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mechanism is also a key factor that determines the impact of the MAUP [
10
].
Therefore, our study is trying to provide information concerning the impact of the
aggregation mechanisms on the results of potential accessibility analysis.
The paper is divided into
five main sections. After the introduction, the potential
accessibility approach is outlined. Then, in the third section, a case study area of the
Mazovia region is presented, including the network and population data involved in
the analysis. The same section covers the data processing procedure and three
different potential accessibility models are presented in detail. In the fourth section
the empirical results are presented followed by the conclusions in the
final section.
2 The Potential Accessibility Approach
In transport studies, several different meanings are ascribed to the term
'
accessi-
bility
, comprising issues relating to land use policy, infrastructure equipment,
quality of transport networks, opportunities for interaction at the society level etc.
The potential accessibility approach enables the observer to present one face of the
multifaceted phenomenon of accessibility. Potential accessibility studies are
focused on one or more of the following main themes:
'
Assessment of the scale and pattern of regional accessibility disparities [
12
-
14
]
Examination of the impact of accessibility on regional development, e.g. in
terms of the location of manufacturing
rms [
15
] or population distribution [
16
]
Evaluation of new transport
investments,
including their
impact on the
improvement of overall accessibility [
17
−
19
] and/or the degree of territorial
cohesion [
20
23
].
-
The proposed methodology, which is tested in the research presented here, can
be applied in all of the above mentioned types of investigation. Nevertheless, due to
the fact that calculations are extremely work-intensive and time-consuming, efforts
should be made to provide some limits to the area of study.
Potential accessibility models are based on the distance, travel time or cost
between all pairs of origin-destination nodes within the given model assuming a
greater impact of larger centres than smaller ones, and a diminishing importance of
more distantly located destinations [
24
,
25
]. Its mathematical description presents
as follows:
X
A
i
¼
g
ð
M
j
Þ
f
ð
c
ij
Þ
:
ð
1
Þ
j
where g(M
j
) is the function of destination attractiveness, and f(c
ij
) is a distance
decay function. In the analysis presented below we use time, calculated as travel
time by private car, as a distance decay element. The destination attractiveness
(so-called
) is measured as the total population attributed to a given network
node (i.e. municipality or grid cell). The distance decay function responds to the
'
mass
'
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