| The course runs from 5 - 9 July 2027. |
The overall aim of this course is to equip health researchers and public health practitioners with existing R experience to build, evaluate, interpret and communicate spatial models and risk maps for health applications.
Course objectives
At the end of the course, students should be able to:
- Prepare, manipulate and visualise spatial health data in R, including vector and raster data.
- Distinguish areal, point-occurrence and continuous-space data structures and relate them to research questions.
- Build and interpret spatial areal models, geostatistical models and ecological niche models.
- Generate predictions from these model types and select evaluation strategies suited to the prediction task.
- Interpret risk and suitability maps, including uncertainty and limitations, and communicate findings for a defined audience.
Who is this course for?
The course is for health researchers, epidemiologists, public health practitioners and postgraduate researchers who already use R and want to develop applied skills in spatial modelling and risk mapping. Participants may work in universities, public health agencies, ministries of health or humanitarian and international organisations.
Previous GIS experience is helpful but not required however prior practical experience using R is required. Participants should be comfortable importing data, manipulating data frames, writing and running scripts, and fitting and interpreting generalised linear models. Introductory R and GLM materials will be provided as optional pre-reading and will not be taught during the course. Prior spatial analysis experience is not required.
Assessment
There is no formal summative assessment. Formative learning will be supported through practical exercises, discussion of model choices, interpretation of predictions and maps, peer feedback and group presentations.
This course is delivered in-person and online over 5 days. The course combines short lectures and live demonstrations with guided R practicals, participant-led discussion of participants' own research questions and datasets. There will also be group model comparison, map critique and a final map communication activity. Participants will work with prepared health and environmental datasets; sharing their own data is not required.
Day 1
Spatial data and GIS in R, including spatial data structures, CRS and projections, joins, buffers, distances, raster and vector data, and exploratory mapping.
Day 2
Student research questions and datasets; areal data, neighbourhood structures, spatial autocorrelation, baseline areal models, spatial random effects and model interpretation.
Day 3
Geostatistical models and spatial dependence, followed by ecological niche modelling using occurrence and environmental data.
Day 4
Prediction from areal, geostatistical and ENM approaches; predictive evaluation and comparison across model types.
Day 5
Interpreting risk and suitability maps, uncertainty and limitations, map communication workshop and student group presentations.
Fees for 2027 entry
Full fee: £1,900
Special tuition fee rate for offer holders from LMICs
Additional discounts are available for offer holders from Low- or Middle-income countries (LMICs). A limited number of LMIC offer holders will receive a 50% discount on the course fee (new course fee: £950).
Eligibility Criteria for 50% discount:
Applicants who wish to apply for the special tuition fee rate:
- must hold an offer of admission for the course and
- must be a national of, and currently be resident in, a Low- or Middle- income country (LMIC) - World Bank definition
How to apply for a 50% discount
Applicants only need to submit proof they meet the eligibility criteria at the time of admission.
Applying for this course
Applications for 2027 are now open and can be made via our online application form.
The application deadline will be 23:59 (UK time) on Saturday 05 June 2027. We strongly advise that you apply early as courses may close earlier than the stated deadline if they become full.
Please read LSHTM's Admissions policies prior to submitting your application.
LSHTM may cancel courses two weeks before the first day of the course if numbers prove insufficient. In those circumstances, course fees will be refunded.