| Title: | Spatiotemporal Point Patterns Simulation |
|---|---|
| Description: | Generates artificial point patterns marked by their spatial and temporal signatures. The resulting point cloud may exhibit inherent interactions between both signatures. The simulation integrates microsimulation (Holm, E., (2017)<doi:10.1002/9781118786352.wbieg0320>) and agent-based models (Bonabeau, E., (2002)<doi:10.1073/pnas.082080899>), beginning with the configuration of movement characteristics for the specified agents (referred to as 'walkers') and their interactions within the simulation environment. These interactions (Quaglietta, L. and Porto, M., (2019)<doi:10.1186/s40462-019-0154-8>) result in specific spatiotemporal patterns that can be visualized, analyzed, and used for various analytical purposes. Given the growing scarcity of detailed spatiotemporal data across many domains, this package provides an alternative data source for applications in social and life sciences. |
| Authors: | Monsuru Adepeju [cre, aut] |
| Maintainer: | Monsuru Adepeju <[email protected]> |
| License: | GPL-3 |
| Version: | 1.3.4 |
| Built: | 2025-02-19 05:00:50 UTC |
| Source: | https://github.com/manalytics/stppsim |
Simulates spatial locations to serve as origins of walkers. If provided, spaces covered by restriction features are avoided. Final origins are assigned probability values indicating the strengths of the origins.
artif_spo(poly, n_origin=50, restriction_feat = NULL, n_foci=5, foci_separation = 10, mfocal = NULL, conc_type = "nucleated", p_ratio)artif_spo(poly, n_origin=50, restriction_feat = NULL, n_foci=5, foci_separation = 10, mfocal = NULL, conc_type = "nucleated", p_ratio)
poly |
(An sf or S4 object) a polygon shapefile defining the extent of the landscape |
n_origin |
number of locations to serve as
origins for walkers. Default: |
restriction_feat |
(An S4 object) optional
shapefile containing features
in which walkers cannot walk through.
Default: |
n_foci |
number of focal points amongst the origin
locations. The origins to serve as focal
points are based on random selection. |
foci_separation |
a value from |
mfocal |
the c(x, y) coordinates of a single point,
representing a pre-defined |
conc_type |
concentration of the rest of the
origins (non-focal origins) around the focal ones. The options
are |
p_ratio |
the smaller of the
two terms of proportional ratios.
For example, a value of |
The focal origins (n_foci) serve as the central locations
(such as, city centres). The foci_separation indicates
the nearness of focal origins from one another.
The conc_type argument allows a user to specify
the type of spatial concentration exhibited by the non-focal
origin around the focal ones.
If restriction_feat is provided, its features help
to prevent the occurrence of any events in the areas
occupied by the features.
Returns a list detailing the properties of the generated spatial origins with associated strength (probability) values.
#load boundary of Camden load(file = system.file("extdata", "camden.rda", package="stppSim")) boundary = camden$boundary # get boundary landuse <- camden$landuse spo <- artif_spo(poly = boundary, n_origin = 50, restriction_feat = landuse, n_foci=5, foci_separation = 0, mfocal = NULL, conc_type = "dispersed", p_ratio=20)#load boundary of Camden load(file = system.file("extdata", "camden.rda", package="stppSim")) boundary = camden$boundary # get boundary landuse <- camden$landuse spo <- artif_spo(poly = boundary, n_origin = 50, restriction_feat = landuse, n_foci=5, foci_separation = 0, mfocal = NULL, conc_type = "dispersed", p_ratio=20)
Data comprising 'Theft' and 'Criminal Damage'
records of Camden Borough of London, UK
for the year 2021 (Source: https://data.police.uk/).
