Difference between revisions of "Group11 proposal"

From ISSS608-Visual Analytics and Applications
Jump to navigation Jump to search
 
(15 intermediate revisions by 2 users not shown)
Line 1: Line 1:
−
[[File:G11 TitleBanner.png|frameless|1700px|left|Group 11: A Geospatial Analysis on Nutrients & Health in Greater London]]
+
[[File:G11 TitleBanner2.png|1700px|frameless|SGSAS]]
 
<div>
 
<div>
 
{|style="background-color:#607080;" width="100%" cellspacing="0" cellpadding="0" valign="top" border="0"  |
 
{|style="background-color:#607080;" width="100%" cellspacing="0" cellpadding="0" valign="top" border="0"  |
−
| style="font-family:Century Gothic; font-size:100%; solid #103080; background:#20a0d0; text-align:center;" width="25%" |  
+
| style="font-family:Century Gothic; font-size:100%; solid #103080; background:#20a0d0; text-align:center;" width="20%" |  
 
;
 
;
 
[[Group11_proposal| <font color="#FFFFFF">Proposal</font>]]
 
[[Group11_proposal| <font color="#FFFFFF">Proposal</font>]]
  
−
| style="font-family:Century Gothic; font-size:100%; solid #103080; background:#607080; text-align:center;" width="25%" |  
+
| style="font-family:Century Gothic; font-size:100%; solid #103080; background:#607080; text-align:center;" width="20%" |  
 
;
 
;
 
[[Group11_poster| <font color="#FFFFFF">Poster</font>]]
 
[[Group11_poster| <font color="#FFFFFF">Poster</font>]]
  
−
| style="font-family:Century Gothic; font-size:100%; solid #103080; background:#607080; text-align:center;" width="25%" |  
+
| style="font-family:Century Gothic; font-size:100%; solid #103080; background:#607080; text-align:center;" width="20%" |  
 
;
 
;
 
[[Group11_application| <font color="#FFFFFF">Application</font>]]
 
[[Group11_application| <font color="#FFFFFF">Application</font>]]
  
−
| style="font-family:Century Gothic; font-size:100%; solid #103080; background:#607080; text-align:center;" width="25%" |  
+
| style="font-family:Century Gothic; font-size:100%; solid #103080; background:#607080; text-align:center;" width="20%" |
 +
;
 +
[[Group11_user_guide| <font color="#FFFFFF">Application User Guide</font>]]
 +
 
 +
| style="font-family:Century Gothic; font-size:100%; solid #103080; background:#607080; text-align:center;" width="20%" |  
 
;
 
;
 
[[Group11_research_paper| <font color="#FFFFFF">Research Paper</font>]]
 
[[Group11_research_paper| <font color="#FFFFFF">Research Paper</font>]]
−
 
 
|}
 
|}
 
</div>
 
</div>
Line 34: Line 37:
  
 
== 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).
+
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.
 
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.
Line 61: Line 64:
  
 
== Storyboard & Visualization Features ==
 
== Storyboard & Visualization Features ==
−
* Data Import and Manipulation
+
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).
−
[[File:Storyboard1.png|500px|thumb|none|Data Import and Manipulation]]<br>
+
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]]
  
−
* EDA – Distribution, Heatmap, Choropleth
 
−
[[File:Storyboard2.png|500px|thumb|none|EDA – Distribution, Heatmap, Choropleth]]<br>
 
−
 
−
* Analytical – k-means, LCA, hierarchical clustering
 
−
[[File:Storyboard3.png|500px|thumb|none|Analytical – k-means, LCA, hierarchical clustering]]<br>
 
−
[[File:Storyboard4.png|500px|thumb|none|Analytical – k-means, LCA, hierarchical clustering]]<br>
 
−
[[File:Storyboard5.png|500px|thumb|none|Analytical – k-means, LCA, hierarchical clustering]]<br>
 
−
[[File:Storyboard6.png|500px|thumb|none|Analytical – k-means, LCA, hierarchical clustering]]
 
−
 
−
== Data Source & Preparation ==
 
−
In January 2018, Google BigQuery published a Google Analytics sample with twelve months (Aug 2016 to Aug 2017) of obfuscated Google Analytics 360 data on the Google Merchandise Store, a real ecommerce store that sells Google-branded merchandise. The data is typical of what an ecommerce website would see and includes the following information:
 
−
 
−
* Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display traffic
 
−
* Content data: information about the behaviour of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc.
 
−
* Transactional data: information about the transactions on the Google Merchandise Store website.
 
−
 
−
However, data for some fields is obfuscated, such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.
 
