Poverty Population and Its Educational Accessibility: An Evaluation

using Geospatial Database in Ecuador

Gustavo Navas

, Anthony Loyaga

, Jefferson Barahona

and Julio Proa

no Orellana

Universidad Polit

ecnica Salesiana, Mor

an Valverde S/N y Rumichaca, Quito, Ecuador

Keywords:

Geospatial, GIS, Voronoi, Educational Centers, Spacial Analysis, Poverty.

Abstract:

Education is the most critical aspect against inequality. So, the accessibility to educational centers is one of

the biggest problems that governments and academia are trying to solve in Latin America. Finding an optimal

location is challenging because different variables can be considered, such as serving the poor, accessing to

roads, and optimal distribution. This work presents a methodology that uses geospatial techniques to evaluate

the level of access of poor people to educational centers considering location and accessibility. This method-

ology is multi factorial and can be generalized to multiple scenarios in resource planning. In this work, a case

study of Ecuadorian educational centers is analyzed. So, an Ecuadorian geospatial database was created that

includes educational centers’ location, the distribution of the poor population, Voronoi diagrams, and road

networks. All the analysis were carried out using the PostGIS tool and Google Cloud Platform for the im-

plementation. The results show that 90% of educational centers serve at least 50% of the most impoverished

population.

1 INTRODUCTION

Education is the most critical aspect to mitigate in-

equality problems. However, according to UNICEF

(UNICEF, 2019), more than 60 million children will

not access primary education in 2030. On the other

hand, urban and regional areas have experienced con-

siderable growth (Navarrete and Luj

an-Mora, 2018).

It has led to the authorities’ different planning and

distribution of educational facilities to ﬁnd the most

suitable location (Brock and Schaefer, 2015). Educa-

tional institutions in Ecuador are characterized by low

educational quality and the absence of minimum con-

ditions for girls, boys, and young people. On the other

hand, education in Ecuador has lacked an academic

level of quality in the poorest populations. In 2005,

the educational project of the Educational Units of the

Millennium began, which aims to reduce this gap and

reach children with limited economic resources. The

Millennium Educational Units (EMU) is a project for

the endowment of public educational institutes of pri-

mary and secondary levels. It was created to improve

https://orcid.org/0000-0002-2811-0282

https://orcid.org/0000-0002-8532-114X

https://orcid.org/0000-0002-9150-8891

https://orcid.org/0000-0001-6378-6854

its education and reach the poor sectors. For this rea-

son, its buildings are located in sectors historically

relegated to the national level, which present high

indices of unsatisﬁed basic needs and social prob-

lems. Each EMU is built to guarantee access for

the school population in rural areas permanently ex-

cluded from educational services. EMUs are based on

various location criteria for their construction. How-

ever, it is challenging to choose the optimal location

of educational centers based on spatial analyses. It is

essential to fulﬁlling all social demands considering

poverty and accessibility. Various methodologies and

approaches help planners and governments to address

system inefﬁciency problems through Geographic In-

formation Systems (GIS) (Al-Sabbagh, 2020), (Bulti

et al., 2019), (Boix and Olivella, 2007). The Ge-

ographic Information System provides reliable data

with spatial references that decision-makers could use

to guarantee the optimal distribution of schools based

on the impact of the children served. The study of lo-

cation optimization is not new. Thus, since 1909 We-

ber has studied the optimization of the location of a

company in a region (Okabe and Suzuki, 1997). How-

ever, in recent works, there are applications of these

studies related to the location of schools. In (Jabbar

and Laffta, 2020), the authors propose a methodology

to analyze location sites of private secondary schools

Navas, G., Loyaga, A., Barahona, J. and Orellana, J.

Poverty Population and Its Educational Accessibility: An Evaluation using Geospatial Database in Ecuador.

DOI: 10.5220/0011075200003185

In Proceedings of the 8th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2022), pages 147-154

ISBN: 978-989-758-571-5; ISSN: 2184-500X

147

in the Al-Jihad neighborhood in Baghdad-Iraq. This

methodology determines the spatial distribution pat-

terns of the private secondary schools in the study area

and the distance between the secondary school sites

in the neighborhoods and, ﬁnally, in the appropriate

locations by analyzing the spatial distribution of the

schools and determining suitable locations. In an-

other study conducted in Jeddah, Saudi Arabia (Mu-

rad et al., 2020), the authors used a geodatabase that

incorporates data on education and population. Based

on these data, spatial and network analyzes were used

to understand the location distribution, student den-

sity, and accessibility of schools in the study region.

