Using k-Means and Self Organizing Maps in Clustering Air Pollution Distribution in Makassar City, Indonesia

Air pollution is an important environmental problem for specific areas, including Makassar City, Indonesia. The increase should be monitored and evaluated, especially in urban areas that are dense with vehicles and factories. This is a challenge for local governments in urban planning and policy-making to fulfill the information about the impact of air pollution. The clustering of starting points for the distribution areas can ease the government to determine policies and prevent the impact. The k-Means initial clustering method was used while the Self-Organizing Maps (SOM) visualized the clustering results. Furthermore, the Geographic Information System (GIS) visualized the results of regional clustering on a map of Makassar City. The air quality parameters used are Suspended Particles (TSP), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), Carbon Monoxide (CO), Surface Ozone (O3), and Lead (Pb) which are measured during the day and at night. The results showed that the air contains more CO, and at night, the levels are reduced in some areas. Therefore, the density of traffic, industry and construction work contributes significantly to the spread of CO. Air conditions vary, such as high CO levels during the day and TSP at night. Also, there is a phenomenon at night that a group does not have SO2 and O3 simultaneously. The results also show that the integration of k-Means and SOM for regional clustering can be appropriately mapped through GIS visualization.

Spatio-Temporal Dynamics of European Innovation—An Exploratory Approach via Multivariate Functional Data Cluster Analysis

We apply a functional data approach for mixture model-based multivariate innovation clustering to identify different regional innovation portfolios in Europe, considering patterns of specialization among innovation types. We combine patent registration data and other innovation and economic data across 225 regions, 13 years, and eight patent classes. The approach allows us to form several regional clusters according to their specific innovation types and captures spatio-temporal dynamics too subtle for most other clustering methods. Consistent with the literature on innovation systems, our analysis supports the value of regionalized clusters that can benefit from flexible policy support to strengthen regions as well as innovation in a systematic context, adding technology specificity as a new criterion to consider. The regional innovation cluster solutions for IPC classes for ‘fixed constructions’ and ‘mechanical engineering’ are highly comparable but relatively less comparable for ‘chemistry and metallurgy’. The clusters for innovations in ‘physics’ and ‘chemistry and metallurgy’ are similar; innovations in ‘electricity’ and ‘physics’ show similar temporal dynamics. For all other innovation types, the regional clustering is different. By taking regional profiles, strengths, and developments into account, options for improved efficiency of location-based regional innovation policy to promote tailored and efficient innovation-promoting programs can be derived.

Prefrontal, striatal, and VTA subnetwork dynamics during novelty and exploration

Multiple distinct brain areas have been implicated in memory including the prefrontal cortex (PFC), striatum (STR), and ventral tegmental area (VTA). Information-exchange across these widespread networks requires flexible coordination at a fine time-scale. In the present study, we collected high-density recordings from the PFC, STR, and VTA of male rats during baseline, encoding, consolidation, and retrieval stages of memory formation. Novel sub-regional clustering analyses identified patterns of spatially restricted, temporally coherent, and frequency specific signals that were reproducible across days and were modulated by behavioral states. Clustering identified miniscule patches of neural tissue. Generalized eigen decomposition (GED) reduced each cluster to a single time series. Amplitude envelope correlation of the cluster time series was used to assess functional connectivity between clusters. Dense intra- and inter regional functional connectivity characterized the baseline period, with delta oscillations playing an outsized role. There was a dramatic pruning of network connectivity during encoding. Connectivity rebounded during consolidation, but connections in the theta band became stronger, and those in the delta band were weaker. Finally, during retrieval, connections were not as severely reduced as they had been during encoding, and specifically theta and higher-frequency connections were stronger. Underlying these connectivity changes, the anatomical extent of clusters observed in the gamma band in the PFC and in both the gamma and delta bands in the VTA changed markedly across behavioral conditions. These results demonstrate the brain's ability to reorganize functionally at both the intra- and inter-regional levels during different stages of memory processing.

