Application of Spatial Regression to the 2017 DKI Jakarta Regional Election

Chotib, Fauzan Zahid Abiduloh, Beti Nurbaiti, Mohamad Axel Putra Hadiningrat


The DKI Jakarta elections for 2017-2022 have captured the attention of researchers due to their intriguing nature. One particularly fascinating aspect is the unexpected victory of candidate pair (Paslon) number 3, Anies-Sandi, which was initially not favored by most opinion polls. This study explores how political preference factors, as described in the sociological model, contribute to the formation of base and non-base areas of Anies-Sandi voters in the DKI Jakarta elections for the 2017-2022 period. This study also evaluates the spatial impact of the influence of these social factors. The novelty of research that examines the application of spatial regression in the 2017 DKI Jakarta Pilkada is an interesting innovation that has never been studied before. The base and non-base areas are identified per sub-district with the location quotion (LQ) formula, whereas the identification of global spatial patterns uses the Moran index calculation formula. The free variables in the spatial regression model are designed to adopt a sociological model of political preferences that includes the free variables of sex ratio, percentage of young voters, percentage of highly educated population, percentage of formal sector workers, and percentage of non-Muslim population. The results of this study found that the Anies-Sandi base and non-voter base areas were spread to most sub-districts in DKI Jakarta, but their distribution did not form a particular global spatial pattern. However, it was found that there was an error-type spatial dependency in the first- and second-round LQ models, so it was necessary to perform spatial regression of errors. After spatial regression error, it was found that the sex ratio had a positive and significant effect on LQ in the first round, and the percentage of formal sector workers and the percentage of non-Muslim population had a negative and significant effect on LQ in the first and second rounds. Meanwhile, the lambda coefficient is positive and significant in the first and second rounds of the LQ error spatial regression models, which shows the spatial effect in the residual model. In terms of goodness of fit, the error spatial regression model is more efficient than the OLS model, which is indicated by the relatively low AIC value of the spatial error model compared with the OLS model.


Keywords: political geography, electoral geography, location quotion, Moran’s index, spatial regression.

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