Central Research Question

My Central Research Question hopes to concern the mapping and mathematical analysis of poverty level based on remote and untraditional data other than conventional socioeconomic index. It will be proposed as: To what extent the remote detection and data science methods can improve the comprehensiveness and reliability of poverty assessment and how can a multidimensional assessing system be established in the rural area of China? It is important to notice that China is actually at the very last stage of eradicating absolute poverty which is promised to due by the end of this year. Therefore, a dynamic dataset becomes urgent and necessary to establish instead of a static one. Data and information related to poverty should be refreshed from time to time in order to maintain the planned process of poverty eradication, which is much more challenging to achieve in the rural area where data collection is inefficient and outdated. The necessity to bring new technologies and data science methodologies into the process of poverty assessment is for the dynamic of data.

It should be considered to best fit the category of evaluative central question. It attempts to analyze the effectiveness as well as its limitation to conduct remote geospatial dataset in terms of assessing poverty level in rural areas, which involves a process of making judgments. Furthermore, an underlying comparing of the new data science methods versus traditional socioeconomic index assessment intrinsically exists by this central method question. Though it also partly relates to the category of exploratory question as it also tries to find out what factors are contained and what is happening in the process of constructing a better systematic assessment on poverty level, the core still lies on the part of evaluation and judgment making, to see the accessibility of this proposal based on new technology and data science methodologies. Plus the process to investigate the construction of new poverty assessing system also contains the comparing mentioned above. In the consideration of the type of puzzle, it seems to be comparative puzzle in this case. Nonetheless, causal/predictive puzzle indeed more fits the core of my question. Comparing and contrast are only methods or ways that will be used to solve my question. Instead, a change of effect and phenomenon in the process of adding remote technology and data science methods into ordinary poverty evaluation is the goal, or the “puzzle” to be solved.

Proposed sub-research question:

There are many socioeconomic factors that more narrowly reflect a niche human activity level other than the general ones such as GDP. How can these factors become used and interpreted for the analysis of poverty level, (e.g. electricity uses, night-time lighting, greenhouse gas level etc.)?

How does the data collection based on remote detection (e.g. satellite, mobile phone etc.) work to tell the regional poverty level?

What factors are necessary to construct a complete system of poverty assessment?