Difference between revisions of "ANLY482 AY2017-18 T2 Group 22"

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[[ANLY482_AY2017-18_T2_Group_22 | <font color="#b1260e" size=2 face="Century Gothic"><b>Home</b></font>]]
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[[ANLY482_AY2017-18_T2_Group_22 | <font color="#006400" size=3 face="Century Gothic"><b>Home</b></font>]]
  
 
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| style="padding:0.2em; font-size:100%; background-color:#008000; text-align:center; color:#F5F5F5" width="10%" |  
[[DHL Freighters Project Proposal | <font color="#f1cf0e" size=2 face="Century Gothic"><b>Project Proposal</b></font>]]
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[[Kiva Project Overview | <font color="#f1cf0e" size=3 face="Century Gothic"><b>Project Overview</b></font>]]
  
 
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[[DHL Freighters Project Findings| <font color="#f1cf0e" size=2 face="Century Gothic"><b>Project Findings</b></font>]]
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[[Kiva_Project_Findings_Final| <font color="#f1cf0e" size=3 face="Century Gothic"><b>Project Findings</b></font>]]
  
 
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[[DHL Freighters Project Management| <font color="#f1cf0e" size=2 face="Century Gothic"><b>Project Management</b></font>]]
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[[Kiva Project Management| <font color="#f1cf0e" size=3 face="Century Gothic"><b>Project Management</b></font>]]
  
 
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[[DHL Freighters Documentation| <font color="#f1cf0e" size=2 face="Century Gothic"><b>Documentation</b></font>]]
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[[Kiva Documentation| <font color="#f1cf0e" size=3 face="Century Gothic"><b>Documentation</b></font>]]
  
 
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[[DHL Freighters About Us| <font color="#f1cf0e" size=2 face="Century Gothic"><b>About Us</b></font>]]
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[[Kiva About Us| <font color="#f1cf0e" size=3 face="Century Gothic"><b>About Us</b></font>]]
  
 
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[[Main_Page| <font color="#f1cf0e" size=2 face="Century Gothic"><b>ANLY482 Main Page</b></font>]]
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[[ANLY482_AY2017-18_Term_2| <font color="#f1cf0e" size=3 face="Century Gothic"><b>ANLY482 Main Page</b></font>]]
 
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<div style="background: #FFD700; line-height: 0.3em; border-left: #008000 solid 13px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;"><font face ="Elephant" color= "black" size="3">Project Description</font></div></div>
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Kiva is an online crowdfunding platform which extends financial services in the form of loans to the poor and financially excluded people around the world, who are otherwise unable to raise funds from financial institutions and banks given their financial capacity and background. Since its inception, Kiva lenders have provided over $1 billion USD in loans to over 2 million people. This project provides a journey of starts with an overview of the business and the motivations and activities for people in Philippines taking up these loans, followed by kernel density analysis to observe how the intensity of spatial point patterns differ across the different islands and provinces, and lastly the implementation of Exploratory Spatial Data Analysis using measures of spatial autocorrelation and Local Indicators of Spatial Association (LISA) to gain insights into the impact of neighbouring areas and presence of clusters, by using Queen’s case contiguity-based weights, and 2 distance-based weighting methods, namely the K-Nearest Neighbour and the Inverse Distance Weighting.
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Latest revision as of 16:37, 15 April 2018



Home

 

Project Overview

 

Project Findings

 

Project Management

 

Documentation

 

About Us

 

ANLY482 Main Page


Project Description
Kivalogo.png

Kiva is an online crowdfunding platform which extends financial services in the form of loans to the poor and financially excluded people around the world, who are otherwise unable to raise funds from financial institutions and banks given their financial capacity and background. Since its inception, Kiva lenders have provided over $1 billion USD in loans to over 2 million people. This project provides a journey of starts with an overview of the business and the motivations and activities for people in Philippines taking up these loans, followed by kernel density analysis to observe how the intensity of spatial point patterns differ across the different islands and provinces, and lastly the implementation of Exploratory Spatial Data Analysis using measures of spatial autocorrelation and Local Indicators of Spatial Association (LISA) to gain insights into the impact of neighbouring areas and presence of clusters, by using Queen’s case contiguity-based weights, and 2 distance-based weighting methods, namely the K-Nearest Neighbour and the Inverse Distance Weighting.