Both of the studies mentioned above have used GIS

tools to ﬁnd the location of schools, considering dif-

ferent parameters. However, the authors do not men-

tion the optimal location and whether their methodol-

ogy can be applied to the entire country. All of these

works are applied to small cities. In contrast, in (Botto

et al., 2015), the authors locate schools in Uruguay

based on proximity criteria to target households and

prioritize socioeconomic vulnerability. The main idea

is to use an optimal allocation algorithm to distribute

the population. Therefore, the current supply should

be sufﬁcient to cover the demand. However, the au-

thor does not consider other parameters such as road

accessibility.

Other approaches include additional parameters.

For example, in (Al-Sabbagh, 2020), the authors pro-

pose a study that uses GIS tools to locate elementary

schools based on different strategies such as current

elementary school distribution, average nearest neigh-

bor, hotspot analysis, and clustering analysis. Fur-

thermore, in (Sezer et al., 2018) the study applies the

network analysis to locate the closest schools in the

city of Usak. The study also considers the number of

students, teachers, classrooms, roads, and buildings.

These data were obtained from the Usak municipality

zoning plan and the OpenStreetMap vector. In all of

these previous works, the use of bigdata is missing.

Therefore, the main contribution of this work is to

extend previous methodology (Navas et al., 2019) to

evaluate the impact of the school’s location in the pop-

ulation with attendance poverty in Ecuador, but taking

into account the road accessibility and the distribution

of the Voronoi Diagram as optimal distribution. It is

worth noting that the combination of economic data

(poverty) and GIS tools such as Voronoi and road ac-

cessibility is a challenge because our study belongs to

all of the country (bigdata).

So, in this work, an extension of a novel methodol-

ogy is proposed for evaluating the location of schools

based on multiple factors such as poverty level, road

accessibility, access distance, and a Voronoi Diagram.

It is challenging because the use of mixed bigdata,

including demographic and spacial data (Yan et al.,

2010).

2 METHODS

Before starting the description of the methodology,

terms and basic operations of spatial data analysis are

brieﬂy explained.

• The Voronoi Diagram. It is generated from

N spatial objects (geographical points or lines),

which must generate N Voronoi polygons that de-

limit each object, where the number of sides of

this polygon is according to the number of clos-

est neighbors, consequently generating a polygon

that contains the area of inﬂuence of the object

(Erwig, 2000).

• The Buffer Operator. Returns a buffer zone at

a speciﬁed distance from a geographic point or

line. The type of geographic object obtained is a

polygon, and what is inside the polygon is called

a buffer zone.

• The Intersection Operator. Returns a zone of in-

ﬂuence corresponding to the intersection or com-

mon area between two classes of different geo-

graphic entities. The Intersection. is that opera-

tor where there are two areas that give place to the

intersection or common area between them.

• Population Density. The ρ, corresponds to to

population density in number of inhabitants per

square kilometer (hab/km

Next, the mathematical formalization is established in

a subsequent subsection to show how it is applied and

ﬁnally land the case study’s solution.

2.1 Methodology Development

The objective of this methodology is to establish pos-

sible locations that are the most appropriate accord-

ing to the variable considered. In our case study the

location corresponds to new sites to locate new pre-

university study centers and the parameter to consider

is poverty and the accessibility. From step 1 to step

8, the existing locations are analyzed based on the pa-

rameter studied to order them from highest to lowest

depending on the parameter. Starting from step 9, the

procedure is given to establish the new locations. The

process is explained bellow (see Figure 1).

Step 1: First, set the generators ”s1

” that are the fun-

damental input for steps 2 and 3 that generate the di-

agrams of Voronoi and Buffer tool respectively. The

GISTAM 2022 - 8th International Conference on Geographical Information Systems Theory, Applications and Management

148

generator turns out to be the geographical location of

all the units that exist, are located within area S and

which is being studied. In our case study they are

educational units and it is always considered a point

type.

Figure 1: Methodology development diagram.

