The Promotion of Large-Scale Developed Land in

Third Urban Research Symposium
Urban Development for Economic Growth and Poverty Reduction
The World Bank
Institute of Applied Economic Research - IPEA
The Promotion of Large-Scale Developed Land in Venezuela:
Where It Should Take Place?
Benjamín Reif
breif@reifconsultores.com
President, Reif Consultores Asociados SNC
Professor of Architecture and Urban Studies, Central University of Venezuela
Antonio Suárez
asuarez@reifconsultores.com
Project Coordinator, Reif Consultores Asociados SNC
Professor of Game Theory, Andres Bello Catholic University
April 4 - 6, 2005
Brasilia, DF, Brazil
The Promotion of Large-Scale Developed Land in Venezuela:
Where It Should Take Place?
Benjamín Reif and Antonio Suárez
ABSTRACT
This paper describes a study done in Venezuela in order to facilitate the decision-making process
relating to spatially focused poverty reduction policies and programs. Maximization of benefits for
poor people is one of the challenges faced by poverty reduction policies and programs. Such
maximization must be based on policy implementation within the context of the places where poor
people live. Focusing consists mainly of two dimensions: the vertical dimension and the horizontal
or spatial one. Vertical dimension refers to the identification of target-groups as candidates for
being the object of poverty reduction policies. On the other hand, spatial dimension relates to the
identification of places or sites where poverty reduction policy implementation is necessary.
Because of scarcity of resources for policy implementation, criteria for prioritisation must be applied.
Similarly, the identification of priority groups within the sector of a particular policy and the selection
of priority geographical areas must be addressed. Methods and tools for this purpose are needed
to help planners to assess problems and issues and to convey knowledge to support strategic
decision-making. On the other hand, a stable institutional mechanism is required for allowing
formulation, pursuit and the evaluation of projects according to the policy.
This study was developed for Venezuelan National Housing Council to help identify municipalities,
where policy benefits should receive priority within target groups. The priority should be based on
its multiplier effects, mainly its economic potential. The economic potential of a spatial unit is
defined as the capacity to generate jobs when the conditions that directly affect the production of
goods and services in this spatial unit are improved. At the same time, areas within each
municipality should be identified for large-scale land developments. Environmental and risks
issues, accessibility and other relevant factors were considered in the selection of potential sites.
The methodology used to estimate the Economic Potential consisted in classifying and grouping
the spatial units by using multiple regression techniques on significant variables such as
infrastructure, human resources and market potential. This process was performed at a municipal
level, covering the whole national territory composed, at the time when the study was completed,
by 286 municipalities.
On the other hand, the methodology used to identify areas within each municipality for large-scale
land developments was based on the overlapping of geographical data. Among the variables
employed are agricultural land potential, present land use, geomorphology, vegetation, ecological
fragility, risk issues and accessibility.
The combination of the economic potential of a municipality and the environmental analysis of its
territory lead to the location of new integrated housing compounds, with better perspectives of
sustainable social and economic development for their inhabitants.
CONTENTS
Page
1.
Introduction
1
2.
Economic Potential
2.1
Methodology
2.2
Limitations
2
3
12
3.
Identification of Sites
3.1
Estimating Environmental Sensibility
3.2
Incorporation of other variables
3.3
Obtaining Synthesis Map
13
14
17
20
4.
Conclusions
21
1.
INTRODUCTION
The battle against poverty demands, in first place, the promotion of employment
and productive investment, in such a way that the population would be capable of
generating income and thus have a better quality of life. Creating employment and
generating wealth must be accompanied by a framework that would guarantee a
style of life worthy of the population. This would require giving the families housing
with the primary services (water, electricity, etc.) and access to health, education
and recreation, among other services. It also implies the development of new
integrated housing compounds, which in some cases, are necessary to develop
along with new economic activities, away from traditional cities.
