Kiva Project Findings Final
| Interim | Final |
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Contents
Area of study
The bulk of its loans, in terms of both amount and quantity, are funded in the Philippines, thus being the country of focus in our analysis. The Republic of Philippines is made up of 7107 tropical islands, having a total square area of 300,000km2 and being 65% mountainous (Net Industries, 2018). Although the country’s official languages are Filipino (Tagalog) and English, it is a country that has diverse regional cultures, with three languages serving as regional lingua francas: Ilokano in Northern and Central Luzon, Tagalog in Central and Southern Luzon, and Cebuano in the Visayas and Mindanao.
Philippines are divided into three major island groups: Luzon, Visayas, and Mindanao.
Luzon, the most populous and largest island in the Philippines, home to the country’s capital and major metropolis Manila. It leads the country in agriculture and industrial manufacturing, and more than half of the Filipino population lives on Luzon (Britannica). Luzon also consists of Palawan Island, a large island southwest of Manila.
Visayas is an island group located in the centre of Philippines. It consists of seven large islands and several hundred smaller ones, and the region is famous for agriculture and fishing (Britannica).
Mindanao is the second largest main island after Luzon. The island has narrow coastal plains, with broad, fertile basins and extensive swamps(Britannica). Mindanao has the strongest Muslim presence in Philippines amongst the three islands, whose dominant religion is Roman Catholic, and is home to most of the ethnic minorities. Agriculture is a key industry like other islands, while its textile and timber industries are also important due to deposits of raw materials.
With vast ethnic, cultural, economic and religious differences between various provinces, geospatial analysis is used to identify how do Kiva’s loan attributes differ across the Philippines.
TO BE REMOVED
Figure 1: Screenshot of loan_themes_by_region.csv
The screenshot above of loan_themes_by_region.csv shows a snippet of the old geocode of the Kiva regions having many missing values (14536 out of 15736 records). As there is far too many missing records for this column to derive any meaningful information regarding shifts in location regions for particular loan themes, we removed this column entirely.
Analysis
Kernel Density Analysis
Methodology
Kernel density function is a non-parametric method of estimating the probability density function (PDF) of a continuous random variable, and is non-parametric as the underlying distribution for the variable is not assumed. Each sample point will have its own weight function which represents its influence of the density values in the surrounding neighbourhood, and each ‘bump” is centred at the datum and spreads out symmetrically to cover the neighbouring values. The size of the “bump” represents the probability assigned at the neighbourhood of values around that datum, and the estimated model is the summation of all the kernel function “bumps”. G22_KDE_formula.png
The Gaussian Kernel function, represent by k(u), follows a normal distribution curve to represent the intensity of different points
