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首页> 外文期刊>International Journal of Environment and Geoinformatics >Spatially Constrained Clustering of Nigerian States: Perspective from Social, Economic and Demographic Attributes
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Spatially Constrained Clustering of Nigerian States: Perspective from Social, Economic and Demographic Attributes

机译:尼日利亚国家的空间受限聚类:从社会,经济和人口统计属性的角度看

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Creationand differentiation of regions are some of the basic tasks in geographicanalysis. Regionalisation attempts to create a generalised representation ofthe processes which is taking place at the level of the amalgamated geographicunits. To this end, this study examined the combined use of demographic,economic and poverty characteristics of States across Nigeria to create regionsrelevant for economic and development planning. The study utilised dependencyratios derived from gridded age structure data, Gross domestic product (GDP),poverty index. K-Means and Max-p algorithm were used for identification ofregions. Correlation analysis showed thatYouth dependency and total dependency have a strong statistically significantpositive relationship (r=0.998, p<0.01) indicating that dependency in thecountry is driven by youth. The best K-Mean clustering implementation withoutconsidering contiguity identified 12 regions with a ratio of between and totalsum of squares (RBTSS) of 0.789. The Max-p algorithm was tested with populationconstrain, the best result identified 9 regions with RBTSS of 0.611 constrainedby a minimum population of 8% and implemented with the greedy local searchalgorithm, this was the same for the simulated annealing approach (SA). Withhigh dissimilarity still common across a handful of the regions identified, afurther test was carried out using a minimum bound of 3 States and the SA localsearch approach. The best result identified 11 contiguous regions with only oneregion having a relatively high within region dissimilarity and a RBTSS of0.626. The results confirmed that there are more than 6 regions as currentlydefined for the country. The analyses showcased an example of knowledgediscovery from a spatial dataset which could support regional developmentplanning. From the results, there is a clear need for re-examination of currentregions and designing of better-defined regions to ensure that development isguided by evidence.
机译:区域的创建和区分是地理分析的一些基本任务。区域化试图创建在合并的地理单位级别上正在发生的过程的通用表示。为此,这项研究审查了尼日利亚各州人口,经济和贫困特征的综合利用,以建立与经济和发展规划有关的地区。该研究利用了从年龄结构数据,国内生产总值(GDP),贫困指数得出的依赖关系。采用K-Means和Max-p算法进行区域识别。相关分析表明,青年依赖和总依赖之间具有很强的统计学显着正相关关系(r = 0.998,p <0.01),表明该国的依赖是青年驱动的。最佳的K-Mean聚类实现方案不考虑连续性,可确定12个区域之间的比率与平方和之和(RBTSS)为0.789。用种群约束对Max-p算法进行了测试,最佳结果确定了9个区域,其中RBTSS为0.611,最小种群为8%,并采用贪婪的局部搜索算法来实现,这与模拟退火方法(SA)相同。由于在确定的少数地区之间仍普遍存在高度差异,因此使用3个州的最小范围和SA localsearch方法进行了进一步测试。最好的结果是确定了11个连续区域,其中只有一个区域具有较高的区域内相似度,RBTSS为0.626。结果证实,该国目前有6个以上的地区。分析显示了从空间数据集中发现知识的示例,该数据可以支持区域发展规划。从结果来看,显然需要对当前地区进行重新审查,并设计出定义更好的地区,以确保以证据为指导进行发展。

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