The National Housing Council (El Consejo Nacional de la Vivienda – CONAVI - in
Spanish), the Venezuelan public agency in charge of housing and habitat policies
in the country, identified this need several years ago. What is presented in this
paper is one of the solutions formulated to attend to these requirements. It is a
methodological process, which responds to the query as to where future integrated
housing developments should be placed. This process follows two paths: on one
hand, places where there exists economic potential to be developed must be
considered while on the other, the places chosen must be capable of bearing with,
from the environmental perspective, the development of human activities. In this
way, economic development in a sustainable manner would be promoted.
This paper describes the methodology designed for CONAVI, in its two main
components.
1
2.-
ECONOMIC POTENTIAL
Economic potential is defined as the capacity that a spatial unit has to generate
employment, when the conditions that directly affect the production of goods and
offering of services in this unit improve. These conditions refer to two dimensions.
On one hand, the Supply conditions that are related to all those factors that directly
or indirectly affect the cost structure of a determined activity. This means that the
production units will have the tendency to locate in places in which the expected
costs will be lower. The main conditioning factors that could affect costs are the
presence of developed infrastructure and the availability of skilled labour. On the
other hand, the Demand conditions are those market characteristics that stimulate
economic activity, through an expected increase in the benefits that the activity can
generate. The factor of demand considered is the existence of population strata
with its request for goods and/or services.
The methodology used to estimate economic potential was based on classifying
and grouping together the spatial units and applying multiple regressions referring
to significant variables to each group. The unit of analysis consisted in
municipalities, which are the smallest territorial entities. They are equivalent to
Counties in Anglo-Saxon countries.
As has been aforementioned, the variable used as a proxy for economic activity is
the level of employment registered in a particular municipality. This choice is
justified because is the only economic information available at municipal level.
Likewise, the temporary variation of employment could not be used, due to the
unavailability of this information at this level.
2
This study is based on the premise that the economic activity of a given zone is
determined by the availability of a number of factors. The following have been
considered in this study:
•
Infrastructure
•
Human resources
•
Potential of the local market
The Infrastructure factor refers to the existence of installations and services
offered by the municipality, such as roads, health and education, among others. It
is assumed that by improving the infrastructure and services, the level of economic
activity within a particular municipality would rise. This factor was measured
through the percentage of the population who have access to infrastructure and
road services, health, education, primary services and communications, among
others.
The Human Resources factor is defined as the quantity and quality of personnel
available in a specific location. It is assumed that on improving this component, the
level of economic activity which can take place in a specific municipality would
rise. As a proxy for this factor, the percentage of population in each level of
education was used, from the illiterate to the population with college education.
The Potential of Local Market factor refers to the potential demand for goods and
services by the residents of the municipality. It is assumed that on improving this
component, the level of economic activity of a chosen municipality could rise. This
3
factor is estimated based on the existing level of poverty, measured through the
index of unsatisfied basic needs.
2.1
Methodology
This study is based on a multivariable analysis of the aspects related to the
economic potential. This analysis was conducted in three phases:
•
Factor estimation
•
Group construction
•
Estimation of Economic Potential
2.1.1 Factor Estimation
The objective of this phase was the analysis of the statistical information available,
consequently leading to the derivation of the factors associated with the level of
economic activity of a municipality.
The factorial analysis technique was used, which consisted in identifying variables
or factors which explained the configuration of correlations/covariances of a set of
variables observed. The method of factorial extraction was factorisation through
the main axis, which consists in obtaining a common punctuation which, on being
multiplied by a simulated factorial coefficient, would reproduce the original data
with a tolerance factor of 1% above or below the original value. The number of
iterations required varied between 6 and 10 in the majority of the cases.
An attempt to identify three factors related to those factors theoretically identified,
was made:
4
•
Infrastructure
•
Human resources/personnel
•
Potential of the local market
In the case of the “infrastructure”, a double factorization was made. Firstly,
components through factorial analysis applied to simple variables were estimated,
with a total of five components being used (see Table 1). Secondly, the
components to estimate the infrastructure factor were factorized. On the remaining
factors, the calculations were made based on simple variables.
