https://journal.stats.id/index.php/ijsa/issue/feed Indonesian Journal of Statistics and Its Applications 2020-05-30T10:03:33+00:00 Agus M Soleh agusms@apps.ipb.ac.id Open Journal Systems <p><strong>Indonesian Journal of Statistics and Its Applications (<a href="http://u.lipi.go.id/1510202061">eISSN:2599-0802</a>)&nbsp;(formerly named <a href="https://journal.ipb.ac.id/index.php/statistika" target="_blank" rel="noopener">Forum Statistika dan Komputasi</a>),&nbsp;</strong><strong>established since 2017</strong><strong>, </strong>publishes scientific papers in the area of statistical science and the applications. The published papers should be research papers with, but not limited to, following topics: experimental design and analysis, survey methods and analysis, operation research, data mining, statistical modeling, computational statistics, time series and econometrics, and statistics education.&nbsp;&nbsp; All papers were reviewed by peer reviewers consisting of experts and academician across universities and agencies.&nbsp;This journal is <strong>nationally accredited&nbsp;(SINTA 3)</strong> by Directorate General of Research and Development Strengthening (DGRDS),&nbsp;Ministry of Research, Technology and Higher Education of the Republic of Indonesia No.: <a href="http://arjuna.ristekdikti.go.id/files/berita/Surat_Pemberitahuan_Hasil_Akreditasi_Jurnal_Ilmiah_Elektronik_Periode_III_Tahun_2019_dan_Lampiran.pdf" target="_blank" rel="noopener">14/E/KPT/2019, dated 10 May 2019</a>.&nbsp;</p> <p><strong>Scope:</strong><br>Indonesian Journal of Statistics and Its Applications is a refereed journal committed to the Statistics and its applications.</p> https://journal.stats.id/index.php/ijsa/article/view/180 ANALISIS PENGARUH PENGETAHUAN TENTANG SEKS TERHADAP PERILAKU SEKSUAL REMAJA DI INDONESIA MENGGUNAKAN REGRESI LOGISTIK MULTINOMIAL 2020-05-30T10:03:33+00:00 Muhammad Ricky Pranata ricky.pranata@bps.go.id Ray Sastri raysastri@stis.ac.id <p>Sexual impulse will begin to appear in a person when entering adolescent age. The adolescent does different things to fulfill their sexual impulse such as holding hands, hugging, kissing, touching and even having sex. Because this is a new experience to them, they need a lot of information about sexuality such as the reproductive system, sexually transmitted diseases, and others. They can get it in school, the internet, or discuss it with others. The way they deal with their sexual impulse is largely determined by their individual characteristics, knowledge, and discussion partners. This study aims to determine the effect of individual characteristics, knowledge, and information sources on adolescent sexual behavior. This study uses data from the Indonesian Demographic and Health Survey (SDKI) in 2012 with a unit of analysis adolescence age of 15─19 years and is never married. The method of analysis uses multinomial logistic regression with adolescent sexual behavior as response variables divided into three categories; quiet (ignore it), minor sexual activity, and serious (touching the sensitive area and or having sex). The conclusion is the individual's background, sexual knowledge, and sources of information influence sexual behavior both in boy and girl. Serious sexual behavior tends to occur in adolescents who do not attend school, a man who understands about contraception, girls who misunderstand about pregnancy, and those who discuss sexuality with friends.</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/328 PERBANDINGAN BEBERAPA METODE KLASIFIKASI DALAM MEMPREDIKSI INTERAKSI FARMAKODINAMIK 2020-05-30T10:03:20+00:00 Hasnita Hasnita hasnita.handayani@gmail.com Farit Mochamad Afendi fmafendi@gmail.com Anwar Fitrianto anwarstat@gmail.com <p>One mechanism for Drug-Drug Interaction (DDI) is pharmacodynamic (PD) interactions. They are interactions by which the effects of a drug are changed by other drugs at the site of receptor. The interactions can be predicted based on Side Effects Similarity (SES), Chemical Similarity (CS) and Target Protein Connectedness (TPC). This study aims to find the best classification technique by first applying the scaling process, variable interaction, discretization and resampling technique. We used Random Forest, Support Vector Machines (SVM) and Binary Logistic Regression for the classification. Out the three classification methods, we found the SVM classification method produces the highest Area Under Cover (AUC) value compared to the other, which is 67.91%.