Fuzzy Geographically Weighted Clustering Research Articles

Grouping Districts / Cities in Central Sulawesi Province Based on Poverty Indicators Using the Fuzzy Geographically Weighted Clustering -Artificial Bee Colony Method

Introduction: Poverty is the main problem that is the focus of attention of the government in Indonesia. In general, poverty is a person's inability to meet basic basic needs in every aspect of life. Cluster analysis is a solution to map this problem. Method: Fuzzy Geographically Weighted Clustering-Artificial Bee Colony (FGWC-ABC) is one clustering method that is an integration of classical fuzzy clustering methods and geodemographic elements. Artificial Bee Colony is a metaheuristic algorithm that is used as a global optimization to increase cluster accuracy. Artificial Bee Colony can efficiently and effectively solve various function optimization problems in various cases. Result and Discussion: The research results obtained 3 optimum clusters with each cluster characteristic relatively different based on poverty indicators. Cluster 1 with low poverty, cluster 2 with high poverty, and cluster 3 with moderate poverty. Conclusion: By using the IFV validity index, 3 optimum clusters were obtained with different characteristics of each cluster based on its indicators. Cluster 1 consists of three regencies/cities with low poverty status, cluster 2 consists of seven regencies/cities with high poverty status, and cluster 3 consists of six regencies/cities with moderate poverty status.

Fuzzy Geographically Weighted Clustering Analysis for Sectoral Potential Gross Regional Domestic Product in West Sumatera

Gross Regional Domestic Product is the sum of the added value of all goods and services produced or produced in an area that arises as a result of various economic activities in a certain period. Each region certainly has its own advantages and potential, such as in sectors or business fields. GRDP inequality occurs due to differences in geographical conditions and natural resources in each region. The method that can be used to overcome this inequality is cluster analysis. Fuzzy Geographically Weighted Clustering is a clustering method which is an integration of the classical fuzzy clustering method and geo-demographic elements, so that the clusters formed will be sensitive to geographic effects. The results of the research obtained 3 optimum clusters with different characteristics. Cluster 1 has high potential, cluster 2 has low potential and cluster 3 has medium potential in forming GRDP.

Fuzzy Geographically Weighted Clustering Method for Grouping Provinces in Indonesia Based on Welfare Indicators Aspects of Information and Communication Technology (ICT)

The welfare of the people is a task and goal that must be realized by the Republic of Indonesia. To find out the condition of the welfare of the Indonesian people, it can be seen from 8 areas of Indonesia's welfare indicators. Indicators The welfare of the Indonesian people is undergoing a digital transformation of Information and Communication Technology (ICT) in 2021. However, there was a gap in ICT development due to geographical conditions and the distribution and dynamics of each region's society. Cluster analysis is a solution for target setting for better future decisions. Fuzzy Geographically Weighted Clustering (FGWC) is one of the cluster methods with fuzzy logic that considers geographical and population elements in grouping targets. The results of the research resulted in 3 optimum clusters with different characteristics of each cluster based on indicators of ICT aspects of people's welfare. Cluster 1 has a low status of ICT indicators of people's welfare and is located in the middle or at the end of the island, provinces from cluster 2 have a medium status of ICT indicators of people's welfare with a medium area, while cluster 3 has a high status of ICT indicators of people's welfare with a large area or dense populations.

Optimization Fuzzy Geographically Weighted Clustering with Gravitational Search Algorithm for Factors Analysis Associated with Stunting

Stunting is a significant threat to the quality of human resources in Indonesia because stunting does not only involve physical growth disorders but can also cause children to be vulnerable to disease and experience disorders of brain development and intelligence. Many factors cause stunting, not only malnutrition in pregnant women and toddlers. Grouping can be done to make it easier to see the characteristics of the factors causing stunting in Indonesia. The grouping is done based on the similarity of the characteristics of the factors causing stunting in each province. This study used Fuzzy Geographically Weighted Clustering (FGWC) with Gravitational Search Algorithm (GSA) to group and assess the best cluster using the Partition Coefficient validity index, Classification Entropy, Separation Index, Xie & Beni's Index, and IFV Index. Furthermore, a difference test was conducted to determine the dominant factor causing stunting in the formed cluster. The results showed that the FGWC-GSA gave the best clustering results on the fuzziness value of 2 with the number of clusters 2. Cluster 1 consisted of 16 provinces, and cluster 2 consisted of 18 provinces. Based on the T-test, the variables of infants who received exclusive breastfeeding had significant differences between clusters. Therefore, cluster 2 is a cluster that has dominant problems related to exclusive breastfeeding.