In Table 1 and Table 3, the communalities and factor coefficients for each one of
the factors related to the potential of economic development are presented.
Table 1
a.- Infrastructure Factor Components
Communality
Factor
Coefficient
Transport Component
Population (%) with access to:
Paved roads
Unpaved roads
Sea/River ports
Airports
Educational Services Component
Population (%) with access to:
Maternal education sites
Primary education sites
Secondary education sites
Universities/Technical schools
Health Services Component
Population (%) with access to:
Rural primary health services
Urban primary health services
Hospitals
Basic Services Component
Population (%) with access to:
Water supply services
Sewerage services
Electricity services
Telecomunications Component
Population (%) with access to:
Mail and telegraph services
Telephone services
0,9420
0,9620
0,8320
0,7900
1,0000
0,2920
0,2677
0,1450
0,2953
0,8744
0,8864
0,8998
0,8993
1,0000
0,2456
0,2490
0,2528
0,2526
0,9534
0,8914
0,9540
1,0000
0,2678
0,2504
0,4818
0,8632
0,8579
0,8389
1,0000
0,3372
0,3351
0,3277
0,7569
0,8031
1,0000
0,4852
0,5148
5
b.- Infrastructure Factor
Communalities
Factor Coefficients
Education Health
Basic
Telecom
Transport Services Services Services Services
0,9690
0,9767
0,9678
0,9786
0,9585
0,1706
0,2087
0,2068
0,2091
0,2048
1,0000
As can be observed, the level of the communalities vary between 0,76 and 0,99,
the majority being over 0,80, which indicates that the factors extracted acceptably
reflect the variations of the variables on which they were estimated. On the other
hand, the factorial coefficients, on adding up the unit, indicate the contribution that
each variable gives to the variation of its factor. For example, in Table 1, it can be
observed that within the infrastructure factor, the presence of Educational Services
(the factorial coefficient of which is 0,2087), contributes more to the variation of the
factor than the component Transport (0,1706).
Table 2
Human Resources Factor
Variable: Population per Educational Level (%)
Communalities
Factor Coefficients
Iliteracy Primary Secondary Univ/Tech
0,8400
0,9100
0,8400
0,9100
0,2400
0,2600
0,2400
0,2600
Total
1,0000
Table 3
Potential of Local Market Factor
Variable: Population per standard of living (%)
Communalities
Factor Coefficients
Extreme
Poverty
0,9380
0,5070
Unsatisfied
Basic Needs
0,9614
0,4930
Total
1,0000
Sources:
OCEI, XII National Population and Housing Census, 1990
MSAS, Epidemiology Annals, 1994.
M.E., Statistical Departments / Educational Statistical System, 1997
FIDES, Investments need map, 1998
The estimated factors, in this way, are expressed in a graded scale. The grading of
a factor is equal to the original variable multiplied by its factorial coefficient. In this
case, the factorial value takes the form of a weighted sum of variables, the weights
6
being the coefficients of the factors. These weights express the presence and
importance of each of the variables that was their basis, that is, represents a
measurement of the contribution level of each factor. Thus, each one has its
corresponding measure. For example, for “personnel” , in a municipality where
only primary and secondary school education exist, and in which 90% of the
population has obtained only primary school education and 10%, secondary
school, their weight in the factor would be 25,8. This is the result of 90 x 0,26 (the
factorial coefficient of primary school education) plus 10 x 0,24 (coefficient
of
secondary school education).
2.1.2 Building Groups
The objective of this stage was to obtain groups from municipalities’
characteristics, as there were reflected in factors. Groups were built in accordance
with similarity in the three factors showed earlier. Cluster analysis was the
technique used; the aim was the maximization of the variation inter-groups
compared with intra-groups one.