</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/250 ON GENERALISATION OF GOMPERTZ-MAKEHAM DISTRIBUTION 2020-05-30T10:03:27+00:00 Akinlolu Olosunde akinolosunde@gmail.com Tosin Adekoya adekoyatosin01@gmail.com <p>In this paper an exponentiated generalised Gompertz-Makeham distribution. An exponentiated generalised family was introduced by Codeiro, et. al., which allows greater flexibility in the analysis of data. Some Mathematical and Statistical properties including cumulative distribution function, hazard function and survival function of the distribution are derived. The estimation of model parameters are derived via the maximum likelihood estimate method.</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/510 PENERAPAN ANALISIS LASSO DAN GROUP LASSO DALAM MENGIDENTIFIKASI FAKTOR-FAKTOR YANG BERHUBUNGAN DENGAN TUBERKULOSIS DI JAWA BARAT 2020-05-30T10:02:54+00:00 Stephan Chen stephanchen97@gmail.com Khairil Anwar Notodiputro khairil@apps.ipb.ac.id Septian Rahardiantoro rahardiantoro_14@apps.ipb.ac.id <p>Tuberculosis is the deadliest infectious disease in Indonesia, and West Java is a province with the largest number of tuberculosis cases in Indonesia. This research was conducted to identify variables and groups of variables that could explain the number of tuberculosis cases in West Java. The data used has many explanatory variables, and these variables form groups. LASSO and group LASSO analysis can be used for variables selection and handle data that has many explanatory variables, and group LASSO analysis can be used on data with grouped variables. The results of the LASSO analysis, variables that can explain the number of tuberculosis cases in West Java are the number of people with disabilities, the number of pharmacy staff, the number of malnourished people, the number of people working and the number of cities. According to the group LASSO analysis, the variables that can explain the number of tuberculosis cases in West Java are variables in the health and environmental groups. The government can focus on these factors if they want to reduce the number of tuberculosis cases in West Java.</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/552 COVARIANCE BASED-SEM ON RELATIONSHIP BETWEEN DIGITAL LITERACY, USE OF E-RESOURCES, AND READING CULTURE OF STUDENTS 2020-05-30T10:02:39+00:00 Reny Rian Marliana renyrianmarliana@gmail.com Leni Nurhayati leninurhayati82@gmail.com <p>In this paper, a relationship model among latent variables using Covariance Based-Structural Equation Modeling (CB-SEM) is studied. The latent variables are digital literacy, use of e-resources and reading culture of students. The goal of the study is to build a simultaneously model between those three variables, determine the influence of digital literacy on the use of e-resources and reading culture of students, and the influence of the use of e-resources on reading culture of students. The parameters of the model are estimated by the Maximum Likelihood method. This study took data from 256 questionnaires of students at STMIK Sumedang. Results showed that digital literacy significantly influences the use of e-resources and the reading culture of students. In contrast, there are no significant influences on the use of e-resources on the reading culture of the student.</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/573 ROBUST SPATIAL REGRESSION MODEL ON ORIGINAL LOCAL GOVERNMENT REVENUE IN JAVA 2017 2020-05-30T10:02:25+00:00 Winda Chairani Mastuti windachairani1@gmail.com Anik Djuraidah anikdjuraidah@apps.ipb.ac.id Erfiani Erfiani erfiani@apps.ipb.ac.id <p>Spatial regression measures the relationship between response and explanatory variables in the regression model considering spatial effects. Detecting and accommodating outliers is an important step in the regression analysis. Several methods can detect outliers in spatial regression. One of these methods is generating a score test statistics to identify outliers in the spatial autoregressive (SAR) model. This research applies a robust spatial autoregressive (RSAR) model with S- estimator to the Original Local Government Revenue (OLGR) data. The RSAR model with the 4-nearest neighbor weighting matrix is the best model produced in this study.