These units can be the geolocated location of the

subject studied and point type. For example, in the

case of education, it would be the location of edu-

cational centers, or in business, it could be the loca-

tion of product distribution centers, etc. Step 2: The

generators from step 1 will be used for the Voronoi

diagrams, the ﬁrst classiﬁcation of these limits comes

given by the result of applying the Voronoi diagram in

a established area. Voronoi diagrams cover the entire

space of study. Step 3: Corresponds to the Buffer tool

around the generators set in the ﬁrst step. One feature

is that there must be as many areas generated by the

Voronoi diagram as areas generated by the Buffer, and

these must be equal in quantity to the number of ge-

ometries generated in step 2.

(ν, generators, distance) = β

(ν

,s1

,d) (1)

where B

occupies a subspace of S

Step 4: Intersection operator between the generated

areas in steps 2 and 3. For this case there can be two

types of polygons resulting.

• That the buffer is completely within the Voronoi

diagram correspondent.

• Let the two areas intersect in a common area.

= B

∩ ν

(2)

The i index traverses each of the generators for both

the polygons of Voronoi as well as for the Buffer ar-

eas generated.

Step 5: It is a new generator ”s2

” that corresponds to

ways land, river, etc. found in area S.

Step 6: It is a buffer around the generators ”s2

” re-

lated to the access roads, which were established in

step 5. This buffer corresponds to an area of inﬂuence

of the road, depending on the closeness to her.

(S,generators,distance) = β

(ν

,s2

,d)r (3)

Step 7: It is an intersection operation between the

results from step 4 and step 6. Giving rise to a new

result that is:

= B

∩ I

(4)

Applying I

, we have:

= ( B

∩ ( B

∩ ν

)) (5)

This relates the areas linked to the units with accessi-

bility.

Step 8: It is an intersection between the geopoliti-

cal division with the analyzed parameter ”Γ

” and the

step 7 result ”I

”. It gave rise to a new result that is

formalized as follows manner.

= Γ

∩ I

(6)

Applying I

, we have:

Fij

= Γ

∩ ( B

∩ ν

)) (7)

corresponds to the number of political divisions,

which contains the parameter to be analyzed. There-

fore, the index j is different from index i, since j cor-

responds to the number of political divisions within

”I

i”, The parameter in Γ

, can be diverse, in some

cases, it could be the poverty index and in another

moment could be the Population Density H. The in-

tersection gives rise to a series of sub-areas for each

unit provided in generator ”s1

”. Every sub-area in-

cludes its value of the parameter.

It is analyzed for sub-areas for each generator

1, the procedure described by (Navas et al., 2019)

for I

Fij

through the formula 8 of weighting a group

of data X

,...,X

, considering a series of coefﬁ-

cients or parameters for each political division, called

Poverty Population and Its Educational Accessibility: An Evaluation using Geospatial Database in Ecuador

149

weights W ,W

,...,W

. It can determine the level of

the parameter in each weighted political division of

each irregular polygon.

+ w

+ ... + w

)

+ w

+ ... + w

)

(8)

Step 9: Incorporate the population density parameter

H at the level of political divisions it corresponds to

H j

, and select those political divisions that incorpo-

rate the conditions of selection. These selected divi-

sions will be called ”candidate political division” Γ

∈ Γ

H j

; (9)

where Γ

meets selection condition.

Step 10: Location of the centroid for each of the Can-

didate political divisions.

∀ Γ

∈ Γ

H j

∃ s3

(10)

Where ”s3

”, is the centroid of the divisions of each

political candidate Γ

. And Where ”s3

” is a new

generator.

Step 11: With the Buffer tool around the generator

obtained in step 10, it was generated the area of inﬂu-

ence of each candidate centroid ”s3

”. One character-

istic is that there must be as many areas generated by

the buffer tool as the number of candidate centroids

”s3

” that was generated in step 10.

