Geographical Weighted Regression Model for Poverty Analysis in Jambi Province

Inti Pertiwi Nashwari(1*)

(1) Bogor Agricultural University
(*) Corresponding Author


Agriculture sector has an important contribution to food security in Indonesia, but it also huge contribution to the number of poverty, especially in rural area. Studies using a global model might not be sufficient to pinpoint the factors having most impact on poverty due to spatial differences. Therefore, a Geographically Weighted Regression (GWR) was used to analyze the factors influencing the poverty among food crops famers. Jambi Province is selected because have high number of poverty in rural area and the lowest farmer exchange term in Indonesia. The GWR was better than the global model, based on high value of R2, lowers AIC and MSE and Leung test. Location in upland area and road system had more influence to the poverty in the western-southern. Rainfall was significantly influence in eastern. The effect of each factor, however, was not generic, since the parameter estimate might have a positive or negative value.


food crop farmer; Geographically Weighted Regression; poverty; spatial analysis


ADB (2009). Kesejahteraan Petani dan Pengentasan Kemiskinan [internet]. [diacu 2015 Oktober 24]. (in bahasa indonesia).

Anselin, L. (2013). Spatial econometrics: methods and models (Vol. 4). Springer Science & Business Media.

Ali, K., Partridge, M. D., & Olfert, M. R. (2007). Can geographically weighted regressions improve regional analysis and policy making?. International Regional Science Review, 30(3), 300-329.

Badan Pusat Statistik (2013). Laporan Hasil Sensus Pertanian 2013 (Pencacahan Lengkap), BPS Provinsi Jambi, Jakarta. (in bahasa Indonesia).

Badan Pusat Statistik (2014). Analisis Sosial Ekonomi Petani di Indonesia. Hasil Survei Pendapatan Rumah Tangga Usaha Pertanian. Sensus Pertanian 2013, BPS, Jakarta.

Badan Pusat Statistik (2015). Statistik Potensi Desa Provinsi Jambi 2014, BPS, Jambi. (in bahasa Indonesia).

Badan Pusat Statistik (2015). Indeks Harga yang Diterima Petani (It), Indeks Harga yang Dibayar Petani (Ib), dan Nilai Tukar Petani (NTP) Menurut Provinsi, 2008-2014 [internet]. [diacu 2015 Mei 25]. Tersedia di bahasa Indonesia).

Badan Pusat Statistik (2016). Profil Kemiskinan Indonesia September 2015, BPS, Jakarta.(in bahasa Indonesia).

Bekti, R.D., and Sutikno (2012). Spatial durbin model to identify influential factors of diarrhea. J. Math. Statistics, 8: 396-402
Brunsdon, C., Fotheringham, A. S., & Charlton, M. (2002).

Geographically weighted summary statistics—a framework for localised exploratory data analysis. Computers, Environment and Urban Systems, 26(6), 501-524.

Cappelo, R (2009). Spatial Spillover and Regional Growth : a Cognitive Approach. European Planning Studies, 17(5)

Fischer, M.M., and Getis, A (2010). Handbook of Applied Spatial Analysis, Software Tools, Methods and Applications, Springer, New York.

Fotheringham, A. S., Charlton, M. E., & Brunsdon, C. (1998). Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and planning A, 30(11), 1905-1927.

Gujarati, D.N (2010). Basic Econometrics. Mc Graw-hill Companies, New York.

Hill, R. C., Griffiths, W. E., and Lim, G. C (2011). Principles of econometrics, John Wiley & Sons, Inc, USA.

Jajang (2014). Modifikasi Statistik Getis Lokal pada MatriksPembobot AMOEBA untuk Model Panel Spasial dan Kajian Performanya. Sekolah Pascasarjana IPB, Bogor. (in bahas indonesia)

Kam, S. P., Hossain, M., Bose, M. L., & Villano, L. S. (2005). Spatial patterns of rural poverty and their relationship with welfare-influencing factors in Bangladesh. Food Policy, 30(5), 551-567.

Lisanty, N., & Tokuda, H. (2015). Comprehending Poverty in Rural Indonesia: An In-depth Look inside Paddy Farmer Household in Marginal Land Area of Banyuasin District, South Sumatra Province. International Journal of Social Science Studies, 3(3), 129-137.

Minot, N., Baulch, B., & Epprecht, M. (2006). Poverty and inequality in Vietnam: Spatial patterns and geographic determinants (p. 148). Washington, DC: International Food Policy Research Institute.

Muta’ali (2012). Daya Dukung Lingkungan Untuk Perencanaan Pengembangan Wilayah. Badan Penerbit Fakultas Geografi, Universitas Gadjah Mada

Pasaribu, E (2015). Dampak Spillover dan Multipolaritas Pengembangan Wilayah Pusat-Pusat Pertumbuhan di Kalimantan. Sekolah Pascasarjana. IPB.

Saefuddin, A., Setiabudi, N. A., & Achsani, N. A. (2011). On comparison between ordinary linear regression and geographically weighted regression: With application to Indonesian poverty data. European Journal of Scientific Research, 57(2), 275-285.

Saefuddin, A., Setiabudi, N. A., & Fitrianto, A. (2012). On comparison between logistic regression and geographically weighted logistic regression: With application to Indonesian poverty data. World Applied Sciences Journal, 19(2), 205-210.

Smajgl, A., & Bohensky, E. (2013). Behaviour and space in agent-based modelling: poverty patterns in East Kalimantan, Indonesia. Environmental modelling & software, 45, 8-14.

Sudarlan, R.I., and Yusuf, AA (2015). Impact Of Mining Sector To Poverty And Income Inequality In Indonesia: A Panel Data Analysis, International Journal Of Scientific & Technology Research, 4 (06) : 195-200.

Sudaryanto, T., Susilowati, S. H., & Sumaryanto, S. (2009). Increasing Number of Small Farms in Indonesia: Causes and Consequences. In European Association of Agricultural Economists, 111th Seminar.

Sumarto, S, and de Silva, I (2013). Poverty-growth-Inequality Triangle: The Case of Indonesia (No. 57135), University Library of Munich, Germany.

Suryahadi, A., & Hadiwidjaja, G. (2011). The role of agriculture in poverty reduction in Indonesia. Jakarta: SMERU Research Institute.

Teguh, D., & Nurkholis, N. (2011). Finding out of the Determinants of Poverty Dynamics in Indonesia: Evidence from Panel Data (No. 41185). University Library

Thongdara, R., Samarakoon, L., Shrestha, R. P., & Ranamukhaarachchi, S. L. (2012). Using GIS and spatial statistics to target poverty and improve poverty alleviation programs: A case study in northeast Thailand. Applied Spatial Analysis and Policy, 5(2), 157-182.

Warr, P (2013). Food Security, Agriculture, and Poverty in Asia.
World Bank (2007). Era Baru Dalam Pengentasan Kemiskinan di Indonesia. Indopov. The World Bank. Jakarta

Yamauchi, F., and Muto, M. S, Chowdhury, R. Dewina, and S. Sumaryanto (2010). Are Schooling and Roads Complementary? Evidence from Rural Indonesia. JICA Research Institute Working Paper, 10

Yosnofrizal (2015). Petani dan Jerat Kemiskinan [internet]. [diacu 2015 September 25]. Tersedia di


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