OpenStreetMap (OSM) is a collaborative project to create a free editable geographic database of the world. The geodata underlying the maps is considered the primary output of the project (Wikipedia contributors 2021). OpenStreetMap was born in 2004 in the UK, at a time when map data sources were controlled by private and governmental players. They were expensive and highly restrictive which made them accessible only by large companies.
Data is the most important asset. It validates a research story and a conclusions; it provides a platform of confidence for other researchers who might continue your work; and it is a resource that can be used by researchers in other fields to undertake new work, perhaps completely unrelated to your own research interests. Well-organised data that is accessible to the research community can continue to provide extended benefit and value long after your projects have been completed.
Introduction This post offers some technique on how to analyse data from a surveys and questionnaires in R, provides tips on visualizing survey data, and exemplifies how survey and questionnaire data can be analyzed.
Questionnaires and surveys are widely used in research and thus one of the most common research designs. Questionnaires elicit three types of data:
Factual Behavioral Attitudinal While factual and behavioral questions are about what the respondent is and does, attitudinal questions tap into what the respondent thinks or feels.
OpenStreetMaps is a great source of spatial data. Most common programming languages have packages for downloading data from OSM. In this tutorial we are going to see how to download hosptial features data using R’s osmdata (Padgham et al. 2017) package and plot it using ggplot (Wickham 2016), and interactively using tmap (Tennekes 2018). This requires some knowledge of spatial data structures.
The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a quasi-global rainfall data set. As its title suggests it combines data from real-time observing meteorological stations with infra-red satellite data to estimate precipitation. CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.
The global dataset covers the area from \(40^\circ\)N to \(4^\circ\)S and from \(20^\circ\)W to \(50^\circ\)E with a spatial resolution of 0.
