Wykorzystanie zdjęć LANDSAT w badaniu struktury sieci osadniczej w wybranych krajach pozaeuropejskich LANDSAT images usage in research of settlement network structure in chosen non-European countries
The main aim of the article is to assess the usage of the
following methods for the settlement structure analysis: satellite
images interpretation, Zipf rule and nearest neighbour
index. This assessment is carried out during the analysis of
the relationship between socio-economic development level
and the diversification and complexity of settlement
Settlement network development was especially investigated
in the 1950. and 1960. R. B. Potter (1999) summarising
heretofore research stated that the balanced and hierarchised
settlement network does not develop in LEDCs. Therefore it
was interesting to check if such a statement is not exaggerated.
The analytical data were obtained from LANDSAT ETM+
images interpretation. Due to it, the coefficients of variation
for particular countries were calculated to investigate settlement
network diversification. The diversification was presented
also on choropleth maps. Afterwards it was analysed
if the Zipf rules (classic, using population number data and
modified, using built-up areas values obtained from satellite
images) describe the settlement network complexity in countries
under investigation. The nearest neighbour index was
used to check spatial complexity of the networks.
By the networks diversification analysis, it was proved
that there was a relationship between socio-economic level
and networks diversity in the countries under investigation,
although there are also other processes influencing this relationship.
There is intense spatial dynamism in core regions
and neighbouring peripheries in the countries with the most
diverse networks (the most developed counties). In the least
diverse countries there is the low spatial dynamism or there
were significant decentralisation forces in the past.
By the networks complexity analysis, the development
level and networks complexity relationship was revealed. In
the relatively less developed countries the settlement networks
were far from equilibrium (primate city pattern) and
built-up areas dispersed. In more developed countries settlement
networks were close to equilibrium and concentrated,
so compact urbanised areas were present. Changes in networks
complexity take place in the most developed areas,
however, peripheries are spread across the overwhelming part
of the countries, which is irrespective to the level of their
development. Hence, networks’ structures enhancement and
their dispersion occur only in some parts of the countries.
Therefore R. B. Potter’s and others statements seem to underestimate
the LEDCs’ processes.
The visual satellite images interpretation allowed the
analysis of the data rarely used in settlement structure researches.
This enabled the analysis with lack of population
statistical data limitations, e.g. unreliable data not covering
the whole population within the city or town, data not comparable
between different countries (problem of different city
definitions), from different years for each country. The comparison
of modified and classic Zipf rules showed that the
modification was correct. The data availability for modified
rule was also much greater. This method occurred useful in
LEDCs as spatial urbanised areas expansion is characteristic
for the urbanisation process there (Cohen, 2006). The nearest
neighbour index NNI analysis was also much more precise
and due to the greater data availability, statistically significant. The conclusions of the methodological aim can be extrapolated,
providing for the limitations described, for other
countries not being covered by the research.
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