Socio Economic and Spatial Methodologies: a better understanding of socioeconomic assessment in rural area by: Iwan Rudiarto, Aulisa Rahmi, Umrotul Farida 5th RRPG International Conference “Managing Rural Change in a Turbulent World: Towards a Resilient and Sustainable Rural Society” Kuala Lumpur, 25-29November 2013

OUTLINE Introduction Theoretical Background Method and Analysis: Study Area, Samples, and Sampling Technique Process of Analysis Spatial Interpolation Results Socioeconomic Characteristics Homogenous Socioeconomic Zone Conclusions

INTRODUCTION Methodological problems in regional assessment of mountain farming system sustainability arise due to representative household surveys, which are bound to a certain scale in which they serve as models for a case. In a micro level survey, conditions of farm families are evaluated in detail only in limited number of surveyed households or villages, valid only for certain cases and classified using cluster analysis rather than their position in space  regionalization has become necessary to cover all related issues and problems for the whole study area. The differentiation of socio economic characteristics and its supporting environmental aspects, normally presented in the tables format in order to show the specification and the significant position of one area compared to the other  what about geographic location? How socioeconomic can be linked into its spatial aspect? How GIS can be applied to integrate socioeconomic and infrastructure development? And How the data can be generalized?

THEORETICAL CONCEPT Vertical Linkages and Hierarchical System in Farming System Linkage between these systems: Decision making Resource availability Resource use Transportation Markets Social activities Cultural needs Values Objectives Source: Doppler, 1999

METHOD AND ANALYSIS (Spatial Interpolation) Estimating the value of a field variable at unsampled sites within the area covered by sample locations (Zhang and Goodchild, 2002). Applying Inverse Distance Weight (IDW)  to generate grid data in the un-sampled area. IDW method determines the output values for each location from all points within a specified radius. This technique creates weights according to the distances between the interpolated location (x,y) and each neighbors. IDW Spline Trend Krigging

METHOD AND ANALYSIS (Study Area, Samples, and Sampling Technique) Dieng Plateau Kejajar sub district in Wonosobo district, Kedungjati sub district in Grobogan District, and Bumijawa sub district in Tegal district  central java province. Randomly selected farm family using standardized questionnaires to 75 families in Dieng sub district, 68 families in Kedungjati sub district, and 100 families in Bumujawa sub district distributed in each three study areas

METHOD AND ANALYSIS (Process of Analysis) Family Survey Geographic Position GPS (X,Y) Socio Economic Data Location of Surveyed Family DATA JOINING Non Spatial + Spatial Data Spatially Significant Test (Spatial Autocorrelation) Spatial Interpolation Selected Spatial Data Spatial Reclassification Integration of Selected Data Classification of Homogenous Zone Physical Data: - Index of Erosion Risk - Slope Map (%) - Elevation Map (m) Spatially Socio Economic Description

METHOD AND ANALYSIS (Spatial Interpolation) Inverse Distance Weight (IDW) was selected to generate grid data in the unsampled area. IDW method assumes that the variable being mapped decreases in influence with distance from its sampled location. IDW method determines the output values each location from all points within a specified radius.

a) b) Example of spatial interpolation using IDW method; a) point samples, and b) interpolated point map based on family income,Dieng Plateau.

RESULTS (Different Socioeconomic Chracteristics) By using IDW method in interpolating socioeconomic data from sampled points, all the unknown value of un-sampled area within the study area are able to be identified. However, the interpolation results may over or underestimate the conditions at the edge of surface. The interpolation, particularly at the border of study area and at the less sample point area, is often continued with unrealistic values. Once the last sampling point is passed, the derived trend continues with the same gradient as before the sampling point and makes the values rise or decline inappropriately in some cases.

Farm Income Weigthed Education Index

a) b) Distribution of a) family income, and b) cash - balance in Kedungjati sub district

a) b) Distribution of a) education index, and b) farm area in Bumijawa sub district

RESULTS (Homogenous Socioeconomic Zone) Homogenous zone and the average socio-economic value of Dieng sub district

Socio-Economic Characteristics Average Values Zone 3 Zone 2 Zone 1 Education Index 2,46 3,23 3,77 Family Income (per month) 567.000,00 1.077.142,86 1.561.666,67 Farm Income 472.714,29 961.428,57 906.666,67 Off-Farm Income 94.285,71 115.714,29 655.000,00 Total Farm Area (ha) 0,08 0,26 0,33 Total Land Area for House (m2) 80,97 103,94 134,13 Altitude Level (m asl) 748,682 1225,24 880,616 Homogenous socioeconomic zones of Bumijawa sub district

CONCLUSIONS Spatial description of socioeconomic development among the region can be distinguished explicitly based on the altitudinal level of household samples such as high, middle, and low area. Spatial description from the zoning map shows the relation of economic, social, as well as environmental aspect, i.e.: higher family income normally shows higher level of education and located on lower level of environmental risk. The descriptions of problems, potentials, and advantages of study area can be clearly identified  decisions on further rural development policies can be proposed.

MATURNUWUN TERIMAKASIH THANK YOU DANKE SCHOEN GRAZIE GRACIAS MERCI DANK U