2. Investigación documental
2.6. Identificación de requerimientos
The participant agents’ attribute was “personal radius”, thus the radius of personal activity range, describing the longest distance that a participant agent was willing to travel for attending a church activity. The personal radii of participants were influenced by both participants’ preferences and socioeconomic statuses. The preference refers to an individual’s subjective tendency toward long travel or short. Because the socioeconomic status could determine whether a household was able to afford a vehicle, this factor, in turn, could influence the travel to church activities. When a vehicle was present, participants might be willing to travel to distant churches, thus reflecting a large value of personal radi- us. Alternately, personal radii might be small if walking or public transport was the option. In this study, a normal distribution, an exponential distribution, and a log-normal distribution were used to simulate the diversity of participants’ personal radii. The values of personal radii were derived from probability distributions via randomly generated probabilities (Park and Bera 2009). Successful methods of deriving values from probability distributions had been demonstrated by previous studies (Viskov 1986, Wichura 1988, Peterson 1998). One of the most using methods was the transformation of the equation of proba- bility distribution, which was conducted by using f(x) to derive the solution of x.
Normal distribution was defined by equation (1) (Viskov 1986),
√
(1) where x is the variable, f(x) is the corresponding probability, e is the natural logarithm, µ is the mean
value of the variable, and σ is the standard deviation. Equation (1) could be transformed to equation (2)
by using f(x) to derive the solution of x,
12 · |ln · √2| , !0 # $
√ , % 0 &' 1( (2) where x is the probability of the distribution of personal radii, f(x) is the corresponding personal radius,
and (-1)i is used to defined whether the personal radius is greater or smaller than the mean. The value of
i is randomly either 0 or 1. Via equation (2), the values of personal radii could be derived from a normal
distribution by substituting a randomly generated x (probabilities). In equation (2), x was randomly gen-
erated from a uniform distribution (Park and Bera 2009). The range of this uniform distribution is [0,
√ ], which is derived from equation (1). In equation (1), the range of f(x) is [0,
√ ] (f(x) becomes maximum when x = µ). Since the x of equation (2) is the same variable to the f(x) of equation (1), they
share the same range.
Exponential distribution was defined by equation (3) (Reynolds 1988),
λ*+, , 0 (3) where x is the variable, f(x) is the corresponding probability, e is the natural logarithm, and λ is the recip-
rocal of the mean (λ = 1 / µ). Equation (3) can be transformed to equation (4) by using f(x) to derive the
solution of x,
|-./|
* , 0 # $ 0 (4) where x is the probability and f(x) is the corresponding value of the variable. Since λ = 1 / µ, equation (4)
· |ln |, !0 # $1( (5) The values of personal radii could be derived from exponential distribution by substituting ran- domly generated probability (variable x) in equation (5). The value of x was randomly generated from a
uniform distribution and the range of this uniform distribution is [0,1]. This range was derived from
equation (3). In equation (3), the range of f(x) is [0,
1] (f(x) is maximized when x = 0). As the x of equation
(5) and the f(x) of equation (3) are the same variable, they share the same range.
Log-normal distribution was defined by equation (6),
+√
23
, 4 0
(6) where x is the variable, f(x) is the corresponding probability, e is the natural logarithm, µ and σ are, re-spectively, the mean and the standard deviation of variables’ nature logarithms. In this paper, the mean and standard deviation of variables of equation (6) were denoted as m and s. The values of µ and σ can
be derived by equation (7) and (8).
ln √657 5 (7) ln1 56 (8) Equation (6) could be transformed to equation (9) by using f(x) to derive the solution of x,
89 ·-. +·√ 7171, :0 # $√ 1, % 0 &' 1;
(9) The values of personal radii could be derived from log-normal distribution by substituting ran- domly generated probability (variable x) in equation (9). The value of x was randomly generated from a
uniform distribution and the range of this uniform distribution is [0,
√
1].
In normal distribution, µ (mean value) reflects the centralization of the data, while standard de-
ization of the derived personal radii. When µ was a small value, the derived personal radii were central-
ized to small values, which meant that the distance that participants were willing to travel to attend church activities was small. On the contrary, when µ was a large value, the derived personal radii were
centralized to large values, meaning that participants were willing to travel to distant churches. The standard deviation (σ) of equation (2) was used to adjust the diversity of personal radii among partici-
pants. When σ was small, the derived personal radii would be more centralized to the mean and less
varied. When σ was large, the derived personal radii would be less centralized to the mean but more
dispersed.
In contrast to normal distribution, the mean and standard deviation in log-normal distribution also reflect the concentration or variation of data despite of the distribution of data is different in log- normal. In log-normal distribution, the mean is larger than the mode (the value that appears most often in a set of data), which means the majority of data is smaller than the mean. In equation (9), as the mean increases, the derived personal radii would relatively increase with the majority of derived values concentrated on smaller values than the mean. Besides, in log-normal distribution, the variation of standard deviation would reflect on both of the concentration and diversity of data. In equation (9), as the standard deviation decreases, the diversity of derived personal radii reduces while the probability curve of log-normal becomes more and more proximate to which of normal distribution (the mode tend to equal to the mean). Alternatively, as the standard deviation increases, the derived personal radii be- come more and more dispersed and the mode becomes smaller and smaller than the mean.
In exponential distribution, λ (reciprocal of µ) was used to adjust both the centralization and var-
iation of the derived personal radii. The probability curve of exponential distribution is descending (Reynolds 1988). When µ was small (λ was thus large), the slope of probability curve would be relatively
steep, and most data would be smaller than the mean. However, when µ was large (λ was thus small),
balanced at both sides of the mean value. In equation (5), as µ decreased, the derived personal radii
would be increasingly concentrated on small values, and their variation would tend to be small. As µ in-
creased, though, the derived personal radii would be more dispersed, and their centralization would tend to disappear. Both large value of µ in exponential distribution and large value of σ in normal distri-
bution as well as log-normal distribution could result in diversified personal radii, but the difference be- tween these three is that the former one is one tail while the latter two are two tails. This study derived personal radii using different probability distributions to investigate the influence of participants’ activi- ty ranges on the affiliation network of churches.