Version 1
: Received: 16 January 2021 / Approved: 18 January 2021 / Online: 18 January 2021 (13:13:23 CET)
How to cite:
Das, A. A Visual Analytics Approach for Finding Geospatial and Temporal Patterns From covid19 Government Backed Business Support Scheme. Preprints2021, 2021010339 (doi: 10.20944/preprints202101.0339.v1).
Das, A. A Visual Analytics Approach for Finding Geospatial and Temporal Patterns From covid19 Government Backed Business Support Scheme. Preprints 2021, 2021010339 (doi: 10.20944/preprints202101.0339.v1).
Cite as:
Das, A. A Visual Analytics Approach for Finding Geospatial and Temporal Patterns From covid19 Government Backed Business Support Scheme. Preprints2021, 2021010339 (doi: 10.20944/preprints202101.0339.v1).
Das, A. A Visual Analytics Approach for Finding Geospatial and Temporal Patterns From covid19 Government Backed Business Support Scheme. Preprints 2021, 2021010339 (doi: 10.20944/preprints202101.0339.v1).
Abstract
Bounce Back Loan is amongst a number of UK business financial support schemes launched by UK Government in 2020 amidst pandemic lockdown. Through these schemes, struggling businesses are provided financial support to weather economic slowdown from pandemic lockdown. £43.5bn loan value has been provided as of 17th Dec2020. However, with no major checks for granting these loans and looming prospect of loan losses from write-offs from failed businesses and fraud, this paper theorizes prospect of applying spatiotemporal modelling technique to explore if geospatial patterns and temporal analysis could aid design of loan grant criteria for schemes. Application of Clustering and Visual Analytics framework to business demographics, survival rate and Sector concentration shows Inner and Outer London spatial patterns which historic business failures and reversal of the patterns under COVID-19 implying sector influence on spatial clusters. Combination of unsupervised clustering technique with multinomial logistic regression modelling on research datasets complimented by additional datasets on other support schemes, business structure and financial crime, is recommended for modelling business vulnerability to certain types of financial market or economic condition. The limitations of clustering technique for high dimensional is discussed along with relevance of an applicable model for continuing the research through next steps
Keywords
COVID19; Bounce Back Loans ; BBLS; Clustering, Geospatial; Temporal
Subject
MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Arunav Das