# Using cluster analysis on municipal statistical data to configure public
policies about Water, Sanitation and Hygiene in Venezuela

Saúl E. Buitrago-Boret<EMAIL_ADDRESS>Roger Martínez-Rivas
<EMAIL_ADDRESS>Josefina Flórez-Diaz<EMAIL_ADDRESS>Rodrigo
Mijares-Seminario<EMAIL_ADDRESS>Elena Rincón<EMAIL_ADDRESS>Universidad Simón Bolívar, Caracas, Venezuela

###### Abstract

Objective: The aim of this research is to demonstrate how the use of
hierarchical cluster analysis on 366 municipalities and other minor entities
(parishes) of Venezuela, could be useful to consider regional differences and
similarities between territorial entities when designing national public
policies of Water, Sanitation and Hygiene (WASH) based on evidence.
Methods and results: Consider data from various sources to characterize the
population of Venezuela through their territorial entities. Select variables
at the level of the territorial entities to cover demographic characteristics,
mortality and nutrition, coverage of reliable water and sanitation services,
access to education, and access to information and communication technologies.
Classify the territorial entities into a limited number of mutually exclusive
groups using hierarchical clustering techniques and based on proximity in the
multi-dimensional space. Adjust of assignments, reallocating some entities
into a different group based on the specialists’ opinion about its hierarchy
in the cities regional system and its geographic location. Define an indicator
to verify the consistency of the groups built. Conduct a statistical analysis
to confirm separation of the groups. Demonstrate the utility of the results
with some examples of common analysis when building a sanitary public policy,
using seven distinct groups of recommendations depending on each cluster.
Conclusions: Cluster analysis can be a useful method to analyse relevant
differences between territorial entities when designing national public
policies based on evidence.

###### keywords:

Cluster analysis; classification; hierarchical methods; municipalities
segmentation; multivariate analysis; WASH; water, sanitation, and hygiene.

††journal: Journal of LaTeX Templates

##  1 Introduction

Access to adequate water and sanitation facilities does not necessarily lead
to better health. Besides this affirmation, identify how relevant are several
social determinants to explain differences in many water-related diseases, is
a complex problem difficult to solve, much more when designing a public
policy. Reducing diseases and mortality rates on waterborne and other water-
related diseases is due not only to inadequate quality and quantity of water
for human consumption, but also to poor hygiene practices that are commonly
associated with poverty, bad access to education and adequate information,
among other determinants.

Even within the same nation, there are differences in social determinants by
region, and between urban and rural areas (see [1, 2, 3]). Consequently, it is
essential to perform analyses that discover regional differences, to design
strategies that promote a reduction of water-borne diseases adapted to each
context.

The political, economic, and social crisis that Venezuela is going through,
and the weakening of the competent public institutions have negatively
affected WASH services, both at the level of infrastructure and service
provision, to which are added inadequate hygienic practices at home (see [4,
5, 6, 7, 8, 9]).

Based on the most recent statistical data available at the level of federal
entities and municipalities, an epidemiological profile of Venezuela was
prepared regarding some water-borne diseases (WBD), demographic and
socioeconomic indicators were described, the coverage of water services and
sanitation, the assistance to educational services, and the access to
information and communication technologies was analysed. These analyses made
it possible to indicate the situation of the country and reflect regional
differences, which were categorized through a hierarchical cluster analysis.

The politic-administrative division of Venezuela covers 23 states, 1 Capital
District and 1 federal dependencies. Internally each state is divided into
municipalities. The country has 335 municipalities that are also divided in
parishes.

Data / information processing and analysis from various sources was carried
out with the aim of characterizing the population of Venezuela. To process the
data, at the states level of Venezuela, a database was created, and some basic
statistical analyses were carried out, which led to tables of total data; this
to graphically visualize some characteristics of the territorial entities.

Figure 1: On the top, deaths from diarrhoea and gastroenteritis of presumed
infectious origin x 10.000 inhabitants, by federal entity. Venezuela. 2014.
See [10, 11]. On the bottom, illness from diarrhoea in children less than 4
years of age and all ages x 10.000 inhabitants, by federal entity. Venezuela.
2016\. See [10,12].

Fig. 1 (top) shows data related to deaths from diarrhoea and gastroenteritis
of presumed infectious origin registered in Venezuela in 2014 (see [10, 11]);
it illustrates the relative importance of deaths with respect to the total
population of each federal entity.

