Project

Spatial Analysis

Goal: We publish book on the theory and application of spatial and spatio-temporal statistics. That present method generated by new applications, or in which new approach is applied to a major practical case. At the same time, we also share the step of analysis using R Software. So, that it makes some of your tasks simpler and easier. For further information, please visit www.rezzyekocaraka.com/book

Date: 1 August 2017

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Rezzy Eko Caraka
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The exposure rate to air pollution in most urban cities is really a major concern because it results to a life-threatening consequence for human health and wellbeing. Furthermore, the accurate estimation and continuous forecasting of pollution levels is a very complicated task. In this paper, one of the space-temporal models, a vector autoregressive (VAR) with neural network (NN) and genetic algorithm (GA) was proposed and enhanced. The VAR could tackle the issue of multivariate time series, NN for nonlinearity, and GA for parameter estimation determination. Therefore, the model could be used to make predictions, such as the information of series and location data. The applied methods were on the pollution data, including NOX, PM2.5, PM10, and SO2 in Taipei, Hsinchu, Taichung, and Kaohsiung. The metaheuristics genetic algorithm was used to enhance the proposed methods during the experiments. In conclusion, the VAR-NN-GA gives a good accuracy when metric evaluation is used. Furthermore, the methods can be used to determine the phenomena of 10 years air pollution in Taiwan.
Rezzy Eko Caraka
added a research item
Implement and enhance the performance of spatial fuzzy clustering using Fuzzy Geo- graphically Weighted Clustering with various optimization algo- rithms, mainly from Xin She Yang (2014) with book entitled Nature- Inspired Optimization Algorithms. The optimization algorithm is useful to tackle the disadvan- tages of clustering inconsistency when using the traditional approach. The distance measure- ments option is also provided in order to increase the quality of clustering results. The Fuzzy Ge- ographically Weighted Clustering with nature inspired optimisation algorithm was firstly devel- oped by Arie Wahyu Wijayanto and Ayu Purwari- anti (2014) <doi:10.1109/CITSM.2014.7042178> using Artificial Bee Colony algorithm
Rezzy Eko Caraka
added a research item
Global warming arising from climate change can increase the spread of deadly diseases. Effort is needed to develop a set of policies for the government to stem or reduce health risks from global warming. The purpose of this paper is to examine more detail and comprehensively about the relationship among climate and event disease count in Taiwan using the partial least square latent regression model. The results obtained that of the 17 types of diseases in Taiwan, that has the most significant loading factor is Amoebiasis, Malaria and Chikungunya. At the same time, climate variables that have the biggest most significant factor are Number day with max temp more than 30, Number day Temp more than 25, and Rainfall PH. Cronbach's Alpha infectious disease 0.9696 and climate 0.2813. At the same time, the value of Dillon Goldstein's rho infectious disease 0.974 and climate 0.6404, respectively. Abstract Global warming arising from climate change can increase the spread of deadly diseases. Effort is needed to develop a set of policies for the government to stem or reduce health risks from global warming. The purpose of this paper is to examine more detail and comprehensively about the relationship among climate and event disease count in Taiwan using the partial least square latent regression model. The results obtained that of the 17 types of diseases in Taiwan, that has the most significant loading factor is Amoebiasis, Malaria and Chikungunya. At the same time, climate variables that have the biggest most significant factor are Number day with max temp more than 30, Number day Temp more than 25, and Rainfall PH. Cronbach's Alpha infectious disease 0.9696 and climate 0.2813. At the same time, the value of Dillon Goldstein's rho infectious disease 0.974 and climate 0.6404, respectively.
Rezzy Eko Caraka
added 2 research items
The decreasing area of mangroves is an ongoing problem since, between 1980 and 2005, one-third of the world’s mangroves were lost. Rehabilitation and restoration strategies are required to address this situation. However, mangroves do not always respond well to these strategies and have high mortality due to several growth limiting parameters. This study developed a land suitability map for new mangrove plantations in different Southeast Asian countries for both current and future climates at a 250-m resolution. Hydrodynamic, geomorphological, climatic, and socio-economic parameters and three representative concentration pathway (RCP) scenarios (RCP 2.6, 4.5, and 8.5) for 2050 and 2070 with two global climate model datasets (the Centre National de Recherches Météorologiques Climate model version 5 [CNRM-CM5.1] and the Model for Interdisciplinary Research on Climate [MIROC5]) were used to predict suitable areas for mangrove planting. An analytical hierarchy process (AHP) was used to determine the level of importance for each parameter. To test the accuracy of the results, the mangrove land suitability analysis were further compared using different weights in every parameter. The sensitivity test using the Wilcoxon test was also carried out to test which variables had changed with the first weight and the AHP weight. The land suitability products from this study were compared with those from previous studies. The differences in land suitability for each country in Southeast Asia in 2050 and 2070 to analyze the differences in each RCP scenario and their effects on the mangrove land suitability were also assessed. Currently, there is 398,000 ha of potentially suitable land for mangrove planting in Southeast Asia, and this study shows that it will increase between now and 2070. Indonesia account for 67.34% of the total land area in the “very suitable” and “suitable” class categories. The RCP 8.5 scenario in 2070, with both the MIROC5 and CNRM-CM5.1 models, resulted in the largest area of a “very suitable” class category for mangrove planting. This study provides information for the migration of mangrove forests to the land, alleviating many drawbacks, especially for ecosystems.
The diagnosis of a hazard can be classified into three key domains, particularly regarding the natural hazards, non-natural hazards and social hazards. The disasters which have actually happened in West Papua require considerable attention and consideration of the Indonesian Government, despite since they have handled as much as they can to provide solutions and make people feel secure and pleasant. The purpose of this study is to calculate the location-based social vulnerability in West Papua involves the components of Information, Technology, and Communication, Food Access, Natural Disaster, Social Protection Statement, Access to Financial Services, Description of the source of household income, Number of event floods, number of earthquake disasters, COVID-19 death cases, and Number of incidents of protest which are obtained from the National Socio-Economic Survey (SUSENAS) official statistics with the main focus of research on the millennial generation. After employ clustering of variables around latent variables with connectivity value of 3.9400794, Dunn 0.9373, and Silhouette 0.6333. Each factor provide a sign indicating a positive or negative effect on social vulnerability and finally a location cluster will be formed based on the index obtained.