Note: Police.uk data is aggregated at monthly
scale (yyyy-mm). But, the data provided here has been
disaggregated to daily scale by adding fake
'daily' stamps (to give yyyy-mm-dd). So, caution should
be taken when interpreting the results based on
full date.
camden_crimescamden_crimes
A matrix containing four variables
x: x coordinate
y: y coordinate
date: date of occurence
type: types of crime
Generates a boundary (polygon) around a set of points, using Convex Hull technique (Eddy, W. F, 1977).
chull_poly(xycoords, crsys = NULL)chull_poly(xycoords, crsys = NULL)
xycoords |
(matrix) A 2-column
coordinate vectors of points: |
crsys |
Optional string specifying the coordinate reference system (crs) of the resulting boundary, e.g., the crs string "+proj=longlat +datum=WGS84" transform the resulting boundary to wgs84 system. |
Draws an arbitrary boundary around spatial points by joining the outer-most points by lines.
Returns a "SpatialPolygonsDataFrame" object representing the boundary surround the spatial points
Eddy, W. F. (1977). A new convex hull algorithm for planar sets. ACM Transactions on Mathematical Software, 3, 398–403.10.1145/355759.355766.
data(xyt_data) #extract xy coordinates only xy <- 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)data(xyt_data) #extract xy coordinates only xy <- 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)
To compare the sizes of two areas (boundary shapefiles).
compare_areas(area1, area2, display_output = FALSE)compare_areas(area1, area2, display_output = FALSE)
area1 |
(as |
area2 |
(as |
display_output |
(logical) Whether to print output
in the console.
Default: |
Compares the sizes of two areas (polygon shapefiles).
The two shapefiles can be in any crs,
and any spatial object formats. If enabled, the output (a value)
comparing the area of the two polygons is printed. This value can
be used to scale some specific spatial parameters, including
n_origin, s_threshold, and step_length.
Returns a plot and a text (string) comparing the sizes of two areas.
#load 'area1' object - boundary of Camden, UK load(file = system.file("extdata", "camden.rda", package="stppSim")) camden_boundary = camden$boundary #load 'area2' - boundary of Birmingham, UK load(file = system.file("extdata", "birmingham_boundary.rda", package="stppSim")) #run compare_areas(area1 = camden_boundary, area2 = birmingham_boundary, display_output = FALSE)#load 'area1' object - boundary of Camden, UK load(file = system.file("extdata", "camden.rda", package="stppSim")) camden_boundary = camden$boundary #load 'area2' - boundary of Birmingham, UK load(file = system.file("extdata", "birmingham_boundary.rda", package="stppSim")) #run compare_areas(area1 = camden_boundary, area2 = birmingham_boundary, display_output = FALSE)
Checks if date is in a
specified format (i.e. 'yyyy-mm-dd').
date_checker(x)date_checker(x)
x |
A date or a vector of date values |
Returns "TRUE" if all
date entries are in the specified format
("yyyy-mm-dd),
and FALSE if at least one date is not
in the format.
Returns TRUE or FALSE
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) #> TRUEdate_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
Extracts the bounding (edges) coordinates of a polygon object.
extract_coords(poly)extract_coords(poly)
poly |
(An sf or S4 object) A polygon shapefile. |
Given a spatial polygon object, the function extracts its bounding coordinates.
Returns 2-column xy coordinates representing points of directional change along the boundary.
#load boundary of Camden load(file = system.file("extdata", "camden.rda", package="stppSim")) boundary = camden$boundary # get boundary extract_coords(poly=boundary)#load boundary of Camden load(file = system.file("extdata", "camden.rda", package="stppSim")) boundary = camden$boundary # get boundary extract_coords(poly=boundary)
Models the global temporal pattern, as combining the long-term trend and seasonality.
gtp(start_date, trend = "stable", slope = NULL, shortTerm = "cyclical", fPeak = 90, show.plot =FALSE)gtp(start_date, trend = "stable", slope = NULL, shortTerm = "cyclical", fPeak = 90, show.plot =FALSE)
start_date |
the start date of the temporal pattern.
The date should be in the format |
trend |
specifies the direction of the
long-term trend. Options are:
|
slope |
slope of the long-term trend when
an |
shortTerm |
type of short- to medium-term
fluctuations (patterns) of the time series.
Options are: |
fPeak |
first seasonal
peak of cyclical short term. Default value is |
show.plot |
(logical) Shows 'gtp'.
Default is |
Models the GTP for anchoring the temporal trends and patterns of the point patterns to be simulated.