The higher prevalence of Delta Amacuro, Amazonas, and Zulia with respect to
the national total can be observed in the registry of deaths associated with
the ingestion of water and food in unsafe conditions. In these three states,
the prevalence is double the national average. It can also be noted that the
participation of children under 5 years of age in the composition of the
general rate of deaths due to diarrhoea and gastroenteritis in these three
entities accounted for more than half of the cases, which tells us about the
precariousness of water, sanitation and hygiene, limitations in vaccination,
limitation in child deworming campaigns, precariousness of the shelter and the
socioeconomic situation of the families, among other possible reasons.

The states Trujillo, Apure, Vargas, Yaracuy, and Portuguesa, they also
register prevalence above the national average. In these cases, although the
population group of children under 5 years of age has a lower incidence, the
conditions of access to water and hygiene in homes must also be weak.

Fig. 1 (bottom) shows 2016 s records of illness from diarrhoea in children
less than 4 years of age and all ages by 10 thousand of inhabitants (see [10,
12]). According to this data, the number of sicks is higher than deaths,
remaining a high incidence of children under 4 years, and a major prevalence
of Delta Amacuro, Amazonas, and Zulia adding Falcón, Yaracuy and Cojedes over
the rest of federal entities in Venezuela.

In this research, the objects under study will be the 335 municipalities that
make up Venezuela, excluding the Libertador Municipality (the only
municipality of the Distrito Capital) for which the 22 parishes that comprise
compound it will be considered, in the same way, we will proceed with the
Vargas municipality (the only municipality in the Vargas state), considering
its 11 parishes. Thus, ending with 366 objects for the study. According to the
Living Conditions Survey ENCOVI 2020 (see [13, 14, 15, 16, 17, 18]), the
country registered in 2020 a declining population of the order of 28.5 million
inhabitants, distributed in 366 municipalities and parishes referred above.
The municipalities and the parishes present notable differences regarding
population size, percentage of urban and rural population, levels of human
development, coverage of water and sanitation services, presence of indigenous
communities and informal settlements, among other key attributes to identify
the most convenient type of intervention.

The idea pursued in this research is to demonstrate how the use of
hierarchical cluster analysis on territorial entities of Venezuela, could be
useful to consider regional differences and similarities between territorial
entities when designing national public policies of Water, Sanitation and
Hygiene (WASH) based on evidence.

##  2 Methods

To process the data from the ENCOVI 2020 survey, at the municipalities of
Venezuela and parishes of Distrito Capital and Vargas state, a database was
created.

Regarding data on the coverage of WASH and other services at homes, the ENCOVI
survey (see [13, 14, 15, 16, 17, 18]) includes 26 attributes / variables at
the level of territorial entities; of these, 15 were selected ($x_{1}$ to
$x_{15}$) within the following 6 areas:

  1. 1.

coverage of reliable water and sanitation services

     * (a)

Percentage of households with safely managed drinking water supplies
($x_{1}$).

     * (b)

Percentage of households using safely managed sanitation services ($x_{2}$).

  2. 2.

health status of the population

     * (a)

Mortality rate for children under 5 years of age ($x_{3}$).

     * (b)

Percentage of children under 5 years of age who are underweight ($x_{4}$).

  3. 3.

socioeconomic data

     * (a)

Percentage of households living below the extreme poverty line ($x_{5}$).

     * (b)

Total employment rate ($x_{6}$).

  4. 4.

demographic situation

     * (a)

Percentage of population ($x_{7}$).

     * (b)

Percentage of adolescent mothers ($x_{8}$).

     * (c)

Demographic dependency ratio ($x_{9}$).

  5. 5.

access to education in public institutions

     * (a)

Rate of attendance at a teaching centre for children from 3 to 5 years old
($x_{10}$).

     * (b)

Rate of attendance at a teaching centre for children from 6 to 11 years old
($x_{11}$).

     * (c)

Rate of attendance at a teaching centre for adolescents aged 12 to 17 years
($x_{12}$).

     * (d)

Percentage of the population aged 15 to 64 that reaches at least complete
primary education ($x_{13}$).

  6. 6.

access to information and communication technologies

     * (a)

Percentage of households that have computers ($x_{14}$).

     * (b)

Percentage of households that have internet access ($x_{15}$).

