Module resources
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8.9 Markscheme
The markscheme can be found on Moodle with a break down of how much percent is given to each criteron. Read it closely and often when preparing for the final exam.
8.10 Old practicals
The module has developed over several years and there is some content that we no longer cover but still exsits in old practical books, specifically:
8.11 Events / meet other spatial data professionals
The UK ESRI conference is normally free to go to and whilst (in my opinion) it’s mostly just ESRI marketing their latest products it is good to go along and see how industry are using spatial software. It might also be useful for dissertation ideas / networking.
8.12 Cool stuff to explore
New packages and functions that i’ve recently come across that are worth exploring..
- RStudio cloud — like RStudio but online, great for collaborating, although it now (2020) has caps on how much you can use it.
- Animating plots
- Bivariate maps
- Rayshader
- R Cartography package
- Data is beautiful reddit
- Network visualisation
8.13 Books/reading resources
There are a lot of free online books for geospatial analysis, especially using R, check out:
- Data Visualization A practical introduction
- R Programming for Data Science
- Everything you need for Exploratory Data Analysis & Visualization
- What They Forgot to Teach You About R
- RMarkdown for Scientists
- RStudio recommended books
- R Graphics Cookbook
- Hadley Wickham’s website
- Geocomputation with R
- Happy Git and GitHub for the useR
- Bookdown information pages
- Interactive web-based data visualization with R, plotly, and shiny
- YaRrr! The Pirate’s Guide to R
- Engineering Production-Grade Shiny Apps
- Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny
- Statistical Inference via Data Science
- Fundamentals of Data Visualization
- 21 Recipes for Mining Tiwtter Data with rtweet
- Learning statistics with R: A tutorial for psychology students and other beginners
- Crime mapping in R
If you want to produce more efficent R code then have a look at the Efficient R programming book.
…that’s a lot of books, should we read all of them? No, be selective, a lot of the same material will be covered in each book. Follow your interests, read widely and think about how you could apply these ideas / concepts to different topics or disciplines!
8.14 New developments
Every year there is a useR conference that provides a load to tutorials on the latest developments and research…check them out here:
Other useful tutorials can be found at:
8.15 Data
This is by no means an extensive data list, but summarises data used within some of the practicals alongside a few additions that you might want to explore when sourcing data for your assignments. You are not limited to these data sets for you assessment.
- Google dataset search
- Tesco store data (London)
- NHS data (ready for R)
- US City Open Data Census
- nomis
- ONS geoportal
- UK data service
- ONS
- Edina (e.g. OS mastermap)
- Open Topography
- USGS Earth Explorer
- Geofabrik (OSM data)
- Global weather data (points)
- London data store
- Air b n b data
- NASA SocioEconomic Data and Applications Center (SEDAC)
- UN environmental data explorer
- World pop
- World pop github
- DIVA-GIS
- DEFRA
- US Cesus data
- TFL open data
- TFL cycling data
- EU tourism data
- NASA EARTHDATA
- Camden air action
- Kings data on air pollution
- Uber travel time data
- Eurostat
- London Tube PM2.5 levels
- Bike docking data in an R package
- UK COVID data
- R package for COVID data
- Tidy Tuesday data (although look for spatial data)
- Correct statistical tests
8.16 Data lists
Awesome public datasets have a wide range all data (some geographic, some not): Robin Wilson has authored one of the most extensive data lists that i’ve come across