Estimasi distribusi PDRB kawasan Kedungsepur secara spasial menggunakan Geographically Weighted Regression untuk mendukung pengembangan wilayah

https://doi.org/10.22146/mgi.95224

Adik Amin Nashrudien(1*), Retno Widodo Dwi Pramono(2)

(1) Magister Perencanaan Wilayah dan Kota, Fakultas Teknik, Universitas Gadjah Mada
(2) Magister Perencanaan Wilayah dan Kota, Fakultas Teknik, Universitas Gadjah Mada
(*) Corresponding Author

Abstract


Abstrak Salah satu tujuan utama perencanaan pembangunan adalah menghasilkan kebijakan dan program untuk mendorong pertumbuhan ekonomi agar kualitas hidup masyarakat yang tinggal dan bekerja meningkat. Perencanaan yang cermat diperlukan kerincian data aktivitas ekonomi yang cukup rinci paling tidak hingga pada tingkat komunitas yang dapat menggambarkan heterogenitas dari wilayah untuk analisis yang lebih detail dan lebih mendalam mengenai dinamika produksi dan transaksi yang merupakan jantung dari analisis ekonomi. Data Produk Domestik Regional Bruto (PDRB) yang ada saat ini hanya pada skala makro yang mengasumsikan nilainya merata di seluruh wilayah administrasi dan tidak menggambarkan heterogenitas hingga pada tingkat komunitas dan sebaran sentra-sentra kegiatan ekonomi. Penurunan skala (downscaling) PDRB secara spasial perlu dilakukan. Penelitian ini bertujuan menguji teknik downscaling untuk menghasilkan nilai estimasi dengan skala yang lebih kecil dari pada nilai aktual dengan menggunakan metode Geographically Weighted Regression (GWR) dari data nighttime light dan tutupan lahan yang ditambahkan data indeks vegatasi. Indeks vegetasi ditambahkan untuk meningkatkan akurasi dari hasil estimasi PDRB. Karena data tutupan lahan saja tidak cukup sensitif terhadap sebaran produktivitas lahan. Hasil penelitian menunjukkan bahwa GWR downscaling dengan menambahkan indeks vegatasi mempunyai nilai R2 yang mendekati nilai 1 yaitu sebesar 0.9980, 0.9992, dan 0.9991 untuk masing-masing estimasi PDRB sektor primer, sektor sekunder dan tersier, dan total PDRB. Nilai R2 yang mendekati nilai 1 menunjukkan bahwa metode yang dilakukan efektif untuk mengestimasi nilai PDRB downscaling.

Abstract One of the main goals of development planning is to produce policies and programs to stimulate economic growth so that the quality of life of the people living and working improves. Careful planning requires detailed economic activity data that are sufficiently detailed at least to the community level that can depict the heterogeneity of the region for a more detailed and in-depth analysis of the dynamics of production and transactions that are at the heart of economic analysis. The current Gross Domestic Product (GDP) data only on a macro scale assumes equal values across the administrative territory and does not describe heterogeneity up to the community level and the spread of economic activity centers. A spatial downscaling of GDP needs to be done. The study aims to test downscaling techniques to generate estimates of a scale smaller than the actual value using the Geographically Weighted Regression (GWR) method of nighttime light and land cover data added to the vegetation index data. Vegetation index is added to improve the accuracy of the GDP estimates because land coverage data alone is not sensitive enough to the land productivity spread. The results of the study showed that GWR downscaling by adding the vegetable index has R2 values that are close to the value of 1, i.e. 0.9980, 0.9992, and 0.9991 for each estimate of GDP of the primary, secondary and tertiary sectors, and total GDP. A value of R2 that is close to value 1 indicates that the method used is effective to estimate the GDP value of downscaling.

 

Submitted: 2024-03-31 Revisions:  2024-09-25 Accepted: 2024-09-11 Published: 2025-03-17



Keywords


PDRB; PDRB grid; geographically weighted regression (GWR); GWR downscaling; nighttime light



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DOI: https://doi.org/10.22146/mgi.95224

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