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Статті в журналах з теми "Geographically Weighted Panel Regression"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Xiao, Daiquan, Xuecai Xu, and Li Duan. "Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model." Journal of Advanced Transportation 2019 (August 20, 2019): 1–15. http://dx.doi.org/10.1155/2019/8521649.

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This study is intended to investigate the influencing factors of injury severity by considering the heterogeneity issue of unobserved factors at different arterials and the spatial attributes in geographically weighted regression models. To achieve the objectives, geographically weighted panel logistic regression model was developed, in which the geographically weighted logistic regression model addressed the injury severity from the spatial perspective, while the panel data model accommodated the heterogeneity attributed to unobserved factors from the temporal perspective. The geo-crash data

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2

Rusgiyono, Agus, and Alan Prahutama. "GEOGRAPHICALLY WEIGHTED PANEL REGRESSION WITH FIXED EFFECT FOR MODELING THE NUMBER OF INFANT MORTALITY IN CENTRAL JAVA, INDONESIA." MEDIA STATISTIKA 14, no. 1 (2021): 10–20. http://dx.doi.org/10.14710/medstat.14.1.10-20.

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One of the regression methods used to model by region is Geographically Weighted Regression (GWR). The GWR model developed to model panel data is Geographically Weighted Panel Regression (GWPR). Panel data has several advantages compared to cross-section or time-series data. The development of the GWPR model in this study uses the Fixed Effect model. It is used to model the number of infant mortality in Central Java. In this study, the weighting used by the fixed bisquare kernel resulted in a significant variable percentage of clean and healthy households. The value of R-square is 67.6%. Also

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3

Ningrum, A. S., A. Rusgiyono, and A. Prahutama. "Village classification index prediction using geographically weighted panel regression." Journal of Physics: Conference Series 1524 (April 2020): 012040. http://dx.doi.org/10.1088/1742-6596/1524/1/012040.

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4

Azizah, Aliyah Husnun, Nurjannah Nurjannah, Adji Achmad Rinaldo Fernandes, and Rosita Hamdan. "GEOGRAPHICALLY WEIGHTED PANEL LOGISTIC REGRESSION SEMIPARAMETRIC MODELING ON POVERTY PROBLEM." MEDIA STATISTIKA 16, no. 1 (2023): 47–58. http://dx.doi.org/10.14710/medstat.16.1.47-58.

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Regression analysis is a statistical method used to investigate and model the relationship between variables. Furthermore, a regression analysis was developed that involved spatial aspects, namely Geographically Weighted Regression (GWR). GWR modeling consists of various types, one of which is Geographically Weighted Logistic Regression Semiparametric (GWLRS), an extension of the Logistic GWR model that produces local and global parameter estimators. In this study, it is proposed to combine the GWLRS model using panel data or Geographically Weighted Panel Logistic Regression Semiparametric (GW

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5

Martha, Shantika, Yundari Yundari, Setyo Wira Rizki, and Ray Tamtama. "PENERAPAN METODE GEOGRAPHICALLY WEIGHTED PANEL REGRESSION (GWPR) PADA KASUS KEMISKINAN DI INDONESIA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 15, no. 2 (2021): 241–48. http://dx.doi.org/10.30598/barekengvol15iss2pp241-248.

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To analyze the factor affecting poverty during several periods by considering some geographical factors, we can use a geographically weighted panel regression (GWPR) method. GWPR is a combination of the geographically weighted regression (GWR) model and the panel regression model. The research conducts to identify the factors affecting the percentage of poor people in 34 provinces in Indonesia during 2015-2019. The results show that a suitable GWPR model is a fixed-effect model (FEM) with an exponential adaptive kernel function. Referring to the model, the province is divided into four groups

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6

Febrianti, Endah, Budi Susetyo, and Pika Silvianti. "Pemodelan Tingkat Kriminalitas di Indonesia Menggunakan Analisis Geographically Weighted Panel Regression." Xplore: Journal of Statistics 12, no. 1 (2023): 91–109. http://dx.doi.org/10.29244/xplore.v12i1.950.

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Kriminalitas merupakan salah satu masalah sosial ekonomi yang sampai saat ini belum terselesaikan di Indonesia. Meski Indonesia masuk kategori negara yang aman dikunjungi, kenyataannya masih banyak masyarakat Indonesia yang mengalami tindak kriminalitas. Penyelesaian masalah sosial ekonomi ini menjadi sangat penting karena menyangkut keamanan dan kenyamanan masyarakat. Penelitian ini bertujuan mengidentifikasi faktor-faktor yang memengaruhi tingkat kriminalitas di Indonesia dan menentukan model terbaik dari setiap provinsi dengan membandingkan antara model regresi data panel dan model Geograph

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7

Mar'ah, Zakiyah, and Sifriyani Sifriyani. "GEOGRAPHICALLY WEIGHTED PANEL REGRESSION (GWPR) FOR COVID-19 CASE IN INDONESIA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 2 (2023): 0879–86. http://dx.doi.org/10.30598/barekengvol17iss2pp0879-0886.