Urban vulnerability to floods investigation in jakarta, Indonesia: A hybrid optimized fuzzy spatial clustering and news media analysis approach

Jakarta, known as the capital city of Indonesia and the national economic activity center, is also known as the market for floods. Moreover, the regions of Jakarta become vulnerable, especially the slum areas and this COVID-19 pandemic. This study proposes implementing Fuzzy Geographically Weighted Clustering (FGWC) with Chaotic Flower Pollination Algorithm (CFPA) in urban vulnerability analysis of Jakarta at the subdistrict level. Moreover, this study also implements the named entity recognition (NER) from local news media to extract the history of floods in Jakarta during 2019–2020 to support the urban vulnerability. This study found that the CFPA can improve the performance of FGWC. Subsequently, the clustering results showed that each cluster has its urban vulnerability problem. Most regions outside central Jakarta have more vulnerability problems, particularly in population dynamics, floods history, and slum areas. On the other hand, the news analysis showed that the most frequent flood happened between October and February. Therefore, the government and society must collaborates to overcome the urban vulnerability problems and mitigates the floods to increase the regional resilience of Jakarta.

Micro, Small, and Medium Enterprises’ Business Vulnerability Cluster in Indonesia: An Analysis Using Optimized Fuzzy Geodemographic Clustering

The COVID-19 pandemic has caused effects in many sectors, including in businesses and enterprises. The most vulnerable businesses to COVID-19 are micro, small, and medium enterprises (MSMEs). Therefore, this paper aims to analyze the business vulnerability of MSMEs in Indonesia using the fuzzy spatial clustering approach. The fuzzy spatial clustering approach had been implemented to analyze the social vulnerability to natural hazards in Indonesia. Moreover, this study proposes the Flower Pollination Algorithm (FPA) to optimize the Fuzzy Geographically Weighted Clustering (FGWC) in order to cluster the business vulnerability in Indonesia. We performed the data analysis with the dataset from Indonesia’s national socioeconomic and labor force survey (SUSENAS and SAKERNAS). We first compared the performance of FPA with traditional FGWC, as well as several known optimization algorithms in FGWC such as Artificial Bee Colony, Intelligent Firefly Algorithm, Particle Swarm Optimization, and Gravitational Search Algorithm. Our results showed that FPAFGWC has the best performance in optimizing the FGWC clustering result in the business vulnerability context. We found that almost all of the regions in Indonesia outside Java Island have vulnerable businesses. Meanwhile, in most of Java Island, particularly the JABODETABEK area that is the national economic backbone, businesses are not vulnerable. Based on the results of the study, we provide the recommendation to handle the gap between the number of micro and small enterprises (MSMEs) in Indonesia.

Fuzzy Geographically Weighted Clustering dengan Gravitational Search Algorithm pada Kasus Penyandang Masalah Kesejahteraan Sosial di Provinsi Jawa Tengah

One of the indicators of the success of social welfare development in Central Java was decreasing the population of people with social welfare problems (PMKS). One exertion that can be done was grouping or clustering the areas in Central Java-based on 26 indicators of PMKS. Fuzzy Geographically Weighted Clustering (FGWC) algorithm is a clustering analysis that observing the effect of the area. However, FGWC has a limitation in the initialization centroid phase that makes it trapped to local optimal. The limitation can be addressed with the Gravitational Search Algorithm (GSA) approach. The purpose of GSA was to optimize the value objective function. This research applied FGWC-GSA on PMKS in Central Java Province contained 26 indicators. Some validity indexes were applied to determine the best cluster. This research clustering the areas of Central Java into two clusters. The first cluster contained 24 districts and cities, and the second cluster contained 11 districts

Revisiting social vulnerability analysis in Indonesia: An optimized spatial fuzzy clustering approach

In previous social vulnerability studies, the use of indices in assessing social vulnerability can only describe conditions of social vulnerability in general without being able to indicate which factors are dominant in measuring the level of social vulnerability. Therefore this study aims to fill the gap by implementing the relative approach using the clustering method to characterize the dominant factors of social vulnerability in Indonesia at the district level utilizing hybridization of the Fuzzy Geographically Weighted Clustering (FGWC) with Intelligent Firefly Algorithm (IFA). Then the Kruskal-Wallis test is used to obtain the dominant factor of social vulnerability in each formed cluster. The study found that each district in Indonesia has its own prevailing social vulnerability problems. The majority of regions on the west coast of Sumatera Island, such as Nias and Mentawai District and Eastern Indonesia, such as Sumba Barat Daya and Intan Jaya District, are the districts with the most problem, particularly in socioeconomic aspects. The results of this study can be used to support the prevention, mitigation, response, and recovery for reduction disaster programs in Indonesia.

Compare of Clustering School Operational Aid Using Fuzzy Cluster Means and Fuzzy Geographically Weighted Clustering Method

The use of appropriate Cluster method will support the distribution of School Operational Aid (BOS) fund. Clustering is needed to classify the amount of School Operational Aid (BOS) funds with other influential variables with the aim as the consideration in making policy on the distribution and amount of School Operational Aid funds. Compare of method Fuzzy Cluster Means and Fuzzy Geographically Weighted Clustering were used. The variables used in this study were the School Operational Aid (BOS) funds, total coaching costs, and total regency/city management costs in Central Java Province. The best result of the clustering process was Fuzzy Geographically Weighted Clustering use cluster 3.

