formed in line with their infrastructure network, but their urban area did not
necessarily become intensive by dint of the infrastructure network’s pattern of
expansion. The cities in colder regions have relatively high income, and they
depend on car transportation, and people can decide their residential places
without deeply considering the public transportation network. On the other
hand, in cities in warmer and drier regions (including desert regions), residents
settled in an intensive manner centered around limited sources of water. Finally,
in cities in warmer and more humid regions because there was no need to share
sources of heat and because sources of water were also not limited, people had a
high degree of freedom in where they chose to reside, but for cultural reasons,
they might instead choose to share an intensive settlement pattern.
• Elevation diversity inside the urban area (STD): A negative correlation was
demonstrated. The presence of a diverse range of elevations inside the urban
area suggested that the city was located in a region, where differences in elevation
made expansion of the urban area difficult.
• Percentage of size of agricultural land (11) (farmland): A positive correlation was
demonstrated. Of all the land cover classes, it was possible that farmland restrictions were weakest when the shape of the urban area’s expansion was amorphous.
• With the RPI model, we obtained significant correlations for both socioeconomic
framework and population density. we now present the following hypotheses on
the meaning of those correlations.
• Total population: A positive correlation was demonstrated. Urban area size
expands in concert with increases in total population. When an urban area’s
size expands its perimeter is extended, which was thought to make it easier for
its RPI to increase.
• GDP/cap: A positive correlation was demonstrated. Increase in per capita GDP
led to an increase in per capita car ownership. As a result, urban area expansion
followed motor vehicle roads, leading the shape of the urban area as a whole to
become complex.
• Population density: A negative correlation was demonstrated. Complexity of
shape of an urban area suggested that its population was distributed in a comparatively dispersed manner. As a result of the appearance of a dispersal trend in
settlement distribution at the neighborhood level, RPI and population density
were believed to be negatively correlated.
6.2.3.2 Hypotheses on the Correlations Between Population Density
and the Ecological Environmental Elements
• Degree of mixing of land cover classes: A positive correlation was demonstrated.
Diversity of land cover classes inside the urban area suggested that there were
many regions, where it would be difficult to bring about conversion to residential
land easily.
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Y. Uchiyama and K. Hayashi
necessarily become intensive by dint of the infrastructure network’s pattern of
expansion. The cities in colder regions have relatively high income, and they
depend on car transportation, and people can decide their residential places
without deeply considering the public transportation network. On the other
hand, in cities in warmer and drier regions (including desert regions), residents
settled in an intensive manner centered around limited sources of water. Finally,
in cities in warmer and more humid regions because there was no need to share
sources of heat and because sources of water were also not limited, people had a
high degree of freedom in where they chose to reside, but for cultural reasons,
they might instead choose to share an intensive settlement pattern.
• Elevation diversity inside the urban area (STD): A negative correlation was
demonstrated. The presence of a diverse range of elevations inside the urban
area suggested that the city was located in a region, where differences in elevation
made expansion of the urban area difficult.
• Percentage of size of agricultural land (11) (farmland): A positive correlation was
demonstrated. Of all the land cover classes, it was possible that farmland restrictions were weakest when the shape of the urban area’s expansion was amorphous.
• With the RPI model, we obtained significant correlations for both socioeconomic
framework and population density. we now present the following hypotheses on
the meaning of those correlations.
• Total population: A positive correlation was demonstrated. Urban area size
expands in concert with increases in total population. When an urban area’s
size expands its perimeter is extended, which was thought to make it easier for
its RPI to increase.
• GDP/cap: A positive correlation was demonstrated. Increase in per capita GDP
led to an increase in per capita car ownership. As a result, urban area expansion
followed motor vehicle roads, leading the shape of the urban area as a whole to
become complex.
• Population density: A negative correlation was demonstrated. Complexity of
shape of an urban area suggested that its population was distributed in a comparatively dispersed manner. As a result of the appearance of a dispersal trend in
settlement distribution at the neighborhood level, RPI and population density
were believed to be negatively correlated.
6.2.3.2 Hypotheses on the Correlations Between Population Density
and the Ecological Environmental Elements
• Degree of mixing of land cover classes: A positive correlation was demonstrated.
Diversity of land cover classes inside the urban area suggested that there were
many regions, where it would be difficult to bring about conversion to residential
land easily.
118
Y. Uchiyama and K. Hayashi