SOCIOECONOMIC STATUS FROM SPACE: EXAMPLE OF ESTIMATING THAILAND’s SUB-DISTRICT HOUSEHOLD INCOME BASED ON REMOTELY SENSED AND GEOSPATIAL DATA
Bibliographic record
Abstract
Abstract. The socioeconomic data, such as household income, is an important indicator of people’s well-being. However, due to the limited resource in many developing countries such as Thailand, the data obtained from household income surveys are often incomplete. As a result, the annual household survey usually contains a gap at the municipality household level. In this study, we aim to quantify the household income with K-NN imputation models at the sub-district level using satellite imageries and geospatial data as proxies to socioeconomic indicators. We examined the role of satellite and geospatial data in household income estimation, applied the K-NN imputation methods to estimate the missing income data by using various geographical and statistical variables, and quantified how these data improved the accuracy of sub-district household income estimation. Our results illustrated a significant correlation between sub-district household income and geographical data extracted from day-night satellite data, such as night light intensity (r = 0.53), urban density (r = 0.44), residential area (r = 0.68), urban area (r = 0.64), and statistical data as well as household expenditure (r = 0.97). These can be used to improve the socioeconomic indicators’ estimation as well as household income in sub-district level. The income imputation from geographical data perform better result than purely statistical variables. Especially, the night light intensity can infer the wealth of people living in large scale areas, while day-time satellite images can be interpreted for land use and land cover also implying socioeconomic status. Such socioeconomic proxy from space provides spatially explicit information in further study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".