Rezzy Eko Caraka
added a research item
Many indicators of financial vulnerability in households are possible. To identify the signs of household indebtedness, we analyze the information about household debt as well as demography information , loan amount request, monthly expenses, monthly housing cost, monthly income, and total current debt which are compiled from household survey data, credit data, and fintech data. The results of data processing by using logistic regression, feature selection with Boruta, Random Forest, and text mining, we obtain spatial cluster based on province. We also find compelling reasons why the debtor applies to credit market based on dominated variables. ' Renovasi rumah, modal usaha, biaya pendidikan' dominate based on the number of loans, on the other hand, ′ Ongkos kerja/ Transportation fare' is dominate based on Male and Female, while ' Anak Masuk Sekolah / registration new students' dominate based on categorized married/single status.
Rezzy Eko Caraka
added a research item
Geographically, Indonesia is a meeting point of three continental plates. Scilicet, the Eurasian Plate, the Indo-Australian Plate, and the Pacific Plate. Therefore, Indonesia is part of the infamous volcanic zone called the ”Ring of Fire” and one of the areas prone to natural disasters such as volcanic eruptions, earthquakes, tsunamis, floods, and landslides. This study aims to capture the spatial pattern and identify the causes of social vulnerability in the districts/cities in Indonesia using the biclustering method. The data is extracted from the Indonesian National Socio-Economic Survey (SUSENAS) by BPS-Statistics in 2014. The biclustering result indicates that each district/city has its own social vulnerability characteristics and shows that the vulnerable aspects of each district/city are different. The adjacent observations tend to have social vulenrability characteristics. The results of this study can be used as a reference for national disaster mitigation policy in Indonesia.
Rezzy Eko Caraka
added a research item
Geographically, Indonesia is a meeting point of three continental plates. Scilicet, the Eurasian Plate, the Indo-Australian Plate, and the Pacific Plate. Therefore, Indonesia is part of the infamous volcanic zone called the "Ring of Fire" and one of the areas prone to natural disasters such as volcanic eruptions, earthquakes, tsunamis, floods, and landslides. This study aims to capture the spatial pattern and identify the causes of social vulnerability in the districts/cities in Indonesia using the biclustering method. The data is extracted from the Indonesian National SocioEconomic Survey (SUSENAS) by BPS-Statistics in 2014. The biclustering result indicates that each district/city has its own social vulnerability characteristics and shows that the vulnerable aspects of each district/city are different. The adjacent observations tend to have social vulenrability characteristics. The results of this study can be used as a reference for national disaster mitigation policy in Indonesia.
Rezzy Eko Caraka
added 2 research items
Monthly rainfall analysis and forecasts are made to be able to provide a clear picture of the rain and climate conditions that have occurred and will occur in the territory of Indonesia. The information can be known in an area that has the most substantial maximum rainfall and in an area which has the lowest rainfall described and presented spatial pattern by using the ArcView-GIS. The purpose of making monthly rainfall forecasts is to be used and utilized by users, especially in the agriculture and irrigation sectors to plan and make decisions within the next 1 to 3 months related to climatic conditions, especially rainfall. In this paper, we use annual rainfall data in six location East Java. We analysis ENSO phenomena as well as rainfall forecasting in January – March 2018 by using generalized space-time autoregressive and get an accuracy MAPE out sample amount 2.95% dan RMSE out sample amount 4.77.
Rezzy Eko Caraka
added a research item
Indonesian people are mostly like spicy. Therefore, Chili becomes an ingredient in cooking that cannot be separated from Indonesian people. In certain months there is too much demand from the public and inversely proportional to the stock of chili. So, appreciate the chili prices to rise. In the heart of statistics, several methods can be used as forecasting as well as data series from univariate, bivariate and multivariate cases. The reason for choosing the method is following the analysis needs and available data. One of the popular methods in the multivariate case is Generalized Spacetime Autoregressive (GSTAR) the advantage of this method is that it can capture the characteristics of location-based data as well as time. For this reason, in this paper, we used chili prices data in 4 major cities in Java Province, Indonesia. Such as Jakarta, Bandung, Semarang, and Yogyakarta. As a result, GSTAR (21) I (1with inverse weighted be the best models because it fulfilled white noise and normal multivariate assumption with the lowest RMSE and MAPE
Hasbi Yasin
added a research item
Air pollution is one of the most concerned problems on earth today. It is closely related with and mostly generated from the transportation and industrialization sectors, as well as from the environmentally degrading effect of the urban physical development. Air pollution promotes the lower level of air quality, which in turn promotes the greater risk on health, especially that of the human being. This research aims to aid the government in the policy making process related to air pollution mitigation by developing a standard index model for air polluter (Air Polluter Standard Index—APSI) based on the Geographically Weighted Multivariate Regression (GWMR) approach. This GWMR-based APSI model is aimed as the indicator of the prevailing air pollution. Two of the five polluting elements in the APSI are Nitrogen Dioxide (NO2) and Sulfur Dioxide (SO2). The GWMR approach used in modeling the APSI in this research is a spatial multivariate regression model, which is expected to be able to show the effect of the air polluters to the level of air pollution with regard to the geographical aspects of the prevailing event. In this case, the result concluded that there are no significance differences between Multivariate Regression and GWMR in this case. But GWMR is the best model to modeling NO2 and SO2 because it has smallest AIC and MSE.
Rezzy Eko Caraka
added a research item
Mixed Regressive-Spatial Autoregressive Models (MR-SAM) is one spatial model with an area approach that takes into account the spatial influence of lag on the dependent variable. The advantage of this model is we can know the location has spatial effect or not. In this paper uses MR-SAM to determine and analyze the factors that affect the category of the poor in Central Java. MR-SAM is one of parametric regression, before using the model we must fulfill assumptions. In a nutshell, at significant α=5% number of poverty in central java can be explained (statistically significant) by GDP, number of people didn’t finish primary school, and number of people who didn’t finished high school.
Rezzy Eko Caraka
added an update
Syntax spatial data panel using GUI MATLAB or R can be downloaded as follows. Password can be found in the introduction section of spatial data panel books
 
