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@ -25,8 +25,6 @@
\section{Introduction} \label{sec:introduction} \section{Introduction} \label{sec:introduction}
\section{Method} \label{sec:method}
The purpose of this report is to re-analyse the data presented in the paper by The purpose of this report is to re-analyse the data presented in the paper by
\cite{dong2018methods}, which investigates the effect that protests (as an \cite{dong2018methods}, which investigates the effect that protests (as an
example of disruptive social behaviours in general) have on consumer example of disruptive social behaviours in general) have on consumer
@ -39,11 +37,13 @@
(OECD). Although \cite{dong2018methods} investigate temporal and spatial (OECD). Although \cite{dong2018methods} investigate temporal and spatial
effects on consumer spending, for the purposes of this analysis, only the effects on consumer spending, for the purposes of this analysis, only the
spatial effect of variables (with relation to the geographical distance from spatial effect of variables (with relation to the geographical distance from
the event) is considered. The dataset consists of variables measured as a the event) is considered.
function of the distance from the event (in km), including: the number of
customers, the median spending amount, the number of transactions, and the
total sales amount.
\section{Method} \label{sec:method}
The dataset consists of variables measured as a function of the distance
from the event (in km), including: the number of customers, the median
spending amount, the number of transactions, and the total sales amount.
The re-analysis is conducted on the data provided in the The re-analysis is conducted on the data provided in the
paper\cite{dong2018methods}, using Python in conjunction with packages such as paper\cite{dong2018methods}, using Python in conjunction with packages such as
pandas, matplotlib, numpy and seaborn, to process and visualise the data. As pandas, matplotlib, numpy and seaborn, to process and visualise the data. As