Returns a time series (list) of 365 data points representing 1-year global temporal pattern.
gtp(start_date = "2020-01-01", trend = "stable", slope = NULL, shortTerm = "cyclical", fPeak = 90, show.plot = FALSE)gtp(start_date = "2020-01-01", trend = "stable", slope = NULL, shortTerm = "cyclical", fPeak = 90, show.plot = FALSE)
Generates a system of square grids over an area (boundary shapefile).
make_grids(poly, size = 350, show_output = FALSE, interactive = FALSE)make_grids(poly, size = 350, show_output = FALSE, interactive = FALSE)
poly |
(as |
size |
Size of square grids to be
created. For example, the input |
show_output |
(logical) Display the output.
Default: |
interactive |
(logical) to show
interactive map of the grids generated.
Default: |
Generates a square grid system in a shapefile
format (in the same crs as the input poly).
If interactive argument is TRUE, an interactive
map is shown from which the centroid coordinates
of any grid can be displayed by hovering the mouse
over the grid. If internet connection is
available on the PC, a basemap (OpenStreetmap) is
added to help identify places.
Returns a "SpatialPolygonsDataFrames" object representing a system of square grids covering the polygon area.
#load boundary of Camden load(file = system.file("extdata", "camden.rda", package="stppSim")) boundary = camden$boundary make_grids(poly=boundary, size = 350, show_output = FALSE, interactive = FALSE)#load boundary of Camden load(file = system.file("extdata", "camden.rda", package="stppSim")) boundary = camden$boundary make_grids(poly=boundary, size = 350, show_output = FALSE, interactive = FALSE)
This function uses the Knox test for space-time clustering to quantify the spatio-temporal association between events (Credit: Wouter Steenbeek).
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,...)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,...)
x |
a vector of x coordinates |
y |
a vector of y coordinates |
time |
a vector of time. This can be of type integer, numeric, or date |
sds |
A vector of break points of the spatial intervals. For example c(0,50,120,300) to specify spatial intervals from 0-50, 50-120, 120-300 meters. Or c(0,50,100,Inf) to specify spatial intervals from 0-50, 50-100, and 100-Inf meters. (More accurately, on the scale of the provided x and y coordinates. For example, data may be projected in feet and thus the distances refer to feet instead of meters). |
tds |
A vector of break points of the temporal intervals. For example c(0,2,4,Inf) to specify temporal intervals from 0-2, 2-4, 4-Inf days. |
s_include.lowest |
the descriptions above are ambiguous on how exactly the spatial break points are handled. For example, does c(0,100,200) refer to 0-100, 101-200? Or to 0-99 and 100-199? s_include.lowest follows the arguments of cut (see ?cut). Logical, indicating if a spatial distance equal to the lowest (or highest, for right = FALSE) 'breaks' value should be included. Default = FALSE. See vignette("NearRepeat_breaks") for details. |
s_right |
logical, indicating if the spatial intervals should be closed on the right (and open on the left) or vice versa. Default = FALSE. See vignette("NearRepeat_breaks") for details. |
t_include.lowest |
t_include.lowest follows the arguments of cut (see ?cut). Logical, indicating if a temporal distance equal to the lowest (or highest, for right = FALSE) 'breaks' value should be included. Default = FALSE. |
t_right |
logical, indicating if the temporal intervals should be closed on the right (and open on the left) or vice versa. Default = FALSE. See vignette("NearRepeat_breaks") for details. |
method |
The method to calculate the spatial distances between crime events. Methods possible as in the 'dist' function (see ?dist). Default is 'manhattan', which seems to be a fair approximation of the distance travelled by a road network. Alternatively, the user can specify 'euclidean' to get the 'as the crow flies' distance. |
nrep |
The number of replications of the Monte Carlo simulation (default = 999). |
saveSimulations |
Should all simulated contingency tables be saved as a 3-dimensional array? Default = FALSE |
future.seed |
A logical or an integer (of length one or seven), or a list of length(X) with pre-generated random seeds. Default = TRUE. See R package future.apply for details. |
... |
(optional) Additional arguments passed to future_lapply() |
Further details available at: https://github.com/wsteenbeek/NearRepeat.
