment of analytical data with respect to
laboratory reference materials (as shown in fig5).
21. 21
Exploratory data analyses are carried out to assess the data
distribution.
Multivariate analytical techniques such as robust factor analyses
and hierarchical cluster analyses are used to investigate statistical
and spatial correlations between elements.
Mapping
R and Python code have been developed to automate the
process of exploratory data analysis, spatial data analysis, data
interpolation.
Map production using the arcPy mapping module is done.
Online viewer
Finally , a web mapping service and online viewer for the
mapped datasets, with live links to a managed database is
developed.
23. CASE STUDY 2
23
Acta Silvae (2013) illustrated the use of R programming language
in the analyses of spatial data.
Objective
The aim of this article is to demonstrate the R’s potential for the
spatial data processing and presentation.
Study area
Snežnik (south Slovenia) forest measuring 20 ha.
Increases in altitude from 820 m to 880 m.
Silver fir and European beech are the dominant tree species. The
terrain is characterized by abundant sinkholes.
24. 24
Methodology
Manipulation of vector data :
Coordinates were recorded using GPS devices and exported to
a text file.
This text file was imported into the R environment using the
library ‘map tools’ .
Geospatial spatial interpolation:
A spatial interpolation (kriging) of the temperature throughout
the research area using the library gstat .
A variogram model which is a function of the spatial
dependence of random variables is to be selected.
The point measurements were used to create a continuous
temperature field in raster format .
26. 26
LiDAR data processing:
R is used as a tool for large amounts of data processing,
programming of the raw LiDAR data for 1 km2.
Has a size of 539,468 KB (539 MB) and contains 20,736,221
rows and 62,208,663 data points.
In R, an algorithm is written to eliminate points that represent
forest trees in the whole cloud of points, yielding a point of the
terrain.
27. 27
Fig. 8: The 3D point cloud (gray) of longitudinal profile in the research area. The red points are
marked on the floor, which were determined based on the algorithm written in R.
28. 28
-A digital elevation model (DEM) was produced based on these classified
points.
Fig. 9: 3D elevation model based on LiDAR data. The surface coloured with a colour range
of the altitude value
29. SUMMARY
29
R has become a high quality open-source software environment
for statistical computing and graphics
It has a high performance GIS tool that can be used for
geospatial data production, analysis, and mapping.
R allows the usage of many control flows, loops and user-
defined functions, multiple input and output data formats.
It gives the opportunity to codify the existing data and
functions.
The entire process of analyzing data within R is run through a
written script and syntax, which means that it is simple to rerun
these analyses if needed.
30. References
30
Bivand et Albrecht implementing functions for spatial
statistical analysis using the r language journal of geographical
systems, 2:307-317, 2000.
B.bajat (2012) spatial modelling of population concentration
using geographically weighted regression method, journal of
the geographical institute sass 01/2011; 61:151-167.
Howarth (1983) vol.2: ‘statistics and data analysis in
geochemical prospection’, in handbook of exploration
geochemistry, pages 69-73, elsevier, Amsterdam,
31. 31
M. Mcclelland et. wang(2010) ‘a python package for using r
in python’, journal of statistical software, code snippets.
Thibaul et al. Using an R package for exploratory spatial data
analysis april 2012, volume 47, issue 2.
(2012) ‘Statda: Statistical Analysis for Environmental Data.’
URL: http://CRAN.R-project.org/package=statda. R package
version 1.6.2.