Rezzy Eko Caraka
added a research item
Buku ini membantu pembaca dalam mendapatkan pehamanan tentang statistika spatial dari bantuan software R dan juga GUI MATLAB. Buku ini merupakan pengembangan dari buku pertama penulis tentang Geographically Weighted Regreession (GWR) Sebuah Pendekatan Regresi Geogragis yang terdiri dari 4 pembahasan pokok. Yaitu : Pengantar Spasial Data Panel, Model Spasial Data Panel dengan R, Dasar Graphical User Interface (GUI), Spatial Data Panel dngan GUI. Buku ini dapat digunakan sebagai bahan pustaka akademisi dan prakitis yang tertarik dengan statistika spasial
Rezzy Eko Caraka
added an update
Buku ini membantu pembaca dalam mendapatkan pehamanan tentang statistika spatial dari bantuan software R dan juga GUI MATLAB. Buku ini merupakan pengembangan dari buku pertama penulis  tentang Geographically Weighted Regreession (GWR) Sebuah Pendekatan Regresi Geogragis yang terdiri dari 4 pembahasan pokok. Yaitu : Pengantar Spasial Data Panel, Model Spasial Data Panel dengan R, Dasar Graphical User Interface (GUI), Spatial Data Panel dngan GUI. Buku ini dapat digunakan sebagai bahan pustaka akademisi dan prakitis yang tertarik dengan statistika spasial
 
