Mapping crime data in R: Live code demonstration
University of Manchester
Entry (no or almost no prior knowledge)
Do you want to display crime data on maps but don’t know how?
Crime data often contains spatial components. As a result, analyses of crime data can create patterns that are clearly linked to geography. Naturally, putting the data or analysis on a map makes a lot of sense. Unfortunately, if you have never matched statistical data to spatial data then you may not know how easy it can be to make beautiful data maps!
That’s where these free workshops come in. The aim of the workshops is to teach participants to use the R statistical and graphical environment to map open-source police recorded crime statistics onto geographic representations.
This is the second of two free Mapping crime data in R workshops. The first session will cover some fundamental theories and concepts surrounding GIS and spatial data. This session will be a live code demonstration using R to explore these concepts.
This workshop is suitable for intermediate (or higher) users of R but there is no need to have experience with GIS software or spatial data. Users should be comfortable with how to set the working directory in R, how to read in data, how to write basic R functions and how to save scripts and output files.
Level: (higher) intermediate
Experience of R: Yes. R and Rstudio must be already installed and working
Knowledge of GIS/Spatial Data: None
Target audience: Researchers/anyone interested in crime and spatial data
Coding material and further instructions will be available mid-January via our GitHub page
Presenter: Nadia Kennar
This event will be livestreamed on our UK Data Service YouTube channel but the chat will be disabled. By registering and attending the Zoom event you will be able to ask questions and interact.
Recordings of UK Data Service events are made available on our YouTube channel and, together with the slides, on our past events pages soon after the event has taken place.
Website and registration:
Frameworks for Research and Research Designs, Data Collection, Data Quality and Data Management , ICT and Software, Research Management and Impact
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