the respective response variable and the ecological environment indicators as
explanatory variables. We examined all the explanatory variables shown in
Table 6.2 in each of the two models, taking density and RPI, respectively, as the
response variable. As a result, based on a significance probability for each explanatory variable, ultimately six indicators in Table 6.3 were selected for the model with
RPI as its response variable, and six indicators in Table 6.4 were selected for the
model with population density as its response variable. While basically with all
variables the significance probability was 10% or less, we still included it for annual
average temperatures considering their usefulness for examination purposes,
although this probability only just exceeded 10% in the RPI-as-response-variable
model.
The greatest difference between the two models was that one had socioeconomic
indicators among its explanatory variables and the other did not. Total population
and per capita GDP, which were significant in the PI model, were not significant in
the population density model. This suggested that in cities with more than four
million people, population density was regulated by the ecological environment
rather than by the city’s minimum socioeconomic framework in the form of its total
population and GDP. However, because the contribution for the population density
model was comparatively low at 0.51, it cannot be said that the ecological environmental elements chosen regulated density in any dominant manner. It is possible that
for population density in particular, there are other ecological environmental elements as well as comparatively more important elements in a city’s socioeconomic
framework, which are relevant. As elements that ought to be analyzed in the future in
this regard, possible examples include not the percentage of the size of agricultural
land but rather the productivity of that land and family systems and systems of land
ownership. At present, these are elements for which it is difficult to get quantitative
data from cities in all regions of the world.
6.2.3 The Ecological Environment’s Influence on Urban
Morphology
In this subsection, with respect to the meaning of the correlations obtained through
the foregoing multiregression analysis, we discuss the ecological environmental
elements that ought to be taken into consideration in order to affect a review of the
compact city model, presenting a hypothesis for each element.
6.2.3.1 Hypotheses for the Correlations Between RPI and the Individual
Ecological Environmental Elements
• Annual average temperatures: A negative correlation was demonstrated. For
cities in colder regions, out of a need to share heat sources, their urban area
6 Generation of Urban Morphologies Through Long-Term Evolution of. . .
115
explanatory variables. We examined all the explanatory variables shown in
Table 6.2 in each of the two models, taking density and RPI, respectively, as the
response variable. As a result, based on a significance probability for each explanatory variable, ultimately six indicators in Table 6.3 were selected for the model with
RPI as its response variable, and six indicators in Table 6.4 were selected for the
model with population density as its response variable. While basically with all
variables the significance probability was 10% or less, we still included it for annual
average temperatures considering their usefulness for examination purposes,
although this probability only just exceeded 10% in the RPI-as-response-variable
model.
The greatest difference between the two models was that one had socioeconomic
indicators among its explanatory variables and the other did not. Total population
and per capita GDP, which were significant in the PI model, were not significant in
the population density model. This suggested that in cities with more than four
million people, population density was regulated by the ecological environment
rather than by the city’s minimum socioeconomic framework in the form of its total
population and GDP. However, because the contribution for the population density
model was comparatively low at 0.51, it cannot be said that the ecological environmental elements chosen regulated density in any dominant manner. It is possible that
for population density in particular, there are other ecological environmental elements as well as comparatively more important elements in a city’s socioeconomic
framework, which are relevant. As elements that ought to be analyzed in the future in
this regard, possible examples include not the percentage of the size of agricultural
land but rather the productivity of that land and family systems and systems of land
ownership. At present, these are elements for which it is difficult to get quantitative
data from cities in all regions of the world.
6.2.3 The Ecological Environment’s Influence on Urban
Morphology
In this subsection, with respect to the meaning of the correlations obtained through
the foregoing multiregression analysis, we discuss the ecological environmental
elements that ought to be taken into consideration in order to affect a review of the
compact city model, presenting a hypothesis for each element.
6.2.3.1 Hypotheses for the Correlations Between RPI and the Individual
Ecological Environmental Elements
• Annual average temperatures: A negative correlation was demonstrated. For
cities in colder regions, out of a need to share heat sources, their urban area
6 Generation of Urban Morphologies Through Long-Term Evolution of. . .
115