View source: R/emerging-hostpot-analysis.R

emerging_hotspot_analysis | R Documentation |

Emerging Hot Spot Analysis identifies trends in spatial clustering over a period of time. Emerging hot spot analysis combines the Getis-Ord Gi* statistic with the Mann-Kendall trend test to determine if there is a temporal trend associated with local clustering of hot and cold spots.

emerging_hotspot_analysis( x, .var, k = 1, include_gi = FALSE, nb_col = NULL, wt_col = NULL, nsim = 199, threshold = 0.01, ... )

`x` |
a spacetime object and must be a spacetime cube see details for more. |

`.var` |
a numeric vector in the spacetime cube with no missing values. |

`k` |
default |

`include_gi` |
default |

`nb_col` |
Optional. Default |

`wt_col` |
Optional. Default |

`nsim` |
default |

`threshold` |
default |

`...` |
unused. |

Emerging Hot Spot Analysis is a somewhat simple process. It works by first calculating the Gi* statistic for each location in each time period (time-slice). Next, for each location across all time-periods, the Mann-Kendall trend test is done to identify any temporal trend in Gi* values over all time periods. Additionally, each location is classified into one of seventeen categories based on ESRI's emerging hot spot classification criteria.

The Mann-Kendall trend test is done using `Kendall::MannKendall()`

. `Kendall`

is not installed with sfdep and should be installed prior to use.

If you would like to use your own neighbors and weights, they must be created
in the `geometry`

context of a spacetime object. The arguments `nb_col`

and `wt_col`

must both be populated in order to use your own neighbor and weights
definitions.

In addition to identifying neighbors in space, emerging hotspot analysis also
incorporates the same observations from `k`

periods ago-called a time lag. If
the time lag k is 1 and the unit of time is month, the neighbors for the
calculation of Gi* would include the spatial neighbors' values at time `t`

and the same spatial neighbors' values at time `t-1`

. If `k = 2`

, it would include
`t`

, `t-1`

, and `t-2`

.

Presently, there is no method of missing value handling. If there are missing
values, the emerging hot spot analysis will fail. Be sure to fill or omit
time-slices with missing values *prior* to using emerging hot spot analysis.

Returns a data.frame.

How Emerging Hot Spot Analysis works, Emerging Hot Spot Analysis (Space Time Pattern Mining), and the video Spatial Data Mining II: A Deep Dive into Space-Time Analysis by ESRI.

if (FALSE) { df_fp <- system.file("extdata", "bos-ecometric.csv", package = "sfdep") geo_fp <- system.file("extdata", "bos-ecometric.geojson", package = "sfdep") # read in data df <- readr::read_csv(df_fp, col_types = "ccidD") geo <- sf::read_sf(geo_fp) # Create spacetime object called `bos` bos <- spacetime(df, geo, .loc_col = ".region_id", .time_col = "time_period") # conduct EHSA ehsa <- emerging_hotspot_analysis( x = bos, .var = "value", k = 1, nsim = 99 ) ehsa }

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