ANALISIS FAKTOR KEMISKINAN KABUPATEN/KOTA DI KALIMANTAN, SULAWESI, BALI DAN NUSA TENGGARA

Poverty alleviation planning should be started with data analysis in advance. One of the poverty data sources available in Indonesia is the Regency/City Poverty Data and Information Catalog, published by the Central Statistics Agency (BPS). From the catalog published in the time series can be observed where the poverty rate decreases along with the increasing budget for poverty reduction. In 2005, there were 35.1 million people (15.97%) of the country living under the poverty line and in 2015 reduced to be 28.51 million people which equaled 11.13% of the total population of Indonesia. This research aims to analyze poverty factors in 175 regents and cities located on the islands of Kalimantan, Sulawesi, Bali, and Nusa Tenggara using data from BPS. The principal component analysis (PCA) is the main analytical instrument that was used in this research. The poverty data from BPS has 9 aspects/factors and PCA analysis results in the same number of main components/factors. The difference in the result of these two observations is seen in variable members in each component that could be occurred because BPS conducts grouping of variables before the population data collection gets started, while PCA classifies variables based on data that has been collected or after the population data collection is completed. PCA results can be utilized for further research purposes such as regional clustering, implementation of evaluation, and planning. Meanwhile, the BPS poverty aspect displayed in a more structured arrangement, makes it is easier to observe for publications and more practical to use when conducting population data collection.

CLUSTERING OF THE ECONOMY AS A MEANS OF DEVELOPING AN INNOVATION INFRASTRUCTURE

The experience of the functioning of national innovation systems in the developed countries indicates that the development of an innovative model of the national economy is impossible without the formation of an active innovation infrastructure. The article deals with the problems of formation and development of the innovation infrastructure of the country in difficult social and economic conditions based on clustering of the economy. The purpose of the research is to analyze the role and impact of clusters on self-organization and self-development of the country’s innovation infrastructure in the conditions of limited financial resources. The research investigates the tendencies of the spread of regional clustering and its influence on the creation of a network of infrastructure companies, which, on a commercial basis, offer and distribute services. Clustering of regions is accompanied by the spread of the impact of a growing number of clusters (innovation structures of the network type) on the national economy and its innovation infrastructure. A certain infrastructural environment develops around the hub and the core of the cluster. It includes a number of companies specializing in innovative services, which can offer their services on a commercial basis not only to companies in this cluster, but also to all nearby innovative enterprises. The spread of clustering in all regions can serve as a basis for further self-organization and self-development of subsidiary companies under the influence of market mechanisms of management and growth of the innovation infrastructure of clusters into a basic platform of the innovation infrastructure of the country. Activation of entrepreneurship in clusters is carried out through the concentration of business activity and inter-sectoral cooperation. Vertical cluster associations are formed based on the synergy of enterprises, institutions, organizations, and individuals that are heterogeneous in the field of activity and form of ownership. The state, creating a favorable innovation environment, regulates the boundary conditions for innovation infrastructure subjects and clusters, which independently self-organize themselves under the influence of market mechanisms and expand their services to all regions, forming a holistic innovation infrastructure of the country.

Multilevel Twin Models: Geographical Region as a Third Level Variable

The classical twin model can be reparametrized as an equivalent multilevel model. The multilevel parameterization has underexplored advantages, such as the possibility to include higher-level clustering variables in which lower levels are nested. When this higher-level clustering is not modeled, its variance is captured by the common environmental variance component. In this paper we illustrate the application of a 3-level multilevel model to twin data by analyzing the regional clustering of 7-year-old children’s height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in children’s height is attributable to regional clustering, which is 7% of the variance explained by between-family or common environmental components. Since regional clustering may represent ancestry, we also investigate the effect of region after correcting for genetic principal components, in a subsample of participants with genome-wide SNP data. After correction, region no longer explained variation in height. Our results suggest that the phenotypic variance explained by region might represent ancestry effects on height.

Regional Clustering Based On HDI Components In Indonesia

This research aims to see the distribution of HDI in Indonesia as seen from its grouping based on its constituent components. The panel data regression model is formed from the results of cluster analysis using the K-means method using the Manhattan distance. The data used for cluster analysis is the panel data of the HDI components by province in 2010-2019. The results of the cluster analysis form 5 optimum groups, with the characteristics of group 1, HDI is relatively moderate, group 2 is also relatively moderate but not better than group 1, group 3 is relatively high, group 4 is relatively low, and group 5 is relatively very high.

Innovation in the British Video Game Industry since 1978

This article examines the evolution of the video game industry in Britain from its start in 1978. The industry originated with passionate hobbyists and amateurs who benefited from the national broadcaster's campaign to expand computer literacy. Unlike the regional clustering of the industry in the United States and Japan, the British industry was dispersed geographically, consisting of mini-clusters with porous boundaries. During the 2000s, the fragmented British industry was largely acquired by U.S. and Japanese multinational companies and became part of global value chains, but the development of mobile gaming and digital distribution provided opportunities for a new generation of start-ups to emerge in Britain.