Movatterモバイル変換

Near Repeat calculator using the Knox test

Description

This function uses the Knox test for space-timeclustering to quantify the spatio-temporalassociation between events (Credit: Wouter Steenbeek).

Usage

NRepeat(x, y, time, sds, tds, s_include.lowest = FALSE,s_right = FALSE, t_include.lowest = FALSE, t_right = FALSE,method = "manhattan", nrep = 999, saveSimulations = FALSE,future.seed = TRUE,...)

Arguments

Details

Further details available at:https://github.com/wsteenbeek/NearRepeat.

Value

An object of type "knox", i.e. a list with four tables.For each spatial and temporal distance combination,(1) The countsof observed crime pairs, (2) The Knox ratios based on the mean of thesimulations, (3) The Knox ratios based on the median of thesimulations, (4) p-values.

References

Steenbeek W. Near Repeat. R package version 0.1.1. 2018.URL: https://github.com/wsteenbeek/NearRepeat

Examples

## Not run: # Generate example data. Suppose x and y refer to meters distance.set.seed(10)(mydata <- data.frame(x = sample(x = 20, size = 20, replace = TRUE) * 20,                     y = sample(x = 20, size = 20, replace = TRUE) * 20,                     date = as.Date(sort(sample(20, size = 20, replace = TRUE)),                     origin = "2018-01-01")                     ))# Near Repeat calculation using 0-100 meters and 100-Inf meters, and three# temporal intervals of 2 daysset.seed(38673)NRepeat(x = mydata$x, y = mydata$y, time = mydata$date,           sds = c(0,100,Inf), tds = c(0,2,4))# Add a 'same repeat' spatial interval of 0.001 meters, and use Euclidean# distanceset.seed(38673)NRepeat(x = mydata$x, y = mydata$y, time = mydata$date,           sds = c(0,0.001,100,Inf), tds = c(0,2,4),           method = "euclidean")# Only do 99 replicationsset.seed(38673)NRepeat(x = mydata$x, y = mydata$y, time = mydata$date,           sds = c(0,0.001,100,Inf), tds = c(0,2,4),           method = "euclidean", nrep = 99)# The plot() function can be used to plot a Heat Map of Near Repeat results# based on p-valuesset.seed(4622)myoutput <- NRepeat(x = mydata$x, y = mydata$y, time = mydata$date,                       sds = c(0,100,200,300,400), td = c(0,1,2,3,4,5))# The default range of p-values that will be highlighted (0-.05) can be# adjusted using the 'pvalue_range' parameter. By default the Knox ratios# are printed in the cells, but this can be adjusted using the 'text'# parameter. The default is "knox_ratio". Possible values are "observed",# "knox_ratio", "knox_ratio_median", "pvalues", or NA.## End(Not run)

Artificial spatial origins

Description

Simulates spatial locations to serveas origins of walkers. If provided, spaces coveredby restriction features are avoided. Finalorigins are assigned probability valuesindicating the strengths of the origins.

Usage

artif_spo(poly, n_origin=50, restriction_feat = NULL,n_foci=5, foci_separation = 10, mfocal = NULL,conc_type = "nucleated", p_ratio)

Arguments

Details

The focal origins (n_foci) serve as the central locations(such as, city centres). Thefoci_separation indicatesthe nearness of focal origins from one another.Theconc_type argument allows a user to specifythe type of spatial concentration exhibited by the non-focalorigin around the focal ones.Ifrestriction_feat is provided, its features helpto prevent the occurrence of any events in the areasoccupied by the features.

Value

Returns a list detailing theproperties of the generated spatial originswith associatedstrength (probability) values.

