Identifying Local Spatially Dependent Driving Factors of Village Development in Jambi Province

  • Andrea Emma Pravitasari Regional Development Planning Division, Department of Soil Science and Land Resource, Faculty of Agriculture, Bogor Agricultural University, Jl. Meranti, IPB Darmaga Campus, Bogor, West Java, Indonesia, Postcode: 16680
  • Ernan Rustiadi Regional Development Planning Division, Department of Soil Science and Land Resource, Faculty of Agriculture, Bogor Agricultural University, Jl. Meranti, IPB Darmaga Campus, Bogor, West Java, Indonesia, Postcode: 16680
  • Jane Singer Graduate School of Global Environmental Studies (GSGES), Kyoto University, Yoshida Honmachi, Sakyo, Kyoto, Japan 606-8501
  • Juniadi Juniadi Department of Economics and Development Studies, University of Jambi; Jl. Lintas Jambi - Muara Bulian Km. 15, Mendalo Darat, Jambi Luar Kota, Kota Jambi, Jambi 36122
  • Setyardi P. Mulya Center for Regional Systems, Analysis, Planning and Development (P4W/CRESTPENT), Bogor Agricultural University (IPB), Jl. Raya Pajajaran, Bogor, Indonesia, Postcode: 16144
  • Lutfia N. Fuadina Regional Development Planning Division, Department of Soil Science and Land Resource, Faculty of Agriculture, Bogor Agricultural University, Jl. Meranti, IPB Darmaga Campus, Bogor, West Java, Indonesia, Postcode: 16680
Keywords: Geographically Weighted Regression (GWR); infrastructure; local spatial relationship; spatial variability; Village Development Index (VDI)

Abstract

Village is the smallest unit of administrative boundaries in Indonesia. There are more than 74,000 villages in our country with various characteristics. One way that can be used to determine the hierarchy of village development is by analyzing the village development index (VDI). The objectives of this study are to calculate VDI and to identify spatial variations in the relationship between VDI and its driving factors in every location. In this study, Jambi Province was selected as the research location. There are 1543 villages in Jambi. The VDI in this study were determined by considering the number of facilities/infrastructure and the accessibility to access those facilities. Villages with higher VDI are more developed rather than other villages. To identify the driving factors of village development, 62 variables from PODES and spatial data were included in the Ordinary Least Square (OLS) model. Based on OLS results, we found 17 variables which statistically significant affecting village development in Jambi. Then, Geographically Weighted Regression (GWR) model was employed to identify the spatial relationship of driving factors to village development. The seventeen variables included number of education, health, and economic facilities; accessibility; percentage of built-up area, household working in agricultural sector, household using electricity, household living in slum area, poor people; number of criminality and people suffering from malnutrition. Studies of the interdependencies between these driving forces which affecting village development in the region remain limited. The presented findings show that the local driving forces affecting village development in Jambi Province vary spatially.

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References

Bappeda Jambi. (2011). Analisis Pertumbuhan Ekonomi Provinsi Jambi Tahun 2010.
Bebbington A, Dharmawan L, Fahmi E, Guggenheim S. (2006). Local Capacity, Village Governance, and The Political Economy of Rural Development in Indonesia. World Development. 34(11): 1958-1976.
Clement F, Orange D, Williams M, Mulley C, and Epprecht M. (2009). Drivers of afforestation in Nothern Vietnam: assessing local variations using geographically weighted regression Applied Geography 29 (4) 561-576 http://dx.doi.org/10.1016/j.apgeog.2009.01.003
Farrow A, Larrea C, Hyman G, and Lema G. (2005). Exploring the spatial variation of food poverty in Ecuador Food Policy 30 510-531 http://dx.doi.org/10.1016/j.foodpol.2005.09.005
Fotheringham A S, Brunsdon C, and Charlton M. (2002). Geographically weighted regression: The analysis of spatially varying relationships England: John Wiley & Sons.
Imron MA. (2011). Regional Autonomy Proliferation of Region and Psudo Local Government in Indonesia. Kawistara. 1(2): 103-212.
Jaimes N B P, Sendra J B, Delgado M G, and Plata R F. (2010) Exploring the driving forces behind deforestation in the state of Mexico (Mexico) using geographically weighted regression Applied Geography 30: 576-591 http://dx.doi.org/10.1016/j.apgeog.2010.05.004
Malczewski J and Poetz A 2005 Residential burglaries and neighborhood socioeconomic context in London, Ontario: global and local regression analysis The Professional Geographer 57 516-529 http://dx.doi.org/10.1111/j.1467-9272.2005.00496.x
Ogneva-Himmelberger, Pearsall, Y H., and Rakshit R 2009 Concrete evidence and geographically weighted regression: A regional analysis of wealth and the land cover in Massachusetts Applied Geography http://dx.doi.org/10.1016/j.apgeog.2009.03.001
Pravitasari AE, Rustiadi E, Mulya SP, Setiawan Y, Fuadina LN, Murtadho A. (2018). Identifying the drivinig forces of urban expansion and its environmental impact in Jakarta-Bandung Mega Urban Region. IOP Conference Series (in publishing).
Pravitasari AE, Saizen I, Tsutsumida N, Rustiadi E, and Pribadi DO. (2015). Local spatially dependent driving forces of urban expansion in an emerging Asian Megacity: the case of greater Jakarta (Jabodetabek) The Journal of Sustainable Development 8 (1): 108-119.
Rustiadi E, Saifulhakim S, Panuju DR. (2009). Perencanaan dan Pengembangan Wilayah (Regional Planning and Development). ISBN: 9794616877. Jakarta: Yayasan Obor Indonesia. 514 p.
Saleh A. (2016). Concept of Village Regrouping as an Alternative Strategy for Sustainable Micro Regional Development. Procedia-Social and Behavioral Science. 216: 933-937.
Sitorus RPS, Leonataris C, Panuju DR. (2012). Analisis Pola Perubahan Penggunaan Lahan dan Perkembangan Wilayah di Kota Bekasi, Provinsi Jawa Barat (Analysis of Land Use Change Patterns and Regional Development in Bekasi Municipality, West Java Province). Journal of Soil Science and Environment. 14(1): 21-28.
Su S, Xiao R., and Zhang Y (2012) Multi-scale analysis of spatially varying relationships between agricultural landscape patterns and urbanization using geographically weighted regression Applied Geography 32 (2) 360-375 http://dx.doi.org/10.1016/j.apgeog.2011. 06.005
Yu D L (2006). Spatially varying development mechanisms in the Greater Beijing area: A geographically weighted regression investigation Annals of Regional Science 40: 173-190 http://dx.doi.org/10.1007/s00168-005-0038-2.
Published
2019-02-22