Next, new variables, $z_{1}$ to $z_{15}$, were defined as follows. The
variation ranges of each attribute $x_{i}$ were reduced to the intervals
defined by their minima and maxima, and then normalized to the range 0 to 100,
for the population of the 366 territorial entities, as shown in equation 111It
is also known as the min-max normalization method “Min-Max: Min-Max
normalization is the process of taking data measured in its engineering units
and transforming it to a value between 0 and 1. Whereby the lowest (min) value
is set to 0 and the highest (max) value is set to 1. This provides an easy way
to compare values that are measured using different scales or different units
of measure. The normalized value is defined as:
$MM(x_{ij})=(x_{ij}-x_{min})/(x_{max}-x_{min})$”, (see [19]). 1.

$z_{i}=\frac{x_{i}-\min_{1\leq j\leq 15}x_{j}}{\max_{1\leq j\leq
15}x_{j}-\min_{1\leq j\leq 15}x_{j}}100,\quad{\rm for}\ 1\leq i\leq 15.$ (1)

Some basic statistical analyses were carried out, which led to tables of total
data and radial charts; this to graphically visualize some characteristics of
the territorial entities.

When analysing the data, it is evident that within the territorial entities
there are relevant differences regarding the six areas named before.

To develop a water, sanitation and hygiene strategy adapted to these regional
differences, a hierarchical cluster analysis was carried out for the 366
territorial entities to group them into relatively homogeneous categories. The
idea is to discover associations and structures in the data that are not
obvious a priori, but that can be useful once they have been found. The
objective of this type of analysis is not to statistically explain the
phenomenon, but to classify some objects (in this case territorial entities
defined by common characteristics), into a limited number of mutually
exclusive groups.

The goal of cluster analysis is to group objects into clusters such that
objects within the same cluster are similar in some sense, and objects from
different clusters are dissimilar (see [20, 21]). The hierarchical clustering
algorithms selected proceed agglomerative (bottom-up). In agglomerative
algorithms, each object starts as a singleton cluster and the “clusters” are
merged successively. Most clustering methods form clusters based on the
proximity between data points in the multi-dimensional space. A commonly used
measure of proximity between a pair of data points (objects) is the squared
Euclidean Distance. Hierarchical algorithms output a rooted tree structure
that can be represented as a dendrogram. All analyses are conducted using
Statistics Toolbox of MATLAB (see [22]).

The hierarchical dendrogram corresponding to the analysis carried out, using a
solid red cut line, is shown in Fig. 2, where we can see 3 large very well
differentiated groups. The group on the left with 23 territorial entities of
the Distrito Capital and the States of Anzo tegui, Carabobo, Mérida, Miranda,
Nueva Esparta, and Táchira. The central group that contains most of the
country’s territorial entities (335 between municipalities and parishes). And
the group on the right with 8 municipalities in the states of Amazonas,
Barinas, and Delta Amacuro.

Figure 2: Dendrogram associated with the cluster analysis for the 366
territorial entities. Year 2020.

With this general overview, it was decided to review in detail the composition
of each group. Carrying out a group cut further down (shown by the dash red
line on the dendrogram in Fig. 2 produces 16 subgroups, where the original
group on the left remains the same, the original in the centre is divided into
10 subgroups and the original on the right is divided into 5 subgroups.

Next, based on these 16 new subgroups, the municipalities were classified into
three large categories, relocating some subgroups of municipalities that,
according to a taxonomic analysis, belonged to the original central group, but
in the opinion of the specialists they would be better located in some of the
extreme original groups, if other variables were considered, such as hierarchy
in the regional system of cities and remote geographic location with respect
large metropolitan areas. The illustration below the dendrogram in Fig. 2
shows the 7 subcategories Ia (cluster 16), Ib (cluster 15), IIa (clusters 7
and 8), IIb (cluster 2), IIb (cluster 2), IIIa (clusters 3, 4, 8, 10 and 13),
IIIb (cluster 1), and IIIc (clusters 5, 6, 11, 12, and 14).

The characteristics of each category are described below.
Category I: Municipalities and parishes that represent jurisdictions within
metropolitan areas that are generally well served with respect to the coverage
of water and sanitation services, with favourable indicators regarding their
health, demographic, and socioeconomic situation, with high percentages of
households with access to information and communication technologies.
Category II: Territorial entities in intermediate and smaller cities, some
capitals of federal entities (the vast majority of the country’s
municipalities), with less favourable indicators, but distant from the other
categories in most of the indicators used.
Category III: Corresponds to municipalities located in remote areas, far from
main populated centres; It comprises mostly border municipalities, with high
infant mortality rates, unfavourable indicators regarding their demographic
and socioeconomic situation, with great limitations in access to educational
services and information and communication technologies.