Examples

#load boundary of Camdenload(file = system.file("extdata", "camden.rda",package="stppSim"))boundary = camden$boundary # get boundarylanduse <- camden$landusespo <- artif_spo(poly = boundary, n_origin = 50,restriction_feat = landuse, n_foci=5, foci_separation = 0,mfocal = NULL, conc_type = "dispersed", p_ratio=20)

Records of crimes of Camden Borough of London,UK, 2021 (Source: https://data.police.uk/data/)

Description

Data comprising 'Theft' and 'Criminal Damage'records of Camden Borough of London, UKfor the year 2021 (Source:⁠https://data.police.uk/⁠).Note: Police.uk data is aggregated at monthlyscale (yyyy-mm). But, the data provided here has beendisaggregated to daily scale by adding fake'daily' stamps (to giveyyyy-mm-dd). So, caution shouldbe taken when interpreting the results based onfull date.

Usage

camden_crimes

Format

A matrix containing four variables

• x: x coordinate

• y: y coordinate

• date: date of occurence

• type: types of crime

Boundary surrounding a set of points

Description

Generates a boundary (polygon) arounda set of points, using Convex Hull technique(Eddy, W. F, 1977).

Usage

chull_poly(xycoords,crsys = NULL)

Arguments

Details

Draws an arbitrary boundary aroundspatial points by joining the outer-mostpoints by lines.

Value

Returns a "SpatialPolygonsDataFrame"object representing the boundarysurround the spatial points

References

Eddy, W. F. (1977).A new convex hull algorithm for planar sets.ACM Transactions on Mathematical Software,3, 398–403.10.1145/355759.355766.

Examples

data(xyt_data)#extract xy coordinates onlyxy <- matrix(as.numeric(xyt_data[,1:2]),,2)bry <- chull_poly(xy, crsys = NULL)#visualise result#plot(bry) #to plot#points(xy[,1], xy[,2], add=TRUE)

Compare two areas

Description

To compare the sizes of twoareas (boundary shapefiles).

Usage

compare_areas(area1, area2,display_output = FALSE)

Arguments

Details

Compares the sizes of two areas (polygon shapefiles).The two shapefiles can be in anycrs,and any spatial object formats. If enabled, the output (a value)comparing the area of the two polygons is printed. This value canbe used to scale some specific spatial parameters, includingn_origin,s_threshold, andstep_length.

Value

Returns a plot and a text (string) comparingthe sizes of two areas.

Examples

#load 'area1' object - boundary of Camden, UKload(file = system.file("extdata", "camden.rda",package="stppSim"))camden_boundary = camden$boundary#load 'area2' - boundary of Birmingham, UKload(file = system.file("extdata", "birmingham_boundary.rda",package="stppSim"))#runcompare_areas(area1 = camden_boundary,area2 = birmingham_boundary, display_output = FALSE)

Date (Format) Checker

Description

Checks if date is in aspecified format (i.e.'yyyy-mm-dd').

Usage

date_checker(x)

Arguments

Details

Returns"TRUE" if alldate entries are in the specified format(⁠"yyyy-mm-dd⁠),andFALSE if at least one date is notin the format.

Value

Returns TRUE or FALSE

Examples

date_list_1 <- c("2021-09-12", "2016-xx-02","09/08/2012")date_checker(date_list_1)#> FALSE (Entries 2 and 3#are incorrect date inputs)date_list_2 <- c("2021-09-12", "1998-03-09")date_checker(date_list_2)#> TRUE

Coordinates extraction

Description

Extracts the bounding (edges) coordinatesof a polygon object.

Usage

extract_coords(poly)

Arguments

Details

Given a spatial polygon object,the function extracts its bounding coordinates.

Value

Returns 2-column xy coordinatesrepresenting points of directionalchange along the boundary.

Examples

#load boundary of Camdenload(file = system.file("extdata", "camden.rda",package="stppSim"))boundary = camden$boundary # get boundaryextract_coords(poly=boundary)

Global temporal pattern (GTP)

Description

Models the global temporal pattern,as combining the long-term trend and seasonality.

Usage

gtp(start_date, trend = "stable",slope = NULL, shortTerm = "cyclical",fPeak = 90, show.plot =FALSE)

Arguments

Details

Models the GTP for anchoring the temporaltrends and patterns of the point patterns to be simulated.

