Package index
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hotspot_count() - Count points in cells in a two-dimensional grid
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hotspot_change() - Identify change in hotspots over time
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hotspot_kde() - Estimate two-dimensional kernel density of points
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hotspot_dual_kde() - Estimate the relationship between the kernel density of two layers of points
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hotspot_gistar() - Identify significant spatial clusters of points
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hotspot_classify() - Classify hot-spots
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hotspot_classify_params() - Control the parameters used to classify hotspots
Data wrangling
Tools for working with data that is used to identify hotspots. Most data wrangling is done automatically by the analysis functions listed above, but you can use the functions below to control in more detail how this is done.
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hotspot_clip() - Extract points inside polygon
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hotspot_grid() - Create either a rectangular or hexagonal two-dimensional grid
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st_transform_auto() - Toggle between lon/lat and UTM co-ordinates
Plotting results
These methods for the autoplot() function automatically produce charts that are tailored to displaying the results produced by one of the hotspot_*() family of functions.
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autoplot(<hspt_c>) - Plot map of hotspot classifications
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autoplot(<hspt_d>)autolayer(<hspt_d>) - Plot map of changes in grid counts
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autoplot(<hspt_k>)autolayer(<hspt_k>) - Plot map of kernel-density values
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autoplot(<hspt_n>)autolayer(<hspt_n>) - Plot map of grid counts
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memphis_population - Populations of census blocks in Memphis in 2020
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memphis_precincts - Memphis Police Department Precincts
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memphis_robberies - Personal robberies in Memphis in 2019
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memphis_robberies_jan - Personal robberies in Memphis in January 2019