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Poverty Mapping East Java Province, Indonesia

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Poverty Mapping East Java Province, Indonesia

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With satellite imagery and point of interest (POI), poverty can be identified from space! Poverty as One of SDGs’ Main GoalsSDGs Goal: No PovertyConsidering that poverty is still a problem in many countries, “poverty alleviation” in 2030 will become the “backbone” of the goals of the sustainable development agenda (SDGs)

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Spatially granular poverty index (SGPI) for urban poverty mapping in Jakarta metropolitan area (JMA): a remote sensing satellite imageries and geospatial big data approach

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Spatially granular poverty index (SGPI) for urban poverty mapping in Jakarta metropolitan area (JMA): a remote sensing satellite imageries and geospatial big data approach

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Spatially granular poverty index (SGPI) for urban poverty mapping in Jakarta metropolitan area (JMA): a remote sensing satellite imageries and geospatial big data approach - Earth Science Informatics

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Accurate and comprehensive urban poverty monitoring is undoubtedly essential to support the urban poverty alleviation targets in many developing countries. The currently available urban poverty monitoring in Indonesia is, however, primarily depends on the expensive and resource-demanding Indonesia National Socio-Economic Survey (SUSENAS). Despite its high-quality poverty statistics, such national-scale survey collection methods are indispensably labor and cost expensive to scale. Alternative data is essentially required to be explored for supporting government poverty data in order to increase the data granularity. In this study, we aim to develop the spatially granular poverty index (SGPI) consisting of multisource remote sensing satellite imageries and big data sources. With 1 km resolution, the SGPI is composed of nighttime light (NTL), normalized difference built-up index (NDBI), carbon monoxide (CO) and nitrogen dioxide (NO2) pollution levels, education and healthcare Point of Interest (POI) data, as alternative economic activity and poverty proxy indicators. Three different approaches of the equal-weighted sum method, the Pearson correlation coefficient method weighted method, and PCA weighted method are compared and evaluated to combine different indicators. Our result shows that the SGPI built in the scope of Jakarta Metropolitan Area (JMA) by using the equal weighted sum with Yeo-Johnson transformation (EWS-YJ) has the highest correlation with official poverty data which is 0.954. Based on visual identifications through high-resolution satellite imagery, the areas with a relatively high SGPI value are densely populated, and vice versa. Our findings suggest that multi-source remote sensing and geospatial big data integration are promising alternative approaches for granular urban poverty mapping based on spatial characteristics.

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Deep Learning Approach using Satellite Imagery Data for Poverty Analysis in Banten, Indonesia

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Deep Learning Approach using Satellite Imagery Data for Poverty Analysis in Banten, Indonesia

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Deep Learning Approach using Satellite Imagery Data for Poverty Analysis in Banten, Indonesia

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Satellite imageries data provides abundant geospatial features of infrastructures, land uses, land covers, and economic activity footprints that are potential for domainspecific tasks. In this study, we investigate the use of satellite imageries data as spatial-based proxy indicators in predicting the percentage of poverty in Banten Province, Indonesia using a deep learning approach. The poverty dataset is taken from the Village Potential Data Survey (PODES) 2018 results published by Statistics Indonesia (BPS) as the assumed ground-truth labels. Our finding reveals a correlation between the night-time light satellite imagery and the percentage of poverty, hence the regression model to predict the percentage of poverty is constructed using convolutional neural networks (CNN) architecture. The correlation between night-time image data and the percentage of poverty in each village is negative 52 percent under log transformation. Our proposed model generates a promising root mean squared error (RMSE) of 5.3023 which is potentially beneficial to support the construction and monitoring of poverty statistics in Indonesia.