&nbsp; The coefficient of the RSAR model gives a more relevant result. Median absolute deviation (MdAD) and median absolute percentage error (MdAPE) values ​​in the RSAR model with 4-nearest neighbor give smaller results than the SAR model.</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/330 KLASIFIKASI FAKTOR-FAKTOR PENYEBAB PENYAKIT DIABETES MELITUS DI RUMAH SAKIT UNHAS MENGGUNAKAN ALGORITMA C4.5 2020-05-30T10:03:12+00:00 Dewi Rahma Ente dewi01136@gmail.com Sri Astuti Thamrin tuti@unhas.ac.id Samsul Arifin Zula4717@gmail.com Hedi Kuswanto hedikuswanto454@gmail.com Andreza Andreza andrezafauzialghifary@gmail.com <p>Diabetes mellitus (DM) is one of the chronic and deadly diseases that are widely observed in various countries today. This disease continues and is increasing to a very alarming stage. This study aims to identify and see the relationship between factors that influence DM disease. The method used in this research is C4.5 algorithm which is one of the algorithms used to make predictive classifications. Classification is one of the processes in data mining that aims to find patterns in relatively large data that use the representations in the form of decision trees. This method is applied to data from medical records of patients with DM in 2014-2018 taken from the Hasanuddin University Teaching Hospital. The results obtained indicate that there are four factors that influence the prediction of a patient's DM status namely; Fasting Blood Glucose (GDP), LDL Cholesterol, Triglycerides, and Body Weight.</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/331 ANALISIS REGRESI DATA PANEL PADA INDEKS PEMBANGUNAN GENDER (IPG) JAWA TENGAH TAHUN 2011-2015 2020-05-30T10:03:06+00:00 Intan Lukiswati lukisintan@gmail.com Anik Djuraidah anikdjuraidah@gmail.com Utami Dyah Syafitri utamids@apps.ipb.ac.id <p>The Gender Development Index (GDI) is a measure of the level of achievement of gender-based human development in Indonesia. Central Java Province is the largest province in Java with a GDI rate which tends to increase during the period of 2011 to 2015. Central Java's GDI, when compared to other provinces on Java Island, ranks third after DKI Jakarta and DI Yogyakarta. Central Java’s GDI consists of several observations for a certain period of time so that panel data regression analysis can be used. The purpose of this study was to model the GDI of women in Central Java with panel data regression and find out which explanatory variables significantly affected women's GDI in Central Java from 2011 to 2015. The results of this study indicate that explanatory variables that significantly influence women's GDI in Central Java are life expectancy, primary school enrollment rates, high school enrollment rates, and per capita expenditure.</p> <p>&nbsp;</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/502 KAJIAN REGRESI KEKAR MENGGUNAKAN METODE PENDUGA-MM DAN KUADRAT MEDIAN TERKECIL 2020-05-30T10:03:00+00:00 Khusnul Khotimah khusnulrfa@gmail.com Kusman Sadik kusmansadik@gmail.com Akbar Rizki akbar.ritzki@gmail.com <p>Regression is a statistical method that is used to obtain a pattern of relations between two or more variables presented in the regression line equation. This line equation is derived from estimation using ordinary least squares (OLS). However, OLS has limitations that are highly dependent on outliers data. One solution to the outliers problem in regression analysis is to use the robust regression method. This study used the least median squares (LMS) and multi-stage method (MM) robust regression for analysis of data containing outliers. Data analysis was carried out on generation data simulation and actual data. The simulation results of regression analysis in various scenarios are concluded that the LMS and MM methods have better performance compared to the OLS on data containing outliers. MM method has the lowest average parameter estimation bias, followed by the LMS, then OLS. The LMS has the smallest average root mean squares error (RMSE) and the highest average R2 is followed by the MM then the OLS. The results of the regression analysis comparison of the three methods on Indonesian rice production data in 2017 which contains 10% outliers were concluded that the LMS is the best method. The LMS produces the smallest RMSE of 4.44 and the highest R2 that is 98%. MM's method is in the second-best position with RMSE of 6.78 and R2 of 96%. OLS method produces the largest RMSE and lowest R2 that is 23.15 and 58% respectively.