An object of type "knox", i.e. a list with four tables. For each spatial and temporal distance combination,(1) The counts of observed crime pairs, (2) The Knox ratios based on the mean of the simulations, (3) The Knox ratios based on the median of the simulations, (4) p-values.
Steenbeek W. Near Repeat. R package version 0.1.1. 2018. URL: https://github.com/wsteenbeek/NearRepeat
## 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 days set.seed(38673) NRepeat(x = mydata$x, y = mydata$y, time = mydata$date, sds = c(0,100,Inf), tds = c(0,2,4)) # Add a 'same repeat' spatial interval of 0.001 meters, and use Euclidean # distance set.seed(38673) NRepeat(x = mydata$x, y = mydata$y, time = mydata$date, sds = c(0,0.001,100,Inf), tds = c(0,2,4), method = "euclidean") # Only do 99 replications set.seed(38673) NRepeat(x = mydata$x, y = mydata$y, time = mydata$date, sds = c(0,0.001,100,Inf), tds = c(0,2,4), method = "euclidean", nrep = 99) # The plot() function can be used to plot a Heat Map of Near Repeat results # based on p-values set.seed(4622) myoutput <- NRepeat(x = mydata$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)## 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 days set.seed(38673) NRepeat(x = mydata$x, y = mydata$y, time = mydata$date, sds = c(0,100,Inf), tds = c(0,2,4)) # Add a 'same repeat' spatial interval of 0.001 meters, and use Euclidean # distance set.seed(38673) NRepeat(x = mydata$x, y = mydata$y, time = mydata$date, sds = c(0,0.001,100,Inf), tds = c(0,2,4), method = "euclidean") # Only do 99 replications set.seed(38673) NRepeat(x = mydata$x, y = mydata$y, time = mydata$date, sds = c(0,0.001,100,Inf), tds = c(0,2,4), method = "euclidean", nrep = 99) # The plot() function can be used to plot a Heat Map of Near Repeat results # based on p-values set.seed(4622) myoutput <- NRepeat(x = mydata$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)
Generates an n probability
values in accordance with a specified
proportional ratios.
p_prob(n, p_ratio = 20)p_prob(n, p_ratio = 20)
n |
a number of data points. |
p_ratio |
the smaller of the
terms of specified proportional ratios. For instance, for a
|
Proportional ratios are used to divide the
area under curve (auc) of an exponential function
such that for any given percentage ratios a:b, the
auc is divided into b:a.
Returns a dataframe with a probability field.
p_prob(n = 15, p_ratio = 20)p_prob(n = 15, p_ratio = 20)
Boundary coordinates of Camden Borough of London
polypoly
A dataframe containing one variable:
x: x coordinate
y: y coordinate
Tests whether a polygon
has the correct geometry,
namely; S4 or sf. Also, tests
that there is a valid projection attached to
the polygon.
poly_tester(poly)poly_tester(poly)
poly |
(as |
Returns an error message if the polygon is not in the correct geometry or CRS.
Returns error messages, or mute
#load boundary of Camden load(file = system.file("extdata", "camden.rda", package="stppSim")) boundary = camden$boundary # get boundary poly_tester(poly=boundary)#load boundary of Camden load(file = system.file("extdata", "camden.rda", package="stppSim")) boundary = camden$boundary # get boundary poly_tester(poly=boundary)
Generates spatiotemporal point patterns based on a set of synthesized origins.
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, ...)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, ...)
n_events |
number of points
(events) to simulate. Default: |
start_date |
the start date of the temporal pattern.
The date should be in the format |
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: |
n_origin |
number of locations to serve as
origins for walkers. Default: |
restriction_feat |
(An S4 object) optional
shapefile containing features
in which walkers cannot walk through.
Default: |
field |
a number in the range of |
n_foci |
number of focal points amongst the origin
locations. The origins to serve as focal
points are based on random selection. |
foci_separation |
a value from |
mfocal |
the c(x, y) coordinates of a single point,
representing a pre-defined |
conc_type |
concentration of the rest of the
origins (non-focal origins) around the focal ones. The options
are |
p_ratio |
the smaller of the
two terms of proportional ratios.
