SPAN supports researchers across the full data lifecycle—preparing, analysing, and transforming research data into usable, insightful, and visually compelling outputs for reporting and publication.
SPAN can source and prepare research data for analysis and visualisation. SPAN can also source publicly available research datasets, including Census data, Climate and environmental data, Health and demographic data. Supported dataset types include tabular datasets, images and remote sensing data (e.g. LiDAR), spatial data such as points (e.g. trees), lines (e.g. roads), and polygons (e.g. buildings).
Researchers may also combine their own data—from spreadsheets, databases, or field collection—with public datasets, live data feeds, or data shared by partner organisations.
SPAN uses the R statistical environment and Python to perform large‑scale data cleaning, transformation, complex joins, and geospatial wrangling. Automated, reproducible pipelines are developed to produce outputs for analysis and visualisation.
SPAN provides multidisciplinary analytical support tailored to individual research needs. SPAN applies spatial statistical methods to analyse location‑based data and uncover spatial patterns and relationships using ESRI products and R/RStudio.
SPAN applies advanced statistical and spatial modelling using R, Python and ArcGIS Pro, including predictive modelling, clustering, regression analysis, suitability modelling, and interactive 3D visualisation to produce publication ready figures, infographics, and high-quality visual products.
SPAN develops custom scripts and workflows to automate analysis, schedule processing tasks, implement logging and error handling, and support long-term reproducibility through clear handover documentation.