Difference between revisions of "Group08 application"
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− | + | | Sub tab 3: Sankey diagram | |
|[[File:Tab 6.png|frameless|500px]] | |[[File:Tab 6.png|frameless|500px]] | ||
|| This third tab of Flow shows a Sankey diagram of the selected Source and Destination from the Aggregation Filters tab. | || This third tab of Flow shows a Sankey diagram of the selected Source and Destination from the Aggregation Filters tab. | ||
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− | | Sub tab | + | | Sub tab 4: Origin Destination Matrix (Flow Select without dominance) |
[[File:Tab 7.png|frameless|500px]] | [[File:Tab 7.png|frameless|500px]] | ||
|| '''COMPONENTS OF USER INPUTS PANEL''' | || '''COMPONENTS OF USER INPUTS PANEL''' | ||
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| '''GW REGRESSION TAB'''|| '''Instructions''' | | '''GW REGRESSION TAB'''|| '''Instructions''' | ||
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− | | Sub tab 1: | + | | Sub tab 1: Regression Model |
[[File:Tab 8.png|frameless|500px]] | [[File:Tab 8.png|frameless|500px]] | ||
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− | + | The Gravity Model plots a regression analysis of Centrality measures (X, Dependent Variable) against Tap in or Tap out (Y, Independent Variable). | |
− | [1] On the filter, user can choose the X variable, Y variables, and the confidence level. | + | [1] On the filter, user can choose the X variable, Y variables, and the confidence level. |
− | [2] | + | [2] On the top of the result panel, user can toggle between Regression Model and Model Assumptions. Based on the confidence level chosen, the model will show if the model is statistically significant. |
+ | The coefficient of the model can also be found below. Since we normalized all centrality measures, the weight of the coefficient can tell us the importance of the centrality measure to the overall passenger flow. In the case above, we can see that degree centrality is the most important centrality measure. | ||
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− | | Sub tab | + | | Sub tab 2: Model Assumption |
[[File:Tab 9.png|frameless|500px]] | [[File:Tab 9.png|frameless|500px]] | ||
|| '''COMPONENTS OF USER INPUTS PANEL''' | || '''COMPONENTS OF USER INPUTS PANEL''' | ||
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− | [1] | + | Lastly, we did some testing using Durbin Watson test to detect the presence of auto correlation in the residuals the significance of the model. |
+ | |||
+ | [1] Users can select the level of confidence and also the independent variables for the regression model. | ||
− | [2] | + | [2] |
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Revision as of 18:31, 26 April 2020
Rshiny application: [Click here]
Github: [Click here]