This technique was applied and three groups of municipalities were identified:
group A is the lowest in all factors scale. It is characterised by low infrastructure
and human resources levels, and has great poverty problems, so Local Market
Potential is limited. On the other hand, group C is the opposite of the A one; factors
scale levels are high in all factors. Group B is intermediate. Since there are very
many municipalities in this group (and a huge variety of them), it has been divided
in three subgroups according to factors scale progression. It is to notice that the
7
density of municipalities is different in each group; for the low level of the scale,
municipalities are more homogeneous than in the high ones. In a similar way, it is
relevant to point out this categorization is adequate with other social indicators. For
example, group A municipalities have less than 18.000 residents, and an
urbanization value of around 50%. For C group municipality’s urbanisation reach
95%.
This classification was verified statistically with discriminant analysis and was
establish that the groups are statistically stable.
The distribution of municipalities was contrasted with economic activity, expressed
by employment. Cluster analysis was carried out, including employment as an
additional variable. The results confirmed the previous classification. From this
result, it can be inferred the existence of a relationship between economic activity
and the factors considered.
2.1.3 Estimating Economic Potential
The objective of the last stage was to estimate the economic potential for each
municipality group. This was made it in two steps.
Firstly, it was took into consideration the existence of a relationship between factor
endowment and economic activity (measured by employment) for all municipalities.
This analysis was done to build a framework for estimating the economic potential
in each municipal group. For this analysis, all the 286 municipalities were taken.
The multiple regression method by ordinary least squares was used.
8
Table 4
Relationship between factors and economic activity
Constant
8.138,94
t
Infrastructure
0,2946
29,54
Factors
Human
Local Mark
resources potential
0,2888
1,7215
25,36
58,36
R2
0,9377
On table 4 it can be seen that the determination coefficient is high, and the variable
coefficients are positive, implying that a proportional relationship between factor
endowment and economic activity (measured by employment) exists. An increase
in municipality factors endowments may cause an increase in its employment. It
should be recall that regression coefficients only means association among
variables. Nevertheless, they can be interpreted as variations in the economic
activity due to variations in the associate factors. Therefore, an increase of one
point in the scale of local market potential, is associated to an increase of almost
two (1,7215) jobs, while the infrastructure index increase, needed to generate one
job, is almost four points (see Table 4).
The second step consisted on estimating the economic potential, defined as the
growth of the economic activity, as a consequence of a unitary increase in the
factor endowment, for each municipal category. There were carried out regressions
between factors and economic activity, expressed as employment. This technique
was applied for each municipal group. The variations of economic activity due to a
unitary variation of the localization factors are presented in table 5.
Table 5
Economic Potential per municipality group
Coefficient
2
R
N
Group A
1,5046
0,9207
73
B1
2,3396
0,9506
125
Group B
B2
4,9539
0,9854
51
B3
5,6515
0,9543
17
Group C
1,8836
0,9302
20
Total
2,3048
0,9377
286
9
These coefficients can be interpreted as the expected variations in economic
activity due to a unitary variation in the factor endowment, and they constitute the
economic potential of a municipal category.
For example, suppose that in a
municipality belonging to group A, investments are carried out in infrastructure;
educational programs are executed, that resulted in an elevation of the population's
educational level; plus poverty reduction programs are carried out successfully. Let
us suppose that these actions imply that the index localization factors increases
one point, then, 1.5 jobs are expected to be created in such municipality. If the
municipality belongs to another group, its potential will be different, because the
variation of employment due to variation of the localisation factors is higher.
The high correlation among factors, coincidences in scale factors in each group,
and the homogeneity in the relationships between employment and factors inside
each group, lead us to consider the building of an index or location endowment
vector to reflect, in a single axis, the characteristics identified by factors.
Consequently, this index could be related with employment, in a way that could
bring a graphic idea of the relationship between both variables. As a measure of
the factors, this index consisted in a scale. For building it, normalization of factors
scale was required. It was decided that the scale will run from 0 to 100.000.
Subsequently, for each factor it was calculated the distance between each point
and the maximum of its scale, and moved this distance to the scale from 0 to
100.000. Then each scale was added in the single index assuming a weight similar
to each one. In practice, this process was similar to the calculation of a sum of all
10
factor scales. This way, a unique scale of factor endowment was obtained, which
varied from 0 to 100.000.
