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