Assignment on Analytics in Practice.

Analytics in Practice

Analytics in Practice
MEMORANDUM
DATE:
TO: Sally Capp, Lord Mayor of the city of Melbourne
FROM:
SUBJECT: Suburb-Specific Management of flow of people in the City of Melbourne
I am writing to inform you of a proposal on how utilize the data on the city of Melbourne to
develop an adaptable plan for the allocation of traffic related resources for the different suburbs
in the city.
The biggest challenge that face the world’s biggest cities is the management of the flow of
people in ways that provides them with convenience while at the same time satisfying their
personal preferences (Bari & Naser, 2010). Among the proposals for management of the flow
of people in big cities is the improvement and encouraging of the use of public transport (Han,
Shan & Bin, 2013). Whereas this is a very climate-conscious solution, it caters mostly for the
environmental sustainability while ignoring the social sustainability of communities. Developing
a balanced approach to management of the flow of people that caters for both environmental
sustainability and social sustainability hence becomes a point of interest. Satisfying the personal
preferences of the residents in the big cities plays a key role in ensuring the social sustainability
of the communities of the residents of the cities (Emily, 2018). With respect to the flow of
people in cities, personal preferences of the residents may include a preference for private
transportation to public transportation. Therefore, it is important to consider options that will
complement the approach of improving and encouraging of the use of public transport.
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Analytics in Practice
In order to develop an approach that will allow private transportation to coexist with public
transportation in a sustainable manner, there is need to understand the characteristics of the
transport networks in different parts of a city, in this case the City of Melbourne. Transportation,
just like other human behaviors and experiences develop and evolve depending on the
environment. This therefore implies that due to the different human, natural and structural
differences in different suburbs of the city of Melbourne, there is a high likelihood of the
differentiation in the behavior of individuals using the transport networks.
Cluster analysis provides the best statistical approach in understanding the characteristics of the
transportation networks in different parts of the city of Melbourne. Cluster analysis refers to a
data analysis approach that focuses on the use of attributes of the elements (observations) in a
data set to group them depending on the extent to which they are similar (Malki & Rizk, 2016;
Daie & Li, 2016; Liu & Denxiao, 2015). The current dataset of the traffic in the city of
Melbourne contains information on the dates, suburbs, vehicle classification, speed limits for the
specific location and speed of the observed vehicle. Through cluster analysis, similarity in
vehicle classification, speed limits and vehicle speeds can be used to group the suburbs in the
city of Melbourne. In each of the resultant groups, specific traffic approaches can be developed
with additional resources being directed to the suburbs that are in clusters with highest incidents
of high vehicle speeds. The cluster analysis can further be conducted for different days of the
week to allow for resource allocation for resources that can be redirected on a daily basis. In
addition, the cluster analysis can be conducted on regular intervals, say monthly, which will
allow the developed strategy to be adaptable to the dynamic nature of the big cities’ populations.
Additional datasets on traffic jams, accidents, traffic offenses and vehicle emission levels, will
provide more attributes which can be used in the cluster analysis model to generate more detailed
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