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Coronavirus disease 2019 (COVID-19) is a newly emerging infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) which was declared a pandemic by the World Health Organization (WHO) on March 11th, 2020. The response to this ongoing pandemic requires extensive collaboration across the scientific community to contain its impact and limit further transmission. Modeling to see cause-and-effect relationships in an event usually uses the Multiple Linear Regression (Ordinary Least Square) method. But in the case of Covid-19, the spread of the virus occurred from one l

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8

Arum, Prizka Rismawati, and Siva Alfian. "Pemodelan Pertumbuhan Ekonomi di Jawa Barat Menggunakan Metode Geographically Weighted Panel Regression." J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika 15, no. 2 (2022): 219–27. http://dx.doi.org/10.36456/jstat.vol15.no2.a5506.

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Salah satu tujuan negara adalah meningkatkan pertumbuhan ekonomi. Diperlukan pembangunan ekonomi untuk mewujudkan tujuan tersebut demi mencapai masyarakat yang sejahtera. Salah satu indikator pertumbuhan ekonomi adalah Produk Domestik Regional Bruto (PDRB). Data yang digunakan yaitu data sekunder tentang produk domestik regional bruto, jumlah penduduk miskin, pengeluaran pemerintah, rata - rata lama sekolah, tingkat partisipasi angkatan kerja, fasilitas kesehatan, tingkat pengangguran terbuka, pada tahun 2018 - 2020 di Provinsi Jawa Barat. Ternyata terdapat autokorelasi spasial dalam data ters

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9

Li, Chao, and Shunsuke Managi. "Estimating monthly global ground-level NO2 concentrations using geographically weighted panel regression." Remote Sensing of Environment 280 (October 2022): 113152. http://dx.doi.org/10.1016/j.rse.2022.113152.

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10

Chotimah, Chusnul, Sutikno, and Setiawan. "Modelling of Income Inequality in East Java Using Geographically Weighted Panel Regression." IOP Conference Series: Materials Science and Engineering 546 (June 26, 2019): 052019. http://dx.doi.org/10.1088/1757-899x/546/5/052019.

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11

Nabila Miftakhurriza, Jelita Zalzabila, Siswanto, Anisa Kalondeng, Andi Isna Yunita, and Samsir Aditya Ania. "GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODELING ON LIFE EXPECTANCY RATE IN SOUTH SULAWESI." Parameter: Journal of Statistics 4, no. 2 (2024): 93–102. https://doi.org/10.22487/27765660.2024.v4.i2.17267.

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Geographically Weighted Panel Regression (GWPR) is one of the panel data regression approaches used in spatial data analysis. This study uses the global Fixed Effect Model (FEM) panel regression model and the local GWPR model to examine Life Expectancy Rate (LER) at the district/city level in South Sulawesi Province in 2019-2021. LER is an important indicator that reflects the health and welfare of the community. This research aims to develop a GWPR model that can explain variations in LER and identify factors that affect that variable, so that it can help stakeholders in allocating resources

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12

Amelia Fadila Rahman, Syafriandi Syafriandi, Nonong Amalita, and Zilrahmi. "Geographically Weighted Panel Regression Modeling on Human Development Index in West Sumatra." UNP Journal of Statistics and Data Science 1, no. 3 (2023): 232–39. http://dx.doi.org/10.24036/ujsds/vol1-iss3/63.

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The Human Development Index (HDI) is an important issue that has a negative impact on the field of human development and people's welfare in West Sumatra Province. An effort to overcome the problem of the HDI is to identify the influencing factors. A method that can be used to identify influencing factors and explain the influence of characteristic areas of observation is Geographically Weighted Panel Regression (GWPR). GWPR is a combination of panel data regression method with GWR which is used when the data has the influence of spatial heterogeneity. The purpose of this study is to form a GW

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13

Renta, Apriani Monica, Herawati Netti, Sutrisno Agus, and Widiarti. "Geographically Weighted Panel Regression (GWPR) and Geographically and Temporally Weighted Regression (GTWR) Methods in the Influence of Factors Affecting the Minimum Wage of Each Province in Indonesia." RESEARCH REVIEW International Journal of Multidisciplinary 9, no. 7 (2024): 106–15. http://dx.doi.org/10.31305/rrijm.2024.v09.n07.015.

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The study compares the Geographically Weighted Panel Regression (GWPR) method and the Geographically Temporal Weighted Regression (GTWR) method using a spatial and temporal approach in the data. Both of these methods are developments by one of the Geographically Weighted Regression (GWR) methods. GWPR, which is a combination of the GWR model with panel data regression to analyze spatial variations between regions on the influence of the provincial minimum wage determination factor in Indonesia. Meanwhile, GTWR is used to analyze the presence of non-stationarity in its data if there is spatial

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14

Mondiana, Yani Quarta, Henny Pramoedyo, Atiek Iriany, and Marjono. "Applied fixed effect of Geographically Weighted Panel Regression (GWPR) with M- Estimator approach to estimate sugarcane yield data in East Java." Journal of Applied and Natural Science 16, no. 2 (2024): 646–52. http://dx.doi.org/10.31018/jans.v16i2.5443.