For example, a value of |
s_threshold |
defines the spatial
perception range of a walker at a given
location. Default: |
step_length |
the maximum step taken by a walker from one point to the next. |
trend |
specifies the direction of the
long-term trend. Options are:
|
shortTerm |
type of short- to medium-term
fluctuations (patterns) of the time series.
Options are: |
fPeak |
first seasonal
peak of cyclical short term. Default value is |
s_band |
distance bandwidth within which
the event re-occurences are maximized (i.e.,
interactions are maximum). Specified as a vector of
two distance values. Default: |
t_band |
temporal bandwidth within which
event re-occurences are maximized (i.e., interactions
are maximum). Specified as a vector of values (in days)
|
slope |
slope of the long-term trend when
an |
interactive |
Whether to run the process in
interactive mode. Default is |
show.plot |
(logical) Shows GTP.
Default is |
show.data |
(TRUE or FALSE) To show the output
data. Default is |
... |
additional arguments to pass from
|
Simulate artificial spatiotemporal patterns and interactions based user specifications.
Returns a list of artificial spatiotemporal point patterns based on user-defined parameters.
## 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 landuse boundary <- stppSim:::boundary landuse <- stppSim:::landuse #In this example, we will use a minimal number of #'n_origin' (i.e. `20`) for faster computation: #simulate data simulated_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)## 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 landuse boundary <- stppSim:::boundary landuse <- stppSim:::landuse #In this example, we will use a minimal number of #'n_origin' (i.e. `20`) for faster computation: #simulate data simulated_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)
Generates spatiotemporal point pattern from origins sampled based on real sample dataset.
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)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)
n_events |
number of points
(events) to simulate. Default: |
ppt |
A 3-column matrix or list containing
|
start_date |
the start date of the temporal pattern.
The date should be in the format |
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: |
s_threshold |
defines the spatial
perception range of a walker at a given
location. Default: |
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: |
restriction_feat |
(An S4 object) optional
shapefile containing features
in which walkers cannot walk through.
Default: |
field |
a number in the range of |
p_ratio |
the smaller of the
two terms of proportional ratios.
For example, a value of |
interactive |
Whether to run the process in
interactive mode. Default is |
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
|
s_interaction |
(string) indicating the
type of spatial interaction to detect.
Default: |
tolerance |
Pvalue to use for the extraction of
space-time interaction in the sample data. Default
value: |
crsys |
(string) the EPSG code of the projection
system of the |
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.
A list of artificial spatiotemporal point patterns and interaction generated based on a sample (real) data.
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.
## 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)## 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)
Snaps points to the nearest segment of a network data.
snap_points_to_lines(points, lines, verbose = FALSE)snap_points_to_lines(points, lines, verbose = FALSE)
points |
point data (sf object) |
lines |
line/street/road network (sf object) |
verbose |
Whether to output processing messages. |
Function snaps points (within 300m) to the nearest segment on a network. The remaining points outside 300m buffer are returned in their original locations (Credit: Michal Kvasnicka)
Point (sf object) with adjusted coordinates to fit on the network data
#get line and point data #load(file = system.file("extdata", "camden.rda", #package="stppSim")) lines <- stppSim:::lines pts <- stppSim:::pts my_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)#get line and point data #load(file = system.file("extdata", "camden.rda", #package="stppSim")) lines <- stppSim:::lines pts <- stppSim:::pts my_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)
Builds a space restriction map from one or more shapefiles. A space restriction raster map showing the restriction levels of various features across the landscape. The function builds on raster- and SimRIv-packages.
space_restriction(shp, baseMap, res, binary = is.na(field), field = NA, background = 1)space_restriction(shp, baseMap, res, binary = is.na(field), field = NA, background = 1)
shp |
shapefile object containing features to serve as obstructions to the movement of walkers. |
baseMap |
if provided, a raster onto which
to stack the restriction features ( |
res |
the desired pixel resolution of the raster to be created, when baseMap is not provided. |
binary |
if TRUE, the shapefile will be rasterized
so that all features are assigned a value of |
field |
a number in the range of |
background |
the value in the range 0 and 1 to assign to all pixels that are not covered by any shapefile object. |
Helps to create a complete space restriction map
with cell values ranging from 0
(minimum restriction level) and 1(maximum restriction level). All
other areas not covered by any features are assigned the value
of background. When stacking additional features to existing
baseMap, only the areas covered by features are updated, while
the remaining areas retain the original values of baseMap.
