Difference between revisions of "ISSS608 2016 17T3 Group12 Report"

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The RainyApp can be accessed through:
 
The RainyApp can be accessed through:
  
−
insert link here.
+
https://weatherwise.shinyapps.io/RainApp/
  
 
=Methodology=
 
=Methodology=
−
 
−
==Data Preparation==
 
−
The data has been downloaded from NEA, which is provided for 58 active weather stations across spread across Singapore. The data is provided for each month and is updated on 10th of every month. This data has been used by us to
 
−
 
−
==Geospatial Interpolation==
 
  
 
{| class="wikitable"
 
{| class="wikitable"
 +
|-
 +
|<center><b>Design Framework</b></center>
 +
|<center><b>Results</b></center>
 +
|-
 +
|[[File:Datasource.png|400px|center]]<br/>
 +
<center>'''Data Source'''</center>
 +
|
 +
National Environment Agency collects data from total 58 weather stations all over Singapore, this data is made available for the public and can be found in their website. Data is provided monthly for each station, and from each station respective rainfall, temperature, wind speed etc are recorded. For this project we are using only rainfall data. Data.gov.sg provided the shape file for the visualisation.
 
|
 
|
−
! Description
 
−
! R Script
 
−
! Output
 
−
|-style="vertical-align: top;horizontal-align: center;"
 
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| Load the Shapefile and use it to create the map with a grid
 
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| [[File:G12-ScriptShp.JPG|framed|]]
 
−
| [[File:SG Grid.png|framed|]]
 
−
|-style="vertical-align: top;horizontal-align: center;"
 
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|Load the coordinates of each weather station and project it to the map
 
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| [[File:G12-ScriptCoords.JPG|framed|]]
 
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| [[File:G12-OutMap.png|framed|]]
 
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|-style="vertical-align: top;horizontal-align: center;"
 
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|Filter the rainfall data based on the filter timeframe
 
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| [[File:G12-ScriptFilter.JPG|framed|]]
 
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| [[File:G12-OutFilter.JPG|framed|]]
 
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|-style="vertical-align: top;horizontal-align: center;"
 
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|Run the fit model for the variogram
 
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| [[File:G12-ScriptVGM.JPG|framed|]]
 
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| [[File:G12-OutVGM.png|framed|]]
 
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|-style="vertical-align: top;horizontal-align: center;"
 
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|Perform the Kriging based on the best fit variogram
 
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| [[File:G12-ScriptKrig.JPG|framed|]]
 
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| [[File:G12-OutKriged.JPG|framed|]]
 
−
|-style="vertical-align: top;horizontal-align: center;"
 
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|Plot the interpolated values to the SG map
 
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| [[File:G12-ScriptOverlay.JPG|framed|]]
 
−
| [[File:G12-OutInter.png|framed|]]
 
−
|}
 
−
 
−
==Shiny Application==
 
−
 
−
{| class="wikitable"
 
 
|-
 
|-
−
! Description
+
|[[File:Shp-grid.png|600px|centre]]
−
! R Script
+
<center>'''Grid Points''' </center>
−
! Output
+
|
 +
From the shape file, we created grid points for interpolation. Interpolation is the important in this visualisation as rainfall from weather stations don't make much sense as its own.
 +
|
 
|-
 
|-
−
| row 1, cell 1
+
|[[File:Point to space.png|600px]]<br/>
−
| row 1, cell 2
+
<center>'''Interpolation''' </center>
−
| row 1, cell 3
+
|
 +
The first graph shows the 58 weather stations and the second one is the interpolated visualisation of the same. It is evident that the first visualisation is not that useful while considering a certain region or the whole Singapore.
 
|-
 
|-
−
| row 2, cell 1
+
 
−
| row 2, cell 2
 
−
| row 2, cell 3
 
 
|}
 
|}
  
−
=Design Framework=
+
=User Guide=
  
 
{| class="wikitable"
 
{| class="wikitable"
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|<center><b>Results</b></center>
 
|<center><b>Results</b></center>
 
|-
 
|-
−
|[[File:Airline.png|600px]]<br/>
+
|[[File:Select weekly-monthly.png|600px]]<br/>
−
<center>'''Airline Traffic'''</center>
+
<center>'''User Interface'''</center>
 
|
 
|
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* The dash board design allows the user to select different airline from the drop-down menu.
+
The application give the user the choice to select visualisation either monthly or weekly. Weekly option is to let the user to dig deep and monthly provides a overall picture. So these two options can cater the requirements of all users.
−
 
 
−
* It shows the network graph of the airlines connecting different parts of India with the size as the betweeness centrality and the colour as the closeness centrality.
 
−
 
 
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* The lines on the map are directed from the origin to the destination airport with the arrow head.
 