Cluster analysis was applied to this index. The classification obtained was similar
to the one previously found. Due to this result, it is possible to see (in a graphic
way) the relationship between the location factor endowment vector and economic
activity. It was found that the classification coincided with the changes of slope of
the economic activity curve. In table 6 and figure 1, the results of this analysis are
shown.
Table 6
Municipalities per group in factor
endowment index
Group
A
B1
B2
B3
C
Total
Factor endowment Índex
Less than
5.576
From
5.577 to
16.984
From
16.985 to
42.921
From
42.922 to
66.740
More than
66.741
73
125
51
17
20
286
Figure 1: Economic Potential
11
This graphic representation allows seeing in a simple way, the relationship among
a group of 22 variables, represented by the location factor endowment index, and
its relationship with employment.
2.2
Limitations
The presented methodology is a first approach to measure economic potential
concept. This methodology can be improved with future research. The results of
the one we have shown has three limitations; data sources, spatial units, and data
temporary reference.
First at all, the analysis is based on available statistical information. The research
was based on statistics collected by the Central Office of Statistic (O.C.E.I.),
statistical divisions of Education and Health ministries, as well as other national
and regional public agencies. That means this research was limited to variables
relevant by these agencies. For future research, it is expected that statistical
sources will be upgraded, validated and enlarged, and the study of other variables
and factors will be possible throughout the consultation with other agencies.
Secondly, all municipalities with information for the oldest source were considered;
in this case, the 1990 National Census. In other words, the results took in
consideration all existent municipalities according to the 1992 Territorial Division;
286 municipalities, then covering all Venezuelan territory. Today there are 335
municipalities.
12
Finally, this research worked with the information of the last available year, in
practical terms, it worked in a period of seven year-old time (1990-1997), because
not all the sources collected information with the same accuracy for one only year.
This constitutes an evident limitation for the results obtained (due to 1990 data was
compared with another of 1997). To face this problem, proportional adjusting
techniques were applied in order to homogenize variables; in this way comparison
was possible. Thus, the analysis showed is transversal, that is to say, the
municipal structure is analyzed for a point in time.
3.
IDENTIFICATION OF SITES
The second stage of this research consisted on the identification of sites for largescale land development. The analysis of these variables necessarily implies
consideration of physical and geographical factors. For the study of these factors,
an analysis of cartographic material was carried out.
The cartographic analysis demands all variables should be susceptible of map
representation. The objective is to compare the spatial distribution of a group of
variables, in a way that a comprehensive approach of a problem can be extracted.
The usual technique consists on overlapping a set of thematic maps. The method
is eminently qualitative; variable synthesis is measured in ordinal scales. For
example environmental sensibility is obtained from the overlapping of vegetation
map, wild life maps and geomorphology map. The spatial distribution of
environmental sensibility levels is obtained, which indicate the places in which
certain activities can be carried out without causing a significant impact on the
natural environment.
13
In this case, the objective was to identify places where to establish human
settlements associated to economic investment projects, assuring environmental
sustainability. This task had to take into account physical and geographical factors.
For incorporating these factors, three steps were followed:
3.1
•
Estimating environmental sensibility
•
Incorporation of other variables
•
Obtaining synthesis map
Estimating Environmental Sensibility
The objective of this step is the generation of a map that reflects the environmental
sensibility to human activities. The environmental sensibility was conceived as the
combination of three components. First, geomorphology defined as the landscape
unit that prevails in an area. Second, the vegetation of the area, which includes its
associated wild life. Finally, the legal protection figures (if any) of the area. In this
component, the categories established by Venezuelan legislation were taking into
account. These areas are denominated Special Administrative Regime Areas
(Areas Bajo Régimen de Administración Espacial or ABRAE).
The components had a set of categories that were evaluated by an expert panel.
