  Different techniques were developed to extract urban agglomerations from a
big dataset. The urban agglomerations are used to understand the structure and
growth of cities. However, the major challenge is to extract urban
agglomerations from big data, which can reflect human activities. Community
urban cluster refers to spatially clustered geographic events, such as human
settlements or activities. It provides a powerful and innovative insight to
analyze the structure and growth of the real city. In order to understand the
shape and growth of urban agglomerations in Switzerland from spatial and
temporal aspects, this work identifies urban clusters from nighttime light data
and street network data. Nighttime light data record lights emitted from human
settlements at night on the earth's surface. This work uses DMSP-OLS Nighttime
light data to extract urban clusters from 1992 to 2013. The street is one of
the most important factors to reflect human activities. Hence, urban clusters
are also extracted from street network data to understand the structure of
cities. Both of these data have a heavy-tailed distribution, which includes
power laws as well as lognormal and exponential distributions. The head/tail
breaks is a classification method to find the hierarchy of data with a
heavy-tailed distribution. This work uses head/tail breaks classification to
extract urban clusters of Switzerland. At last, the power law distribution of
all the urban clusters was detected at the country level.
