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Sustainable Cities and Regions—Statistical Approaches

Sustainable Cities and Regions—Statistical Approaches

Dynamic urbanisation leaves a significant mark on the broadly understood quality
of life, regardless of the size of the city and the country or continent in which it is located.
On one hand, economic progress favours the development of new technologies and the
availability of many kinds of resources accessible almost without any limits to make life easier
[1–5]. On the other hand, the development of urbanised areas, new transport networks,
and higher demand for natural resources causes their depletion, pollution of different
components of the environment, waste production, deforestation, landscape fragmentation,
and biodiversity losses, leading finally to the deterioration of living conditions in the long
term [6–11]. Some of these driving forces and pressures, as well as the responses of the
natural environment, can be described by characterizing their regularities and patterns. Understanding
the quantitative features of many components of socio-environmental systems
makes it easier to undertake proper actions to mitigate undesirable phenomena. Therefore,
various statistical and mathematical techniques (machine learning, regression, classification,
spatial analysis, and others) can be widely used to solve crucial problems in the current development
of cities and regions worldwide to face the challenge of sustainable development
at different scales [12–17]. Mathematical modelling of socio-environmental dependencies
allows the drawing of far-reaching conclusions supporting the decision-making process
for a more sustainable future. Testing broadly understood statistical hypotheses leads to
drawing conclusions about the significance of relationships.

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