In this sense, the result of the cluster analysis is not rigorously followed,
but it does allow a general understanding of the differences that justify
three categories for a future intervention strategy: two extreme categories
and a large central majority group.

Next, the NL2 ($L_{2}$ norm) indicator is defined in equation (2) based on the
Euclidean norm with weights, where certain attributes are given greater
relevance, such as: the percentage of households that have safe managed
drinking water supply services ($x_{1}$), the mortality rate of children under
5 years of age ($x_{3}$), the percentage of households that live below the
extreme poverty line ($x_{5}$), the percentage of adolescent mothers
($x_{8}$), and the percentage of the population aged 15 to 64 that reaches at
least complete primary school ($x_{13}$). The other attributes considered in
the definition of NL2 is the denial of the meaning of the attributes:
mortality rate for children under 5 years of age ($x_{3}$), percentage of
children under 5 years of age who are underweight ($x_{4}$), percentage of
households living below the extreme poverty line ($x_{5}$) and percentage of
adolescent mothers ($x_{8}$), to have positive contributions to the indicator.

$NL2=(\sum_{i=1}^{15}x_{i}^{2}w_{i})^{1/2},\quad{\rm with}\
\sum_{i=1}^{15}w_{i}=1.$ (2)

Fig. 3 shows how the 366 territorial entities are distributed according to the
indicator NL2. We can appreciate how the 7 subcategories are separated. The
abscissa axis corresponds to the number assigned to each territorial entity
and the ordinate axis the calculated value of NL2 for each territorial entity.

A way to verify the validity and accuracy of the results is to analyse and
compare characteristics of specific local entities. The municipalities of
Moran (Lara State), Baruta and El Hatillo (Miranda State) have been chosen for
detailed comparisons.

Table 1: Values of variables for Moran, Baruta and El Hatillo Municipalities.
Year 2020.

Variables | Moran | Baruta | El Hatillo
---|---|---|---
| ID: 170 | ID: 200 | ID: 206
| NL2: 68.81 | NL2: 80.84 | NL2: 78.66
Percentage of households with safely managed drinking water supplies ($x_{1}$) | 77,77% | 91,24 % | 79,14%
Percentage of households using safely managed sanitation services ($x_{2}$) | 69,80% | 99,90 % | 98,39%
Mortality rate for children under 5 years of age ($x_{3}$) | 24,85% | 10,64% | 10,90%
Percentage of children under 5 years of age who are underweight ($x_{4}$) | 9,06% | 3% | 3,19%
Percentage of households living below the extreme poverty line ($x_{5}$) | 67% | 47% | 51%
Total employment rate ($x_{6}$) | 54% | 54% | 50%
Percentage of population ($x_{7}$) | 0,44% | 1,17% | 0,29%
Percentage of adolescent mothers ($x_{8}$) | 15,05% | 5,56% | 6,39%
Demographic dependency ratio ($x_{9}$) | 59% | 39% | 41%
Rate of attendance at a teaching centre for children from 3 to 5 years old ($x_{10}$) | 49,37% | 74,75% | 74,59%
Rate of attendance at a teaching centre for children from 6 to 11 years old ($x_{11}$) | 94,76% | 95,20% | 92,05%
Rate of attendance at a teaching centre for adolescents aged 12 to 17 years ($x_{12}$) | 77,33% | 90,17% | 86,13%
Percentage of the population aged 15 to 64 with at least primary education ($x_{13}$) | 76,53% | 95,43% | 94,45%
Percentage of households that have computers ($x_{14}$) | 12,15% | 50,20% | 46,07%
Percentage of households that have internet access ($x_{15}$) | 7,28% | 47,38% | 42,50%
Cluster (Capital number: Category; Small letter: Subcategory) | IIb | Ia | Ia

As one can observe in Table 1, in general the values of Hatillo and Baruta are
relatively closer between them than when are compared with Moran values.
However, some indicators are too close as to explain differences between
entities (for example: variables $x_{6}$ and $x_{11}$), and in some cases
values are distributed in a way that a single indicator considered alone could
justify a different classification (for example: variable $x_{7}$). These
simple observations allow to asseverate the importance of consider a wide set
of variables and the relevance of analyse them, trying to smooth small
differences and to discover a convincing grouping of entities.

