用地理加权回归方法模拟西爪哇水稻生产

Muhamad Sobari, I. Jaya
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引用次数: 0

摘要

在印度尼西亚,水稻生产因省而异,导致各省之间或大或小的差距。在印度尼西亚,东爪哇、中爪哇和西爪哇省的水稻产量最高。然而,与东爪哇和中爪哇相比,西爪哇每年的大米总消费量最高。在线性回归中,各区域的系数是相同的,但各区域有时会有不同的影响因子,从而产生空间多样性。因此,采用地理加权回归(GWR)方法对西爪哇省县市的水稻生产进行了空间异质性建模。GWR模型采用固定的双平方核函数作为加权函数。该模型包括农业劳动力数量、水稻种子使用数量、两轮拖拉机数量、水泵数量、农民群体数量五个解释变量,以水稻产量为响应变量。GWR模型比全球回归具有更大的系数确定(96.8%)和更小的AIC值(920.76)。在2018-2020年期间,两轮拖拉机的数量和水泵的数量对西爪哇的水稻产量影响最大,两轮拖拉机的数量和农民群体的数量变量对西爪哇大多数县/市的水稻产量有影响。有11组区域具有显著预测变量的相似性。
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Modeling Rice Production in West Java by Means Geographically Weighted Regression
In Indonesia, rice production varies from province to province, resulting in both large and small disparities between provinces. In Indonesia, East Java, Central Java and West Java Provinces have the highest rice production. In contrast to East Java and Central Java, however, the total rice consumption per year in West Java is the highest. In linear regression, the coefficients are the same for all regions, while each region sometimes has different influencing factors, resulting in spatial diversity. Consequently, the Geographically Weighted Regression (GWR) method was used to model the rice production of West Java Provincial regencies/municipalities by accounting for spatial heterogeneity. The GWR model employs the fixed bi-square kernel function as its weighting function. This model includes five explanatory variables, such as number of agricultural labor, number of used rice seed, number of two-wheel tractor, number of water pump, and number of farmer groups, with rice production as the response variable. GWR model has greater coefficient determination (96.8 percent) and smaller AIC values (920.76) than global regression. During the period of 2018-2020, the number of two-wheel tractors and the number of water pumps had the greatest impact on rice production in West Java and the number of two-wheeled tractors and the number of farmer groups variables has an effect on rice production in most regencies/municipalities in West Java. There are 11 groups of areas which has the similarity of significant predictor variables.
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