(S,generators,distance) = β

(ν

,s3

,d)r

(11)

Step 12: the goal of this step is to reduce the number

of areas generated for each candidate centroid, from

step 11, with those.

lik

∈ B

(12)

Where B

lik

, are the reduced candidate areas corre-

sponds to a reduced set of B

Step 13: An intersection between the candidate areas

reduced by the area corresponding to the roads ob-

tained in step 6. B

lik

is carried out, with the area of

inﬂuence of step 8.

kli

= B

lik

∩ B

(13)

2.2 Case of Study

The following case study was carried out in Ecuado-

rian territory. In this way, it was essential to ho-

mogenize the geographic data for proper geoprocess-

ing. Thus, the geographic reference system was deter-

mined in EPSG / SRID 32717. This identiﬁer corre-

sponds to Datum WGS84, UTM zone 17S. The Post-

greSQL Database Management System was used with

its extension for GIS, PostGIS, to carry out this geo-

processing. Steps 1 and 2 are shown in Figure 2.

Step 1: In the Ecuadorian context, the geographical

information on the positioning of educational institu-

tions is registered by the MINEDUC. The point type

geographic records are found in the geographic refer-

ence system corresponding to the EPSG / SRID code

32717 (Guti

errez, 2006) (Llario, 2013).

Step 2: Before creating the Voronoi Polygons, it is

necessary to have geographic information of the poly-

gon type that describes the political boundary of the

country, whose function is to create the Voronoi con-

tour and segment the Ecuadorian territory. This op-

eration results in the Voronoi segmentation of educa-

tional institutions whose external contour is a quadri-

lateral by default. Here, using the intersection opera-

tion, only the Ecuadorian territory is outlined, which

is the region of this case study (Guti

errez, 2006)

(Llario, 2013).

Figure 2: Area of each EMU segmented with Voronoi dia-

grams.

Figure 3: 20km radial buffer.

Step 3: The buffer to each educational institution was

determined for a radius of 20 km as is shown in de-

tail in Figure 3. The proposal of this value is based

on the evidence of students in neighboring communi-

ties up to 20 km away. For this, the operation used

was ST Buffer, which creates a circular polygon with

the speciﬁed radius (Guti

errez, 2006) (Llario, 2013).

Step 4: Taking the segmentation by Voronoi poly-

gons and segmentation by radial buffers of each ed-

ucational institution, we contrast the areas in which

GISTAM 2022 - 8th International Conference on Geographical Information Systems Theory, Applications and Management

150

these two paths converge. It is possible through the

ST Intersection operation, which takes two geome-

tries as parameters and returns the space intersect (see

Figure 4) (Guti

errez, 2006) (Llario, 2013).

Figure 4: Intersection of Voronoi polygons of each EMU

with 20 km buffer.

Step 5: In Figure 5, the geographic information of

the Ecuadorian road network can be found on the ofﬁ-

cial IGM site and the “multiline” type geographic data

set can be found in the EPSG/SRID 4326 reference

system. For the conversion of geographic data to the

EPSG/SRID 32717 reference system speciﬁed in step

1, the ST Transform operation was used (Guti

errez,

2006) (Llario, 2013).

Figure 5: Ecuadorian road network.

Step 6: The zone of inﬂuence of the road network can

also be obtained with the ST Buffer operation. In this

case, being of type line, the parameter within the op-

eration indicates the width of the buffer. For this, the

relative distance of 2 km from the road’s edge was ap-

plied to each side. That distance was estimated con-

sidering that there are records of children who have

to walk up to 30 minutes from their home to reach

school transport on the nearest road. With this, we

know how far the schoolboy can walk, but not the

distance. However, if the ACSM is considered, which

indicates that an average adult can reach a 5 km/h av-

erage walking pace, that is, the adult would walk 2.5

km every 30 min. If we extrapolate this value to the

reality of a student, we can consider that in the best

of cases, it would be expected that they could walk

approximately 2 km (see Figure6 (Guti

errez, 2006)

(Llario, 2013).

Figure 6: Ecuadorian road network buffer.

Step 7: In this step, the expected zone of inﬂuence

of the methodology is determined, where the zone of

inﬂuence of the educational institution (step 4) con-

verges with the buffers that describe the Ecuadorian

road network (previous step). Thus, ﬁnally, through

the ST Intersection operation, it can be seen in 7,

the area of inﬂuence of each educational institution,

including its road accesses (see Figure7 (Guti

errez,

2006) (Llario, 2013).

Figure 7: Coverage area of each EMU through its road ac-

cesses.