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Geographically Weighted Panel Regression (GWPR), a combination of panel regression and geographically weighted regression (GWR), is used to analyze panel data and capture diverse relationship between locations. GWPR was developed on data with panel-fixed effects and applied to modeling data with spatial heterogeneity and time series. One method for estimating parameters in the GWPR is weighted least squares (WLS), which are sensitive to outliers. The present study aimed to use the M- method to estimate GWPR model parameters in data containing outliers using fixed-effect GWPR modeling for the s

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15

Wasani, Desy, Purhadi, and Sutikno. "Parameter estimation and hypothesis testing of geographically and temporally weighted bivariate Gamma regression model." IOP Conference Series: Earth and Environmental Science 880, no. 1 (2021): 012044. http://dx.doi.org/10.1088/1755-1315/880/1/012044.

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Abstract Geographically Weighted Regression (GWR) study potential relationships in regression models that distinguish geographic spaces using non-stationary parameters to overcome spatial effects. The use of gamma regression, namely regression with the dependent variable with a gamma distribution, can be an alternative if the data do not follow a normal distribution. Gamma distribution is a continuous set of non-negative values, generally skewed to the right or positive skewness. Gamma regression is developed to be Bivariate Gamma Regression (BGR) when there are two dependent variables with ga

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16

Ketut, Ananda Kuvera Witana, Susilawati Made, Luh Putu Suciptawati Ni, Ayu Dwi Octavanny Made, Yuliarum Qur'ani Anggun, and Putu Eka Nilakusmawati Desak. "Modelling Mean Years of Schooling (MYS) Districts/Cities of West Kalimantan Province." International Journal of Social Science and Human Research 07, no. 06 (2024): 3605–13. https://doi.org/10.5281/zenodo.11444695.

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Mean years of schooling (MYS) is one of the indicators measuring the quality of education in Indonesia, regulated through the nine-year compulsory education policy. West Kalimantan Province is one of the provinces with the lowest MYS rates in Indonesia, with only one out of its 13 districts/cities fulfilling the compulsory education policy. Therefore, research is needed to obtain the best model and understand the factors that influence the MYS districts/cities of West Kalimantan Province. This study uses the Geographically Weighted Panel Regression (GWPR) analysis method, due to the annual cha

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17

Darma putra, Jimmi, Dina Fitria, Dodi Vionanda, and Admi Salma. "Geographically Weighted Panel Regression for Modeling The Percentage of Poor Population in West Sumatra." UNP Journal of Statistics and Data Science 1, no. 3 (2023): 211–17. http://dx.doi.org/10.24036/ujsds/vol1-iss3/64.

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Geographically Weighted Panel Regression (GWPR) model applies panel regression to spatial data, and parameter estimation is carried out using spatial weight at each observation point. The purpose of this study is to determine the GWPR model and the factors that influence the percentage of poor people in each district/city in West Sumatra Province from 2015 to 2021. And the adaptive bisquare kernel function was used to provide spatial weighting, and Cross-Validation (CV) criteria were used to identify the optimal bandwidth. The research data was secondary data sourced from the official website

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18

Maulana, Akhmad Fajar, Yuana Sukmawaty, and Maisarah Maisarah. "PENERAPAN MODEL GEOGRAPHICALLY WEIGHTED PANEL REGRESSION PADA TINGKAT KEMISKINAN DI PROVINSI KALIMANTAN SELATAN." RAGAM: Journal of Statistics & Its Application 2, no. 2 (2024): 66. http://dx.doi.org/10.20527/ragam.v2i2.11332.

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South Kalimantan Province is one of the provinces in Indonesia which has the lowest poverty rate or percentage of poor people on the island of Kalimantan, even in Indonesia. The percentage of poor people in South Kalimantan Province in March 2022 was 4.49% or in the 2nd lowest poverty position in Indonesia, below the Bangka Belitung Islands Province and above the Bali Province. Geographically Weighted Panel Regression (GWPR) is a local regression model with repeated data at location points for each observation at different times. This study aims to estimate the GWPR model parameters and test t

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19

Meutuah, Yasin, and Maruddani. "PEMODELAN FIXED EFFECT GEOGRAPHICALLY WEIGHTED PANEL REGRESSION UNTUK INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH." Jurnal Gaussian 6, no. 2 (2017): 241–50. https://doi.org/10.5281/zenodo.1065051.

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Human development index is an indicator for assessing the quality of human resources and  measure the results of human development. The achievements of the human development index is not enough if conducting observations in each cities in just one particular time, but the observations  need to be made in some period of time. The distribution in each cities is also a concern, because the conditions are so diverse that led to their spatial effects. Therefore, it is necessary to study these variables in some time periods that affect human development index taking into acc

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20

Amandra, Miko Novri, Widyastutik Widyastutik, and Nimmi Zulbainarni. "Determinan Nilai Tukar Nelayan Di Indonesia Dengan Pendekatan Geographically Weighted Panel Regression (GWPR)." Jurnal Sosial Ekonomi Kelautan dan Perikanan 17, no. 2 (2022): 195. http://dx.doi.org/10.15578/jsekp.v17i2.10940.

