User guide

This guide explains how to use tidymapr and what its results mean. Each chapter follows the same plan:

1. The concept: the question the method answers, and the theory behind it, with its key formula and original references.
2. Code you can copy into RStudio, run on real, openly licensed data shipped with R packages.
3. Figures and tables, made by that code.
4. How to interpret them: what the numbers and colours mean, and what they do not.
5. How to report them: a model paragraph for a methods or results section, with the numbers filled in from the analysis.

Chapter	You will learn	Main functions
1. Install and run in RStudio	Set up R, RStudio and build tools; a first map; your own data; saving figures; troubleshooting	geo_map(), map_*(), geo_save()
2. Cartography concepts	Projections and their distortions; classification methods and GVF; colour schemes; map elements	geo_automap(), geo_automap_explain(), geo_classify(), coord_projection()
3. Spatial autocorrelation	Global Moran’s I and the correlogram; local Moran’s I (LISA); Getis-Ord Gi* hot spots	geo_moran(), geo_lisa(), geo_hotspot()
4. Interpolation	Variograms; ordinary kriging and its variance; IDW; cross-validation; kriging with measurement error	geo_interpolate(), geo_diversity_surface()
5. Species distribution models	Presence-background modelling; Maxent theory and features; response curves; AUC, TSS and Boyce; spatial block cross-validation; ensembles and disagreement	sdm_maxent(), sdm_ensemble()
6. Remote sensing and terrain	Spectral indices; Otsu thresholds; spectral diversity; D8 flow; topographic wetness; 3D terrain	geo_spectral_index(), geo_spectral_diversity(), geo_flow_accumulation(), geo_twi(), geo_map3d()
7. Point clusters and landscapes	DBSCAN and its parameters; FRAGSTATS fragmentation metrics	geo_dbscan(), geo_landscape()

Where to start. New to R: Chapter 1, then Chapter 2. Need a map for a paper: Chapter 2. Analysing field samples: Chapters 3 and 4. Working with species records: Chapter 5. Working with imagery or elevation: Chapter 6.

Going further. The case studies apply these methods to complete research questions, the Gallery shows the code of every map, and the Reference lists every function and argument. For diversity estimation itself, see the companion package tidydiversity; for a point-and-click desktop GIS with the same conventions, see GeoSpaX.

How to cite. Run citation("tidymapr") in R.