Remote sensing data has been used by research studies to derive socioeconomic and energy-related variables. For instance, nighttime light satellite images provide potential possibilities to quantify such variables and its correlation with human activity [7] and poverty incidences [6]Remote sensing of environment and disaster laboratory

Institute of Industrial Science, the University of Tokyo, Japan

Exploring Nighttime Lights, MODIS Global Land Cover andSocio-economic Datasets for Electrification Coverage andPoverty Mapping Studies
Jeark PRINCIPE, Wataru TAKEUCHI
Institute of Industrial Science, the University of Tokyo, JapanThis study is a preliminary work that explores satellite-based data such as nighttime lights from the Visible Infrared Imaging Radiometer Suite (VIIRS)
Day/Night Band (DNB) produced by the Earth Observations Group (EOG) at NOAA/NCEI and MODIS Land Cover Type product (MCD12Q1) as well as acore local poverty indicator from the Community-Based Monitoring System (CBMS) for their potential for electrification coverage and poverty mapping
studies. By doing an overlay analysis of the VIIRS-DNB and MCD12Q1 data, it was found out that about 94.21% of built-up areas in the Philippines are lit
areas or with access to electricity. This percentage is similar to the value reported by the World Bank for “Access to electricity, urban (% of urban population)”for the Philippines which is at 94.94% in year 2013. It was observed also that: unlit built-up pixels were those located near bodies of water (e.g., rivers) mostly
in sandy areas and in bare soil areas; small patches of built-up areas tend to have unlit pixel nearby; and unlit pixels are not observed near large (well-defined)
patches of built-up areas (e.g., Metro Manila). Finally, a series of maps were produced showing the spatial distribution of lit and unlit areas per province and thecorresponding proportion of households with income below the poverty threshold” (POVP). While it is expected to see low and high POVPs for well-lit andlow-lit areas, respectively, the said maps demonstrate how even in well-lit municipalities, high POVP still exists. On the other hand, there were somemunicipalities with relatively low proportion of lit areas but with low POVP. This initial study will become a springboard for succeeding studies for energy
infrastructure support for poverty alleviation.

2. Methodology

1. INTRODUCTION
Remote sensing data has been used by research studies to derivesocioeconomic and energy-related variables. For instance, nighttime lightsatellite images provide potential possibilities to quantify such variables andits correlation with human activity [1] and poverty incidences [2]. Meanwhile,land cover data such as MCD12Q1 is essential as human modifications to land
use/cover have significant implications to sustainable land management [3].
The Community-Based Monitoring System (CBMS) is an “organized processof data collection and processing at the local level and of integration of data inlocal planning, program implementation and impact monitoring” [4].
The objective of this study is to explore VIIRS-DNB, MCD12Q1 andCBMS data to:
1. Estimate the extent of electrification coverage in the Philippines
2. Determine characteristics of built-up areas (derived from MCD12Q1)
which are detected as unlit areas (based on VIIRS-DNB)
3. Show municipal-level spatial distribution of POVPs per province and theircorresponding lit areas.

3. Results

MCD12Q1VIIRS-DNB

Image
Pre-processing

Overlay Analysis

Pre-processed
VIIRS-DNB
CBMS-POVP Data
GIS Pre-processing
Municipal Boundary

QualitativeAnalysis

Municipal Boundarywith POVP attribute
Fig. 1. Methodology of the Study

MCD12Q1 and VIIRS-DNB data were pre-processed by re-projecting them
to a common projection (UTM 51N) and then clipped to include only areas
within the Philippines and extract built-up areas from MCD12Q1. An overlay
analysis was done to these datasets to determine lit and unlit pixels forelectrification coverage assessment in built-up areas. CBMS-POVP Data wasinputted as an attribute to the vector data for the municipal boundary of
selected provinces in the Philippines. A qualitative analysis was done with thepre-processed VIIRS-DNB and Municipal Boundary with POVP attribute tocharacterize poverty level with access to electricity.

4. Remarks

Fig. 3. 2013 Land Cover(MCD12Q1) of the Philippines

Fig. 2. Nightlight at Metro Manila (top), Cebu City (Middle) and Davao City (bottom)• The overlay analysis has estimated that 94.21% of built-up areas in thePhilippines are lit areas or with access to electricity. This percentage issimilar to the value reported by the World Bank for “Access to electricity,urban (% of urban population)” for the country which is at 94.94% in year
2013 [5].
• It was observed that generally, unlit built-up areas were located near bodies
of water (e.g., rivers) mostly in sandy areas; also in bare soil areas.
Moreover, small patches of built-up areas tend to have unlit pixel nearby
and unlit pixels are not observed near large and well-defined patches ofbuilt-up areas.
• This study has explored a global land cover, nightlight and CBMS datasets
to determine the extent of electrification coverage in the Philippines andinvestigated locations of unlit built-up areas.

5. References

Fig. 4. Spatial distribution of nightlight and POVP in selected Philippine provinces[1] D. Li , X. Zhao and X. Li, "Remote sensing of human beings – a perspective from nighttime light," Geo-spatial Information
Science, pp. 69-79, 2016.
[2] M. Xie, N. Jean, M. Burke, D. Lobell and S. Ermon, "Transfer Learning from Deep Features for Remote Sensing andPoverty Mapping," in 30th AAAI Conference on Artificial Intelligence, Phoenix, Arizona, 2016.
[3] M. A. Friedl, D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley and X. Huang, "MODIS Collection 5
global land cover: Algorithm refinements and characterization of new datasets," Remote Sensing of Environment, no. 114, pp.
168-182, 2010.
[4] CBMS INCT, "CBMS Philippines Frequently Asked Questions." CBMS INCT, Manila, 2014.
[5] The World Bank Group, "Access to electricity (% of population)," 2017. [Online]. Available:
http://data.worldbank.org/indicator/EG.ELC.ACCS.ZS. [Accessed May 2017].

For further details, contact: Jeark Principe, Bw-601, 6-1, Komaba 4-chome, Meguro, Tokyo 153-8505 JAPAN (URL: http://wtlab.iis.u-tokyo.ac.jp/ E-mail: japrince@iis.u-tokyo.ac.jp)