'Akmedoids'
packageAuthors:
Adepeju, M., Langton, S., and Bannister, J.
Big Data Centre, Manchester Metropolitan University, Manchester, M15 6BH
Date:
20200109
Abstract
The'akmedoids'
package advances a set of Rfunctions for longitudinal clustering of longterm trajectories and determines the optimal solution based on the CalińskiHarabatz
criterion (Caliński and Harabasz 1974). The package also includes a set of functions for addressing common data issues, such as missing entries and outliers, prior to conducting advance longitudinal data analysis. One of the key objectives of this package is to facilitate easy replication of a recent paper which examined small area inequality in the crime drop (see Adepeju et al. 2019). This document is created to provide a guide towards accomplishing this objective. Many of the functions provided in the akmedoids
package may be applied to longitudinal data in general.
Longitudinal clustering analysis is ubiquitous in social and behavioural sciences for investigating the developmental processes of a phenomenon over time. Examples of the commonly used techniques in these areas include groupbased trajectory modelling (GBTM) and the nonparametric kmeans method. Whilst kmeans has a number of benefits over GBTM, such as more relaxed statistical assumptions, generic implementations render it more sensitive to outliers and shortterm fluctuations, which minimises its ability to identify longterm linear trends in data. In crime and place research, for example, the identification of such longterm
linear trends may help to develop some theoretical understanding of criminal victimisation within a geographical space (Weisburd et al. 2004; Griffith and Chavez 2004). In order to address this sensitivity problem, we advance a novel technique named anchored kmedoids
('akmedoids'
) which implements three key modifications to the existing longitudinal kmeans
approach. First, it approximates trajectories using ordinary least square regression (OLS
) and second, anchors
the initialisation process with median observations. It then deploys the medoids
observations as new anchors for each iteration of the expectationmaximisation procedure (Celeux and Govaert 1992). These modifications ensure that the impacts of shortterm fluctuations and outliers are minimised. By linking the final groupings back to the original trajectories, a clearer delineation of the longterm linear trends of trajectories are obtained.
We facilitate the easy use of akmedoids
through an opensource package using R
. We encourage the use of the package outside of criminology, should it be appropriate. Before outlining the main clustering
functions, we demonstrate the use of a few data manipulation
functions that assist in data preparation. The worked demonstration uses a small example dataset which should allow users to get a clear understanding of the operation of each function.
SN  Function  Title  Description 

1 
dataImputation

Data imputation for longitudinal data 
Calculates any missing entries (NA , Inf , null ) in a longitudinal data, according to a specified method

2 
rates

Conversion of ‘counts’ to ‘rates’  Calculates rates from observed ‘counts’ and its associated denominator data 
3 
props

Conversion of ‘counts’ (or ‘rates’) to ‘Proportion’  Converts ‘counts’ or ‘rates’ observation to ‘proportion’ 
4 
outlierDetect

Outlier detection and replacement  Identifies outlier observations in the data, and replace or remove them 
5 
wSpaces