−
 
−
The is a huge dataset of 400+ variables with a daily data incremental rate of approximately 25MB for 1,500 sessions and 40,000 detailed records. It can be exported to AVRO, JSON or CSV formats.
 
−
 
−
* [https://console.cloud.google.com/marketplace/details/obfuscated-ga360-data/obfuscated-ga360-data?filter=solution-type:dataset&q=analytics&id=45f150ac-81d3-4796-9abf-d7a4f98eb4c6&pli=1 Google Analytics Sample]
 
−
* [https://support.google.com/analytics/answer/3437719?hl=en BigQuery Export schema]
 
−
* [https://ga-dev-tools.appspot.com/dimensions-metrics-explorer/ Google Analytics Dimensions & Metrics Explorer]
 
−
* [https://your.googlemerchandisestore.com/Index Google Official Merchandise Store]
 
  
 
== Software Tools ==
 
== Software Tools ==
Line 93: Line 78:
  
 
== R Packages ==
 
== R Packages ==
−
* rjson: https://cran.r-project.org/web/packages/rjson
 
−
* jsonlite: https://cran.r-project.org/web/packages/jsonlite
 
−
* bigrquery: https://cran.r-project.org/web/packages/bigrquery
 
 
* shiny: https://shiny.rstudio.com
 
* shiny: https://shiny.rstudio.com
−
* shinydashboard: https://cran.r-project.org/web/packages/shinydashboard
+
* shinythemes: https://cran.r-project.org/web/packages/shinythemes
−
* ggplot2: https://cran.r-project.org/web/packages/ggplot2
+
* shinyWidgets: https://cran.r-project.org/web/packages/shinyWidgets
−
* plotly: https://plot.ly/r
+
* RColorBrewer: https://cran.r-project.org/web/packages/RColorBrewer
−
* poLCA: https://cran.r-project.org/web/packages/poLCA
 
 
* tidyverse: https://www.tidyverse.org
 
* tidyverse: https://www.tidyverse.org
−
* trelliscope: https://www.rdocumentation.org/packages/trelliscope/versions/0.9.7
+
* leaflet: https://cran.r-project.org/web/packages/leaflet
−
* ClustGeo: https://cran.r-project.org/web/packages/ClustGeo
+
* tmap: https://cran.r-project.org/web/packages/tmap
 
* spdep: https://cran.r-project.org/web/packages/spdep
 
* 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
 
* GWmodel: https://cran.r-project.org/web/packages/GWmodel
−
* spgwr: https://cran.r-project.org/web/packages/spgwr
+
* plotly: https://cran.r-project.org/web/packages/plotly
−
* geofacet: https://cran.r-project.org/web/packages/geofacet
+
* 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 ==
 
== Team Members ==
Line 121: Line 111:
 
*[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://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://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://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/how-geographicallyweightedregression-works.htm How Geographically Weighted Regression (GWR) works]
−
*[https://en.wikipedia.org/wiki/Greater_London Wikipedia: Greater London]
+
*[https://arxiv.org/abs/1306.0413 GWmodel: an R Package for Exploring Spatial Heterogeneity using Geographically Weighted Models]
−
*[https://geoportal.statistics.gov.uk/datasets/regions-december-2019-boundaries-en-bgc RGN boundaries 2019 BGC]
+
*[https://arxiv.org/abs/1312.2753 The GWmodel R package: Further Topics for Exploring Spatial Heterogeneity using Geographically Weighted Models]
−
*[https://geoportal.statistics.gov.uk/datasets/local-authority-districts-december-2019-boundaries-uk-bgc LAD boundaries 2019 BGC]
+
*[https://arxiv.org/abs/1905.00266 Scalable GWR: A linear-time algorithm for large-scale geographically weighted regression with polynomial kernels]
−
*[https://geoportal.statistics.gov.uk/datasets/wards-december-2019-boundaries-ew-bgc Wards boundaries 2019 BGC]
+
*[http://mural.maynoothuniversity.ie/7850/1/MC_Minkowski.pdf The Minkowski approach for choosing the distance metric in geographically weighted regression ]
−
*[http://geoportal.statistics.gov.uk/datasets/middle-layer-super-output-areas-december-2011-boundaries-ew-bgc MSOA boundaries 2011 BGC]
+
*[https://wiki.smu.edu.sg/1819t3isss608/Group09_Methodology UK's access to health assets and hazards]
−
*[http://geoportal.statistics.gov.uk/datasets/lower-layer-super-output-areas-december-2011-boundaries-ew-bgc LSOA boundaries 2011 BGC]
+
*[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

Retrieved from ‘https://wiki.smu.edu.sg/1920t2isss608/index.php?title=Group11_proposal&oldid=3083’