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DESCRIPTION:Course type: Short CourseDate: 2 June - 3 July 2026Location: On
 lineOverviewSpatial analysis is becoming an increasingly useful tool throu
 ghout public health research with increasing amounts of spatial health dat
 a generated each year. Whether you&rsquo\;re a humanitarian aid worker loo
 king to add map-making to your growing rapid analysis skillset or an early
 -stage PhD student who wants to learn the fundamentals before progressing 
 to geostatistics\, this short course will be well suited to your needs.Our
  hands-on\, practical approach to teaching\, with real-life examples\, mea
 ns you can progress from no previous experience with R to applying R to yo
 ur own work with confidence. We also place a strong emphasis on enabling s
 tudents to continue their learning independently allowing your skillset to
  continue growing beyond the end of the course.Who is this course for?Prac
 tising public health professionals and health researchers interested in ad
 ding expertise in spatial data analysis to their existing skills. Operatio
 nal researchers and in particular those working in humanitarian crises/eme
 rgency deployments are particularly encouraged.No previous experience with
  R or spatial data analysis is required\, but some experience with quantit
 ative data analysis using programmable computer software\, e.g. plotting a
 nd analysing data in Stata\, SAS\, Python or MATLAB is expected. It is als
 o expected that students are familiar with the use of the Generalised Line
 ar Model (e.g. logistic regression\, Poisson regression\, multiple explana
 tory variables) and that computing is\, or will be\, part of their regular
  day-to-day role.&nbsp\; &nbsp\; &nbsp\;Course objectivesAt the end of the
  course\, students should be able to:Read in spatial and non-spatial datas
 ets into R and perform basic data manipulation tasks using the &ldquo\;dpl
 yr&rdquo\; package\, make a variety of plots using the &ldquo\;ggplot2&rdq
 uo\; package and demonstrate an understanding of why different plot types 
 are used for different types of dataManipulate and visualise spatial data 
 using maps with the &ldquo\;ggplot&rdquo\; package and be able to identify
  when different types of data projections should be used.Understand how to
  analyse areal data and be able to implement and interpret simple regressi
 on analyses on areal datasets including the use of multi-level modelsBe ab
 le to write clear\, tidy and intuitive R code that can be reproduced by ot
 hers and know how to conduct a &ldquo\;code review&rdquo\; of the work of 
 others.Identify the key characteristics of point data and understand and i
 mplement a variety of point data analysis techniques\, such as kriging and
  Gaussian process regression.&nbsp\;How to ApplyFor more information and h
 ow to register\, please&nbsp\;click here!Application Deadline: 2 May 2026
DTEND;VALUE=DATE:20260704
DTSTAMP:20260507T121827Z
DTSTART;VALUE=DATE:20260602
LOCATION:
SEQUENCE:0
SUMMARY:Introduction to Spatial Analysis in R
UID:RFCALITEM639137531070446662
X-ALT-DESC;FMTTYPE=text/html:<img src="https://uat.psiweb.org/images/defaul
 t-source/default-album/lshtm.png?sfvrsn=84f2a9db_1&amp\;sf_site_temp=true&
 amp\;sf_site=aa6f9fcc-8c60-4e6d-90ca-8c73a12c9f03" style="max-width:100%\;
 height:auto\;" width="432" height="218" sf-image-responsive="true" sf-size
 ="43370" alt="" title="LSHTM" /><p><strong>Course type: </strong>Short Cou
 rse<br /><strong>Date</strong>: 2 June - 3 July 2026<br /><strong>Location
 : </strong>Online</p><h2>Overview</h2><p>Spatial analysis is becoming an i
 ncreasingly useful tool throughout public health research with increasing 
 amounts of spatial health data generated each year. Whether you&rsquo\;re 
 a humanitarian aid worker looking to add map-making to your growing rapid 
 analysis skillset or an early-stage PhD student who wants to learn the fun
 damentals before progressing to geostatistics\, this short course will be 
 well suited to your needs.</p><p>Our hands-on\, practical approach to teac
 hing\, with real-life examples\, means you can progress from no previous e
 xperience with R to applying R to your own work with confidence. We also p
 lace a strong emphasis on enabling students to continue their learning ind
 ependently allowing your skillset to continue growing beyond the end of th
 e course.</p><h2>Who is this course for?</h2><p>Practising public health p
 rofessionals and health researchers interested in adding expertise in spat
 ial data analysis to their existing skills. Operational researchers and in
  particular those working in humanitarian crises/emergency deployments are
  particularly encouraged.</p><p>No previous experience with R or spatial d
 ata analysis is required\, but some experience with quantitative data anal
 ysis using programmable computer software\, e.g. plotting and analysing da
 ta in Stata\, SAS\, Python or MATLAB is expected. It is also expected that
  students are familiar with the use of the Generalised Linear Model (e.g. 
 logistic regression\, Poisson regression\, multiple explanatory variables)
  and that computing is\, or will be\, part of their regular day-to-day rol
 e.&nbsp\; &nbsp\; &nbsp\;</p><h2>Course objectives</h2><p>At the end of th
 e course\, students should be able to:</p><ul><li>Read in spatial and non-
 spatial datasets into R and perform basic data manipulation tasks using th
 e &ldquo\;dplyr&rdquo\; package\, make a variety of plots using the &ldquo
 \;ggplot2&rdquo\; package and demonstrate an understanding of why differen
 t plot types are used for different types of data</li><li>Manipulate and v
 isualise spatial data using maps with the &ldquo\;ggplot&rdquo\; package a
 nd be able to identify when different types of data projections should be 
 used.</li><li>Understand how to analyse areal data and be able to implemen
 t and interpret simple regression analyses on areal datasets including the
  use of multi-level models</li><li>Be able to write clear\, tidy and intui
 tive R code that can be reproduced by others and know how to conduct a &ld
 quo\;code review&rdquo\; of the work of others.</li><li>Identify the key c
 haracteristics of point data and understand and implement a variety of poi
 nt data analysis techniques\, such as kriging and Gaussian process regress
 ion.&nbsp\;</li></ul><h2>How to Apply</h2><p>For more information and how 
 to register\, please&nbsp\;<strong><a href="https://www.lshtm.ac.uk/study/
 courses/short-courses/Spatial-analysis-R?utm_source=psi&amp\;utm_medium=co
 urse_listing&amp\;utm_campaign=short-course">click here</a>!</strong></p><
 p><strong>Application Deadline: 2 May 2026</strong></p>
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