Whitespace removal  Removes all the leading and trailing whitespaces in a longitudinal data 
#1. Data manipulation
Table 1 shows the main data manipulation functions and their descriptions. These functions help to address common data issues prior to analysis, as well as basic data manipulation tasks such as converting longitudinal data from count
to proportion
measures (as per the crime inequality paper where akmedoids
was first implemented). In order to demonstrate the utility of these functions, we provide a simulated dataset traj
which can be called by typing traj
in R
console after loading the akmedoids
library.
"dataImputation"
functionCalculates any missing entries in a data, according to a chosen method. This function recognises three kinds of data entries as missing. These are NA
, Inf
, null
, and an option of whether or not to consider 0
’s as missing values. The function provides a replacement option for the missing entries using two methods. First, an arithmetic
method which uses the mean
, minimum
or maximum
value of the corresponding rows or columns of the missing values. Second, a regression
method which uses OLS regression lines to estimate the missing values. Using the regression method, only the missing data points derive values from the regression line while the remaining (observed) data points retain their original values. The function terminates if there is any trajectory with only one observation in it. Using the 'traj'
dataset, we demonstrate how the 'regression'
method estimates missing values.
#installing the `akmedoids` packages
install.packages("devtools")
devtools::install_github("manalytics/packages/akmedoids")
#viewing the first 6 rows of 'traj' object
head(traj)
#> location_ids X2001 X2002 X2003 X2004 X2005 X2006 X2007 X2008 X2009
#> 1 E01012628 3 0 1 2 1 0 1 4 0
#> 2 E01004768 9 NA 2 4 7 5 1 3 1
#> 3 E01004803 4 3 0 10 2 3 6 6 8
#> 4 E01004804 7 3 9 3 2 NA 6 3 2
#> 5 E01004807 2 Inf 5 5 6 NA 3 5 4
#> 6 E01004808 8 5 8 4 1 5 6 1 1
#no. of rows
nrow(traj)
#> [1] 10
#no. of columns
ncol(traj)
#> [1] 10
The first column of the traj
object is the id
(unique) field. In many applications, it is necessary to preserve the id
column in order to allow linking of outputs to other external datasets, such as spatial location data. Most of the functions of the akmedoids
provides an option to recognise the first column of an input dataset as the unique field. The dataImputation
function can be used to imput the missing data point of traj
object as follows:
imp_traj < dataImputation(traj, id_field = TRUE, method = 2,
replace_with = 1, fill_zeros = FALSE)
#> [1] "8 entries were found/filled!"
#viewing the first 6 rows
head(imp_traj)
#> location_ids X2001 X2002 X2003 X2004 X2005 X2006 X2007 X2008 X2009
#> 1 E01012628 3 0.00 1 2 1 0.00 1 4 0
#> 2 E01004768 9 6.44 2 4 7 5.00 1 3 1
#> 3 E01004803 4 3.00 0 10 2 3.00 6 6 8
#> 4 E01004804 7 3.00 9 3 2 3.90 6 3 2
#> 5 E01004807 2 3.92 5 5 6 4.36 3 5 4
#> 6 E01004808 8 5.00 8 4 1 5.00 6 1 1
The argument method = 2
refers to the regression
technique, while the argument replace_with = 1
refers to the linear
option (currently the only available option). Figure 1 is a graphical illustration of how this method approximates the missing values of the traj
object.
'dataImputation'
functionObtaining the denominator information (e.g. population estimates to normalise counts) of local areas within a city for noncensus years is problematic in longitudinal studies. This challenge poses a significant drawback to the accurate estimation of various measures, such as crime rates and populationatrisk of an infectious disease. Assuming a limited amount of denominator information is available, an alternative way of obtaining the missing data points is to interpolate and/or extrapolate the missing population information using the available data points. The dataImputation
