The geographically weighted regression approach in analyzing the factors forming economic growth

Rendra Erdkhadifa


East Java has a great position to become one of a province with a predominance of the economic studies which has an allotment of a region with affects culture. Known as the Mataraman based on a culture that is inherited. Derived from the influence of ancient culture Mataram which centered on central java and D.I Yogyakarta. The effect of great culture on Mataraman region is viewed as one characteristic that has the ability on economic activities, with the result that causes differences in economic growth’s indicators as reflection of economic’s prosperity. This study looks at the factor which provide economic growth is based on Mataraman characteristic by Government Expenditure, Gross Domestic Regional Product, Mataraman, Original Local Government Revenue and analyzed using regression and Geographically Weighted Regression to get best model economic growth in Mataraman, East Java. The conclusion of the study is every object has different factor to influence economic growth


Economic Growth; Geographically Weighted Regression; Government Expenditure; Mataraman; Original Local Government Revenue.

Full Text:



Arsyad, L. (1999). Pengantar perencanaan dan pembangunan ekonomi daerah. Badan Penerbitan Fakultas Ekonomi (BPFE).

Arsyad, L. (2010). Ekonomi Pembangunan (5th ed.). Yogyakarta: UPP STIM YKPN.

Casson, M. (1993). Cultural determinants of economic performance. Journal of Comparative Economics, 17(2), 418–442.

Charlton, M., Fotheringham, S., & Brunsdon, C. (2009). Geographically weighted regression. White Paper. National Centre for Geocomputation. National University of Ireland Maynooth.

Lin, C.-H., & Wen, T.-H. (2011). Using geographically weighted regression (GWR) to explore spatial varying relationships of immature mosquitoes and human densities with the incidence of dengue. International Journal of Environmental Research and Public Health, 8(7), 2798–2815.

Liu, K. (1988). Measurement error and its impact on partial correlation and multiple linear regression analyses. American Journal of Epidemiology, 127(4), 864–874.

Matthews, S. A., & Yang, T.-C. (2012). Mapping the results of local statistics: Using geographically weighted regression. Demographic Research, 26, 151.

Müller, M. (2005). Kultur und ökonomische Entwicklung-Eine empirische Untersuchung kultureller Umwelt und unternehmerischer Fähigkeiten in der indonesischen Provinz Papua (West-Neuguinea).

Saragih, J. P., & Khadafi, M. S. (2003). Desentralisasi Fiskal dan Keuangan Daerah dalam Otonomi. Ghalia Indonesia.

Setyaningsih, P., & Rofi, A. (2014). Pekerja Perempuan dan Segmentasi Pasar Kerja Menurut Wilayah Kebudayaan di Provinsi Jawa Timur (Analisa Sakernas 2012). Jurnal Bumi Indonesia, 3(1).

Sukirno, S. (2006). Ekonomi Pembangunan Proses masalah dan Dasar Kebijakan, cetakan ketiga. Penerbit Kencana, Jakarta.

Tizona, A. R., Goejantoro, R., & Wasono, W. (2017). Pemodelan Geographically Weighted Regression (Gwr) Dengan Fungsi Pembobot Adaptive Kernel Bisquare Untuk Angka Kesakitan Demam Berdarah di Kalimantan Timur Tahun 2015. Jurnal Eksponensial, 8(1), 87–94.

Todaro, M. P., & Smith, S. C. (2004). Pembangunan Ekonomi di Dunia Ketiga Edisi Kedelapan. Jakarta: Penerbit Erlangga.

Windle, M. J. S., Rose, G. A., Devillers, R., & Fortin, M.-J. (2009). Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic. ICES Journal of Marine Science, 67(1), 145–154.



  • There are currently no refbacks.

Copyright (c) 2019 Indonesian Journal of Islamic Economics Research

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Indonesian Journal of Islamic Economics Research, p-ISSN 2686-5076 l e-ISSN 2714-5751

This work is licensed under a Creative Commons Attribution 4.0 International License.


Web Analytics Made Easy - StatCounter