Difference between revisions of "ANLY482 AY2017-18 T2 Group 22"
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| − | [[ANLY482_AY2017-18_T2_Group_22 | <font color="# | + | [[ANLY482_AY2017-18_T2_Group_22 | <font color="#006400" size=3 face="Century Gothic"><b>Home</b></font>]] |
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[[Kiva Project Overview | <font color="#f1cf0e" size=3 face="Century Gothic"><b>Project Overview</b></font>]] | [[Kiva Project Overview | <font color="#f1cf0e" size=3 face="Century Gothic"><b>Project Overview</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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[[Kiva Project Management| <font color="#f1cf0e" size=3 face="Century Gothic"><b>Project Management</b></font>]] | [[Kiva Project Management| <font color="#f1cf0e" size=3 face="Century Gothic"><b>Project Management</b></font>]] | ||
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[[Kiva Documentation| <font color="#f1cf0e" size=3 face="Century Gothic"><b>Documentation</b></font>]] | [[Kiva Documentation| <font color="#f1cf0e" size=3 face="Century Gothic"><b>Documentation</b></font>]] | ||
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[[Kiva About Us| <font color="#f1cf0e" size=3 face="Century Gothic"><b>About Us</b></font>]] | [[Kiva About Us| <font color="#f1cf0e" size=3 face="Century Gothic"><b>About Us</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>]] | [[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: # | + | <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. | + | 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
Project Description
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.