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Salah satu pendekatan dalam mengukur kesejahteraan nelayan adalah nilai tukar nelayan (NTN). NTN publikasi Badan Pusat Statistik (BPS) hanya mengukur daya beli nelayan sehingga perlu direformulasi dan dikoreksi berdasarkan pertumbuhan produksi dan tenaga kerja. Penelitian ini bertujuan untuk menganalisis pola spasial dan dependensi spasial serta menganalisis determinan NTN level nasional dan level provinsi. Analisis yang digunakan adalah indeks moran, diagram pencar moran, dan Geographically Weighted Panel Regression (GWPR). Data yang digunakan merupakan data dari 33 provinsi tahun 2015 hingga

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21

Nasri, Ramadhoni, Nurul Gusriani, and Nursanti Anggriani. "GEOGRAPHICALLY WEIGHTED PANEL REGRESSION (GWPR) MODEL FOR POVERTY DATA IN WEST JAVA PROVINCE 2019-2021." Jurnal Diferensial 5, no. 2 (2023): 106–16. http://dx.doi.org/10.35508/jd.v5i2.12213.

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The problem of poverty in West Java shows a pattern that tends to be concentrated in adjacent areas, indicating spatial heterogeneity in the problem. On the other hand, poverty in West Java also shows an increasing trend from year to year so that dynamic changes occur in various regions. From this situation, it is necessary to know the factors that affect poverty spatially using panel data. One way is to model the poverty problem with the Geographically Weighted Panel Regression (GWPR) model. The GWPR model is the development of a regression model that combines Geographically Weighted Regressi

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22

Warsito, Budi, Hasbi Yasin, Dwi Ispriyanti, and Arief Rachman Hakim. "The Step Construction of Geographically Weighted Panel Regression in Air Polluter Standard Index (APSI) Data." E3S Web of Conferences 73 (2018): 12006. http://dx.doi.org/10.1051/e3sconf/20187312006.

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Geographically Weighted Panel Regression or GWPR is a local linear regression model that combines GWR model and panel data regression model with considering spatial effect, especially spatial heterogeneity problem. This article is focused on the soft computation of GWPR model using Fixed Effect Model (FEM). Parameter estimation in GWPR is obtain by Weighted Least Squares (WLS) methods and the resulting model for each location will be different from one to another. This study will compare the fixed-effect GWPR model with several weighting functions. The best model is determined based on the big

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23

Kurnia, Hafsah, Irma Fauziah, and Madona Yunita Wijaya. "Pemodelan kasus tingkat kemiskinan di Indonesia periode 2015-2021 dengan model regresi panel terboboti geografis." Majalah Ilmiah Matematika dan Statistika 24, no. 2 (2024): 99. http://dx.doi.org/10.19184/mims.v24i2.39392.

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Poverty is a major concern of the Indonesian government and the government's efforts to reduce poverty are a national development priority. Therefore, it is interesting to identify the factors that influence poverty in Indonesia. Considering a spatial perspective, Geographically Weighted Panel Regression (GWPR) method is applied to the panel data set of 34 Indonesia provinces over the period 2016-2021. The best fitted model is found when using the adaptive kernel weighting function with poverty rate, length of schooling, provincial minimum wave, human development index, literacy rate, and unem

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24

Deta Erviana, Mustofa Usman, Widiarti, and Khoirin Nisa. "Penerapan Metode Geographically Weighted Panel Regression Pada Indeks Pembangunan Manusia di Indonesia Tahun 2017-2022." Sciencestatistics: Journal of Statistics, Probability, and Its Application 2, no. 1 (2024): 35–47. http://dx.doi.org/10.24127/sciencestatistics.v2i1.5669.

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Regresi linier merupakan metode statistik untuk memeriksa hubungan antara variabel respons dan satu atau lebih variabel prediktor. Dalam sebuah penelitian, satu unit observasi harus diteliti selama beberapa periode waktu, karena mempelajari satu unit dalam satu periode waktu tidaklah cukup. Oleh karena itu, sebuah pendekatan statistik yang disebut analisis regresi panel diciptakan untuk mengintegrasikan data cross-section dan data time series. Namun pada kenyataannya, perbedaan kondisi antar lokasi dipengaruhi oleh efek spasial yang menyebabkan terjadinya heterogenitas spasial. Dikembangkanlah

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25

Raihani, Risti, Sifriyani Sifriyani, and Surya Prangga. "Geographically Weighted Panel Regression Modelling of Dengue Hemorrhagic Fever Data Using Exponential Kernel Function." JTAM (Jurnal Teori dan Aplikasi Matematika) 7, no. 4 (2023): 961. http://dx.doi.org/10.31764/jtam.v7i4.16235.

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Geographically Weighted Panel Regression (GWPR) model is a panel regression model applied to spatial data. This research takes the Fixed Effect Model (FEM) panel regression as the global model and GWPR as the local model for dengue hemorrhagic fever (DHF) in East Kalimantan Province data over the years 2018-2020. DHF is a disease that has the potential to become an extraordinary event which is accompanied by death. In comparison to Indonesia, East Kalimantan Province's DHF Incident Rate (IR) was high in 2020. East Kalimantan's IR is 60.6 per 100,000 population, compared to Indonesia's IR of 40

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26

Tiopan Sitorus, Andrew Lupe, and Elmanani Simamora. "Metode Geographically Weighted Panel Regression (GWPR) Untuk Menganalisis Faktor Yang Mempengaruhi Kemiskinan Di Provinsi Sumatera Utara." Ranah Research : Journal of Multidisciplinary Research and Development 6, no. 1 (2024): 155–67. http://dx.doi.org/10.38035/rrj.v6i1.808.