With this idea in mind, the municipalities Morán (ID:170, NL2:68.81) belonging
to subcategory IIb, and Baruta (ID:200, NL2:80.84) and El Hatillo (ID:206,
NL2:78.66) belonging to category Ia, are highlighted with a black square in
Fig. 3

Figure 3: Territorial entities distributed according to the indicator NL2 (L2
norm). Year 2020.

It can be noted that municipalities Baruta and El Hatillo in Miranda state,
part of the metropolitan area of Caracas, are clearly far from Morán in Lara
state and from other entities categorized in II (compare red and green dots in
Fig. 3). Indeed, according with further analysis, these two first entities
have a more favourable situation than Morán, regarding services available and
hygiene practices at home.

Furthermore, it is convenient to advice that within each municipality and
parish there may also appear some differences, especially in urban areas, that
can justify detailed analysis for future interventions. For example, in the El
Hatillo municipality of Miranda state, a less favourable situation can be
observed in the rural settlement of Gavilán, an inner community, in comparison
with El Hatillo town, its capital. The same situation appears with the
neighbourhoods of La Palomera and Ojo de Agua, slum settlements inside
Municipality of Baruta, compared to residential areas of higher income groups.
In general, the communities located in the urban peripheries have situations
that are more like rural areas or small populated rural centres of the
country.

##  3 Results

The municipalities and parishes of category type I comprise 27 jurisdictions,
presented by 14 parishes of the Distrito Capital and 13 municipalities located
in the states of Anzoátegui, Bolívar, Carabobo, Lara, Mérida, Miranda, Nueva
Esparta, Táchira, and Zulia. The estimated population in 2020 exceeds 6
million 400 thousand inhabitants (22,46% of the total population of the
country). The health conditions, water and sanitation coverage, economic,
demographic, educational and services situation in these jurisdictions are the
most favourable in the national context. They are located within large
metropolitan areas, which ensures access to services, although within them
there may be informal settlements with unfavourable situations, as in the
cases of Baruta and El Hatillo in the State of Miranda. The data analysis
distinguishes two subcategories, Ia (14 parishes and 9 municipalities) and Ib
(4 municipalities).

The municipalities and parishes of category type II comprises 299
jurisdictions, consisting of eight parishes of the Distrito Capital and 291
municipalities located in all federal entities in the country. The estimated
population in 2020 reaches about 21 million inhabitants (73,68% of the total
population of the country). Their health conditions, water and sanitation
coverage, socioeconomic, demographic, educational and services situation is
around the national average. The parishes of the Distrito Capital and several
municipalities are in large metropolitan areas, but the majority belong to
intermediate cities and small rural populations. The data analysis
distinguishes two subcategories, IIa (159 entities) and IIb (140 entities).

The municipalities and parishes of category type III comprise 40
municipalities located in Amazonas, Anzo tegui, Apure, Aragua, Barinas,
Bolívar, Delta Amacuro, Falcón, Guárico, Lara, Mérida, Monagas, Portuguesa,
Táchira, Trujillo, and Zulia. The estimated population in 2020 reaches around
1 million 100 thousand inhabitants (3,86% of the total population of the
country). Their health conditions, water and sanitation coverage,
socioeconomic, demographic, educational and services situation are worse than
the national average (depending on the attribute considered). They correspond
to small rural populations distant from the main cities of the regions to
which they belong, so their access to goods and services is limited. The data
analysis distinguishes three subcategories, IIIa (12 municipalities), IIIb (20
municipalities) and IIIc (8 municipalities).

Table 2 shows the average values for the attributes, and Fig. 4 shows the
radial charts for each of the subcategory belonging to categories I, II and
III.

Table 2: Average values of the 15 attributes in the jurisdictions for each of
the seven subcategories. Year 2020.