−
 
 
−
* The data table on the right-hand side gives user on overall view of the arrival and departure airport of the airline as well as the departure time with frequency representing the no of days it fly’s.
 
 
|
 
|
 
|-
 
|-
−
|[[File:Facet.png|600px|centre]]
+
|[[File:Seelect the variogram.png|600px|centre]]
−
<center>'''Facet''' </center>
+
<center>'''User Interface''' </center>
 
|
 
|
−
* This design allows the user to easily have an overall view of all the airline carriers in india and lets the user see the big picture of the airline operating in different parts of india.
+
Additional to the monthly and weekly option, user can also choose between six different VGMs(Variogram Models).  
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* It also allows the user to see the network analysis with an overall view.
 
 
|
 
|
 
|-
 
|-
−
|[[File:Airline Time.png|600px]]<br/>
+
|[[File:Final output.png|600px]]<br/>
−
<center>'''Airline by Time''' </center>
+
<center>'''User Interface''' </center>
 
|
 
|
−
* The design allows the user to navigate the different hours using the slider.
+
The interpolated graph reveals the rainfall pattern all over Singapore. The intensity of the red colour shows the amount of rainfall received and the blue dotes indicate the NEA points of rainfall recording. The size of the blue dots is also proportional to the amount of rainfall received.
−
* It allows the user to find out which are the time where traffic network is very high and which are the airlines travelling during that period of time.
 
 
|-
 
|-
−
|[[File:Airline Tier1.png|600px]]<br/>
+
 
−
<center>'''Airline by Tier'''</center>
 
−
|
 
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*      This design allows the user to explore the network centrality using both the map as well as the bubble plot to locate the cities of interest on the basis of 3 tier (derived from population).
 
−
|
 
 
|}
 
|}
 +
=Packages Used=
 +
*dplyr
 +
* shiny
 +
* ggplot2
 +
* tidyverse
 +
* ggmap
 +
* sp
 +
* plotly
 +
* shinydashboard
 +
* rgdal
 +
* gstat
 +
* gridExtra
 +
* rvest
 +
 +
=Future Work=
 +
* Currently the group has initially identified 6 variogram distribution, but considering there are 17 vgm implemented in the gstat library a great addition to future works could include comparing the dataset in all 17 vgms and getting the top 5 as options for the users.
 +
* Currently the model is restricted to the Rainfall Data from NEA but future works improvement can include uploading of any dataset where the user just needs to indicate the location of the dataset and the variable to be interpolated and let the application capture the shapefile and map through DIVA-GIS and any open source map to create the interpolation.
 +
 +
=References=
 +
 +
https://wiki.smu.edu.sg/17t2is415g1/IS415_Team_Ninja_Project
 +
 +
https://cran.r-project.org/doc/contrib/intro-spatial-rl.pdf
 +
 +
http://rpubs.com/nabilabd/118172
 +
 +
http://data-analytics.net/cep/Schedule_files/geospatial.html

Latest revision as of 02:21, 7 August 2017

SG Grid.png WeatherWISE

Project Dropbox

Proposal

Poster

Report and App

 



Application

The RainyApp can be accessed through:

https://weatherwise.shinyapps.io/RainApp/

Methodology

Design Framework
Results
Datasource.png

Data Source

National Environment Agency collects data from total 58 weather stations all over Singapore, this data is made available for the public and can be found in their website. Data is provided monthly for each station, and from each station respective rainfall, temperature, wind speed etc are recorded. For this project we are using only rainfall data. Data.gov.sg provided the shape file for the visualisation.

Shp-grid.png
Grid Points

From the shape file, we created grid points for interpolation. Interpolation is the important in this visualisation as rainfall from weather stations don't make much sense as its own.

Point to space.png
Interpolation

The first graph shows the 58 weather stations and the second one is the interpolated visualisation of the same. It is evident that the first visualisation is not that useful while considering a certain region or the whole Singapore.

User Guide

Design Framework
Results
Select weekly-monthly.png
User Interface

The application give the user the choice to select visualisation either monthly or weekly. Weekly option is to let the user to dig deep and monthly provides a overall picture. So these two options can cater the requirements of all users.

Seelect the variogram.png
User Interface

Additional to the monthly and weekly option, user can also choose between six different VGMs(Variogram Models).

Final output.png
User Interface

The interpolated graph reveals the rainfall pattern all over Singapore. The intensity of the red colour shows the amount of rainfall received and the blue dotes indicate the NEA points of rainfall recording. The size of the blue dots is also proportional to the amount of rainfall received.

Packages Used

  • dplyr
  • shiny
  • ggplot2
  • tidyverse
  • ggmap
  • sp
  • plotly
  • shinydashboard
  • rgdal
  • gstat
  • gridExtra
  • rvest

Future Work

  • Currently the group has initially identified 6 variogram distribution, but considering there are 17 vgm implemented in the gstat library a great addition to future works could include comparing the dataset in all 17 vgms and getting the top 5 as options for the users.
  • Currently the model is restricted to the Rainfall Data from NEA but future works improvement can include uploading of any dataset where the user just needs to indicate the location of the dataset and the variable to be interpolated and let the application capture the shapefile and map through DIVA-GIS and any open source map to create the interpolation.

References

https://wiki.smu.edu.sg/17t2is415g1/IS415_Team_Ninja_Project

https://cran.r-project.org/doc/contrib/intro-spatial-rl.pdf

http://rpubs.com/nabilabd/118172

http://data-analytics.net/cep/Schedule_files/geospatial.html

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