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This study aims to analyze the factors of population density, life expectancy, years of schooling, open unemployment rate, per capita monthly food expenditure, population with health complaints, economic growth, human development index, households with access to proper drinking water and households with access to proper sanitation on the percentage of poverty in North Sumatra province. This research is based on secondary data available at the North Sumatra Central Bureau of Statistics in 2017-2021. The factor analysis used in this study is Geographically Weighted Panel Regression (GWPR) which

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27

Mumtaz, Ghina Fadhilla, Suyitno Suyitno, and Sifriyani Sifriyani. "GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODELING OF POVERTY RATES IN TROPICAL RAINFOREST AREAS OF KALIMANTAN." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 2 (2025): 903–16. https://doi.org/10.30598/barekengvol19iss2pp903-916.

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Анотація:

When applied to spatial panel data, the Geographically Weighted Panel Regression (GWPR) model is a localized version of the linear regression model. The Fixed Effect Model (FEM) inside estimator is used as a global model in this investigation. The purpose of this research is to obtain a GWPR model and identify the variables that affect the proportion of the impoverished in 56 districts and cities located in Kalimantan's humid tropical forest region between 2019 and 2022. The Weighted Least Square (WLS) approach, which provides geographic weighting in addition to the Least Square method, is use

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28

Sifriyani, I. Nyoman Budiantara, M. Fariz Fadillah Mardianto, and Asnita. "Determination of the best geographic weighted function and estimation of spatio temporal model – Geographically weighted panel regression using weighted least square." MethodsX 12 (June 2024): 102605. http://dx.doi.org/10.1016/j.mex.2024.102605.

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29

Ananda, Ni Made Shantia, Suyitno Suyitno, and Meiliyani Siringoringo. "Geographically Weighted Panel Regression Modelling of Human Development Index Data in East Kalimantan Province in 2017-2020." Jurnal Matematika, Statistika dan Komputasi 19, no. 2 (2023): 323–41. http://dx.doi.org/10.20956/j.v19i2.23775.

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Анотація:

Geographically Weighted Panel Regression (GWPR) model is a panel regression model applied on spatial data. This research applied Fixed Effect Model (FEM) on panel regression as the global model and GWPR as the local model for Human Development Index (HDI) regencies/municipalities in East Kalimantan Province data over the years 2017-2020. The aim of this research is to obtain the GWPR model of HDI data, as well as to acquire factors that influence it. The parameter of GWPR model was estimated on each observation location using the Weighted Least Square (WLS) method, namely Ordinary Least Square

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Azkiya, Azka Al, Yenni Angraini, and Rahma Anisa. "Penerapan Geographically Weighted Panel Regression dan Data Envelopment Analysis dalam Pemodelan Kemiskinan di Kalimantan Timur." Journal of Regional and Rural Development Planning 8, no. 1 (2024): 41–53. http://dx.doi.org/10.29244/jp2wd.2024.8.1.41-53.

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Indonesia currently still needs to focus on achieving sustainable development goals agreed by all countries in the world. Indonesia presently ranks 82nd out of 163 nations in terms of SDG accomplishment, indicating that there is still plenty of potential for improvement. One of the goals that hasn't been accomplished is ‘no poverty’. Regarding the poverty cases, among all province in Indonesia, East Kalimantan is important to be analyzed, because Penajam Paser Utara and Kutai Kartanegara in East Kalimantan are scheduled to become Indonesia's next capital, Nusantara. The goal of this research i

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Saska, Indria, and Sifriyani Sifriyani. "IMPLEMENTATION OF GEOGRAPHICALLY WEIGHTED PANEL REGRESSION WITH GAUSSIAN KERNEL WEIGHTING FUNCTION IN THE OPEN UNEMPLOYMENT RATE MODEL." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 2 (2025): 733–42. https://doi.org/10.30598/barekengvol19iss2pp733-742.

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This study analyzes the factors influencing the Open Unemployment Rate in Kalimantan using the Geographically Weighted Panel Regression (GWPR) model with Gaussian kernel weighting functions. The GWPR model, a local panel regression approach for spatial data, is compared with the global Fixed Effect Model (FEM). Spatial weighting for parameter estimation employs Fixed Gaussian and Adaptive Gaussian kernels, with the optimum bandwidth determined through Cross Validation (CV), resulting in a minimum CV value of 25.536 for the Adaptive Gaussian Kernel. Local factors identified as influencing the O

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Yansi, Felicitas Avelline, Noeryanti, and Noviana Pratiwi. "PEMODELAN GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MENGGUNAKAN PEMBOBOT KERNEL GAUSSIAN DAN KERNEL BI-SQUARE PADA KEMISKINAN DI PAPUA." Jurnal Statistika Industri dan Komputasi 9, no. 1 (2024): 21–31. http://dx.doi.org/10.34151/statistika.v9i1.4829.