function can be used to perform this task.
The key step towards using the function for this purpose is to create a matrix, containing both the available fields and the missing fields arranged in their appropriate order. All the entries of the missing fields can be filled with either NA
or null
. Below is a demonstration of this task with a sample population dataset with only two available data fields. The corresponding input
matrix is constructed as shown.
#viewing the data first 6 rows
head(population)
#> location_id census_2003 census_2007
#> 1 E01004809 300 200
#> 2 E01004807 550 450
#> 3 E01004788 150 250
#> 4 E01012628 100 100
#> 5 E01004805 400 350
#> 6 E01004790 750 850
nrow(population) #no. of rows
#> [1] 11
ncol(population) #no. of columns
#> [1] 3
The corresponding input
dataset is prepared as follows and saved as population2
:
#> location_ids X2001 X2002 X2003 X2004 X2005 X2006 X2007 X2008 X2009
#> 1 E01004809 NA NA 300 NA NA NA 200 NA NA
#> 2 E01004807 NA NA 550 NA NA NA 450 NA NA
#> 3 E01004788 NA NA 150 NA NA NA 250 NA NA
#> 4 E01012628 NA NA 100 NA NA NA 100 NA NA
#> 5 E01004805 NA NA 400 NA NA NA 350 NA NA
#> 6 E01004790 NA NA 750 NA NA NA 850 NA NA
The missing values are estimated as follows using the regression
method of the dataImputation
function:
pop_imp_result < dataImputation(population2, id_field = TRUE, method = 2,
replace_with = 1, fill_zeros = FALSE)
#> [1] "77 entries were found/filled!"
#viewing the first 6 rows
head(pop_imp_result)
#> location_ids X2001 X2002 X2003 X2004 X2005 X2006 X2007 X2008 X2009
#> 1 E01004809 350 325 300 275 250 225 200 175 150
#> 2 E01004807 600 575 550 525 500 475 450 425 400
#> 3 E01004788 100 125 150 175 200 225 250 275 300
#> 4 E01012628 100 100 100 100 100 100 100 100 100
#> 5 E01004805 425 412.5 400 387.5 375 362.5 350 337.5 325
#> 6 E01004790 700 725 750 775 800 825 850 875 900
Given that there are only two data points in each row, the regression
method will simply generate the missing values by fitting a straight line to the available data points. The higher the number of available data points in any trajectory the better the estimation of the missing points. Figure 1 illustrates this estimation process.
###(ii) "rates"
function
Given a longitudinal data (\(m\times n\)) and its associated denominator data (\(s\times n\)), the 'rates'
function converts the longitudinal data to ‘rates’ measures (e.g. counts per 100 residents). Both the longitudinal and the denominator data may contain different number of rows, but need to have the same number of columns, and must include the id
(unique) field as their first column. The rows do not have to be sorted in any particular order. The rate measures (i.e. the output) will contain only rows whose id's
match from both datasets. We demonstrate the utility of this function using the imp_traj
object (above) and the estimated population data (‘pop_imp_result
’).
#example of estimation of 'crimes per 200 residents'
crime_per_200_people < rates(imp_traj, denomin=pop_imp_result, id_field=TRUE,
multiplier = 200)
#view the full output
crime_per_200_people
#> location_ids X2001 X2002 X2003 X2004 X2005 X2006 X2007 X2008 X2009
#> 1 E01012628 6 0 2 4 2 0 2 8 0
#> 2 E01004768 3.6 2.86 1 2.29 4.67 4 1 4 2
#> 3 E01004803 2.29 1.6 0 4.71 0.89 1.26 2.4 2.29 2.91
#> 4 E01004804 5.09 1.92 5.14 1.55 0.94 1.69 2.4 1.12 0.7
#> 5 E01004807 0.67 1.36 1.82 1.9 2.4 1.84 1.33 2.35 2
#> 6 E01004808 6.4 3.64 5.33 2.46 0.57 2.67 3 0.47 0.44
#> 7 E01004788 4 6.4 3.63 2.29 2 3.56 0.8 2.18 0
#> 8 E01004790 2.86 2.48 4.53 3.35 3.75 3.3 3.29 4.34 2