</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/524 ANALISIS KURVA ROC PADA MODEL LOGIT DALAM PEMODELAN DETERMINAN LANSIA BEKERJA DI KAWASAN TIMUR INDONESIA 2020-05-30T10:02:49+00:00 Muhammad Rizqi Fachrian Nur 15.8765@stis.ac.id Siskarossa Ika Oktora siskarossa@stis.ac.id <p>Binary logistic regression is used for probability modeling or to predict binary response variables (Success / Failure) from one or more explanatory variables that are continuous or categorical. In carrying out this analysis, there are several ways to test the suitability of the resulting model, and one of them is the area under the ROC curve. The application of the analysis method in this study is the determinant of the elderly population to work. The population of the elderly in Indonesia is increasing every year. Many views that the elderly depend on other residents, especially in terms of the economy. However, if seen from the percentage of elderly working in Indonesia, it is increasing, including the elderly in KTI. The purpose of this study is to determine the characteristics of the elderly in KTI, know the factors that influence the decision of the elderly population to work in KTI and find out the tendency of variables that affect the decision of the elderly to work in KTI. The data used are raw data from Badan Pusat Statistik (BPS) was Survei Sosial Ekonomi Nasional (Susenas) Kor March 2018. This study using descriptive analysis methods and binary logistic regression. The results are that the variables that significantly influence the decisions of the elderly to work are residence, gender, age, education, family status, marital status, health complaints, and health insurance. Elderly who has characteristics residing in rural, male sex, classified as young elderly (60-69 years old), has the highest level of elementary school education, has the status of KRT in his family, is married, has no complaints health, and not having health insurance will have a greater tendency to decide to work.</p> <p>&nbsp;</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/543 KAJIAN VALIDITAS INSTRUMEN PENGUKURAN SKALA PENGALAMAN KERAWANAN PANGAN DI INDONESIA 2020-05-30T10:02:44+00:00 Herlina Herlina herlina_1479@apps.ipb.ac.id Bagus Sartono bagusco@gmail.com Budi Susetyo buset008@yahoo.com <p>The results of the FAO study since 2013 through the Voices of Hungry Project (VoH-FAO) have produced measures of the Food Insecurity Experience Scale (FIES). FIES is a global reference scale that becomes a reference for comparing the prevalence of food insecurity between countries and regions. The challenge of using the FIES instrument, each country must carry out linguistic adaptations that are appropriate to the culture and national language. This study aims to analyze the validity of FIES measurements in Indonesia, including internal and external analysis. The Rasch model (RM) used for internal validity analysis. Measurement of the validity and reliability of Indonesian FIES items was calibrated with a global reference scale. Differences in the scale of calibration items with a global reference scale of less than 0.35 indicate that they are standard items. FIES measurements require at least five common items. External analysis of FIES measurements uses the Pearson correlation between district-level aggregation on each FIES item that is answered "yes" and determinant characteristics of household food insecurity. The expected correlation coefficient indicated the direction of a positive correlation and observed the correlation coefficient of item 1501 to 1508, which is getting smaller. Internal analysis of FIES measurements in Indonesia shows the achievement of unidimensional and local independence assumptions. However, item 1501 has identified as an outlier. Then identify unique issues are 1501 and 1504, while unique items in rural subsamples are 1503 and 1508. Unique item differences founded in food expenditure 60 percent or more, i.e., 1502. This shows a discordance with items assumption of parameter invariance. The reliability of the FIES item is 0.78, and this reflects the suitability of the model quite well. External analysis of the FIES measurement identifies item 1501 and 1504 as invalid items (unique items).