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<p>This study investigates spatial economic disparities in Vietnam by applying Local Indicators of Spatial Association (LISA) to high-resolution nighttime light data from 2000 and 2019. The analysis identifies four spatial clusters—High–High (HH), Low–Low (LL), High–Low (HL), and Low–High (LH)—at the district level. HH clusters are concentrated around Hanoi, Ho Chi Minh City, and Hai Phong, while LL clusters persist in underdeveloped areas, such as the northwest and the Mekong Delta. LH clusters found in peri-urban areas reflect the backwash effects of the major growth centers. HL clusters emerging in cities such as Ky Anh and Da Lat signal localized growth driven by industrialization and tourism. These spatial outliers highlight regions that contribute disproportionately to widening disparities—patterns often overlooked by conventional inequality measures. The findings underscore the value of nighttime light data for analyzing economic geography in data-scarce contexts, and support the need for place-based policies tailored to the structurally lagging and emerging growth regions.</p>

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Identifying spatial economic disparities in Vietnam: evidence from nighttime light data

  • 期刊论文

  • 发表日期：

    2025年12月24日

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  • Chao Zhang

摘要

This study investigates spatial economic disparities in Vietnam by applying Local Indicators of Spatial Association (LISA) to high-resolution nighttime light data from 2000 and 2019. The analysis identifies four spatial clusters—High–High (HH), Low–Low (LL), High–Low (HL), and Low–High (LH)—at the district level. HH clusters are concentrated around Hanoi, Ho Chi Minh City, and Hai Phong, while LL clusters persist in underdeveloped areas, such as the northwest and the Mekong Delta. LH clusters found in peri-urban areas reflect the backwash effects of the major growth centers. HL clusters emerging in cities such as Ky Anh and Da Lat signal localized growth driven by industrialization and tourism. These spatial outliers highlight regions that contribute disproportionately to widening disparities—patterns often overlooked by conventional inequality measures. The findings underscore the value of nighttime light data for analyzing economic geography in data-scarce contexts, and support the need for place-based policies tailored to the structurally lagging and emerging growth regions.