#> 9 E01004805 3.76 2.42 5 3.61 4.8 2.21 3.09 3.56 1.85
#check the number of rows
nrow(crime_per_200_people)
#> [1] 9
From the output, it can be observed that the number of rows of the output data is 9. This implies that only 9 location_ids
match between the two datasets. The unmatched ids
are ignored. Note: the calculation of rates
often returns outputs with some of the cell entries having Inf
and NA
values, due to calculation errors and character values in the data. We therefore recommend that users rerun the dataImputation
function after generating rates
measures, especially for a large data matrix.
###(iii) "props"
function {#props}
Given a longitudinal data, the props
function converts each data point (i.e. entry in each cell) to the proportion of the sum of their corresponding column. Using the crime_per_200_people
estimated above, we can derive the proportion of crime per 200 people
for each entry as follows:
#Proportions of crimes per 200 residents
prop_crime_per200_people < props(crime_per_200_people, id_field = TRUE, scale = 1, digits=2)
#view the full output
prop_crime_per200_people
#> location_ids X2001 X2002 X2003 X2004 X2005 X2006 X2007 X2008 X2009
#> 1 E01012628 0.17 0.00 0.07 0.15 0.09 0.00 0.10 0.28 0.00
#> 2 E01004768 0.10 0.13 0.04 0.09 0.21 0.19 0.05 0.14 0.17
#> 3 E01004803 0.07 0.07 0.00 0.18 0.04 0.06 0.12 0.08 0.24
#> 4 E01004804 0.15 0.08 0.18 0.06 0.04 0.08 0.12 0.04 0.06
#> 5 E01004807 0.02 0.06 0.06 0.07 0.11 0.09 0.07 0.08 0.17
#> 6 E01004808 0.18 0.16 0.19 0.09 0.03 0.13 0.16 0.02 0.04
#> 7 E01004788 0.12 0.28 0.13 0.09 0.09 0.17 0.04 0.08 0.00
#> 8 E01004790 0.08 0.11 0.16 0.13 0.17 0.16 0.17 0.15 0.17
#> 9 E01004805 0.11 0.11 0.18 0.14 0.22 0.11 0.16 0.13 0.16
#A quick check that sum of each column of proportion measures adds up to 1.
colSums(prop_crime_per200_people[,2:ncol(prop_crime_per200_people)])
#> X2001 X2002 X2003 X2004 X2005 X2006 X2007 X2008 X2009
#> 1.00 1.00 1.01 1.00 1.00 0.99 0.99 1.00 1.01
In line with the demonstration in Adepeju et al. (2019), we will use these proportion
measures to demonstrate the main clustering function of this package.
"outlierDetect"
functionThis function is aimed at allowing users to identify any outlier observations in their longitudinal data, and replace or remove them accordingly. The first step towards identifying outliers in any data is to visualise the data. A user can then decide a cutoff value for isolating the outliers. The outlierDetect
function provides two options for doing this: (i
) a quantile
method, which isolates any observations with values higher than a specified quantile of the data values distribution, and (ii
) a manual
method, in which a user specifies the cutoff value. The ‘replace_with
’ argument is used to determine whether an outlier value should be replaced with the mean value of the row or the mean value of the column in which the outlier is located. The user also has the option to simply remove the trajectory that contains an outlier value. In deciding whether a trajectory contains outlier or not, the count
argument allows the user to set an horizontal threshold (i.e. number of outlier values that must occur in a trajectory) in order for the trajectory to be considered as having outlier observations. Below, we demonstrate the utility of the outlierDetect
function using the imp_traj
data above.
#Plotting the data using ggplot library
library(ggplot2)
library(reshape2)
#converting the wide data format into stacked format for plotting
imp_traj_long < melt(imp_traj, id="location_ids")
#view the first 6 rows
head(imp_traj_long)
#> location_ids variable value
#> 1 E01012628 X2001 3
#> 2 E01004768 X2001 9
#> 3 E01004803 X2001 4
#> 4 E01004804 X2001 7
#> 5 E01004807 X2001 2
#> 6 E01004808 X2001 8