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Multi-source satellite imagery and point of interest data for poverty mapping in East Java, Indonesia: Machine learning and deep learning approaches - ScienceDirect

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Multi-source satellite imagery and point of interest data for poverty mapping in East Java, Indonesia: Machine learning and deep learning approaches

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Multi-source satellite imagery and point of interest data for poverty mapping in East Java, Indonesia: Machine learning and deep learning approaches

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Developing Relative Spatial Poverty Index Using Integrated Remote Sensing and Geospatial Big Data Approach: A Case Study of East Java, Indonesia

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Developing Relative Spatial Poverty Index Using Integrated Remote Sensing and Geospatial Big Data Approach: A Case Study of East Java, Indonesia

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Developing Relative Spatial Poverty Index Using Integrated Remote Sensing and Geospatial Big Data Approach: A Case Study of East Java, Indonesia

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Poverty data are usually collected through on-the-ground household-based socioeconomic surveys. Unfortunately, data collection with such conventional methods is expensive, laborious, and time-consuming. Additional information that can describe poverty with better granularity in scope and at lower cost, taking less time to update, is needed to address the limitations of the currently existing official poverty data. Numerous studies have suggested that the poverty proxy indicators are related to economic spatial concentration, infrastructure distribution, land cover, air pollution, and accessibility. However, the existing studies that integrate these potentials by utilizing multi-source remote sensing and geospatial big data are still limited, especially for identifying granular poverty in East Java, Indonesia. Through analysis, we found that the variables that represent the poverty of East Java in 2020 are night-time light intensity (NTL), built-up index (BUI), sulfur dioxide (SO2), point-of-interest (POI) density, and POI distance. In this study, we built a relative spatial poverty index (RSPI) to indicate the spatial poverty distribution at 1.5 km × 1.5 km grids by overlaying those variables, using a multi-scenario weighted sum model. It was found that the use of multi-source remote sensing and big data overlays has good potential to identify poverty using the geographic approach. The obtained RSPI is strongly correlated (Pearson correlation coefficient = 0.71 (p-value = 5.97×10−7) and Spearman rank correlation coefficient = 0.77 (p-value = 1.58×10−8) to the official poverty data, with the best root mean square error (RMSE) of 3.18%. The evaluation of RSPI shows that areas with high RSPI scores are geographically deprived and tend to be sparsely populated with more inadequate accessibility, and vice versa. The advantage of RSPI is that it is better at identifying poverty from a geographical perspective; hence, it can be used to overcome spatial poverty traps.

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Mapping Poverty Distribution of Urban Area using VIIRS Nighttime Light Satellite Imageries in D.I Yogyakarta, Indonesia

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Mapping Poverty Distribution of Urban Area using VIIRS Nighttime Light Satellite Imageries in D.I Yogyakarta, Indonesia

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Mapping Poverty Distribution of Urban Area using VIIRS Nighttime Light Satellite Imageries in D.I Yogyakarta, Indonesia -Asian Journal of Business Environment | Korea Science

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Purpose: This study aims to map the spatial distribution of poverty using nighttime light satellite images as a proxy indicator of economic activities and infrastructure distribution in D.I Yogyakarta, Indonesia. Research design, data, and methodology: This study uses official poverty statistics (National Socio-economic Survey (SUSENAS) and Poverty Database 2015) to compare satellite imagery’s ability to identify poor urban areas in D.I Yogyakarta. National Socioeconomic Survey (SUSENAS), as poverty statistics at the macro level, uses expenditure to determine the poor in a region. Poverty Database 2015 (BDT 2015), as poverty statistics at the micro-level, uses asset ownership to determine the poor population in an area. Pearson correlation is used to identify the correlation among variables and construct a Support Vector Regression (SVR) model to estimate the poverty level at a granular level of 1 km x 1 km. Results: It is found that macro poverty level and moderate annual nighttime light intensity have a Pearson correlation of 74 percent. It is more significant than micro poverty, with the Pearson correlation being 49 percent in 2015. The SVR prediction model can achieve the root mean squared error (RMSE) of up to 8.48 percent on SUSENAS 2020 poverty data.Conclusion: Nighttime light satellite imagery data has potential benefits as alternative data to support regional poverty mapping, especially in urban areas. Using satellite imagery data is better at predicting regional poverty based on expenditure than asset ownership at the micro-level. Light intensity at night can better describe the use of electricity consumption for economic activities at night, which is captured in spending on electricity financing compared to asset ownership.