</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/557 PEMODELAN GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) PADA PERSENTASE KRIMINALITAS DI PROVINSI JAWA TIMUR TAHUN 2017 2020-05-30T10:02:34+00:00 Dessy Wulandari Syahputri Yusuf Dessywulandarisy@gmail.com Elvira Mustikawati Putri Hermanto elvira@unipasby.ac.id Wara Pramesti warapra@unipasby.ac.id <p>Crime is everything that exists in Indonesia. Based on BPS data in 2018, East Java Province ranks first in the Province of North Sumatra and the Special Capital Region of Jakarta. This research was conducted to determine the factors that support crime in each Regency / City of East Java Province. The method used in this research is Weighted Geographic Regression (GWR). Geographically Weighted Regression (GWR) is one of the statistical methods used to model variable responses with regional or area-based predictor variables. Based on the GWR results, it is recognized as a variable Population Density Percentage (X1), Open Unemployment Rate (X2), Poor Population (X3), Population who are Victims of Drug Abuse (X4), Human Development Index (X5), and Married Human Population (X6) ) importance in the city of Surabaya. The coefficient of determination (R2) and AIC from GWR is better than the OLS model. This refers to the optimal R2 and AIC values ​​of 91.40% and 129.293.</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/563 PENGGUNAAN ANALISIS KLASTER K-MEANS DALAM PEMODELAN REGRESI SPASIAL PADA KASUS TUBERKULOSIS DI JAWA TIMUR TAHUN 2017 2020-05-30T10:02:30+00:00 Hardani Prisma Rizky hardaniprisky@gmail.com Wara Pramesti warapra@unipasby.ac.id Gangga Anuraga g.anuraga@unipasby.ac.id <p>Tuberculosis (TB) is a contagious infectious disease caused by the bacterium Mycobacterium tuberculosis which can attack various organs, especially the lungs. TB if left untreated or incomplete treatment can cause dangerous complications to death. East Java Province has the second-highest TB case after West Java Province. Therefore we need statistical modeling to analyze the factors that influence TB in East Java Province. The data used in this study were sourced from data from BPS and East Java Provincial Health Offices in 38 districts/cities in East Java Province in 2017. Analysis of data using the OLS regression approach only looked at variable factors but was unable to know the effects of territory. So to overcome this, a spatial regression approach is used by comparing the weight of Queen Contiguity and the results of the k-means cluster analysis to obtain the best model. Based on the results of the analysis, the spatial aspects of the data have met the assumptions of spatial dependencies using the Moran's I test with a p-value of 0.000001295. The weighting matrix used is the k-means cluster weighting matrix k = 2. The test results obtained by the Spatial Autoregressive Moving Average (SARMA) model selected as the best model with the value of the deterrence coefficient (R2) and Akaike Info Criterion (AIC), 87.10% and 586.69. The factors that significantly influence the number of Tuberculosis patients in each district/city in East Java are population density (X2) and the number of healthy houses (X9).</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/582 PENGEMBANGAN ANALISIS GEROMBOL BERHIRARKI DENGAN KETERGANTUNGAN SPASIAL PADA INDIKATOR MAKRO SOSIAL EKONOMI DI KABUPATEN/KOTA PROVINSI SULAWESI TENGAH 2020-05-30T10:02:21+00:00 Iman Setiawan i.setiawan@untad.ac.id Nur’eni Nur’eni eniocy@yahoo.com Sritasarwati Putran csrtp19@gmail.com <p>This paper develops how the hierarchical clustering analysis uses multivariate variables with spatial dependence on macro social-economic indicator data in Regency/City Central Sulawesi Province. Macro social-economic indicator data used in this paper are the number of criminal cases, per capita expenditure, population density, and Human Development Index of Regency/City of Central Sulawesi Province in 2018. To answer this question, Macro social-economic indicator data was reduced to a new variable using principal component analysis. The new variable was used to identify spatial dependency using the Moran index test. Spatial weight, that meets the Moran index test on the alternative hypothesis (there is a spatial dependency between locations), was used as the spatial dependency distance. Cluster analysis using two distance including variable and spatial dependency distance. The results showed that neighboring Regency/City are in the same cluster (spatial dependency occasion). So that there are five clusters Regency/City in Central Sulawesi Province.