Value

Returns a time series (list) of 365data points representing1-year global temporal pattern.

Examples

gtp(start_date = "2020-01-01", trend = "stable",slope = NULL, shortTerm = "cyclical",fPeak = 90, show.plot = FALSE)

Make square grids

Description

Generates a system of square gridsover an area (boundary shapefile).

Usage

make_grids(poly, size = 350,show_output = FALSE, interactive = FALSE)

Arguments

Details

Generates a square grid system in a shapefileformat (in the samecrs as the inputpoly).Ifinteractive argument isTRUE, an interactivemap is shown from which the centroid coordinatesof any grid can be displayed by hovering the mouseover the grid. If internet connection isavailable on the PC, a basemap (OpenStreetmap) isadded to help identify places.

Value

Returns a "SpatialPolygonsDataFrames" objectrepresenting a system of square grids coveringthe polygon area.

Examples

#load boundary of Camdenload(file = system.file("extdata", "camden.rda",package="stppSim"))boundary = camden$boundarymake_grids(poly=boundary, size = 350,show_output = FALSE, interactive = FALSE)

Proportional (probability) distribution

Description

Generates ann probabilityvalues in accordance with a specifiedproportional ratios.

Usage

p_prob(n,  p_ratio = 20)

Arguments

Details

Proportional ratios are used to divide thearea under curve (auc) of an exponential functionsuch that for any given percentage ratiosa:b, theauc is divided intob:a.

Value

Returns a dataframe witha probability field.

Examples

p_prob(n = 15,  p_ratio = 20)

Boundary coordinates

Description

Boundary coordinates of CamdenBorough of London

Usage

poly

Format

A dataframe containing one variable:

• x: x coordinate

• y: y coordinate

Geometry and CoordinateReference System test of a polygon

Description

Tests whether a polygonhas the correct geometry,namely;S4 orsf. Also, teststhat there is a valid projection attached tothe polygon.

Usage

poly_tester(poly)

Arguments

Details

Returns an error message ifthe polygon is not in thecorrect geometry or CRS.

Value

Returns error messages, or mute

Examples

#load boundary of Camdenload(file = system.file("extdata", "camden.rda",package="stppSim"))boundary = camden$boundary # get boundarypoly_tester(poly=boundary)

Stpp from synthetic origins

Description

Generates spatiotemporalpoint patterns based on a set ofsynthesized origins.

Usage

psim_artif(n_events=1000, start_date = "2021-01-01",poly, netw = NULL, n_origin, restriction_feat=NULL, field,n_foci, foci_separation, mfocal = NULL, conc_type = "dispersed",p_ratio=20, s_threshold = 50, step_length = 20,trend = "stable", shortTerm = "cyclical", fPeak=90,s_band = c(0, 200),t_band = c(1, 5, 10),slope = NULL, interactive = FALSE, show.plot=FALSE, show.data=FALSE, ...)

Arguments

Details

Simulate artificial spatiotemporal patternsand interactions based user specifications.

Value

Returns a list of artificial spatiotemporalpoint patterns based on user-defined parameters.

Examples

## Not run: #load boundary and land use of Camden#load(file = system.file("extdata", "camden.rda",#package="stppSim"))#boundary = camden$boundary # get boundary#landuse = camden$landuse # get landuseboundary <- stppSim:::boundarylanduse <- stppSim:::landuse#In this example, we will use a minimal number of#'n_origin' (i.e. `20`) for faster computation:#simulate datasimulated_stpp <- psim_artif(n_events=200, start_date = "2021-01-01",poly=boundary, netw = NULL, n_origin=20, restriction_feat = NULL,field = NULL,n_foci=1, foci_separation = 10, mfocal = NULL,conc_type = "dispersed",p_ratio = 20, s_threshold = 50,step_length = 20,trend = "stable", shortTerm = "cyclical",fPeak=90, s_band = c(0, 200),t_band = c(1, 5, 10),slope = NULL, interactive = FALSE, show.plot=FALSE, show.data=FALSE)#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 set ,#`restriction_feat = landuse` and#`field = "restrVal"`, then the simulation#is performed on a restricted landscape.## End(Not run)