Step 8: The information that describes the level of

poverty (according to the NBI indicator) in Ecuador,

is available at the National Institute of Statistics and

Censuses. The granularity in which this informa-

tion is found is at the parish level, accompanied by

polygon-type data, which describes its spatial posi-

tion and limits as is shown in Figure 8.

In this way, we proceeded to intersect the area of

inﬂuence from the previous step, contrasting the poly-

gons that describe the parishes where each institution

is located, to assign a percentage of these intersections

impact (Guti

errez, 2006) (Llario, 2013). From this

step it was intended to obtain potential locations for

new educational institutions. An update was made,

going from 77 (used in the previous steps) to 97 insti-

tutions. Once the institutions are adjusted to the geo-

graphic reference system used, the study continues.

Step 9: To obtain a candidate political division, the

selection conditions were taken: poverty >= 90%

and population density >= 5 inhabitants/km

, where

the population density data was obtained from the

INEC. Resulting in 379 candidate political divisions

(see Figure 9) (Guti

errez, 2006) (Llario, 2013).

Poverty Population and Its Educational Accessibility: An Evaluation using Geospatial Database in Ecuador

151

Figure 8: Area of inﬂuence of each educational institution

(Area of inﬂuence “Dendritas” style) contrasting with the

poverty levels of each parish.

Step 10: With the results of the previous step, an

ST Centroid operation was performed on each of the

candidate political divisions.

Figure 9: Candidate Political Divisions based on selection

conditions.

Step 11: Once the centroids were obtained, the

ST Buffer operation was performed to obtain their re-

spective 20 km buffers, presenting intersections with

the existing institutions. To eliminate the intersec-

tions, a differential analysis was performed using an

ST Difference operation, obtaining 298 candidate po-

litical divisions.

Step 12: The buffers obtained in the previous step

present overlapping and to simplify them, the follow-

ing points are taken into consideration.

To assign the demand to the solution, the selec-

tion conditions of Candidate Political Divisions were

taken (Step 9), based on the heuristic method. On the

other hand, for the random substitution of candidate

sites, the centroids that cover most of the Candidate

Figure 10: Candidate Political Divisions with centroids

without intersections with existing institutions.

Political Divisions and do not have an intersection of

more than 40% between the existing institutions were

taken.

Next, it seeks to choose the buffers that occupy

the most territory of the Candidate Political Divisions,

based on the Maximum Coverage Model (COBE-

MAX), so that most of the potential users have access

to the institutions.

Taking these considerations into account, a man-

ual selection of these centroids with their corre-

sponding buffers was carried out, obtaining a result

of 90 Candidate Political Divisions (see Figure11

(Guti

errez, 2006) (Llario, 2013).

Figure 11: Result of simpliﬁcation of Candidate Political

Divisions.

Step 13: At the time of the investigation, it was de-

cided to carry out an intersection between the road

map (Step 6) and the result of the previous step with

the ST Intersection operation, in order to have a more

applicable response. With the information obtained, it

was possible to establish 49 possible institutions (see

Figure12 (Guti

errez, 2006) (Llario, 2013).

GISTAM 2022 - 8th International Conference on Geographical Information Systems Theory, Applications and Management

152

Figure 12: Result of simpliﬁcation of Candidate Political

Divisions intersection with Roads.

3 RESULTS

Within the application of the methodology, it has been

possible to categorize the EMUs according to the per-

centage of poverty that each unit covers, conﬁrming

what was stated by (Navas et al., 2019). On the fact

that the units provide coverage to the poorest popula-

tions in Ecuador, as shown in Fig 15.

Figure 13: Educational units with the highest and lowest

levels of poverty coverage.

Among the institutions with a higher poverty level

are EMU Chontapunta, EMU Cuyabeno, EMU Nuevo

Rocafuerte, with 99.7%, 98.7%, and 98.2%, respec-

tively, and those with the lowest impact are the repli-

cas of schools and schools. Emblematic, such as the

case of the R

eplica 28 de Mayo Educational Unit, the

24 de Mayo R

eplica Educational Unit, and the Vi-

cente Rocafuerte R

eplica Educational Unit that reach

a poverty level of 48.9%, 48.5%, and 47.5%, respec-

tively, as shown in Fig 16.