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Geospatial Big Data Approaches to Estimate Granular Level Poverty Distribution in East Java, Indonesia using Machine Learning and Deep Learning Regressions

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Geospatial Big Data Approaches to Estimate Granular Level Poverty Distribution in East Java, Indonesia using Machine Learning and Deep Learning Regressions

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Geospatial Big Data Approaches to Estimate Granular Level Poverty Distribution in East Java, Indonesia using Machine Learning and Deep Learning Regressions | Proceedings of The International Conference on Data Science and Official Statistics

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One of the economic development the focus of the Indonesian government’s efforts is for reducing poverty. In Indonesia, collecting poverty data uses the conventional method, the name is National Socio-Economic Survey (SUSENAS) which takes a large cost, time, and effort. To overcome these limitations, there is a need for additional data to provide more detailed poverty data. Recent studies show that the use of geospatial big data could identify poverty at a granular level, with a lower cost and faster update because of their unique and unbiased capacity to identify physical and socioeconomic phenomena. The integrated multi-source satellite imagery data such as the normalized difference vegetation index (NDVI) for detecting rural areas based on vegetation, built-up index (BUI) for identifying urban areas through building distribution, normalized difference water index (NDWI) for land cover detection, day time land surface temperature (LST) for identifying urban regions based on surface temperature, and pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) to evaluate economic activities based on pollution. Additionally, point of interest (POI) density and minimum POI distance are used to measure area accessibility. Therefore, the contribution of this research is to implement the utilization of geospatial big data to estimate the numbers of poverties at a granular level to the 666 sub-districts in East Java Province using machine learning and deep learning regression models. The evaluation results to estimate sub-district level poverty shows that the best model development using Support Vector Regression (SVR) in machine learning was the best root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) values of 0.365, 0.293, and 0.032 with R-squared of 0.59 and MLP in deep learning algorithm with 0.444, 0.345, and 0.039 values of RMSE, MAE, and MAPE with R2 0.52. In addition, the results of visual identification revealed that high estimates of lower poverty are typically found in urban areas with high accessibility, and these areas are not spatially deprived areas with limited accessibility.

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Announcing the 2023 GEO SDG Award Winners – EO4SDG

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It is that time of year again when we are honored to announce our winners of the annual GEO SDG Awards! Launched in 2019, the GEO SDG Awards, led by the EO4SDG initiative, recognize institutions, organizations, and countries that are applying Earth observations towards the achievement of the Sustainable Development Goals. For the 5th annual GEO SDG Awards, each nomination was carefully reviewed by eight unique panelists representing various countries and sectors. Submissions were reviewed for overall impact, quality, and soundness of the Earth observation endeavors, and their potential to be replicated and scaled up. We congratulate each of our Sectoral and Special Category winners for their exemplary work!

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SATU DATA JABAR AWARDS 2022-BANDUNG, 28 OKTOBER 2022-#rk #ridwankamil #uuruzhanululum #diskominfojabar #jabarjuara #humasjabar #JABAR #awards

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— Badan Pusat Statistik (BPS-Statistics Indonesia)

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Child Poverty From Space Banten Province, Indonesia

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Child Poverty From Space Banten Province, Indonesia

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Granular Mapping of Child Poverty with Geospatial Big Data

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Why is Child Poverty a Crucial Concern?The world has made remarkable progress in advancing development in recent years. However, the stark reality remains that more than 700 million people still live in extreme poverty (UNICEF, 2020). Among the most vulnerable and affected by this dire situation are children. Growing

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Progress poverty mapping discussion BPS and ADB

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Progress discussion of  this project with the Asia Development Bank (ADB)

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Progress discussion of  this project with the Asia Development Bank (ADB)

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United Nations Economic Commission for Europe (UNECE) Machine Learning Group Monthly Discussion

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United Nations Economic Commission for Europe (UNECE) Machine Learning Group Monthly Discussion

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7th UN Internasional Conference on Big Data and Data Science 2022

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7th UN Internasional Conference on Big Data and Data Science 2022

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Laman website ini hanya bisa diakses lewat VPN.

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Progress discussion of  this project with the United Nations International Children's Emergency Fund

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Progress discussion of  this project with the United Nations International Children's Emergency Fund