They established four levels of sensibility (Very High, High, Medium, and Low), and
a scale to measure the sensibility level. Experts were invited to assign values to
each category in all the components. Then, in several Delphi rounds, the definitive
scale was obtained for each component. Results are shown in table 7.
14
Table 7
Environmental Sensibility Scale
a.- Component: Geomorphology
Landscape Unit
Sensibility level
Value
Very High
8
High
6
Medium
4
Low
2
Sensibility level
Value
Very High
8
High
6
Medium
4
Low
2
Paramo
High mountains
Deltaic plain
Swamp
Coastal marshy plain
Low mountains
Gallery
Foot hills
Denudation plain
Maracaibo lake south plains
Dissected mesa
Non-dissected mesa
Peneplain
Isolated hills
Hills systems
Valley
Thectonic depresion
Coastal zone
Flood plain
Sandy plain
Alluvial fan
Denudation forms
b.- Component: Vegetation and wild life
Vegetation type
Paramo
Manglar
Forest
Gallery forest
Brushwood
Xerophyte plants
Forest plantation
Savanna
c.- Component: Special Administrative Regime Areas
Legal Figure
Sensibility level
Value
National park
Natural monument
Very High
8
Wild life refuge
Wild life reserve
High
6
Protective zone
Forest reserve
Forest area under protection
Medium
4
Note: low environmental sensibility level does not correspond to any legal figure
15
For obtaining an environmental sensibility measure, two complementary methods
were applied. First of all, an index of all the components was calculated. It was
necessary to weight each component. Multiple tests with different combinations
were carried out. These tests consisted on consultations to an experts’ panel and
Delphi rounds. The adopted weights are shown in table 8 and the values are
shown in table 9.
Table 8
Weights for environmental components
Components
Weight (%)
Geomorphology
40
Vegetation and Wild life
20
ABRAE
40
Table 9
Values of environmental sensibility levels
Sensibility Level
Values
Very High
From 6,01 to 8,00
High
From 4,01 to 6,00
Medium
From 2,01 to 4,00
Low
Less than 2,00
Secondly, a composition map was made. In other words, each component was
represented in a map. This map showed different areas classified according to the
component categories. Sensibility regions were obtained for each component, so
three maps were obtained: sensibility for geomorphology, sensibility for vegetation
and sensibility for ABRAE. These three maps were overlapped in order to produce
one of global sensibility. The results can be observed in figure 2. It is necessary to
point out this procedure complemented the first one; so each identified area was
16
associated to a numeric value. This value expresses the level of sensibility of such
area.
Figure 2: Environmental Sensibility
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6° 00 '
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#
Fue nte : Elab or ación p rop ia so br e la ba se d e in for ma ción
ca rto gr áfica d e ve get ación , g eo mor folo gí a y Ar ea s
Ba jo Ré gimen de Ad mis tra ción Espe cial. 2 00 0
Aya cu cho
#
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ay en
ATURE S
GRA N S ABA NA
·
#
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SUCRE
Isla de Rat on
·
#
RAUL LEONI
SIGNOS CONVENCI ONAL ES
MANAP IARE
A UT ANA
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Ca rre tera Principa l
Ca rre tera Secund aria
Ca rre tera Loc al
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L ímite de Estad o
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Ca pital d e Estado
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·
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4° 30 '
#
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uj i
4°3 0'
#
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·
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·
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%
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1: 1.000.000
RE PUBL ICA DE BRAS IL
6000 0
0
600 00
1200 00 Mete rs
COORDENADAS G EOGRAFICAS
La Esm eral da
·
#
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·
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#
·
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1° 30 '
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90 °
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10°
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30°
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72 °3 0'
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Incorporation of other variables
Once obtained the environmental sensibility map, it was necessary to incorporate
to it other geographical and economic variables. Three additional variables were
considered:
Environmental cost refers to resources required to improve certain geographical
conditions. The geographical conditions took into account flooding and
accessibility. The flooding is the severity of floods phenomena. Accessibility refers
to the presence or infrastructure that allows reaching an area. Both variables were
17
measured by ordinal categories. The categories of flooding were: severe, partial
(or seasonal) and absent. For accessibility were high, medium, and low.
