Group11 proposal

Proposal

Poster

Application

Application User Guide

Research Paper

Background

Grocery data from in-store purchases of 411 Tesco shops in the Greater London area are used in this R Shiny application. In this project, we will focus on using the nutrients information from this dataset at 4 different spatial granularities, Lower Super Output Areas (LSOA), Middle Layer Super Output Areas (MSOA), ward and Local Authority Districts (LAD).

The analysis is performed, notably through four sections:

1. Exploratory Data Analysis (EDA)
2. Exploratory Spatial Data Analysis (ESDA)
3. Clustering (Hierarchical, GeoSpatial, Skater Clustering)
4. Geographically weighted regression (GWR)

Motivation

The recent availability of this dataset provides us with an opportunity to work on information that is current. This dataset also combines geospatial data with aspatial information that allows us to apply geospatial regression techniques and geospatial clustering to understand nutrition and obesity at different geographic granularity.

Despite the importance of studying food consumption at scale, there is little data about what people actually eat over long periods of time. Our analysis will link these food consumption data of an area in Greater London through both aspatial and geospatial methods. We will attempt to analyze the eating habits of Londoners based on this dataset through a non-biased, non-personalized lens that is prevalent in current web data from social media and geo-referenced media.

Project Objectives

The project aims to deliver an R-Shiny app that provides:

1. Interactive user interface design
2. Nutritional information interfaced with a visual map representation
3. Clustering techniques through both aspatial and geospatial methods
4. Geographically weighted Regression (GWR) of nutritional data and obesity

Proposed Scope and Methodology

1. Analysis of Tesco Grocery dataset with background research
2. Exploratory Data Analysis (EDA) methods in R
3. Exploratory Spatial Data Analysis (ESDA) methods in R
4. Clustering methods for aspatial and geospatial information in R
5. Analysis of geographically weighted regression (GWR) in R
6. R Markdown development for functionality checks
7. R-Shiny app development for user interactivity

A generalized development timeframe for this project is shown below.

Storyboard & Visualization Features

There will be five sections in the final App. Data exploration will be done in the first two sections using scatterplots, correlation plots, and Local Indicator of Spatial Autocorrelation (LISA). The next two sections will be the clustering methods and geographically weighted regression. The last section will show the 4 transformed final data tables used in the application.

Exploratory Data Analysis

Exploratory Spatial Data Analysis

Clustering

Geographically weighted regression

Data Table

Software Tools

• RStudio: https://rstudio.com/

R Packages

• shiny: https://shiny.rstudio.com
• shinythemes: https://cran.r-project.org/web/packages/shinythemes
• shinyWidgets: https://cran.r-project.org/web/packages/shinyWidgets
• RColorBrewer: https://cran.r-project.org/web/packages/RColorBrewer
• tidyverse: https://www.tidyverse.org
• leaflet: https://cran.r-project.org/web/packages/leaflet
• tmap: https://cran.r-project.org/web/packages/tmap
• spdep: https://cran.r-project.org/web/packages/spdep
• rgeos: https://cran.r-project.org/web/packages/rgeos
• sf: https://cran.r-project.org/web/packages/sf
• sp: https://cran.r-project.org/web/packages/sp
• rgdal: https://cran.r-project.org/web/packages/rgdal
• GWmodel: https://cran.r-project.org/web/packages/GWmodel
• plotly: https://cran.r-project.org/web/packages/plotly
• ClustGeo: https://cran.r-project.org/web/packages/ClustGeo
• dendextend https://cran.r-project.org/web/packages/dendextend
• GGally: https://cran.r-project.org/web/packages/GGally
• ggdendro: https://cran.r-project.org/web/packages/ggdendro
• corrplot: https://cran.r-project.org/web/packages/corrplot
• DT: https://cran.r-project.org/web/packages/DT

Team Members

• LI Junyi Darren
• Muhammad Jufri Bin RAMLI
• TEO Lip Peng Raymond

References

• Tesco Grocery 1.0, a large-scale dataset of grocery purchases in London
• Tesco Grocery 1.0 dataset
• Metadata record for: Tesco Grocery 1.0
• Large-scale and high-resolution analysis of food purchases and health outcomes
• Guide to presenting statistics for Super Output Areas (June 2018)
• Data on child obesity and excess weight at small area level
• Wikipedia: Greater London
• Regions (December 2019) Boundaries EN BGC
• Local Authority Districts (December 2019) Boundaries UK BGC
• Wards (December 2019) Boundaries EW BGC
• Middle Layer Super Output Areas (December 2011) Boundaries EW BGC
• Lower Layer Super Output Areas (December 2011) Boundaries EW BGC
• Exploratory Spatial Data Analysis - Jin Chen
• Exploratory Spatial Data Analysis (ESDA) - Chris Brunsdon
• How Geographically Weighted Regression (GWR) works
• GWmodel: an R Package for Exploring Spatial Heterogeneity using Geographically Weighted Models
• The GWmodel R package: Further Topics for Exploring Spatial Heterogeneity using Geographically Weighted Models
• Scalable GWR: A linear-time algorithm for large-scale geographically weighted regression with polynomial kernels
• The Minkowski approach for choosing the distance metric in geographically weighted regression
• UK's access to health assets and hazards
• Corn: The A-maize-ing Crop