The Explanatory on Rural Development Stages Using Geographically Weighted Regression based on the Integration of Socio-Economic, Demographical and Landcover Data

Abstract

In the context of rural development, each rural area has unique characteristics in determining its development level. The monitoring on global land use–land cover (LULC), LULC change, the normalized difference vegetation index (NDVI), estimated data of crop yield and income, and demographical factors include total population and percentage growth rate during 2015 to 2020 have corresponded with the rural development stages (RDS). These parameters are used in the geographically weighted regression (GWR) has resulted that local regression gave the advantages on the perspective of how the rural areas can be managed and to what extent the environment variable can use to assist the RDS. This paper aimed to show the relationship between the RDS and through the analysis of socio-demographical, derived economic data and the trend of LULC change. The final result has shown that the rural areas located in the forested areas, have a remote location and rough topography tend to have the lowest local regression values compare by the range of R2 values at about 0 to 0.15. The GWR has shown that all explanatory variable has a weak positive correlation to the RDS, even though it shows the pattern of clustered in the entire of Way Sekampung.