The explosion in the use of digital services has fundamentally changed many companies to understand their users through analysis of data produced passively or big data. Decuyper et al (2014) said that big data has the potential to guide policy makers by providing an alternative to traditional data sources such as costly and time-consuming manual surveys. For planning purposes, access to reliable and up-to-date statistical data is essential to humanitarian organizations when deciding where and when help is most needed. Many rich data sources exist with the promise of providing early warning and real-time monitoring of vulnerable populations including social media, remittances and mobile phone records.
Covering about relevant research experience. I have many experience in traditional survey data collection, among others : (1) coarse filter application study for molluscs (bivalves and gastropods) conservation efforts, (2) ethnobiological approach to improve sustainable food security and (3) aren (Arenga pinnata) plant management and optimization based on local wisdom. Although a lot of time consuming and costly, all the work can be completed on time because of the experience. This is mainly due to a well-organized record system and well-planned archives, as well as a systematic template maker that greatly facilitates for data smooth analysis.
My ideas for potential approaches to estimate poverty at the household level with social media data is by analyzing poverty based on ecological and ethnobiological principles. There are many choices ecological solutions, but the priority topics to be completed are population interaction, abundance and diversity, species affinity and community classification.
Decuyper, A., Alex R., Amit W.,Jean M.B., Gautier K., Thoralf G., Vincent D.B., Miguel A. & Luengo-Oroz. (2014). Estimating Food Consumption And Poverty Indices With Mobile Phone Data. 22 Nov 2014. Website address : https://arxiv.org/pdf/1412.2595.pdf.