Returns a raster map showing the restriction levels across the landscape.
Paul Murrell (2019). rasterize: Rasterize Graphical Output. R package version 0.1. https://CRAN.R-project.org/package=rasterize
Quaglietta L, Porto M (2019). SiMRiv: Individual-Based, Spatially-Explicit Simulation and Analysis of Multi-State Movements in River Networks and Heterogeneous Landscapes. R package version 1.0.4, <URL: https://CRAN.R-project.org/package=SiMRiv>.
#load boundary of Camden and land use data load(file = system.file("extdata", "camden.rda", package="stppSim")) boundary = camden$boundary # get boundary restrct_map <- space_restriction(shp = boundary, res = 20, binary = TRUE) #plot the result #plot(restrct_space) #Setting 'restrct_space' raster as basemap, the landuse #map can now be stacked onto the basemap as follows: landuse = camden$landuse # get landuse restrct_Landuse <- space_restriction(shp = landuse, baseMap = restrct_map, res = 20, field = "restrVal", background = 1) #plot(restrct_Landuse)#load boundary of Camden and land use data load(file = system.file("extdata", "camden.rda", package="stppSim")) boundary = camden$boundary # get boundary restrct_map <- space_restriction(shp = boundary, res = 20, binary = TRUE) #plot the result #plot(restrct_space) #Setting 'restrct_space' raster as basemap, the landuse #map can now be stacked onto the basemap as follows: landuse = camden$landuse # get landuse restrct_Landuse <- space_restriction(shp = landuse, baseMap = restrct_map, res = 20, field = "restrVal", background = 1) #plot(restrct_Landuse)
To generate graphics depicting the spatial and temporal models of the final simulation
stm(pt, poly, df, crsys = NULL, display_output = FALSE)stm(pt, poly, df, crsys = NULL, display_output = FALSE)
pt |
a data frame with the first three fields being 'x', 'y', and 'z' information. |
poly |
(An sf or S4 object)
a polygon shapefile defining the extent of a landscape.
Default: |
df |
a vector or 1-column data frame containing values for the time series. |
crsys |
(string) the EPSG code of the projection
system of the |
display_output |
(logical) display the output.
Default: |
Incorporated into psim_artif and
psim_real functions to allow the preview of
the spatial and the temporal model of the simulation.
The spatial model is the strength distribution of
origin which is the likeness of the spatial patterns
to be simulated. The temporal model is the preview
of the trend and seasonal patterns to be expected
from the simulation.
A graphics showing the spatial and temporal model of the simulation.
## Not run: #load polygon shapefile load(file = system.file("extdata", "camden.rda", package="stppSim")) camden_boundary = camden$boundary #read xyz data data(xyz) #create a time series t <- seq(0,5,0.5) df <- data.frame(data = abs(min(sin(t))) + sin(t)) #run function stm(pt = xyz, poly=camden_boundary, df=df, crsys = NULL, display_output = FALSE) ## End(Not run)## Not run: #load polygon shapefile load(file = system.file("extdata", "camden.rda", package="stppSim")) camden_boundary = camden$boundary #read xyz data data(xyz) #create a time series t <- seq(0,5,0.5) df <- data.frame(data = abs(min(sin(t))) + sin(t)) #run function stm(pt = xyz, poly=camden_boundary, df=df, crsys = NULL, display_output = FALSE) ## End(Not run)
Learns both the spatial and the temporal properties of a real sample dataset.
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)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)
ppt |
A 3-column matrix or list containing
|
start_date |
the start date of the temporal pattern.