Attributes / Categories | Ia | Ib | IIa | IIb | IIIa | IIIb | IIIc
---|---|---|---|---|---|---|---
Percentage of households with safely managed drinking water supplies ($x_{1}$) | 90,61 | 76,80 | 40,49 | 66,41 | 61,79 | 49,02 | 27,42
Percentage of households using safely managed sanitation services ($x_{2}$) | 99,60 | 97,48 | 81,78 | 84,52 | 85,63 | 65,40 | 22,79
Mortality rate for children under 5 years of age ($x_{3}$) | 14,98 | 18,63 | 10,74 | 27,60 | 31,65 | 29,66 | 51,43
Percentage of children under 5 years of age who are underweight ($x_{4}$) | 4,09 | 6,45 | 4,22 | 9,56 | 10,01 | 10,12 | 14,03
Percentage of households living below the extreme poverty line ($x_{5}$) | 49,36 | 64,50 | 47,00 | 67,66 | 64,33 | 68,80 | 69,63
Total employment rate ($x_{6}$) | 59,43 | 54,00 | 55,52 | 47,84 | 64,33 | 38,75 | 34,50
Percentage of population ($x_{7}$) | 0,33 | 3,76 | 0,33 | 0,15 | 0,07 | 0,14 | 0,04
Percentage of adolescent mothers ($x_{8}$) | 7,03 | 12,78 | 7,32 | 16,20 | 14,78 | 19,94 | 21,51
Demographic dependency ratio ($x_{9}$) | 46,43 | 54,25 | 41,00 | 61,14 | 62,42 | 63,75 | 71,75
Rate of attendance at a teaching centre for children from 3 to 5 years old ($x_{10}$) | 78,15 | 60,62 | 44,60 | 55,98 | 56,39 | 40,64 | 35,02
Rate of attendance at a teaching centre for children from 6 to 11 years old ($x_{11}$) | 37,07 | 96,64 | 90,90 | 95,80 | 94,01 | 89,94 | 70,15
Rate of attendance at a teaching centre for adolescents aged 12 to 17 years ($x_{12}$) | 89,98 | 85,73 | 70,50 | 81,89 | 74,89 | 76,10 | 65,75
Percentage of the population aged 15 to 64 with at least primary education ($x_{13}$) | 97,14 | 92,85 | 69,04 | 80,29 | 72,90 | 70,99 | 47,42
Percentage of households that have computers ($x_{14}$) | 57,80 | 32,50 | 7,12 | 11,24 | 9,99 | 8.08 | 3,34
Percentage of households that have internet access ($x_{15}$) | 48,63 | 23,40 | 3,43 | 5,58 | 3,37 | 3,46 | 0,81

Figure 4: Radial charts of average values in the jurisdictions for each of the
seven subcategories. Year 2020.

Fig. 5 presents the box plot 222WASH and other services at homes outcomes are
presented as box plots, which represent the middle 50% of the data, ranging
from the upper boundary (75th percentile) to the lower boundary (25th
percentile). Box lines indicate median values. Vertical lines extending from
the box indicate minimum and maximum values and dots are outliers. regarding
data on the coverage of WASH and other services at homes in the six areas
defined before (see section Materials and methods) for the 15 attributes for
the seven subcategories coming from the cluster analysis performed. It can be
observed how the seven subcategories separate to each other for each of the 15
attributes.

Figure 5: Box plot regarding data on the coverage of WASH and other services
at homes in the areas and attributes for the seven subcategories. Year 2020.

Fig. 6 shows the 366 territorial entities distributed according to the 7
subcategories obtained by the cluster and taxonomic analysis.

Figure 6: Municipalities and parishes of Venezuela under category Ia, Ib, IIa,
IIb, IIIa, IIIb and IIIc. Top left, Vargas state with 11 parishes. Top centre,
Distrito Capital with 22 parishes. Top right, Nueva Esparta state with 11
municipalities. Bottom, Venezuela with 335 municipalities excluding Distrito
Capital and the Vargas state.

As an example of how a clustering analysis allows design strategies to perform
a water and sanitation program of intervention, Table 3 below shows how
different types of intervention can be more or less suitable to different type
of municipalities, according to their attributes. The taxonomy of
municipalities allows a quick analysis of viability once the universe of local
entities has been well categorized.

Table 3: Example of different types of intervention to different type of
municipalities, according to their attributes. Year 2020. Legend: 5 more
suitable / reliable, 4 relatively suitable / reliable, 3 less reliable / less
suitable, 2 unuseful / unapplicable, 1 need more information.

