Socio-economic mapping : a Study of the MAUP using a Spatial perspective

Abstract

The objective of the study is to explore the MAUP in spatial perspective. Openshaw (1984) noted that the results of the analysis of aggregated spatial data will depend on the scale and zoning being used. These two factors potentially affect analysis of spatially aggregated data and are referred to as the modifiable areal unit problem or MAUP. The MAUP can distort an interpretation of result of the analysis. For example, Minot and Baulch (2005) indicated that the most poor people do not live in the poorest districts but in the two lowland deltas, where poverty incidence is intermediate. Census and survey often provide a main sources of small area statistics. But analyzing of census data often ignores spatial characteristics and usually assumes that observations are distributed independently and identically (IID). This may lead to incorrect results. One observation may be influenced by others at nearby locations. As a result, census data may show inter-dependence between observations, or the IID assumptions are not be appropriate. The study exercises on characteristics of poverty, employment, unemployment, lapor participant rate, literacy rate, and some others socio-economics indicators. The data sources are come from Australian Bureau of Statistics, Indonesian Statistical Office, and Vietnam Statistical Office. The poverty data were obtained from The Poverty Mapping Project at CIESIN (The Center for International Earth Science Information Network). This institution maintain poverty data of several country and provide as well some report on the study of poverty and other related material (Center for International Earth Science Information Network, 2006). Poverty is one of the welfare characteristics, which is a multidimensional phenomena within a society. The basic idea of measuring poverty is to assess the poverty status of each individual within the region and also constructing an index of poverty using the available information on the poor (Sen, 1976). The Foster Greer and Thorbecke index is used in this study. Other measurements are used such as Socio Economic Index for Area - SEIF A and also an index derived by geographical weighted regression. The SEIF A may be used as an alternatif measured of the socio-economic index. The geographical weighted regression method also give a better localize estimates of the index across the geographical regions. The semivariogram models are developed to portray spatial distribution at each level. Some interesting results come out from this study, one of them is the nugget and range of the semivariogram model may give a direction of understanding spatial distribution at the unobserved scale, for example subdistrict level. The semivariogram model also provides a better information than other measure of spatial autocorrelation, i.e. Moran coefficient. The semivariogram model's parameters gives information about variance (si11), inter-dependency in term of geographical distance (range) and also the nugget effect, which discloses an interesting information about a latent inter-relationship of the characteristics within a particular area.