The date should be in the format |
poly |
(An sf or S4 object) a polygon shapefile defining the extent of the landscape |
n_origin |
number of locations to serve as
origins for walkers. Default: |
p_ratio |
(an integer) The smaller of the
two terms of a Pareto ratio.
For example, a value of |
gridSize |
the size of square grid
to use for discretizing the space.
Default is: |
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
|
tolerance |
Pvalue to use for the extraction of
space-time interaction in the sample data. Default
value: |
crsys |
(string) the EPSG code of the projection
system of the |
show.plot |
(TRUE or FALSE) Whether to show some displays. |
Returns an object of the class real_spo,
storing details of the spatiotemporal
properties of the sample data learnt.
an object (list) containing specific spatial and temporal properties of a sample dataset.
Silverman, B.W., 2018. Density estimation for statistics and data analysis. Routledge.
## 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' crime theft <- camden_crimes[which(camden_crimes$type == "Theft"),1:3] #specify the proportion of full data to use sample_size <- 0.3 set.seed(1000) dat_sample <- theft[sample(1:nrow(theft), round((sample_size * nrow(theft)), digits=0), replace=FALSE),] #plot(dat_sample$x, dat_sample$y) #preview stp_learner(dat_sample, start_date = NULL, poly = NULL, n_origin=50, p_ratio=20, gridSize = 150, s_range = 150, tolerance = 0.07, crsys = "EPSG:27700", show.plot = FALSE) ## End(Not run)## Not run: #Goal: To learn the ST properties #of a sample data, for the purpose of #simulating the full dataset (see `psim_real`). data(camden_crimes) #subset 'theft' crime theft <- camden_crimes[which(camden_crimes$type == "Theft"),1:3] #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),] #plot(dat_sample$x, dat_sample$y) #preview stp_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 dynamic object capable of moving and avoiding obstacles on a landscape.
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)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)
n |
number of events to be generated by a walker within a temporal bin. |
s_threshold |
defines the spatial
perception range of a walker at a given
location. Default: |
step_length |
the maximum step taken by a walker from one point to the next. |
poly |
(An sf or S4 object) a polygon shapefile defining the extent of the landscape |
restriction_feat |
(An S4 object) optional
shapefile containing features
in which walkers cannot walk through.
Default: |
field |
a number in the range of |
coords |
a vector of the form c(x, y) giving the
initial coordinates of a walker (i.e., coordinates
of origins).
Default value is |
pt_itx |
To check whether any of the
specified initial origin coordinates
falls outside the boundary.
Default: |
show.plot |
(TRUE or False) To show the time series
plot. Default is |
A walker is propelled by an in-built stochastic
transition matrix
and a specified set of spatial and temporal
parameters. The transition
matrix defines two states, namely; the exploratory
and a performative states. A walker is capable
of avoiding obstructions (i.e., restriction_feat)
if included. The resulting number of events may be
slightly different from the value n because of the
stochastic process involved.
Returns a trace of walker's path, and the resulting events.
Quaglietta L, Porto M (2019). SiMRiv: Individual-Based, Spatially-Explicit Simulation and Analysis of Multi-State Movements in River Networks and Heterogeneous Landscapes_. R package version 1.0.4, <URL: https://CRAN.R-project.org/package=SiMRiv>.
#load boundary of Camden load(file = system.file("extdata", "camden.rda", package="stppSim")) boundary = camden$boundary # get boundary walkerpath <- walker(n = 5, s_threshold = 250, step_length = 20, poly = boundary, restriction_feat=NULL, field = NULL, coords = c(0,0), pt_itx = TRUE, show.plot = FALSE) #plot(walkerpath)#load boundary of Camden load(file = system.file("extdata", "camden.rda", package="stppSim")) boundary = camden$boundary # get boundary walkerpath <- 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)
Example spatiotemporal point data of a part of San Francisco City, California, US
xyt_dataxyt_data
A matrix containing three variables
x: x coordinate
y: y coordinate
t: t time
Example data with 'x', 'y', and a 'z' information
xyzxyz
A matrix containing three variables
x: x coordinate
y: y coordinate
z: z height/probability/etc