Improvement Method of Fuzzy Geographically Weighted Clustering using Gravitational Search Algorithm

Geo-demographic analysis (GDA) is a useful method to analyze information based on location, utilizing several spatial analysis explicitly. One of the most efficient and commonly used method is Fuzzy Geographically Weighted Clustering (FGWC). However, it has a limitation in obtaining local optimal solution in the centroid initialization. A novel approach integrating Gravitational Search Algorithm (GSA) with FGWC is proposed to obtain global optimal solution leading to better cluster quality. Several cluster validity indexes are used to compare the proposed methods with the FGWC using other optimization approaches. The study shows that the hybrid method FGWC-GSA provides better cluster quality. Furthermore, the method has been implemented in R package spatialClust.

Improvement of Fuzzy Geographically Weighted Clustering-Ant Colony Optimization Performance using Context-Based Clustering and CUDA Parallel Programming

Geo-demographic analysis (GDA) is the study of population characteristics by geographical area. Fuzzy Geographically Weighted Clustering (FGWC) is an effective algorithm used in GDA. Improvement of FGWC has been done by integrating a metaheuristic algorithm, Ant Colony Optimization (ACO), as a global optimization tool to increase the clustering accuracy in the initial stage of the FGWC algorithm. However, using ACO in FGWC increases the time to run the algorithm compared to the standard FGWC algorithm. In this paper, context-based clustering and CUDA parallel programming are proposed to improve the performance of the improved algorithm (FGWC-ACO). Context-based clustering is a method that focuses on the grouping of data based on certain conditions, while CUDA parallel programming is a method that uses the graphical processing unit (GPU) as a parallel processing tool. The Indonesian Population Census 2010 was used as the experimental dataset. It was shown that the proposed methods were able to improve the performance of FGWC-ACO without reducing the clustering quality of the original method. The clustering quality was evaluated using the clustering validity index.

Fuzzy geographically weighted clustering using artificial bee colony: An efficient geo-demographic analysis algorithm and applications to the analysis of crime behavior in population

Geo-demographic analysis is an essential part of a geographical information system (GIS) for predicting people's behavior based on statistical models and their residential location. Fuzzy Geographically Weighted Clustering (FGWC) serves as one of the most efficient algorithms in geo-demographic analysis. Despite being an effective algorithm, FGWC is sensitive to initialize when the random selection of cluster centers makes the iterative process falling into the local optimal solution easily. Artificial Bee Colony (ABC), one of the most popular meta-heuristic algorithms, can be regarded as the tool to achieve global optimization solutions. This research aims to propose a novel geo-demographic analysis algorithm that integrates FGWC to the optimization scheme of ABC for improving geo-demographic clustering accuracy. Experimental results on various datasets show that the clustering quality of the proposed algorithm called FGWC-ABC is better than those of other relevant methods. The proposed algorithm is also applied to a decision-making application for analyzing crime behavior problem in the population using the US communities and crime dataset. It provides fuzzy rules to determine the violent crime rate in terms of linguistic labels from socioeconomic variables. These results are significant to make predictions of further US violent crime rate and to facilitate appropriate decisions on prevention such the situations in the future.

A novel kernel fuzzy clustering algorithm for Geo-Demographic Analysis

Geo-Demographic Analysis (GDA) is a major concentration of various interdisciplinary researches and has been used in many decision-making processes regarding the provision and distribution of products and services in society. Machine learning methods namely Principal Component Analysis, Self-Organizing Map, K-Means, fuzzy clustering and fuzzy geographically weighted clustering were proposed to enhance the quality of GDA. Among them, the state-of-the-art method – Modified Intuitionistic Possibilistic Fuzzy Geographically Weighted Clustering (MIPFGWC) has some drawbacks such as: (i) using the Euclidean similarity measure often results in high error rate and sensitivity to noises and outliers; (ii) updating the membership matrix by the Spatial Interaction – Modification Model (SIM2) model leads to new centers not being “geographically aware”. In this paper, we present a novel fuzzy clustering algorithm named as Kernel Fuzzy Geographically Clustering (KFGC) that utilizes both the kernel similarity function and the new update mechanism of the SIM2 model to remedy the disadvantages of MIPFGWC. Some supported properties and theorems of KFGC are also examined in the paper. Specifically, the differences between solutions of KFGC and those of MIPFGWC and of some variants of KFGC are theoretically validated. Lastly, experimental analysis is performed to compare the performance of KFGC with those of the relevant algorithms in terms of clustering quality.

A novel intuitionistic fuzzy clustering method for geo-demographic analysis

Geo-Demographic Analysis (GDA) is an important tool to explore the underlying rules that regulate our world, and therefore, it has been widely applied to the development of effective socio-economic policies through the analysis of data generated from Geographic Information Systems (GIS). In GDA applications, clustering plays a major role however, the current state-of-the-art algorithms, namely the Fuzzy Geographically Weighted Clustering (FGWC), have demonstrated several limitations both in terms of speed and in terms of quality of the achieved results. Accordingly, in this paper, we propose a novel clustering algorithm for GDA application, based on recent results regarding intuitionistic fuzzy sets and the possibilistic fuzzy C-means, that aims at overcoming some of the limitations of the existing methods.