#plot function
p < ggplot(imp_traj_long, aes(x=variable, y=value,
group=location_ids, color=location_ids)) +
geom_point() +
geom_line()
print(p)
Figure 2 is the output of the above plot function.
Based on Figure 2 if we assume that observations of x2001
, x2007
and x2008
of trajectory id E01004806
are outliers, we can set the threshold
argument as 20
. In this case, setting count=1
will suffice as the trajectory is clearly separable from the rest of the trajectories.
imp_traj_New < outlierDetect(imp_traj, id_field = TRUE, method = 2,
threshold = 20, count = 1, replace_with = 2)
#> [1] "1 trajectories were found to contain outlier observations and replaced accordingly!"
#> [1] "Summary:"
#> [1] "*Outlier observation(s) was found in trajectory 10 *"
imp_traj_New_long < melt(imp_traj_New, id="location_ids")
#plot function
p < ggplot(imp_traj_New_long, aes(x=variable, y=value,
group=location_ids, color=location_ids)) +
geom_point() +
geom_line()
print(p)
Setting replace_with = 2
, that is to replace the outlier points with the ‘mean of the row observations’, the function generates outputs replotted in Figure 3.
###(v) ‘Other’ functions
Please see the akmedoids
user manual for the remaining data manipulation
functions.
#2. Data Clustering
Table 2 shows the two main functions required to carry out the longitudinal clustering and generate the descriptive statistics of the resulting groups. The relevant functions are akmedoids.clust
and statPrint
. The akmedoids.clust
function clusters trajectories according to the similarities of their longterm trends, while the statPrint
function extracts descriptive and change statistics for each of the clusters. The latter also generates quality
plots for the best cluster solution.
The longterm trends of trajectories are defined in terms of a set of OLS regression lines. This allows the clustering function to classify the final groupings in terms of their slopes as rising
, stable
, and falling
. The key benefits of this implementation is that it allows the clustering process to ignore the shortterm fluctuations of actual trajectories and focus on their longterm linear trends. Adepeju and colleagues (2019) applied this technique in crime concentration research for measuring longterm inequalities in the exposure to crime at findgrained spatial scales.
Their implementation was informed by the conceptual (inequality
) framework shown in Figure 4. That said, akmedoids
can be deployed on any measure (counts, rates) and is not limited to criminology, but rather, any field where the aim is to cluster longitudinal data based on longterm trajectories. By mapping the resulting trend lines grouping to the original trajectories, various performance statistics can be generated.
In addition to the use of trend lines, the akmedoids
makes two other modifications to the expectationmaximisation clustering routines (Celeux and Govaert 1992). First, the akmedoids
implements an anchored medianbased initialisation strategy for the clustering to begin. The purpose behind this step is to give the algorithm a theoreticallydriven starting point and try and ensure that heterogenous trend slopes end up in different clusters (Khan and Ahmad (2004); Steinley and Brusco (2007)). Second, instead of recomputing centroids based on the mean distances between each trajectory trend lines and the cluster centers, the median of each cluster is selected and then used as the next centroid. This then becomes the new anchor for the current iteration of the expectationmaximisation step (Celeux and Govaert 1992). This strategy is implemented in order to minimise the impact of outliers. The iteration then continues until an objective function is maximised.
SN  Function  Title  Description 

