Count regression and machine learning approach for zero-inflated over-dispersed count data. Application to micro-retail distribution and urban form

Alessandro Araldi, Alessandro Venerandi, Giovanni Fusco

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

This paper investigates the relationship between urban form and the spatial distribution of micro-retail activities. In the last decades, several works demonstrated how configurational properties of the street network and morphological descriptors of the urban built environment are significantly related to store distribution. However, two main challenges still need to be addressed. On the one side, the combined effect of different urban form properties should be considered providing a holistic study of the urban form and its relationship to retail patterns. On the other, analytical approaches should consider the discrete, skewed and zero-inflated nature of the micro-retail distribution. To overcome these limitations, this work compares two sophisticated modelling procedure: Penalised Count Regression and Machine Learning approaches. While the former is specifically conceived to account for retail count distribution, the latter can capture non-linear behaviours in the data. The two modelling procedures are implemented on the same large dataset of street-based measures describing the urban form of the French Riviera. The outcomes of the two modelling approaches are compared in terms of prediction performance and selection frequencies of the most recurrent variables among the implemented models.

Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2020
Subtitle of host publication20th International Conference, Proceedings
EditorsOsvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Chiara Garau, Ivan Blecic, David Taniar, Bernady O. Apduhan, Ana Maria A.C. Rocha, Eufemia Tarantino, Carmelo Maria Torre, Yeliz Karaca
Place of PublicationCham, Switzerland
Pages550-565
Number of pages16
DOIs
Publication statusPublished - 29 Sep 2020
Event20th International Conference on Computational Science and Its Applications - Cagliari, Italy
Duration: 1 Jul 20204 Jul 2020
https://2020.iccsa.org/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12252 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Computational Science and Its Applications
Abbreviated titleICCSA 2020
Country/TerritoryItaly
CityCagliari
Period1/07/204/07/20
Internet address

Keywords

  • feature selection
  • machine Learning
  • penalised models
  • retail distribution
  • street-network configuration
  • urban form

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