Kenya (field research)

# Field Research in Kenya

Energy access is multidimensional. In order to quantify and address energy demand and energy needs of the rural population, it is highly necessary to implement tools that are not limited to a purely binary assessment (household connected/not connected to the grid). Assessing energy needs shall take into account a wider range of attributes, measuring energy access in terms of affordability, reliability, quality, and safety of energy, as well as include the possibility of using modern and alternative sources (e.g., solar home systems, mini-grids). The purpose of this study was to assess energy access of 137 households in the region of Arequipa (Peru). The approach implemented is based on the Multi-Tier Framework (MTF), developed by The World Bank.

The objective of this project is to analyze the access to energy (electricity and cooking solution) in a rural area of Kenya. The research has been conducted by Hillary Kipcoech Korir, a master student at the Panafrican University for Water and Energy Sciences (PAUWES) of Tlemcen (Algeria).

Data collection, processing and visualization have been supported by the HEDERA Impact Toolkit Software. Relevant information concerning the use, associated costs and several attributes describing access to electricity and cooking solutions has been collected the customers of the financial institution in rural and remote areas using the App HEDERA collect. The HEDERA Impact Toolkit allows to efficiently evaluating a baseline for monitoring progress towards Sustainable Development Goal 7 following the Multi-tier Framework (MTF), recently established by The World Bank, and the Progress out of Energy Poverty Index (PEPI) N. Realpe, PhD Thesis 2017

import os,sys, folium, pandas
HIT_PATH = '../../../../src/' # local path to HIT src code
sys.path.insert(0, os.path.normpath(os.path.join(os.path.abspath(''), HIT_PATH)))
import hedera_types as hedera
import odk_interface as odk
from pivottablejs import pivot_ui
import matplotlib.pyplot as plt
#plt.rcParams["font.family"] = "TW Cen MT"
plt.rcParams.update({'font.size': 18})
odk_data_dir = '../../../../../ODK_Collect_Data/Hillary_Kenya/'

odk_folder_dir = 'HEDERA_SDG7/'
#odk_folder_dir = 'HEDERA_SDG7_19_07_05/'
## @brief name of the file (this should not be changed, it is set from ODK)
odk_data_name = 'HEDERA_SDG7_results.csv'

# initialize the institution
mfi = hedera.mfi(4)

delimiter='-')
mfi.HH = odk.households(data)
collection_overview = odk.overview(mfi.HH,mfi)


## Collection overview

### Map

The Map allows to visualize the location of the collected GPS data. Missing data points are displayed with coordinated (0,0)

#Define initial geolocation
initial_location = [-0.2, 35.6]
map_osm = folium.Map(initial_location, zoom_start=9)
colors = {0: hedera.tier_color(0), 1 : hedera.tier_color(1), 2 : hedera.tier_color(2),
3 : hedera.tier_color(3), 4 : hedera.tier_color(4), 5: hedera.tier_color(5)}
mfi.HH.apply(lambda row:folium.CircleMarker(location=[row["GPS_Latitude"], row["GPS_Longitude"]],
popup=' MTF Index: ' + str(row['E_Index'])).add_to(map_osm), axis=1)
map_osm


### Data per location

Data have been collected in five different locations.

mfi.plot_collection_overview()


### Attributes

import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
mfi.tier_barh(hedera.keys().attributes_electricity[0:8],
hedera.names('en').e_attributes[0:8],legend=True)


The MTF Index is given, for each household, by the minimum ranking among all considered attributes.

import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})


## Power sources

### Primary sources of electricity and illumination

import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
mfi.electricity_sources_summary(legend=True)


### Use of Multiple Power Sources

import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
collection_overview = odk.overview(mfi.HH,mfi)
odk.plot_electricity_sources(collection_overview,'en')


import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
mfi.stacked_tier_per_category('E_Index',hedera.keys().powerSources,
'primary_electricity_source',
hedera.names('en').powerSources,legend=True)


### Primary Cooking Fuels

import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
mfi.cooking_fuels_summary(legend=True)


import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
mfi.tier_barh(hedera.keys().attributes_cooking[0:4],hedera.names('en').c_attributes[0:4],legend=True)


### MTF Index (Cooking solutions)

The MTF Index, for each household, is given by the minimum ranking among all attributes

import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})

import matplotlib.pyplot as plt