1 
akmedoids.clust

Anchored kmedoids clustering

Clusters trajectories into a k number of groups according to the similarities in their longterm trend and determines the best solution based on the CalinskiHarabatz criterion

2 
statPrint

Descriptive (Change) statistics and plots

Generates the descriptive and change statistics of groups, and also plots the groups performances 
#Visualising the proportion data
#view the first few rows
head(prop_crime_per200_people)
#> location_ids X2001 X2002 X2003 X2004 X2005 X2006 X2007 X2008 X2009
#> 1 E01012628 0.17 0.00 0.07 0.15 0.09 0.00 0.10 0.28 0.00
#> 2 E01004768 0.10 0.13 0.04 0.09 0.21 0.19 0.05 0.14 0.17
#> 3 E01004803 0.07 0.07 0.00 0.18 0.04 0.06 0.12 0.08 0.24
#> 4 E01004804 0.15 0.08 0.18 0.06 0.04 0.08 0.12 0.04 0.06
#> 5 E01004807 0.02 0.06 0.06 0.07 0.11 0.09 0.07 0.08 0.17
#> 6 E01004808 0.18 0.16 0.19 0.09 0.03 0.13 0.16 0.02 0.04
prop_crime_per200_people_melt < melt(prop_crime_per200_people, id="location_ids")
#plot function
p < ggplot(prop_crime_per200_people_melt, aes(x=variable, y=value,
group=location_ids, color=location_ids)) +
geom_point() +
geom_line()
print(p)
In the following sections, we provide a worked example of clustering with akmedoids.clust
function using the prop_crime_per200_people
object. The statPrint
function will then generate the descriptive summary of the clusters. The prop_crime_per200_people
object is plotted in 5 as follows:
akmedoids.clust
functionDataset:
Each trajectory in Figure 5 represents the proportion of crimes per 200 residents in each location over time. The goal is to first extract the inequality trend lines such as in Figure 4 and then cluster them according to the similarity of their slopes. For the akmedoids.clust
function, a user sets the k
value which may be an integer or a vector of length two specifying the minimum and maximum numbers of clusters to loop through. In the latter case, the akmedoids.clust
function employs either the Silhouette
(Rousseeuw (1987))) or the Calinski_Harabasz
score (Caliński and Harabasz (1974); Genolini and Falissard (2010)) to determine the best cluster solution. The function is executed as follows:
#clustering
quality_plot < akmedoids.clust(prop_crime_per200_people, id_field = TRUE,
method = "linear", k = c(3,8), crit = "Calinski_Harabasz")
#> [1] "solution of k = 3 determined!"
#> [1] "solution of k = 4 determined!"
#> [1] "solution of k = 5 determined!"
#> [1] "solution of k = 6 determined!"
#> [1] "solution of k = 7 determined!"
#> [1] "solution of k = 8 determined!"
#generate performance (quality) plot
quality_plot
#> [[1]]
#>
#> $qualitycriterion
#> [1] "Quality criterion: Calinski_Harabasz"
#>
#> $membership_optimalSolution
#> [1] "C" "D" "E" "B" "E" "A" "A" "D" "C"
#> attr(,"k")
#> [1] 5
#>
#> $qualityCrit.List
#> k qualityCrit
#> 1 3 40.84286
#> 2 4 33.32730
#> 3 5 118.79617
#> 4 6 82.56149
#> 5 7 26.99766
#> 6 8 38.78562
In addition to the performance plot (Figure 6) that shows the CalinkiHarabatz
scores at different values of k
, the akmedoids.clust
function also generates output messages which include the group memberships of the best solution, as determined quality criterion used. From the plot, the best value of k
is the highest at k=5
, and therefore determined as the best solution. It is recommended that the determination based on either of the quality criteria should be used complementarity with users judgement in relation to the problem at hand.
Given a value of k
, the group membership (labels) of its cluster solution can be extracted by typing the following command:
#clustering
cluster_output < akmedoids.clust(prop_crime_per200_people, id_field = TRUE,
method = "linear", k = c(5))
#> [1] "solution of k = 5 determined!"
#vector of group memberships
as.vector(cluster_output$memberships)
#> [1] "C" "D" "E" "B" "E" "A" "A" "D" "C"
Also, note that the indexes of the group memberships correspond to that of the trajectory object (prop_crime_per200_people
) inputted into the function. That is, the membership labels, "C"
, "D"
, "E"
, ....
are the group membership of the trajectories "E01012628"
,"E01004768"
,"E01004803"
,...
of the object prop_crime_per200_people
.
statPrint
function:Given the vector of group membership (labels), such as = c("C", "D", "E", "B",....)
in the example above, and its corresponding trajectory object, prop_crime_per200_people
, the statPrint
function generates both the descriptive
and the change
statistics of the groups. The function also generates the plots of the group memberships
and their performances
in terms of their shares of the proportion
measure captured over time.
In trajectory clustering analysis, the resulting groups are often reclassified into larger classes based on the slopes, such as Decreasing
, Stable
, or Increasing
classes (Weisburd et al. (2004);Andresen et al. 2017). The slope of a group is the angle made by the medoid of the group relative to a reference line (\(R\)). The reference