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Papua merupakan provinsi dengan tingkat kemisikinan tertinggi di Indonesia. Jumlah penduduk miskin di Papua selama tahun 2019-2021 cukup fluktuatif. Kemiskinan disebabkan oleh banyak faktor, antara lain kesulitan dalam memenuhi kebutuhan dasar, kesulitan dalam memperoleh pendidikan dan pekerjaan. Penelitian ini bertujuan untuk melakukan pemodelan Geographically Weighted Panel Regression menggunakan fungsi pembobot adaptive kernel gaussian dan adaptive kernel bi-square pada jumlah penduduk miskin di Papua. Dalam suatu penelitian tidak cukup melakukan pengamatan dalam satu waktu tertentu, tetapi

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Herlina, Marizsa, Shafira Rizq, Eti Kurniati, and Nabila Zahratu Fuadi. "Covid-19 Vaccination Impact on Four Asean Countries’ Stock With Spatial Dependency: A Comparison of Panel and Geographically Weighted Regression." Jurnal Matematika, Statistika dan Komputasi 21, no. 1 (2024): 234–42. http://dx.doi.org/10.20956/j.v21i1.36177.

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Research about various policies and responses toward COVID-19 cases and its impact on stocks has grown recently. It shows that spatial influence is one of the keys in this research. The pandemic is not free from spatial dependence regarding how it indirectly impacts a country’s economy. Each country has different policies to handle COVID-19, such as lockdowns and vaccination. WHO stated that all countries require vaccination to build human immunity against COVID-19 in the future. Naturally, ASEAN implemented this policy; thus, it is crucial to see the extent of the impact of vaccination on the

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Wati, Dia Cahya, Dea Alvionita Azka, and Herni Utami. "The Model of Per-Capita Expenditure Figures in Sumatera Selatan uses a Geographically Weighted Panel Regression." Indonesian Journal of Statistics and Its Applications 5, no. 1 (2021): 61–74. http://dx.doi.org/10.29244/ijsa.v5i1p61-74.

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The Geographically Weighted Panel Regression (GWPR) is a development of a global regression model where the basic idea is taken from a combination of panel data and GWR. The GWPR model is built from the point approach method, which is based on the position of the coordinates of latitude and longitude. The parameters for the regression model at each location will produce different values. GWPR can accommodate spatial effects, so that it can better explain the relationship between response variables and predictors. The purpose of this study is to compare the GWPR model with the Fixed Gaussian an

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Ninda, Faradilla Chairin, Lisma Dinayanti, and Sarni Marniar Berliana. "Pemodelan Geographically Weighted Panel Regression (GWPR) pada Rumah Tangga yang Menempati Rumah Kumuh di Indonesia Tahun 2020—2022." Seminar Nasional Official Statistics 2024, no. 1 (2024): 947–58. http://dx.doi.org/10.34123/semnasoffstat.v2024i1.2028.

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Kawasan kumuh merupakan permasalahan berskala global yang tertuang dalam Suistainable Development Goals (SDGs) tujuan ke-11. Di Indonesia sendiri, terjadi perlambatan performa penurunan persentase rumah tangga yang tinggal di rumah kumuh pada tahun 2020–2022. Dimana rata-rata penurunan tiap tahunnya hanya sebesar 7,7%, yang semula sebesar 12,58% di tahun 2019. Menurut Theory of Slums dan konsep Culture of Poverty, munculnya kawasan kumuh disebabkan oleh karakteristik sosial dan ekonomi. Namun, juga diketahui bahwa kawasan kumuh dipengaruhi oleh karakteristik spasial, karena memiliki pola menye

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Purwanti, Setyorini Indah, Sutikno, and Purhadi. "Parameter estimation and hypothesis testing of geographically and temporally weighted bivariate generalized Poisson regression." IOP Conference Series: Earth and Environmental Science 880, no. 1 (2021): 012043. http://dx.doi.org/10.1088/1755-1315/880/1/012043.

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Abstract Poisson regression is used to model the data with the response variable in the form of count data. This modeling must meet the equidispersion assumption. That is, the average value is the same as the variance. However, this assumption is often violated. Violation of the equidispersion assumption in Poisson regression modeling will result in invalid conclusions. These violations are an overdispersion and an underdispersion of the response variable. Generalized Poisson Regression (GPR) is an alternative if there is a violation of the equidispersion assumption. If there are two correlate

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Gamayanti, Nurul Fiskia, Junaidi Junaidi, Fadjryani Fadjryani, and Nur'eni Nur'eni. "ANALYSIS OF SPATIAL EFFECTS ON FACTORS AFFECTING RICE PRODUCTION IN CENTRAL SULAWESI USING GEOGRAPHICALLY WEIGHTED PANEL REGRESSION." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 1 (2023): 0361–70. http://dx.doi.org/10.30598/barekengvol17iss1pp0361-0370.

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Fulfillment of rice stock in Indonesia to always be distributed based on demand in the community is certainly closely related to the results of rice production. The results of rice production in various regions of Indonesia are very different. This difference can of course be influenced by geographic location or spatial effects between regions. Central Sulawesi, which is one of the provinces with a large population compared to other provinces on the island of sulwesi, has a responsibility to meet the needs of its community, so it is necessary to take into account and increase the production of

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Liu, Yang, Xiaoyu Chen, and Yujie Zhang. "Analysis of Business Environment and Medical Insurance Coverage Rates in the Destination of China’s Migrant Population: Based on Geographically and Temporally Weighted Regression Model for Panel Data." Mathematical Problems in Engineering 2022 (November 19, 2022): 1–12. http://dx.doi.org/10.1155/2022/6540663.