Rezzy Eko Caraka
added 4 research items
We publish a book on the theory and application of spatial and spatio-temporal statistics. That present method generated by new applications, or in which new approach is applied to a major practical case. At the same time, we also share the step of analysis using R Software. So, that it makes some of your tasks simpler and easier. For further information, please visit www.rezzyekocaraka.com/book
Air pollution is one of the most concerned problems on earth today. It is closely related with and mostly generated from the transportation and industrialization sectors, as well as from the environmentally degrading effect of the urban physical development. Air pollution promotes the lower level of air quality, which in turn promotes the greater risk on health, especially that of the human being. This research aims to aid the government in the policy making process related to air pollution mitigation by developing a standard index model for air polluter (Air Polluter Standard Index - APSI) based on the Mixed Geographically Weighted Regression (MGWR) approach using the adaptive bandwidth. The adaptive bandwidth kernel has different bandwidth value in each observation location. Akaike Information Criterion-corrected (AICc) value is used to choose the most optimum bandwidth. The Monte Carlo Simulation is used to tests for regression coefficient non-stationarity. In this research, we also consider seven variables that are directly related to the air pollution level, which are the traffic velocity, the population density, the business center aspect, the air humidity, the wind velocity, the air temperature, and the area size of the urban forest. Based on AICc and MSE value it is know that the MGWR model with adaptive bisquare kernel is the best bandwidth to analyze this model.
Di era yang semakin berkembang, banyak bidang ilmu seperti ekonomi, sosial, lingkungan, kesehatan, meteorologi, klimatologi, geologi dan sebagainya yang menggunakan data yang berkaitan dengan lokasi atau letak gepgrafis suatu tempat. Data yang memuat informasi mengenai lokasi atau letak geografis suatu daerah dan diperoleh dari hasil pengukuran sering disebut data spasial. Buku ini membahas lengkap mengenai metode statistika spasial dan penerapan dalam permasalahan. Bab pertama membahas definisi statistika spasial, Bab dua sampai dengan empat membahas Geographically Weighted Regression (GWR), Geograpichally Weighted Logistic Regression (GWLR), Geograpichally Weighted Logistic Regression Semiparametric (GWLRS), Geographically Weighted Poisson Regression (GWPR) dan bab lima membahas tentang applikasi OpenGeoDa ArcView GIS. Pada buku ini diberikan sejumlah panduan dalam menganalisis dan intepretasi dari metode tersebut khususnya pengoperasian dengan menggunakan software R, ArcView dan OpenGeoDa. R merupakan Bahasa pemrograman untuk komputasi statistik dan grafis.
Rezzy Eko Caraka
added an update
Sulphur dioxide gas (SO2) is derived from the combustion of fuels containing sulphur. Aside from fuel, sulphur is also contained in the lubricant. Sulphur dioxide gas is difficult to detect because it is colourless gas. Sulphur dioxide can cause respiratory disorders, indigestion, headache, chest pain, and nerve. A necessary preventive measures to reduce the impact of air pollutants SO2 particular elements, one of them by making the modeling that can bring the causes and factors resistor element of air pollutants SO2. The modeling is Geographically Weighted Regression (GWR), Temporal Geographically Weighted Regression (GTWR) and Mixed Geographically Weighted Temporal Regression (MGTWR). All three models are regression models spatial, temporal and spatial temporal spatial- combined, which models the effects of air pollutants SO2 element with a direct view of geography and time of occurrence of air pollution. The third model is then compared to obtain the best model in the modeling of air pollutants SO2 elements. Hasbi Yasin
 
