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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:20260430T102356Z
DTSTART;VALUE=DATE:20260602
LOCATION:
SEQUENCE:0
SUMMARY:Introduction to Spatial Analysis in R
UID:RFCALITEM639131414362160471
X-ALT-DESC;FMTTYPE=text/html:<img src="https://psiweb.org/images/default-so
 urce/default-album/lshtm.png?sfvrsn=84f2a9db_1&amp\;sf_site_temp=true&amp\
 ;sf_site=aa6f9fcc-8c60-4e6d-90ca-8c73a12c9f03" style="max-width:100%\;heig
 ht:auto\;" width="432" height="218" sf-image-responsive="true" sf-size="43
 370" alt="" title="LSHTM" /><p><strong>Course type: </strong>Short Course<
 br /><strong>Date</strong>: 2 June - 3 July 2026<br /><strong>Location: </
 strong>Online</p><h2>Overview</h2><p>Spatial analysis is becoming an incre
 asingly useful tool throughout public health research with increasing amou
 nts of spatial health data generated each year. Whether you&rsquo\;re a hu
 manitarian aid worker looking to add map-making to your growing rapid anal
 ysis skillset or an early-stage PhD student who wants to learn the fundame
 ntals before progressing to geostatistics\, this short course will be well
  suited to your needs.</p><p>Our hands-on\, practical approach to teaching
 \, with real-life examples\, means you can progress from no previous exper
 ience with R to applying R to your own work with confidence. We also place
  a strong emphasis on enabling students to continue their learning indepen
 dently allowing your skillset to continue growing beyond the end of the co
 urse.</p><h2>Who is this course for?</h2><p>Practising public health profe
 ssionals and health researchers interested in adding expertise in spatial 
 data analysis to their existing skills. Operational researchers and in par
 ticular those working in humanitarian crises/emergency deployments are par
 ticularly encouraged.</p><p>No previous experience with R or spatial data 
 analysis is required\, but some experience with quantitative data analysis
  using programmable computer software\, e.g. plotting and analysing data i
 n Stata\, SAS\, Python or MATLAB is expected. It is also expected that stu
 dents are familiar with the use of the Generalised Linear Model (e.g. logi
 stic regression\, Poisson regression\, multiple explanatory variables) and
  that computing is\, or will be\, part of their regular day-to-day role.&n
 bsp\; &nbsp\; &nbsp\;</p><h2>Course objectives</h2><p>At the end of the co
 urse\, students should be able to:</p><ul><li>Read in spatial and non-spat
 ial datasets into R and perform basic data manipulation tasks using the &l
 dquo\;dplyr&rdquo\; package\, make a variety of plots using the &ldquo\;gg
 plot2&rdquo\; package and demonstrate an understanding of why different pl
 ot types are used for different types of data</li><li>Manipulate and visua
 lise spatial data using maps with the &ldquo\;ggplot&rdquo\; package and b
 e able to identify when different types of data projections should be used
 .</li><li>Understand how to analyse areal data and be able to implement an
 d interpret simple regression analyses on areal datasets including the use
  of multi-level models</li><li>Be able to write clear\, tidy and intuitive
  R code that can be reproduced by others and know how to conduct a &ldquo\
 ;code review&rdquo\; of the work of others.</li><li>Identify the key chara
 cteristics of point data and understand and implement a variety of point d
 ata analysis techniques\, such as kriging and Gaussian process regression.
 &nbsp\;</li></ul><h2>How to Apply</h2><p>For more information and how to r
 egister\, please&nbsp\;<strong><a href="https://www.lshtm.ac.uk/study/cour
 ses/short-courses/Spatial-analysis-R?utm_source=psi&amp\;utm_medium=course
 _listing&amp\;utm_campaign=short-course">click here</a>!</strong></p><p><s
 trong>Application Deadline: 2 May 2026</strong></p>
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