psim_real {stppSim}

Stpp from real (sample) origins

Description

Generates spatiotemporal point pattern from origins sampled based on real sample dataset.

Usage

psim_real(n_events, ppt, start_date = NULL, poly = NULL,
netw = NULL, s_threshold = NULL, step_length = 20, n_origin=50,
restriction_feat=NULL, field=NA,
p_ratio=20, interactive = FALSE, s_range = 150,
s_interaction = "medium", tolerance = 0.07,
crsys = NULL)

Arguments

n_events

number of points (events) to simulate. Default: 1000. A vector of integer values can be supplied, such as, c(a1, a2, ....)⁠, where ⁠a⁠1, ⁠a'2, ... represent different integer values.

ppt

A 3-column matrix or list containing x - eastings, y - northing, and t - time of occurrence (in the format: ‘yyyy-mm-dd’)

start_date

the start date of the temporal pattern. The date should be in the format "yyyy-mm-dd". The temporal pattern will normally cover 1-year period.

poly

(An sf or S4 object) a polygon shapefile defining the extent of the landscape

netw

(An sf or S4 object) The network path of the landscape (e.g. road and/or street). Default: NULL. If provided each event is snapped to the closest network path/segment.

s_threshold

defines the spatial perception range of a walker at a given location. Default: 250 (in the same linear unit as the poly - polygon shapefile).

step_length

the maximum step taken by a walker from one point to the next.

n_origin

number of locations to serve as origins for walkers. Default:50.

restriction_feat

(An S4 object) optional shapefile containing features in which walkers cannot walk through. Default: NULL.

field

a number in the range of [0-1] (i.e. restriction values) assigned to all features; or the name of a numeric field to extract such restriction values for different classes of feature. Restriction value 0 and 1 indicate the lowest and the highest obstructions, respectively. Default: NULL.

p_ratio

the smaller of the two terms of proportional ratios. For example, a value of 20 implies 20:80 proportional ratios.

interactive

Whether to run the process in interactive mode. Default is FALSE. If TRUE, a user is able to preview the spatial and temporal models of the expected distribution of the final simulated events (points).

s_range

A value (in metres), not less than 150, specifying the maximum range of spatial interaction across the space. For example, for 150m, the intervals of spatial interactions are created as (0, 50], (50 - 100], and (100-150], representing the "small", "medium", and "large", spatial interaction ranges, respectively. If s_range is set as NULL, simulation focusses only on generating point pattern with similar spatiotemporal patterns as the sample dataset.

s_interaction

(string) indicating the type of spatial interaction to detect. Default: "medium" (See parameter 's_range')

tolerance

Pvalue to use for the extraction of space-time interaction in the sample data. Default value: 0.05.

crsys

(string) the EPSG code of the projection system of the ppt coordinates. This is only used if poly argument is NULL. See "http://spatialreference.org/" for the list of EPSG codes for different regions of the world. As an example, the EPSG code for the British National Grid projection system is: "EPSG:27700".

Details

The spatial and temporal patterns and interactions detected in sample datasets are extrapolated to synthetise larger data size. Details of the spatiotemporal interactions detected in the sample dataset are provided. If the street network of the area is provided, each point is snapped to its nearest street segment.

Value

A list of artificial spatiotemporal point patterns and interaction generated based on a sample (real) data.

References

Davies, T.M. and Hazelton, M.L. (2010), Adaptive kernel estimation of spatial relative risk, Statistics in Medicine, 29(23) 2423-2437. Terrell, G.R. (1990), The maximal smoothing principle in density estimation, Journal of the American Statistical Association, 85, 470-477.

Examples

## Not run: 
data(camden_crimes)
#subset 'theft' crime
theft <- camden_crimes[which(camden_crimes$type == "Theft"),]
#specify the proportion of full data to use
sample_size <- 0.3
set.seed(1000)
dat_sample <- theft[sample(1:nrow(theft),
round((sample_size * nrow(theft)), digits=0),
replace=FALSE),1:3]
#plot(dat_sample$x, dat_sample$y) #preview

#load boundary and land use of Camden
#load(file = system.file("extdata", "camden.rda",
#package="stppSim"))
#landuse = camden$landuse # get landuse
landuse <- stppSim:::landuse
#simulate data
simulated_stpp <- psim_real(n_events=2000, ppt=dat_sample,
start_date = NULL, poly = NULL, netw = NULL, s_threshold = NULL,
step_length = 20, n_origin=20,
restriction_feat = NULL, field=NULL,
p_ratio=20, interactive = FALSE, s_range = 150,
s_interaction = "medium", tolerance = 0.07,
crsys = "EPSG:27700")
#If `n_events` is a vector of values,
#retrieve the simulated data for the
#corresponding vector element by using
#`simulated_stpp[[enter-element-index-here]]`, e.g.,
#to retrieve the first dataframe, use
#simulated_stpp[[1]].

#The above example simulates point patterns on
#an unrestricted landscape. If \code{restriction_feat = landuse} and \code{field = "restrVal"},
then the simulation
#is run with the landuse features as restrictions
#on the landscape.

## End(Not run)