Examples of public policies on Water, Sanitation and Hygiene / Categories | Ia | Ib | IIa | IIb | IIIa | IIIb | IIIc
---|---|---|---|---|---|---|---
Designing / building / improving large and medium water & sanitation infrastructure systems | 5 | 5 | 5 | 5 | 3 | 2 | 2
Designing / building / improving small and domestic water & sanitation systems | 3 | 3 | 3 | 3 | 5 | 5 | 5
Providing information to ensure safe water, sanitation, and hygiene practices at household level through workshops | 4 | 4 | 4 | 4 | 5 | 5 | 5
Providing information to ensure safe water, sanitation and hygiene practices at household level using TICs | 5 | 5 | 4 | 3 | 2 | 2 | 2
Humanitarian assistance and donations on WASH services | 1 | 1 | 1 | 1 | 5 | 5 | 5

##  4 Conclusions

Venezuela has a great diversity of urban and rural contexts, with very unequal
infrastructure and WASH services conditions, additionally, there are great
cultural and geographical differences that must be considered in the
application of strategies for the promotion of best WASH practices at
household level.

Regarding our original objective, to characterize the population of Venezuela,
we find strong support for the idea that municipalities clusters in Venezuela
are associated with households with safely managed drinking water supplies,
and using safely managed sanitation services, mortality rate for children
under 5 years of age, children under 5 years of age who are underweight,
households living below the extreme poverty line, employment rate, population
rate, adolescent mothers, demographic dependency, attendance at a teaching
centre for children, and for adolescents, population aged 15 to 64 that
reaches at least complete primary, households with computers, and internet
access.

Cluster analysis of the most recent statistical data available at the level of
municipalities of Venezuela related to WASH and other services at homes can
segment the municipalities into seven distinct groups, Ia, Ib, IIa, IIb, IIIa,
IIIb, and IIIc, for unique recommendations in the future in the field of
Water, Sanitation and Hygiene interventions.

The entities belonging to category I correspond to better served
municipalities and parishes, with more benevolent indicators, belonging to
main metropolitan areas. The entities in category II are municipalities in
intermediate and smaller cities, some capitals of federal entities, with less
favourable indicators. Finally, the entities in category III are remote
municipalities, far from metropolitan areas, with unfavourable indicators in
almost all aspects.

This segmentation of groups is relevant when it is planning a humanitarian
WASH Intervention at national and subnational levels. Close municipalities
with a menu of options to provide information to the people, with several
community facilities near to vulnerable communities are more affordable to
plan the distribution of human, material and technical resources than those
far locations where exist big gaps on water, sanitation and hygiene
conditions, with historical disadvantages on socioeconomic and demographic
indicators, and where transportation, points of information and availability
of resources are hard to overcome.

Despite some potential limitations, this approach yields valuable intelligence
to inform local government planning service and at the same time offers great
potential for further research into its use in informing possible preventative
WASH services.

## Abbreviations

BM: Banco Mundial; ENCOVI: Encuesta Nacional de Condiciones de Vida; INE:
Instituto Nacional de Estad stica; OCHA: UN Office for the Coordination of
Humanitarian Affairs; PAHO: Pan American Health Organization; WASH: Water,
Sanitation and Hygiene; WHO: World Health Organization.