Stpp from real (sample) origins

Description

Generates spatiotemporal point patternfrom 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

Details

The spatial and temporal patterns andinteractions detected in sample datasetsare extrapolated to synthetise largerdata size. Details of the spatiotemporalinteractions detected in the sampledataset are provided. If the street networkof the area is provided, each point issnapped to its nearest street segment.

Value

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

References

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

Examples

## Not run: data(camden_crimes)#subset 'theft' crimetheft <- camden_crimes[which(camden_crimes$type == "Theft"),]#specify the proportion of full data to usesample_size <- 0.3set.seed(1000)dat_sample <- theft[sample(1:nrow(theft),round((sample_size * nrow(theft)), digits=0),replace=FALSE),1:3]#plot(dat_sample$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 landuselanduse <- stppSim:::landuse#simulate datasimulated_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)

Snapping point to network

Description

Snaps points to the nearest segmentof a network data.

Usage

snap_points_to_lines(points, lines,verbose = FALSE)

Arguments

Details

Function snaps points (within 300m)to the nearest segment on a network. The remainingpoints outside 300m buffer are returned intheir original locations (Credit: Michal Kvasnicka)

Value

Point (sf object) with adjusted coordinatesto fit on the network data

Examples

#get line and point data#load(file = system.file("extdata", "camden.rda",#package="stppSim"))lines <- stppSim:::linespts <- stppSim:::ptsmy_points <- snap_points_to_lines(points=pts,lines=lines,verbose = FALSE)#preview result#ggplot()+#geom_sf(data = lines, col = 'red')+#geom_sf(data = pts, shape = 1)

Space restriction raster map

Description

Builds a space restriction mapfrom one or more shapefiles. A space restrictionraster map showing the restriction levelsof various features across the landscape.The function buildson raster- and SimRIv-packages.

Usage

space_restriction(shp, baseMap, res, binary = is.na(field),field = NA, background = 1)

Arguments

Details

Helps to create a complete space restriction mapwith cell values ranging from 0(⁠minimum restriction level⁠) and 1(⁠maximum restriction level⁠). Allother areas not covered by any features are assigned the valueofbackground. When stacking additional features to existingbaseMap, only the areas covered by features are updated, whilethe remaining areas retain the original values ofbaseMap.

Value

Returns a raster map showing the restrictionlevels across the landscape.

References

1. Paul Murrell (2019). rasterize: Rasterize GraphicalOutput. R package version 0.1.https://CRAN.R-project.org/package=rasterize

2. Quaglietta L, Porto M (2019). SiMRiv: Individual-Based,Spatially-Explicit Simulation and Analysis of Multi-StateMovements in River Networks and Heterogeneous Landscapes.R package version 1.0.4, <URL:https://CRAN.R-project.org/package=SiMRiv>.

Examples

#load boundary of Camden and land use dataload(file = system.file("extdata", "camden.rda",package="stppSim"))boundary = camden$boundary # get boundaryrestrct_map <- space_restriction(shp = boundary,res = 20, binary = TRUE)#plot the result#plot(restrct_space)#Setting 'restrct_space' raster as basemap, the landuse#map can now be stacked onto the basemap as follows:landuse = camden$landuse # get landuserestrct_Landuse <- space_restriction(shp = landuse,baseMap = restrct_map,res = 20, field = "restrVal", background = 1)#plot(restrct_Landuse)

Spatial and temporal model

Description

To generate graphics depicting the spatialand temporal models of the final simulation

Usage

stm(pt, poly, df, crsys = NULL,display_output = FALSE)

Arguments

Details

Incorporated intopsim_artif andpsim_real functions to allow the preview ofthe spatial and the temporal model of the simulation.The spatial model is the strength distribution oforigin which is the likeness of the spatial patternsto be simulated. The temporal model is the previewof the trend and seasonal patterns to be expectedfrom the simulation.