argument is specified as 1
, 2
or 3
, representing the mean trajectory
, medoid trajectory
, or a horizontal line with slope = 0
, respectively. Let \(\vartheta_{1}\) and \(\vartheta_{n}\) represent the angular deviations of the group medoids with the lowest slope (negative) and highest (positive) slopes, respectively.
If we subdivide each of these slopes into a specified number of equal intervals (quantiles), the specific interval within which each group medoid falls can be determined. This specification is made using the N.quant
argument. Figure 7 illustrates the quantiles subdivisions for N.quant = 4
.
In addition to the slope composition of trajectories found in each group, the quantile location of each group medoid can be used to further categorise the groups into larger classes. We refer users to the package user manual
for more details about these parameters. Using the current example, the function can be ran as follows:
#assigning k=5 cluster membership to a variable
clustr < as.vector(cluster_output$memberships)
#plotting the group membership
print(statPrint(clustr, prop_crime_per200_people, id_field=TRUE, reference = 1, N.quant = 4, type="lines", y.scaling="fixed"))
#> $descriptiveStats
#> group n n(%) %Prop.time1 %Prop.timeT Change %Change
#> 1 A 2 22.2 30 4 26 650
#> 2 B 1 11.1 15 5.9 9.1 154.2
#> 3 C 2 22.2 28 15.8 12.2 77.2
#> 4 D 2 22.2 18 33.7 15.7 46.6
#> 5 E 2 22.2 9 40.6 31.6 77.8
#>
#> $changeStats
#> group sn %+ve Traj. %ve Traj. Qtl:1st4th
#> 1 A 1 0 100 4th (ve)
#> 2 B 2 0 100 3rd (ve)
#> 3 C 3 100 0 1st (+ve)
#> 4 D 4 100 0 3rd (+ve)
#> 5 E 5 100 0 4th (+ve)
#assigning k=5 cluster membership to a variable
clustr < as.vector(cluster_output$memberships)
#plotting the group membership
print(statPrint(clustr, prop_crime_per200_people, id_field=TRUE, reference = 1, N.quant = 4, type="stacked"))
#> $descriptiveStats
#> group n n(%) %Prop.time1 %Prop.timeT Change %Change
#> 1 A 2 22.2 30 4 26 650
#> 2 B 1 11.1 15 5.9 9.1 154.2
#> 3 C 2 22.2 28 15.8 12.2 77.2
#> 4 D 2 22.2 18 33.7 15.7 46.6
#> 5 E 2 22.2 9 40.6 31.6 77.8
#>
#> $changeStats
#> group sn %+ve Traj. %ve Traj. Qtl:1st4th
#> 1 A 1 0 100 4th (ve)
#> 2 B 2 0 100 3rd (ve)
#> 3 C 3 100 0 1st (+ve)
#> 4 D 4 100 0 3rd (+ve)
#> 5 E 5 100 0 4th (+ve)
The above printouts are generated along with the plot of group memberships as shown in Figure 8 (see Table 3 for the description of the output table fields).
In the context of the longterm inequality study, broad conclusions can be made from these outputs regarding relative crime exposure in the area represented by each group or class (Adepeju et al. 2019). For example, whilst relative crime exposure have declined in 33.3% (groups A
and B
) of the study area, the relative crime exposure have risen in 44.4% (groups D
and E
) of the area. The relative crime exposure can be said to be stable
in 22.2% (group C
) of the area, based on its close proximity to the reference line. The medoid of the group falls within the \(1^{st}(+ve)\) quantile (see Figure 8). In essence, we determine that groups A
and B
belong to the Decreasing
class, while groups D
and E
belong to the Increasing
class.
It is important to state that this proposed classification method is simply advisory; you may devise a different approach or interpretation depending on your research questions and data.
By changing the argument type="line"
to type="stacked"
, a quality plot
is generated instead (see Figure 9). Note that these plots make use of functions within the ggplot2
library (Wickham 2016). For a more customised visualisation, we recommend that users deploy the ggplot2
library directly.
SN  field  Description 

1 
group

group membershp

2 
n

size (no.of.trajectories.)

3 
n(%)

% size

4 
%Prop.time1

% proportion of obs. at time 1 (2001)

5 
%Prop.timeT

proportion of obs. at time T (2009)

6 
Change

absolute change in proportion between time1 and timeT

7 
%Change

% change in proportion between time 1 and time T

8 
%+ve Traj.

% of trajectories with positive slopes

9 
%ve Traj.

% of trajectories with negative slopes

10 
Qtl:1st4th

Position of a group medoid in the quantile subdivisions

#Conclusion
The akmedoids
package has been developed in order to aid the replication of a placebased crime inequality investigation conducted in Adepeju et al. (2019). Meanwhile, the utility of the functions in this package are not limited to criminology, but rather can be applicable to longitudinal datasets more generally. This package is being updated on a regular basis to add more functionalities to the existing functions
and add new functions to carry out other longitudinal data analysis.
We encourage users to report any bugs encountered while using the package so that they can be fixed immediately. Welcome contributions to this package which will be acknowledged accordingly.
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