DEFORESTATION DATA
By
Lucio Muñoz
munoz@interchange.ubc.ca
or at http://www.truesustainability.com
Deforestation Data Tables
The information collected with the help of
deforestation data collection tables(See a sample copy
here), in this case quantitative, is organized in deforestation data
tables. These tables show specific information relevant to each country and to
the whole region, which is the raw quantitative data used to develop the
qualitative information that provides the basis for qualitative comparative
approach introduced in this section.
See the
deforestation data tables here
Deforestation
Data Change Tables
The determination of data changes through
time leads to the generation of data change information, in this case,
quantitative data changes, which are organized in deforestation data change
tables.
See the
deforestation data change tables here
Quantitative changes are usually
classified in trichotomy forms such as neutral(0), positive(+), and
negative(-), but can also be classified in dichotomy form as positive(+) and
non-positive(-), where the non-positive entries include also de neutral entry.
See trichotomy
deforestation data change tables here
See dichotomy
deforestation data change tables here
Generating qualitative
information from deforestation data tables
Qualitative approaches can be used to
produce qualitative rankings, which can be a powerful and simple tool to
appreciate specific conditions within countries, similarities and differences
between countries, and similarities and differences between specific country
conditions and related regional situations. These different appreciations can
be done at a particular point in time or through time. Two specific qualitative
tools are presented here, a country-country ranking tool and the country-region
tool.
*Country-Country Ranking
By placing countries in ascending or
descending order in terms of the state of specific factor at a specific point
in time we can generate qualitative ranking information that is simple, and
easy to communicate.
Once the orderings are done, these
rankings provide an insight on how specific factors in one country compare to
similar factors in other countries. It facilitate the visualization of
similarities and differences, in terms of specific conditions and policy
options available as it is easy to identify who is first or who is last or who
is at about the middle. Qualitative ranking based on ascending order has been
used to handle data from the five Central American Countries presented here,
which leads to five different types of rankings, 1,2,3,4,and 5, where type 1 =
the one with highest value or first place and type 5 = the one with the lowest
value or last place.
See the
country-country ranked data tables here
*Country-Region Ranking
By using regional values such as actual or
average states as benchmarks, individual countries can be ranked into three
qualitative groups: below regional(BR) or below average(BA) values; regional(R)
or average(A) values; and above regional(AR) or above average(AA) values. For
example, an entry AA1 means that this factor beside being above average(AA) it
is the one of the highest value(type 1) or an entry of BR5 indicates that this
factor besides being below regional values(BR) it is the one with the lowest
value(type 5). Once the classification is done, these rankings provide an
insight on how specific factors in one country compare to similar regional
factors. It permits the appreciation of similarities and differences, in terms
of specific conditions and policy options as it is easy to identify particular
countries within groups. Qualitative ranking procedures based on grouping has
been used to codify data for country-region comparisons.
See the country-region
ranked data tables here
Generating Qualitative
Information from Deforestation Data Change Tables
Qualitative procedures can be used to
transform the information in the trichotomy and dichotomy deforestation data
change tables into qualitative information. Two procedures are introduced here,
qualitative trend coding and ascending qualitative ranking. The qualitative
trend coding leads to the generation of trichotomy and dichotomy qualitative
trends, both country specific trends and regional trends. which are organized
in deforestation data trend tables. The ascending ranking procedure classifies
countries according to the intensiveness of absolute trends as compared to
similar trends in other countries or similar regional trends. All these procedures
are described below
*Trichotomy Data Trend
Tables
Using the qualitative coding procedure of
neutral pressures(0) when changes are zero; of increasing pressures(1) when
trends are expected to have a positive impact on deforestation; and decreasing
pressures(2) when trends are expected to have a negative impact on
deforestation, then we can transform the quantitative changes in the trichotomy
deforestation data change tables into qualitative data, and eliminate this way
the illusion of precision attached to quantitative data. This approach is
consistent with the trichotomy notion that changes in factors believed to be
associated with deforestation may lead to increasing pressures, decreasing
pressures or neutral pressures. Notice that increasing pressures on
deforestation, type 1 trend can result from either a positive trend or a
negative trend depending on the factor in question. For example, a positive
change in the levels of land area dedicated to agriculture is expected to
indicate increasing pressure on deforestation, but also a negative change on
the levels of natural forest areas remaining would indicate increasing
pressures on deforestation too. See the rules
followed when classifying deforestation pressures.
See the
trichotomy data trend tables here
*Dichotomy Data Trend Tables
Using the qualitative coding procedure of
non-increasing pressures(2) when changes are zero or are expected to have a
negative impact on deforestation and increasing pressures(1) when changes are
expected to have a positive impact on deforestation, then we can transform the
quantitative changes in the dichotomy deforestation data change tables into
qualitative data too, and eliminate this way again the illusion of precision
attached to quantitative data. This approach is consistent with the dichotomy
view that changes in factors believed to be associated with deforestation may
lead to inceasing pressures or non-increasing pressures. In a similar way as
explained above, increasing pressures on deforestation may come from positive
or negative changes depending on the factor in question. See the rules followed when classifying
deforestation pressures.
See the
dichotomy data trend tables here
*Country-Country
Ranked Data Change Tables
Based on the magnitude of the absolute
change of specific factors at specific points in time, countries can be ordered
in ascending form from type 1,…..to type "n", where 1 = the
highest or more intensive absolute change and n = the lowest or the less
intensive absolute change. Since there is information about five countries in
the trichotomy deforestation data change tables, each piece of information of
each country is assigned a rank from 1 to 5 depending on its intensiveness with
respect to similar factors in other countries.
See the
country-country ranked deforestation data change tables here
*Country-Region Ranked Data
Change Tables
Using the regional absolute change as a
benchmark, absolute changes in specific aspects of each country can be
classified as belonging to one of three groups: below regional(BR) or below
average(BA) values; at regional (R) or average(A) values; and above regional(AR)
or above average(AA) values. Using this procedure to codify the information in
the trichotomy deforestation data change tables, then country-region ranked
data change tables can be generated.
See the
country-region ranked deforestation data change tables here
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to Lucio Muñoz.
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