</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/599 COMPARISON OF K-MEANS CLUSTERING METHOD AND K-MEDOIDS ON TWITTER DATA 2020-05-30T10:02:17+00:00 Cahyani Oktarina cahyanioktarina10@gmail.com Khairil Anwar Notodiputro khairilnotodiputro@gmail.com Indahwati Indahwati indah.stk@gmail.com <p>The presidential election is one of the political events that occur in Indonesia once in five years. Public satisfaction and dissatisfaction with political issues have led to an increase in the number of political opinion tweets. The purpose of this study is to examine the performance of the k-means and k-medoids method in the Twitter data and to tweet about the presidential election in 2019. The data used in this study are primary data taken from Muhyi's research, then mining the text against data obtained. Because this data has been processed by Muhyi to analyze the electability of the 2019 presidential candidate pairs, for this journal needs a preprocessing was carried out to analyze the tendency of tweets to side with the candidate pairs of one or two. The difference in the pre-processing of this research with previous research is that there is a cleaning of duplicate data and normalizing. The results of this study indicate that the optimal number of clusters resulting from the k-means method and the k-medoid method are different.</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/610 KAJIAN SIMULASI PERBANDINGAN METODE REGRESI KUADRAT TERKECIL PARSIAL, SUPPORT VECTOR MACHINE, DAN RANDOM FOREST 2020-05-30T10:02:13+00:00 Asep Andri Fauzi asepandrif@gmail.com Agus M. Soleh agusms@apps.ipb.ac.id Anik Djuraidah anikdjuraidah@gmail.com <p>Highly correlated predictors and nonlinear relationships between response and predictors potentially affected the performance of predictive modeling, especially when using the ordinary least square (OLS) method. The simple technique to solve this problem is by using another method such as Partial Least Square Regression (PLSR), Support Vector Regression with kernel Radial Basis Function (SVR-RBF), and Random Forest Regression (RFR). The purpose of this study is to compare OLS, PLSR, SVR-RBF, and RFR using simulation data. The methods were evaluated by the root mean square error prediction (RMSEP). The result showed that in the linear model, SVR-RBF and RFR have large RMSEP; OLS and PLSR are better than SVR-RBF and RFR, and PLSR provides much more stable prediction than OLS in case of highly correlated predictors and small sample size. In nonlinear data, RFR produced the smallest RMSEP when data contains high correlated predictors.</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement## https://journal.stats.id/index.php/ijsa/article/view/628 GROWTH EXTERNALITIES ON THE ENVIRONMENTAL QUALITY INDEX OF EAST JAVA INDONESIA, SPATIAL ECONOMETRICS MODEL OF STIRPAT 2020-05-30T10:02:09+00:00 Rahma Fitriani rahmafitriani@ub.ac.id Herman Cahyo Diartho hermancahyo.feb@unej.ac.id Septya Hadiningrum septya03@gmail.com <p>East Java has shown strong economic growth, which negatively affects its environmental quality. Analysis of the functional relationship between economic growth and environmental quality is important to direct the growth without further deteriorate the environmental quality in this area. It is assumed that growth produces some externalities on environmental quality. The spread of technological information, economic productivity, population growth or investment, can be the source of the growth externalities. The objective of this study is to test the significance of the involved growth externalities on East Java’s environmental quality. Using spatial data, the externalities are accommodated in a spatial version of the STIRPAT model. It is estimated using per city/regency 2015 data. The analysis indicates that local density, local agricultural productivity, neighboring density, and neighboring mining activity significantly affect the local environmental quality. The latter two are the main sources of the growth externalities.</p> 2020-02-28T00:00:00+00:00 ##submission.copyrightStatement##