HIERARCHICAL CLUSTER ANALYSIS OF DISTRICTS/CITIES IN NORTH SUMATRA PROVINCE BASED ON HUMAN DEVELOPMENT INDEX INDICATORS USING PSEUDO-F

Human development is needed to create prosperity and assist development in a country. In realising this, it is necessary to first look at the quality of human resources in the country, so that its use is more targeted. The measure used as a standard for the success of human development in a country is the Human Development Index (HDI). HDI figure are calculated from the aggregation of three dimensions, namely longevity and healthy living, knowledge, and decent standard of living. The longevity and healthy living dimension is represented by the Life Expectancy. Average Years of Schooling (AYS) and Expected Years of Schooling (EYS) are indicators representing the knowledge dimension. Meanwhile, the decent standard of living dimension is represented by the Expenditure per Capita indicator. The purpose of this study is to explain the characteristics of each cluster obtained from Hierarchical Cluster Analysis of districts/cities in North Sumatra Province based on HDI indicators in 2022 using Pseudo-F. The methods used are Hierarchical Cluster Analysis and Calinski-Harabasz Pseudo-F Statistic. The main concept of this method is to determine the optimum number of groups. This research uses secondary data obtained from BPS. The sample size in this study are 33 districts/cities and the number of variables are 4 variables. The results of the analysis of this study are the formation of 4 clusters with the best method is Ward. Cluster 1 consists of four members, namely Medan City, Pematang Siantar City, Binjai City, and Padang Sidempuan City, where this cluster has a very high HDI level. Meanwhile, Cluster 4 is a cluster that has a very low HDI level with four cluster members, namely Nias District, South Nias District, North Nias District, and West Nias District. Thus, it can be seen that there is a gap between regions in North Sumatra Province

Determining the Optimum Number of Clusters in Hierarchical Clustering Using Pseudo-F

Poverty refers to the condition where a person cannot meet the basic necessities based on the minimum living standards. Statistics Indonesia proxied an increase in the poverty rate in North Sumatra Province in 2021 from 8.75% to 9.01%. However, this increase is exclusive to North Sumatra Province, which has Indonesia's 3rd largest number of districts/cities. This study discussed mapping the North Sumatra Province region based on 10 poverty factor variables. The 10 variables are life expectancy, health complaints, poverty line, Gross Regional Domestic Product (GRDP), population growth rate, Expected Years of Schooling (EYS), Human Development Index (HDI), labor force participation rate, open unemployment rate, and district/city minimum wage. The Hierarchical Clustering analysis was employed to compare single, complete, and average linkage methods. The best method was determined based on the pseudo-F statistic value. 4 clusters had complete linkage methods, each of which possessed varied characteristics. Cluster 1 contains cities with the lowest poverty rate, including Medan City and  Pematang Siantar City. Cluster 2 consists of cities with low poverty rates, while Cluster 3 consists of cities with high poverty rates. Cities that are included in Cluster 4 have very high poverty rates, including South Nias District and Pakpak Bharat District. The clusters present significant poverty rate gaps among North Sumatra Province regions

Raum und Mobilität : ein neuer Ansatz zur Erfassung verkehrlicher Klimaschutzpotenziale im Personenverkehr ; das Beispiel Wolfsburg

Mit dem Klimaschutz-Teilkonzept Mobilität für die Stadt Wolfsburg wurde eine neue Methode erprobt, mit der sich klimaschutzrelevante Handlungspotenziale an der Schnittstelle von Stadt- und Verkehrsplanung identifizieren lassen. Die Methode erlaubt es, bei der Abschätzung von Potenzialen zur Minderung verkehrsbedingter CO2-Emissionen stadträumliche Gegebenheiten zu erfassen und eine räumliche Differenzierung vorzunehmen. Sie stellt neben den für die Stadt- und Verkehrsplanung schon bestehenden Ansätzen die Analyse von variierenden CO2-Minderungspotenzialen innerhalb der Teilräume einer Stadt in den Vordergrund und ist eine sinnvolle Ergänzung zu den im verkehrsplanerischen Kontext oft angewandten Verkehrssimulationen

Understanding an ice stream in Greenland at the EastGRIP camp: An international-multidisciplinary effort and knowledge transfer among scientists

The East Greenland Ice-core Project (EastGRIP) aims to retrieve a 2650m long ice core by drilling through the Northeast Greenland Ice Stream (NEGIS). Scientists from a variety of physical sciences and engineers hope to gain new knowledge on ice stream dynamics and about the past climate. The project has many partners from all around the world, who contribute to the science, logistics and economics of the project. Most of the data is processed and analysed in the field. As there is limited capacity in the field camp, scientists and technical staff are encouraged to learn different working techniques to support their project partners. The community also invites scientists, who work on theoretical aspects on the ice stream to communicate the perspective on the procedures and challenges of data collection in the field. And so it happens that earth system modellers learn how to drill an ice core or non-scientific staff helps with the preparation of the ice core samples. Also, the project partners are motivated to bring together scientists at early stages in their careers to gain fieldwork experience. Once each season the field camp also welcomes high school students and teachers as a part of a joint science and education programme