The median is between the Victoria Del Portete

EMU and the Sim

on Plata Torres EMU with a poverty

value of 80.46%, unlike Navas’s work, which placed

him in the Paiguara EMU (76.65%), which gives a

difference in poverty of 4.01%. Likewise, the arith-

Figure 14: Educational units with the highest and lowest

levels of poverty coverage.

metic mean changes from 74.29% to 78.33%, which

means that 43 EMU are above the mean.

Table 1: Potential Institutions with Locations.

Name

EMU

Province Canton Parish

Poverty

Inhab/

km²

Axis

Rioverde Esmeraldas Rioverde Chumunde 99.97 6.96

679507.1571

19276

10077535.2657

998

Estero Palma

Real

Esmeraldas Rioverde Rocafuerte 99.23 44.36

681846.8710

54963

10107094.2821

584

Rio Pucon Manab

ı Santa Ana San Pablo 99.59 24.99

604574.3910

3765

9865796.98325

485

La Uni

on Manab

ı Jipijapa La Uni

on 99.33 41.63

561136.7732

79309

9840981.84094

969

Laberintos de

Chiguaza

Morona

Santiago

Huamboya Chiguaza 99.15 12.37

836801.2804

56689

9777508.24942

Laguna de

Colta

Chimborazo Colta

Santiago de

Quito

99.89 102.31

752692.9619

71973

9803005.52237

091

Rio Jord

an Esmeraldas Quininde Malimpia 98.94 15.21

709261.3166

20332

10048759.7177

263

Rio Yescas Manab

ı Chone Eloy Alfaro 98.08 23.9

602172.0862

98068

9955058.45505

Junquillal Guayas Salitre Junquillal 97.96 135.52

626877.5440

93636

9808904.39372

348

Gruta de

la Santa Faz

El Oro Zaruma Guanazan 97.9 15.86

666478.9224

0681

9618913.19839

841

The circular area of each of the units intersects

with the polygons delimited by Voronoi, and with the

buffer of the access roads and, the ease of accessing

it through the tracks. As a result of the execution of

steps 9 to 13, a coverage of 60.62% of the national ter-

ritory was obtained between the existing institutions

and the new ones that are proposed. In order to give

a more tangible result, the ﬁrst 10 institutions where

the level of poverty are the highest were taken. The

results obtained describe a name for the institution,

parish, canton, province, percentage of poverty, pop-

ulation density (inhab/km²) and coordinates in UTM

format zone 17 S with SRID 32717 with their respec-

tive X, Y axis, as can be seen in (Table 1).

Poverty Population and Its Educational Accessibility: An Evaluation using Geospatial Database in Ecuador

153

4 CONCLUSIONS

This document proposes an evaluation methodology

using Geospatial tools to address the optimal location

of schools by taking factors such as poor population,

distance, spatial distribution, and road access. In ad-

dition, a case study is analyzed with educational cen-

ters obtaining promising results. The feasibility and

rationality of the method proposed in this document

are veriﬁed, considering that it has been applied to the

entire country and its road network. The results show

that 90% of the academic units are in areas where

more than 50% of the poor population is concentrated,

and there is also a road axis for access. It should be

noted that the methodology can be easily adapted to

other analysis scenarios and other study areas such as

health, transport, logistics, etc.

A base political division of the study territory in-

cluded the analysis variable, which allowed obtain-

ing the results. All of this could be applied to lo-

cations that offer a service other than those of the

EMUs, which would work similarly. The main ad-

vantage of using an amalgamation of methodologies

to obtain potential institutions over a conventional

method based on existing functions is having several

considerations for the locations. At the same time,

social inclusion is taken into account, giving way to

more humanly applicable results instead of obtaining

an exclusive result for not having some GIS-based

feature. The result can be adjusted to more conve-

nient locations, being able to carry out the process

as many times as necessary, to obtain a response that

is geographically valid. These results, coming from

a heuristic method, are optimal and conform to the

concepts of Spatial Efﬁciency. These institutions are

as close to the population and access roads. In ad-

dition, the concept of Spatial Justice could be consid-

ered, where the educational services of the institutions

are distributed in such a way that the largest possible

part of the Ecuadorian territory is covered. Addition-

ally, as future work, other features can be explored,

such as safety and environment, and other techniques

like reverse Voronoi, to ﬁnd optimal school locations.

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