The combination of these two variables produces the Environmental Cost scale; an
ordinal scale. An unfavorable combination of components corresponds to a high
costs, while favorable conditions correspond to low ones. For instance, an area
with severe flooding and low accessibility has high environmental cost, due to the
high investments needed to correct these conditions. On the contrary, an area with
absent flooding and high accessibility has a low cost. The combination was carried
cartographically, so a map of regions according to its environmental costs was
obtained.
Economic potential, this variable is the cartographic representation of the
economic potential calculated earlier in this research. The map obtained points out
regions where to develop clearly identified economic projects within a positive
economic environment. The economic projects are exogenous proposed. This
distribution can be observed in the figure 3.
18
Figure 3: Economic Potential
Appropriate conditions for other economic activities. Finally, the presence of
other potentialities, such as Oil and Forest ones, were taken into account. The
existence of current activities and/or projects associated with other activities was
also considered. These variables were represented in a map, and can be observed
in the figure 4.
19
Figure 4: Oil and Forest Activities
-72
-7 0
-6 8
-66
-6 4
-6 2
-6 0
CURAÇAO
BONAIRE
#
12
ElVí n c
u lo
12
F ALCON
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#
#
#
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í c
or a
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#
adac a
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q u iv a
#
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· Sant a
#
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#
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a u re
#
u e n a Vis ta
B
#
ISLAS LAS AVES
Ba r a
i v
ed
CONSEJO
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DE LA VIVIENDA
ISLA L A ORCHILA
Mo r u
y
#
#
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t a An a
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i b
ana
CARI RUBANA
·
#
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ISLA LOS ROQUES
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#
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#
#
#
#
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r ag
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#
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#
#
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·
#
#
#
#
·
#
#
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#
·
#
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#
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· Sant a
#
#
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g u n il a
s
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·
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#
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rí o Cat atu mb o
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#
# Sa n An
t o
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·
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%
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POTENCIAL PETROLERO Y POTENCIAL FORESTAL
P ari aguán
#
SANT A MARIA DE IPIRE
SAN JOSE DE GUANIPA
l Pa o d e Ba rc e
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%
MIRANDA
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a
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a r ra n
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B arr anca #·
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INDEPENDENCIA
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g u a ra ma s
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SOTI LLO
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BARINAS
El Re a
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Sa
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Sa n J o sé d e Am
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LAS M ERCEDES
#
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CASACOI MA
a n Die g o d e Ca b ru tic a
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#
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i n
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Fuente: Ma pa ABR AE: ADAM´ S Con sult ing 1 99 3
Ma pa Pe tr oler o: PD VSA 199 7
ATURES
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Obtaining Synthesis Map
So far four thematic maps were produced. The synthesis map is the combination of
those ones. On this map, it is possible to define areas where large scale and
sustainable developed land projects are feasible. In such areas new integrated
housing complex could be built. In other words, the final product of this Project, a
list of recommended areas for large-scale developed land projects, is expressed in
a map, so it is possible to see their geographical location and the interrelations with
the existing towns and cities. In figure 5, a fragment of the synthesis map is shown.
These areas are characterised by Low or Medium level of Environmental
20
Sensibility, Low Environmental Costs, High or Medium level of Economic Potential,
and favourable conditions for other economic activities.
Figure 5: Recommended areas for large-scale land developments
4.
CONCLUSIONS
This methodology has the advantage of incorporating the whole complexity of the
phenomenon, allowing the user to know the reasons why an area is selected.
Additionally, he can modify the selection criteria, obtaining multiple lists of
developing areas.
The combination of the economic potential of a municipality and the environmental
analysis of its territory lead to the location of new integrated housing complex, with
better perspectives of sustainable social and economic development for their
inhabitants.
21