## References

  * BM [2021] BM, Semiannual report of the Latin America and the Caribbean region, Renewing with growth, Technical Report, World Bank, 2021. https://openknowledge.worldbank.org/handle/10986/35329.
  * PAHO and WHO [2017] PAHO and WHO, Health in the Americas, Venezuela (Bolivarian Republic of), Technical Report, Pan American Health Organization and World Health Organization, 2017\. https://openknowledge.worldbank.org/handle/10986/35329.
  * OCHA [2019a] OCHA, Humanitarian Response Plan Venezuela, Humanitarian Program Cycle, Technical Report, United Nations Office for the Coordination of Humanitarian Affairs, 2019a. https://reliefweb.int/sites/reliefweb.int/files/resources/20190814_HRP_VEN_EN.pdf.
  * OCHA [2019b] OCHA, Venezuela: Humanitarian Response Situation Report No.02, Technical Report, United Nations Office for the Coordination of Humanitarian Affairs, 2019b. https://reliefweb.int/report/venezuela-bolivarian-republic/venezuela-humanitarian-response-situation-report-no-02-july.
  * OCHA [2019c] OCHA, Venezuela: Humanitarian Response, Situation Report No.6, Technical Report, United Nations Office for the Coordination of Humanitarian Affairs, 2019c. https://reliefweb.int/report/venezuela-bolivarian-republic/venezuela-humanitarian-response-situation-report-no-6-november.
  * OCHA [2020] OCHA, Venezuela: Informe de situaci n, Technical Report, United Nations Office for the Coordination of Humanitarian Affairs, 2020. https://reliefweb.int/report/venezuela-bolivarian-republic/venezuela-informe-de-situaci-n-diciembre-2020.
  * OCHA [2021] OCHA, Venezuela: Informe de situaci n, Technical Report, United Nations Office for the Coordination of Humanitarian Affairs, 2021. https://reliefweb.int/report/venezuela-bolivarian-republic/venezuela-informe-de-situaci-n-enero-2021.
  * UNICEF [2020] UNICEF, Bolivarian Republic of Venezuela Humanitarian Situation Report, Technical Report, UNICEF Venezuela, 2020. https://www.unicef.org/media/92851/file/Venezuela-Situation-Report-End-of-Year-2020.pdf.
  * UNICEF [2021] UNICEF, Bolivarian Republic of Venezuela Humanitarian Situation Report, Technical Report, UNICEF Venezuela, 2021. https://www.unicef.org/media/96371/file/Venezuela-SitRep-February-2021.pdf.
  * INE [2020] INE, Proyección de la población al 30 de junio 2016 con base al censo 2011, según entidad federal y municipios, 2000-2051, Technical Report, Instituto Nacional de Estad stica, Ministerio del Poder Popular de Planificación, Venezuela, 2020\. https://www.ine.gov.ve/index.php?option=com_content&view=category&id=98&.
  * MPPS [2014] MPPS, Anuario de Mortalidad. Observatorio Venezolano de la Salud, Technical Report, Ministerio del Poder Popular para la Salud, Venezuela, 2014. https://www.ovsalud.org/publicaciones/documentos-oficiales/anuario-mortalidad-2014/.
  * MPPS [2016] MPPS, Semana Epidemiológica 52 - Boletín Epidemiológico. Observatorio Venezolano de la Salud, Technical Report, Ministerio del Poder Popular para la Salud, Venezuela, 2016. https://www.ovsalud.org/publicaciones/documentos-oficiales/boletin-epidemiologico-2016/.
  * ENCOVI [2020a] ENCOVI, Indicador de educación, Universidad Católica Andrés Bello, 2020a. https://insoencovi.ucab.edu.ve/indicador-de-educacion/.
  * ENCOVI [2020b] ENCOVI, Indicador de empleo, Universidad Católica Andrés Bello, 2020b. https://insoencovi.ucab.edu.ve/indicador-de-empleo/.
  * ENCOVI [2020c] ENCOVI, Indicador de vivienda y hogar, Universidad Católica Andrés Bello, 2020c. https://insoencovi.ucab.edu.ve/indicador-de-vivienda-y-hogar/.
  * ENCOVI [2020d] ENCOVI, Indicador de pobreza, Universidad Católica Andrés Bello, 2020d. https://insoencovi.ucab.edu.ve/indicador-pobreza/.
  * ENCOVI [2020e] ENCOVI, Indicador de salud y alimentación, Universidad Católica Andrés Bello, 2020e. https://insoencovi.ucab.edu.ve/salud-y-alimentacion/.
  * ENCOVI [2020f] ENCOVI, Indicadores demográficos, Universidad Católica Andrés Bello, 2020f. https://insoencovi.ucab.edu.ve/indicadores-demografcos/.
  * Mohamad and Usman [2013] I. Mohamad, D. Usman,  Standardization and its effects on K-means clustering algorithm,  Research Journal of Applied Sciences, Engineering and Technology 6 (2013) 3299–3303. doi: 10.19026/rjaset.6.3638.
  * Kaufman and Rousseeuw [1990] L. Kaufman, P. Rousseeuw, Finding groups in data: an introduction to cluster analysis. Wiley Series in Probability and Statistics, John Wiley & Sons, Inc., 1990.
  * Rencher [2002] A. Rencher, Methods of Multivariate Analysis. Wiley-Interscience, 2nd ed., John Wiley & Sons, Inc., 2002.
  * MATLAB [2014] MATLAB, Matlab and statistics toolbox release 2014a, The MathWorks, Inc., Natick, Massachusetts, United States, 2014. https://la.mathworks.com/company/newsroom/mathworks-announces-release-2014a-of-the-matlab-and-simulink-product-families.html.