Rezzy Eko Caraka
added an update
Regression analysis is a statistical analysis that aims to model the relationship between response variables with predictor variables. Geographically Weighted Regression (GWR) is statistical methods used for analyzed the spatial data in local form of regression. Where certain predictor variables influencing the response are global while others are local used the Mixed Geographically Weighted Regression (MGWR) model to solve the problem. The results showed that Weighted Least Square (WLS) can be used to estimate the parameter model and Cross Validation (CV) for the selection of the optimum bandwidth. Goodness of fits tests for a global regression model and MGWR approximated by F distribution as well as on the test of global parameters and local parameters simultaneously and for testing the partial model parameters using the t distribution. The applications of MGWR model in the percentage of poor households in Mojokerto showed that MGWR model differs significantly from the global regression model. Based on Akaike Information Criterion (AIC) values between the global regression model, GWR and MGWR model, it is known that the MGWR model with a weighting Gaussian kernel function is the best model used to analyze the percentage of poor households in Mojokerto (2008) because it has the smallest AIC value. Hasbi Yasin
 
Rezzy Eko Caraka
added an update
Hasbi Yasin Regression analysis is a statistical analysis that aims to model the relationship between response variable with some predictor variables. Geographically Weighted Regression (GWR) is statistical method used for analyzed the spatial data in local form of regression. One of the problems in GWR is how to choose the significant variables. The number of predictor variables will allow the violation of assumptions about the absence of multicollinearity in the data. Therefore, this needs a method to reduce some of the predictor variables which not significant to the response variable. This paper will discuss how to select significant variables by stepwise method. This method is a combination of forward selection method and the backward elimination method.
 
Rezzy Eko Caraka
added 5 project references
Rezzy Eko Caraka
added a project goal
We publish book on the theory and application of spatial and spatio-temporal statistics. That present method generated by new applications, or in which new approach is applied to a major practical case. At the same time, we also share the step of analysis using R Software. So, that it makes some of your tasks simpler and easier. For further information, please visit www.rezzyekocaraka.com/book