SUN’IY INTELLEKT DAVRIDA HUDUDIY RIVOJLANISHNI STATISTIK HISOBLASH METODOLOGIYASINI QAYTA BAHOLASH MEZONLARI (SCOPUS VA WEB OF SCIENCE DA INDEKSLANGAN ILMIY NASHRLAR TAHLILI ASOSIDA)

SUN’IY INTELLEKT DAVRIDA HUDUDIY RIVOJLANISHNI STATISTIK HISOBLASH METODOLOGIYASINI QAYTA BAHOLASH MEZONLARI (SCOPUS VA WEB OF SCIENCE DA INDEKSLANGAN ILMIY NASHRLAR TAHLILI ASOSIDA)

Authors

  • Santjar Sattorov

DOI:

https://doi.org/10.5281/zenodo.19448863

Keywords:

hududiy rivojlanish, statistik metodologiya, sun’iy intellekt, algoritmik o‘rganish, hududiy ekonometrika, katta ma’lumotlar, muqobil ma’lumotlar manbalari, hududiy rivojlanish ko‘rsatkichlari, tushuntiriladigan sun’iy intellekt, bashoratli tahlil.

Abstract

Maqolada sun’iy intellekt (SI) texnologiyalari va katta ma’lumotlarni tahlil qilish vositalarining keng
qo‘llanilishi sharoitida hududiy rivojlanishning statistik hisoblash metodologik asoslarini o‘zgartirishning dolzarb masalasi
ko‘rib chiqiladi. Metodologiyani qayta baholash uchun yettita asosiy mezon aniqlandi: ma’lumotlar manbalarini ma’muriy
registrlardan muqobil ma’lumotlar ekotizimiga o‘tkazish, analitik usullarning chiziqli ekonometrik modellardan chiziqli
bo‘lmagan avtomatlashtirilgan o’rganish algoritmlariga evolyutsiyasi, ma’muriy birliklarning makro-darajasidan mikrohududiy
tahlilga hududiy yechimning o‘tishi, yangi sun’iy intellekt ko‘rsatkichlarini (iqtisodiy murakkablik va raqamli yetuklik
indekslari) ishlab chiqish, statistik funktsiyani tavsifdan bashoratliga o‘tkazish, modellarning tushuntiriladigan sun’iy
intellekt usullari orqali talqin qilinishini ta’minlash va heterojen tuzilgan va tuzilmagan ma’lumotlarni integratsiyalash.
Sin’iy intellekt davrida hududiy statistika metodologiyasining samaradorligi ekonometrik qat’iylikni sintez qilish qobiliyati
va algoritmik tahlilning bashorat qilish imkoniyatlari bilan belgilanadi.
Maqolada bashoratli tahlil va mikrohududiy rejalashtirish imkoniyatlarini hisobga olgan holda hududiy siyosat
metodologiyasini modernizatsiya qilish yo‘nalishlari taklif qilingan.

Author Biography

Santjar Sattorov

Surxondaryo viloyat pedagogika markazi direktori o‘rinbosari
Iqtisodiyot fanlari doktori

References

1. Mewes L., Broekel T. Technological complexity and economic growth of regions // Research Policy. 2022. Vol. 51, iss.

1. DOI: 10.1016/j.respol.2020.104275.

2. Kopczewska K. Spatial machine learning: new opportunities for regional science // The Annals of Regional Science.

2022. Vol. 68. DOI: 10.1007/s00168-021-01101-x.

3. Gómez-Carmona O., Buján-Carballal D. et al. Mind the gap: The AURORAL ecosystem for the digital transformation

of smart communities and rural areas // Technology in Society. 2023. Vol. 74. DOI: 10.1016/j.techsoc.2023.102255.

4. Inoua S. A simple measure of economic complexity // Research Policy. 2023. Vol. 52, iss. 2. DOI: 10.1016/j.

respol.2023.104804.

5. Wang C., Zhang X., Ghadimi P. et al. The impact of regional financial development on economic growth in Beijing–

Tianjin–Hebei region: A spatial econometric analysis // Physica A: Statistical Mechanics and its Applications. 2019. Vol.

521. DOI: 10.1016/j.physa.2019.04.002.

6. Yu Z., Liang Z., Xue L. A data-driven global innovation system approach and the rise of China’s artificial intelligence

industry // Regional Studies. 2022. Vol. 56, iss. 4. DOI: 10.1080/00343404.2021.1954610.

7. Johnson M., Jain R., Brennan-Tonetta P. et al. Impact of big data and artificial intelligence on industry: developing a

workforce roadmap for a data driven economy // Global Journal of Flexible Systems Management. 2021. Vol. 22. DOI:

10.1007/s40171-021-00272-y.

8. Loukis E.N., Maragoudakis M. et al. Artificial intelligence-based public sector data analytics for economic crisis

policymaking // Transforming Government: People, Process and Policy. 2020. Vol. 14, iss. 4. DOI: 10.1108/TG-03-

2020-0046.

9. Feldmeyer D., Meisch C., Sauter H. et al. Using OpenStreetMap data and machine learning to generate socioeconomic

indicators // ISPRS International Journal of Geo-Information. 2020. Vol. 9, iss. 9. DOI: 10.3390/ijgi9090498.

10. Lazzeretti L., Innocenti N., Nannelli M. et al. The emergence of artificial intelligence in the regional sciences: a literature

review // European Planning Studies. 2023. Vol. 31, iss. 3. DOI: 10.1080/09654313.2022.2101880.

11. González-López M., Asheim B.T. Introduction: regional innovation systems and regional innovation policies // Regions

and Innovation Policies. 2020. DOI: 10.4337/9781789904154.00005.

12. Ketova K.V., Kasatkina E.V. et al. Clustering Russian Federation regions according to the level of socio-economic

development with the use of machine learning methods // Economic and Social Changes: Facts, Trends, Forecast.

2021. Vol. 14, iss. 6. DOI: 10.15838/esc.2021.6.72.3.

13. Delcea C., Nica I., Ionescu Ș. et al. Mapping the frontier: a bibliometric analysis of artificial intelligence applications in

local and regional studies // Algorithms. 2024. Vol. 17, iss. 9. DOI: 10.3390/a17090418.

14. Xia S., Zhou Y., Wang Z. et al. Enhancing green innovation through university–industry collaboration and artificial

intelligence: Insights from regional innovation systems in China // The Journal of Technology Transfer. 2025. DOI:

10.1007/s10961-025-10232-8.

15. Masoud N. Artificial intelligence and unemployment dynamics: an econometric analysis in high-income economies //

Technological Sustainability. 2025. Vol. 4, iss. 1. DOI: 10.1108/TECHS-04-2024-0025.

16. Pradhan N., Agrawal A. Mapping fine-scale socioeconomic inequality using machine learning and remotely sensed

data // PNAS Nexus. 2025. Vol. 4, iss. 2. DOI: 10.1093/pnasnexus/pgaf040.

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Published

2026-04-01

How to Cite

Sattorov, S. (2026). SUN’IY INTELLEKT DAVRIDA HUDUDIY RIVOJLANISHNI STATISTIK HISOBLASH METODOLOGIYASINI QAYTA BAHOLASH MEZONLARI (SCOPUS VA WEB OF SCIENCE DA INDEKSLANGAN ILMIY NASHRLAR TAHLILI ASOSIDA). GREEN ECONOMY AND DEVELOPMENT, 4(4). https://doi.org/10.5281/zenodo.19448863
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