GIS-AIDED GEOSPATIAL ANALYSIS OF THE FOOD INDUSTRY OF BULGARIA (2010-2020)

Aleksandra Ravnachka, V. Stoyanova
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Abstract

The current research aims to apply cluster analysis using the software ArcGIS in the study of the food industry in Bulgaria for the period 2010 to 2020. The use of clustering methods is necessary to differentiate homogeneous groups of administrative-territorial units of NUTS 3 level on certain indicators to reveal several features and implement specific economic policies and measures for areas of a cluster and others. The grouping of the areas according to the considered indicators was done with the tool Grouping Analysis. Grouping and classification techniques are some of the most widely used methods in machine learning. We have selected No_spatial_constraint for the Spatial Constraints parameter, for grouping using the K-Means algorithm. Based on the results of the �average intergroup connection� method, the areas are grouped into 7 clusters (food industry, 2010 and 2020; food and beverage products for the period 2010-2020) and into 4 clusters (tobacco production for the period 2010-2020). The selection of indicators based on which the clusters are formed is following the generally accepted indicators for assessing the state and importance of the food industry in the structure of the economy and their information accessibility. The following indicators were used output for 2010 and 2020, employees for 2010 and 2020, and export earnings for 2010 and 2020 for the given territorial unit The territorial distribution of the population, in combination with the historical and modern economic development of the settlements, forms the regional differences in the development of the food industry in the country. The cluster analysis of certain indicators for the assessment of the food industry at the NUTS 3 level for 2010 and 2020 shows some change in the trends in the territorial development of the industry. The cluster analysis shows that there are slight territorial differences at the NUTS 3 level in food production, with large consumer centers and markets being the most important. In the activities of tobacco and beverage production, the territorial differences are minimal.
保加利亚食品工业地理空间分析(2010-2020)
目前的研究旨在应用聚类分析使用软件ArcGIS在2010年至2020年期间保加利亚食品工业的研究。为了在某些指标上区分NUTS 3级行政领土单位的同质组,以揭示若干特征,并为集群和其他地区执行具体的经济政策和措施,有必要使用聚类方法。根据所考虑的指标对区域进行分组是用分组分析工具完成的。分组和分类技术是机器学习中使用最广泛的方法。我们为空间约束参数选择了No_spatial_constraint,以便使用K-Means算法进行分组。根据“平均组间连接”方法的结果,将这些区域划分为7个集群(食品工业,2010年和2020年;2010-2020年期间的食品和饮料产品),并分为4类(2010-2020年期间的烟草生产)。集群形成所依据的指标的选择遵循了评估食品工业在经济结构中的状态和重要性及其信息可及性的普遍接受的指标。人口的地域分布,结合聚落的历史和现代经济发展情况,形成了全国食品工业发展的地域差异。通过对2010年和2020年食品工业NUTS 3级评价指标的聚类分析,可以看出食品工业的区域发展趋势发生了一些变化。聚类分析表明,在食品生产的NUTS 3水平上存在轻微的地域差异,其中大型消费中心和市场是最重要的。在烟草和饮料生产活动中,地域差异很小。
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