Value

A graphics showing the spatial andtemporal model of the simulation.

Examples

## Not run: #load polygon shapefileload(file = system.file("extdata", "camden.rda",package="stppSim"))camden_boundary = camden$boundary#read xyz datadata(xyz)#create a time seriest <- seq(0,5,0.5)df <- data.frame(data = abs(min(sin(t))) + sin(t))#run functionstm(pt = xyz, poly=camden_boundary, df=df,crsys = NULL, display_output = FALSE)## End(Not run)

Learning the spatiotemporal properties ofa sample data

Description

Learns both the spatial and the temporalproperties of a real sample dataset.

Usage

stp_learner(ppt, start_date = NULL, poly = NULL,n_origin=50, p_ratio, gridSize = 150, s_range =  150,tolerance = 0.07,crsys = NULL, show.plot = FALSE)

Arguments

Details

Returns an object of the classreal_spo,storing details of the spatiotemporalproperties of the sample data learnt.

Value

an object (list) containing specific spatialand temporal properties of a sample dataset.

References

Silverman, B.W., 2018. Density estimationfor statistics and data analysis. Routledge.

Examples

## Not run: #Goal: To learn the ST properties#of a sample data, for the purpose of#simulating the full dataset (see `psim_real`).data(camden_crimes)#subset 'theft' crimetheft <- camden_crimes[which(camden_crimes$type =="Theft"),1:3]#specify the proportion of full data to usesample_size <- 0.3set.seed(1000)dat_sample <- theft[sample(1:nrow(theft),round((sample_size * nrow(theft)), digits=0),replace=FALSE),]#plot(dat_sample$x, dat_sample$y) #previewstp_learner(dat_sample,start_date = NULL, poly = NULL, n_origin=50,p_ratio=20, gridSize = 150,s_range =  150, tolerance = 0.07,crsys = "EPSG:27700",show.plot = FALSE)## End(Not run)

A landscape walker

Description

A dynamic object capable of movingand avoiding obstacles on a landscape.

Usage

walker(n = 5, s_threshold = 250, step_length = 20,poly = NULL, restriction_feat=NULL, field = NA, coords=c(0,0),pt_itx = TRUE, show.plot = FALSE)

Arguments

Details

A walker is propelled by an in-built stochastictransition matrixand a specified set of spatial and temporalparameters. The transitionmatrix defines two states, namely; theexploratoryand aperformative states. A walker is capableof avoiding obstructions (i.e.,restriction_feat)if included. The resulting number of events may beslightly different from the valuen because of thestochastic process involved.

Value

Returns a trace of walker's path, and theresulting events.

References

Quaglietta L, Porto M (2019). SiMRiv: Individual-Based,Spatially-Explicit Simulation and Analysis of Multi-StateMovements in River Networks and Heterogeneous Landscapes_.R package version 1.0.4, <URL:https://CRAN.R-project.org/package=SiMRiv>.

Examples

#load boundary of Camdenload(file = system.file("extdata", "camden.rda",package="stppSim"))boundary = camden$boundary # get boundarywalkerpath <- walker(n = 5, s_threshold = 250, step_length = 20,poly = boundary, restriction_feat=NULL, field = NULL,coords = c(0,0), pt_itx = TRUE, show.plot = FALSE)#plot(walkerpath)

Spatiotemporal point data

Description

Example spatiotemporal point dataof a part of San Francisco City,California, US

Usage

xyt_data

Format

A matrix containing three variables

• x: x coordinate

• y: y coordinate

• t: t time

xyz data

Description

Example data with 'x', 'y',and a 'z' information

Usage

xyz

Format

A matrix containing three variables

• x: x coordinate

• y: y coordinate

• z: z height/probability/etc