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Health risk is an important issue in the process of population spatial mobility, and it is also an important issue in the process of urbanization in China. Using the dynamic monitoring data of China’s migrant population and 31 provincial business environment data from 2011 to 2018, this study systematically investigated the spatial distribution and evolution characteristics of the migrant population’s participation in medical insurance in the destination areas and combined it with the Geographically and Temporally Weighted Regression Model for Panel Data (PGTWR) to analyze the impact of the re

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Li, Shoutiao, Zhibang Xu, and Haowei Wang. "Spatiotemporal Characteristics and Factors Driving Exploration of Industrial Carbon-Emission Intensity: A Case Study of Guangdong Province, China." Sustainability 14, no. 22 (2022): 15064. http://dx.doi.org/10.3390/su142215064.

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Research on spatiotemporal characteristics and influencing factors of industrial carbon emissions intensity is crucial to the efforts of reducing carbon emissions. This paper measures the industrial carbon emissions intensity (CI) by energy consumption in Guangdong from 2012 to 2020 and evaluates the regional differences of CI. In addition, we apply the extended STIRPAT (stochastic impacts by regression on population, affluence and technology) and GTWR (geographically and temporally weighted regression) models to reveal the influence of driving factors on CI from spatial–temporal perspectives,

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Sifriyani, Sifriyani, Idris Mandang, F. D. T. Amijaya, and R. Ruslan. "DEVELOPING GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODEL FOR SPATIO-TEMPORAL ANALYSIS OF COVID-19 POSITIVE CASES IN KALIMANTAN, INDONESIA." Journal of Southwest Jiaotong University 57, no. 3 (2022): 113–26. http://dx.doi.org/10.35741/issn.0258-2724.57.3.10.

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This article describes spatial-temporal analysis using the innovation of developing a Geographically Weighted Panel Regression model with a distance weighting function that includes the interaction between spatial and time aspects (GWPR-st). The method is a local regression technique that provides a parameter model that varies in each location through cross-sectional and time-series data observation units. This study develops a new model in spatial statistics and offers new methodologies in Geographic Models and Geographic Information Systems (GIS). This study aims to determine the factors tha

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Nida, Oktarina Safar, and Rahma Nurhamidah. "Determinan Tingkat Pengganguran Terbuka Pada Kabupaten/Kota Di Provinsi Kepulauan Riau Dengan Metode Geographically Weighted Panel Regression (GWPR)." Jurnal Archipelago 2, no. 02 (2023): 195–206. http://dx.doi.org/10.69853/ja.v2i02.57.

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Tingkat Pengangguran Terbuka (TPT) di Provinsi Kepulauan Riau berada pada posisi tertinggi kedua nasional tahun 2022 yaitu sebesar 8,23 persen setelah Jawa Barat. Sebagai provinsi dengan daerah kepulauan, tentunya efek geografis berpengaruh terhadap keadaan ketenagakerjaan di Provinsi Kepulauan Riau. Tujuan penelitian ini adalah mengidentifikasi faktor-faktor yang berpengaruh terhadap TPT pada kabupaten/kota di Provinsi Kepulauan Riau. Model Geographically Weighted Panel Regression (GWPR) digunakan untuk mengakomodasi pengaruh efek geografis atau spasial tersebut. Model yang dihasilkan sudah b

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Asnita, Asnita, Sifriyani Sifriyani, and Meirinda Fauziyah. "ESTIMATION OF GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODEL WITH BISQUARE KERNEL WEIGHTING FUNCTION ON PERCENTAGE OF STUNTING TODDLERS IN INDONESIA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 1 (2024): 0383–94. http://dx.doi.org/10.30598/barekengvol18iss1pp0383-0394.

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Stunting is a condition of failure to thrive in children under five years old due to chronic malnutrition. Efforts that can be made to reduce the incidence of stunting in Indonesia are to identify factors that are thought to affect the incidence of stunting in Indonesia. The analysis methods used in this study are the global Fixed Effect Model (FEM) and the local Geographically Weighted Panel Regression (GWPR) model. FEM is a global regression model that assumes that each individual's model has a different intercept value. While GWPR is a local regression model from FEM that considers aspects

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Wang, Tao, Kai Zhang, Keliang Liu, Keke Ding, and Wenwen Qin. "Spatial Heterogeneity and Scale Effects of Transportation Carbon Emission-Influencing Factors—An Empirical Analysis Based on 286 Cities in China." International Journal of Environmental Research and Public Health 20, no. 3 (2023): 2307. http://dx.doi.org/10.3390/ijerph20032307.

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In order to scientifically evaluate the characteristics and impact outcomes of transportation carbon emissions, this paper uses the panel statistics of 286 cities to measure transportation carbon emissions and analyze their spatial correlation characteristics. Afterwards, primarily based on the current research, a system of indicators for the impact factors of transportation carbon emissions was established. After that, ordinary least squares regression, geographically weighted regression, and multiscale geographically weighted regression models were used to evaluate and analyze the data, and

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Kumbhakar, Subal C., Jingfang Zhang, and Gudbrand Lien. "Locationally Varying Production Technology and Productivity: The Case of Norwegian Farming." Econometrics 11, no. 3 (2023): 20. http://dx.doi.org/10.3390/econometrics11030020.

