Difference between revisions of "Group11 proposal"

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[[File:Group11banner.PNG.png|frameless|1100px|left|Group 11: Google Analytics - Power Up!]]
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[[File:G11 TitleBanner2.png|1700px|frameless|SGSAS]]
 
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== Background ==
 
== Background ==
Google Analytics is a suite of analytical tools to provide insights on website access to aid businesses decisions. It allows businesses to profile their site visitors and how they interact with the website content. It provides Analytics Intelligence for quick answers to common metrics, numerous online reports on audience, advertising, acquisition, behaviour, conversion and user flow, and data analysis with data filtering, manipulation, segmentation and visualization features. A paid version "Google Analytics 360" provides more advanced eCommerce features on who are likely to convert to customers and how best to use the marketing dollars.<br>
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[[File:G11 MapUK.png|1000px|frameless]] <br />
[[File:GaAudienceOverview.png|600px|thumb|none|GA Audience Overview]]
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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).
<br>
+
 
 +
The analysis is performed, notably through four sections:
 +
# Exploratory Data Analysis (EDA)
 +
# Exploratory Spatial Data Analysis (ESDA)
 +
# Clustering (Hierarchical, GeoSpatial, Skater Clustering)
 +
# Geographically weighted regression (GWR)
 +
 
 +
 
 
== Motivation ==
 
== 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 ==
 
== Project Objectives ==
 +
The project aims to deliver an R-Shiny app that provides:
 +
# Interactive user interface design
 +
# Nutritional information interfaced with a visual map representation
 +
# Clustering techniques through both aspatial and geospatial methods
 +
# Geographically weighted Regression (GWR) of nutritional data and obesity
 +
  
 
== Proposed Scope and Methodology ==
 
== Proposed Scope and Methodology ==
 +
# Analysis of Tesco Grocery dataset with background research
 +
# Exploratory Data Analysis (EDA) methods in R
 +
# Exploratory Spatial Data Analysis (ESDA) methods in R
 +
# Clustering methods for aspatial and geospatial information in R
 +
# Analysis of geographically weighted regression (GWR) in R
 +
# R Markdown development for functionality checks
 +
# R-Shiny app development for user interactivity
 +
 +
 +
A generalized development timeframe for this project is shown below. <br />
  
== Project Timeline ==
+
[[File:Gen Gantt2.png|1000px|frameless]]
  
== Visualisation Features ==
+
== 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. <br />
 +
<p>Exploratory Data Analysis <br />[[File:G11 Stb A.jpg|800px|frameless|EDA]]
 +
<p>Exploratory Spatial Data Analysis <br />[[File:G11 Stb B.jpg|800px|frameless|ESDA]]
 +
<p>Clustering <br />[[File:G11 Stb C.jpg|800px|frameless|Clustering]]
 +
<p>Geographically weighted regression <br />[[File:G11 Stb D.jpg|800px|frameless|GWR]]
 +
<p>Data Table <br />[[File:Stb E.jpg|800px|frameless|Data Table]]
  
== Data Source & Preparation ==
 
  
 
== Software Tools ==
 
== Software Tools ==
 +
* RStudio: https://rstudio.com/
  
 
== R Packages ==
 
== 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
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* rgeos: https://cran.r-project.org/web/packages/rgeos
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* sf: https://cran.r-project.org/web/packages/sf
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* sp: https://cran.r-project.org/web/packages/sp
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* rgdal: https://cran.r-project.org/web/packages/rgdal
 +
* GWmodel: https://cran.r-project.org/web/packages/GWmodel
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* plotly: https://cran.r-project.org/web/packages/plotly
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* 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
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* 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 ==
 
== Team Members ==
 +
* LI Junyi Darren
 +
* Muhammad Jufri Bin RAMLI
 +
* TEO Lip Peng Raymond
  
 
== References ==
 
== References ==
 +
*[https://www.nature.com/articles/s41597-020-0397-7 Tesco Grocery 1.0, a large-scale dataset of grocery purchases in London]
 +
*[https://figshare.com/collections/Tesco_Grocery_1_0/4769354/2 Tesco Grocery 1.0 dataset]
 +
*[https://springernature.figshare.com/articles/Metadata_record_for_Tesco_Grocery_1_0_a_large-scale_dataset_of_grocery_purchases_in_London/11799765 Metadata record for: Tesco Grocery 1.0]
 +
*[https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-019-0191-y Large-scale and high-resolution analysis of food purchases and health outcomes]
 +
*[https://geoportal.statistics.gov.uk/datasets/guide-to-presenting-statistics-for-super-output-areas-june-2018 Guide to presenting statistics for Super Output Areas (June 2018)]
 +
*[https://webarchive.nationalarchives.gov.uk/20170110165409/https://www.noo.org.uk/visualisation Data on child obesity and excess weight at small area level]
 +
*[https://en.wikipedia.org/wiki/Greater_London Wikipedia: Greater London]
 +
*[https://geoportal.statistics.gov.uk/datasets/regions-december-2019-boundaries-en-bgc Regions (December 2019) Boundaries EN BGC]
 +
*[https://geoportal.statistics.gov.uk/datasets/local-authority-districts-december-2019-boundaries-uk-bgc Local Authority Districts (December 2019) Boundaries UK BGC]
 +
*[https://geoportal.statistics.gov.uk/datasets/wards-december-2019-boundaries-ew-bgc Wards (December 2019) Boundaries EW BGC]
 +
*[https://geoportal.statistics.gov.uk/datasets/middle-layer-super-output-areas-december-2011-boundaries-ew-bgc Middle Layer Super Output Areas (December 2011) Boundaries EW BGC]
 +
*[https://geoportal.statistics.gov.uk/datasets/lower-layer-super-output-areas-december-2011-boundaries-ew-bgc Lower Layer Super Output Areas (December 2011) Boundaries EW BGC]
 +
*[https://sk.sagepub.com/reference/geography/n406.xml Exploratory Spatial Data Analysis - Jin Chen]
 +
*[https://sk.sagepub.com/reference/geoinfoscience/n64.xml Exploratory Spatial Data Analysis (ESDA) - Chris Brunsdon]
 +
*[https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/how-geographicallyweightedregression-works.htm How Geographically Weighted Regression (GWR) works]
 +
*[https://arxiv.org/abs/1306.0413 GWmodel: an R Package for Exploring Spatial Heterogeneity using Geographically Weighted Models]
 +
*[https://arxiv.org/abs/1312.2753 The GWmodel R package: Further Topics for Exploring Spatial Heterogeneity using Geographically Weighted Models]
 +
*[https://arxiv.org/abs/1905.00266 Scalable GWR: A linear-time algorithm for large-scale geographically weighted regression with polynomial kernels]
 +
*[http://mural.maynoothuniversity.ie/7850/1/MC_Minkowski.pdf The Minkowski approach for choosing the distance metric in geographically weighted regression ]
 +
*[https://wiki.smu.edu.sg/1819t3isss608/Group09_Methodology UK's access to health assets and hazards]
 +
*[https://wiki.smu.edu.sg/18191isss608g1/ISSS608_Group07_Proposal Corn: The A-maize-ing Crop]

Latest revision as of 09:06, 3 May 2020

SGSAS

Proposal

Poster

Application

Application User Guide

Research Paper


Background

G11 MapUK.png
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.

Gen Gantt2.png

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
EDA

Exploratory Spatial Data Analysis
ESDA

Clustering
Clustering

Geographically weighted regression
GWR

Data Table
Data Table

Software Tools

R Packages

Team Members

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

References