The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 109–115, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-109-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 109–115, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-109-2020

  24 Aug 2020

24 Aug 2020

SOCIOECONOMIC STATUS FROM SPACE: EXAMPLE OF ESTIMATING THAILAND’s SUB-DISTRICT HOUSEHOLD INCOME BASED ON REMOTELY SENSED AND GEOSPATIAL DATA

S. Hutasavi and D. Chen S. Hutasavi and D. Chen
  • Laboratory of Geographic Information and Spatial Analysis (LaGISA), Dept. of Geography and Planning, Queen’s University, Kingston, Ontario, Canada

Keywords: Missing household income imputation, Spatial analysis, Night light intensity, K-NN imputation method, Socioeconomic proxy indicators

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.