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In this study, we leverage geographical coordinates and firm-level panel data to uncover variations in production across different locations. Our approach involves using a semiparametric proxy variable regression estimator, which allows us to define and estimate a customized production function for each firm and its corresponding location. By employing kernel methods, we estimate the nonparametric functions that determine the model’s parameters based on latitude and longitude. Furthermore, our model incorporates productivity components that consider various factors that influence production. U

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Yu, Danlin, Yaojun Zhang, Xiwei Wu, Ding Li, and Guangdong Li. "The varying effects of accessing high-speed rail system on China’s county development: A geographically weighted panel regression analysis." Land Use Policy 100 (January 2021): 104935. http://dx.doi.org/10.1016/j.landusepol.2020.104935.

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Simanullang, Endang, Dedi Hakim, Yusman Syaukat, and Widyastutik Widyastutik. "Import of Agricultural Products in the Intra-Regional Comprehensive Economic Partnership (RCEP)." HABITAT 33, no. 3 (2022): 241–50. http://dx.doi.org/10.21776/ub.habitat.2022.033.3.24.

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The flow of agricultural products through imports has the potential to increase with the geographical conditions of adjacent RCEP countries. Economic and non-economic factors can affect imports of agricultural products. This study aims to analyze the spatial effect and the factors that influence the import of agricultural products in Intra RCEP. This study uses a data period from 2013-2019. The analytical method used in this research is Moran's global index, Local Indicator of Spatial Autocorrelation (LISA), and Geographically Weighted Panel Regression (GWPR). The results show a spatial effect

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Zhang, Liguo, Langping Leng, Yongming Zeng, Xi Lin, and Su Chen. "Spatial distribution of rural population using mixed geographically weighted regression: Evidence from Jiangxi Province in China." PLOS ONE 16, no. 4 (2021): e0250399. http://dx.doi.org/10.1371/journal.pone.0250399.

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On the basis of the spatial panel data of 2000, 2005, 2010, and 2015, this study uses a mixed geographically weighted regression model to explore the spatial distribution characteristics and influencing factors of the rural (permanent) population in Jiangxi Province, China. Results show that residents in the county area have a significant spatial positive autocorrelation, especially in the lake and mountain areas and the global Moran’ I index is more than 0.05. The influence of social and economic factors presents spatial homogeneity. The effect of urbanization and per capita disposable income

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Santoso, Edy, Teguh Hadi Priyono, Duwi Yunitasari, and Silvia Anggraini. "The Analysis of Local Regression of Industrial Agglomeration on the Economic Growth in Indonesia." Wiga : Jurnal Penelitian Ilmu Ekonomi 15, no. 1 (2025): 80–90. https://doi.org/10.30741/wiga.v15i1.1402.

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Economic growth is caused by various structural factors, including agglomeration industries and regional spending. This study aims to analyze the influence of industrial agglomeration on economic growth in Indonesia using the Geographically Weighted Panel Regression (GWPR) method. This method analyzes the spatially and temporally varied relationships between dependent and independent variables. This study considers spatial variation to investigate the variability of the economic growth model of each province in Indonesia. This study uses panel data with 34 provinces in Indonesia. Time range fr

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Fan, Xuemei, Ziyue Nan, Yuanhang Ma, Yingdan Zhang, and Fei Han. "Research on the Spatio-Temporal Impacts of Environmental Factors on the Fresh Agricultural Product Supply Chain and the Spatial Differentiation Issue—An Empirical Research on 31 Chinese Provinces." International Journal of Environmental Research and Public Health 18, no. 22 (2021): 12141. http://dx.doi.org/10.3390/ijerph182212141.

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Environmental factors in time and space play a critical role in advancing the sustainable development of the fresh agricultural product supply chain. This paper, availing the panel data of 31 Chinese provinces from 2008 to 2019, constructs a system of indicators assessing the development of the fresh agricultural product supply chain, and obtains the comprehensive development level in the Entropy Weight Method (EWM). Furthermore, it establishes a comparison between optimal solutions generated by the Instrumental Variables Method (IVM) and the Generalized Method of Moments (GMM) over the endoge

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Nugroho, Adin, and Prientananda Ghina Salsabila. "Study of Economic Vulnerability and Its Influence on the Economy in Sumatera Island Using the Household Consumption Expenditure Approach." Proceedings of The International Conference on Data Science and Official Statistics 2023, no. 1 (2023): 430–45. http://dx.doi.org/10.34123/icdsos.v2023i1.293.

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The readiness of a region to face shocks and spillover effects from the surrounding area needed to be developed early. Each region had different economic structure so that the policy and strategy that was used to deal with current and future global uncertainties should be different as well. This study aimed to analyze economic vulnerability and the characteristics of its grouping, and analyze the effect of inflation, unemployment rate, foreign investment, and economic vulnerability towards the economy of provinces in Sumatera. The method performed in this study was Cluster Analysis for groupin

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