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Relevant bibliographies by topics / Geographically Weighted Panel Regression

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Academic literature on the topic 'Geographically Weighted Panel Regression'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Geographically Weighted Panel Regression"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Geographically Weighted Panel Regression"

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Miller, Karen M. "Geographically weighted regression and an extension." Master's thesis, University of Cape Town, 2008. http://hdl.handle.net/11427/4388.

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Kordi, Maryam. "Geographically weighted spatial interaction (GWSI)." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/4112.

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One of the key concerns in spatial analysis and modelling is to study and analyse similarities or dissimilarities between places over geographical space. However, ”global“ spatial models may fail to identify spatial variations of relationships (spatial heterogeneity) by assuming spatial stationarity of relationships. In many real-life situations spatial variation in relationships possibly exists and the assumption of global stationarity might be highly unrealistic leading to ignorance of a large amount of spatial information. In contrast, local spatial models emphasise differences or dissimila

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Yang, Wenbai. "An extension of geographically weighted regression with flexible bandwidths." Thesis, University of St Andrews, 2014. http://hdl.handle.net/10023/7052.

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Various statistical methods have been developed for local spatial analysis. Among them Geographically Weighted Regression (GWR) is a simple yet powerful method to explore spatially varying relationships between variables. This thesis examines how GWR can be extended to investigate spatially varying relationships at various geographical scales within one model. GWR assumes that observations near to a regression location have more influence on the estimation of local regression coefficients than do observations farther away. A single bandwidth is employed in basic GWR to control the rate of dist

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Sikdar, Khokan Chandra. "Application of geographically weighted regression for assessing spatial non-stationarity /." Internet access available to MUN users only, 2003. http://collections.mun.ca/u?/theses,172881.

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HE, Xin. "Modeling church services supply and performance, using geographically weighted regression." Thesis, University of Gävle, Ämnesavdelningen för samhällsbyggnad, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-5801.

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<p>The objective of this study is to develop a multiple linear regression model that measures the relationship between the church services supply and the attendance to the services in the Uppsala diocese, Church of Sweden. By reviewing previous models and examining the nature of data available, two research questions were introduced, namely, the problem of omitted variables and the problem of spatial autocorrelation. For the first question, two methods were compared, namely, the Y-lag method and the first-differenced equation. Statistical tests then showed that the latter was more preferable f

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Burke, Tommy. "Evaluation of visualisations of geographically weighted regression, with perceptual stability." Thesis, University of St Andrews, 2016. http://hdl.handle.net/10023/15680.

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Given the large volume of data that is regularly accumulated, the need to properly manage, efficiently display and correctly interpret, becomes more important. Complex analysis of data is best performed using statistical models and in particular those with a geographical element are best analysed using Spatial Statistical Methods, including local regression. Spatial Statistical Methods are employed in a wide range of disciplines to analyse and interpret data where it is necessary to detect significant spatial patterns or relationships. The topic of the research presented in this thesis is an e

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Yamashita, Kiyoshi. "Understanding urban fire: Modeling fire incidence using classical and geographically weighted regression." Connect to online resource, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1453557.

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Huang, Lixin. "Predicting Hurricane Evacuation Decisions: When, How Many, and How Far." FIU Digital Commons, 2011. http://digitalcommons.fiu.edu/etd/461.

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Traffic from major hurricane evacuations is known to cause severe gridlocks on evacuation routes. Better prediction of the expected amount of evacuation traffic is needed to improve the decision-making process for the required evacuation routes and possible deployment of special traffic operations, such as contraflow. The objective of this dissertation is to develop prediction models to predict the number of daily trips and the evacuation distance during a hurricane evacuation. Two data sets from the surveys of the evacuees from Hurricanes Katrina and Ivan were used in the models' development.

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Ounsi, Karim. "Geographically Weighted Regression as a Predictive Tool for Station-Level Ridership : The Case of Stockholm." Thesis, KTH, Transportplanering, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259500.

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This thesis studies a new regression method, Geographically Weighted Regression (GWR)to predict ridership at the station level for future stations. The case study of Stockholm’s blue lineis used as new stations will be built by 2030. This paper is written in English.Historically, linear regression methods, independent of the geographical location of theobservations, was and is still used as the Ordinary Least Square regression method. With the riseof GIS-softwares these last decades, geographically dependent regression can be used and previouspreliminary studies have shown a dependency between

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White, Megan L. "ASSOCIATING SEVERE THUNDERSTORM WARNINGS WITH DEMOGRAPHIC AND LANDSCAPE VARIABLES: A GEOGRAPHICALLY WEIGHTED REGRESSION-BASED MAPPING OF FORECAST BIAS." UKnowledge, 2014. http://uknowledge.uky.edu/geography_etds/20.

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Severe thunderstorm warnings (SVTs) are released by meteorologists in the local forecast offices of the National Weather Service (NWS). These warnings are issued with the intent of alerting areas in the path of severe thunderstorms that human and property risk are elevated, and that appropriate precautionary measures should be taken. However, studies have shown that the spatial distribution of severe storm warnings demonstrates bias. Greater numbers of severe thunderstorm warnings sometimes are issued where population is denser. By contrast, less populated areas may be underwarned. To investig

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Books on the topic "Geographically Weighted Panel Regression"

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Chris, Brunsdon, and Charlton Martin, eds. Geographically weighted regression: The analysis of spatially varying relationships. Wiley, 2002.

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Brunsdon, Chris, Martin Charlton, and A. S. Fotheringham. Geographically Weighted Regression. Wiley & Sons, Incorporated, John, 2003.

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Oshan, Taylor, and Ziqi Li. Multiscale Geographically Weighted Regression: Theory and Practice. CRC Press LLC, 2023.

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Oshan, Taylor, and Ziqi Li. Multiscale Geographically Weighted Regression: Theory and Practice. Taylor & Francis Group, 2023.

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Oshan, Taylor, and Ziqi Li. Multiscale Geographically Weighted Regression: Theory and Practice. Taylor & Francis Group, 2023.

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Oshan, Taylor, and Ziqi Li. Multiscale Geographically Weighted Regression: Theory and Practice. Taylor & Francis Group, 2023.

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Brunsdon, Chris, Martin Charlton, and A. Stewart Fotheringham. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley & Sons, Incorporated, John, 2003.

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Brunsdon, Chris, Martin Charlton, and A. Stewart Fotheringham. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley & Sons, Incorporated, John, 2007.

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Reviews, Cram101 Textbook. Outlines & Highlights for Geographically Weighted Regression by Fotheringham, ISBN: 0471496162. AIPI, 2007.

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Book chapters on the topic "Geographically Weighted Panel Regression"

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Umi, Khaira, Junaidi, and Fadjryani. "Gender Inequality Index Modeling in Indonesia Using Geographically Weighted Panel Regression Method." In Proceedings of the 4th International Seminar on Science and Technology (ISST 2022). Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-228-6_34.

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Wheeler, David C. "Geographically Weighted Regression." In Handbook of Regional Science. Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-642-36203-3_77-1.

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Shekhar, Shashi, and Hui Xiong. "Geographically Weighted Regression." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_479.

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Yang, Yang. "Geographically Weighted Regression." In Statistical Methods for Global Health and Epidemiology. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35260-8_12.

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Wheeler, David C., and Antonio Páez. "Geographically Weighted Regression." In Handbook of Applied Spatial Analysis. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03647-7_22.

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Shekhar, Shashi, and Hui Xiong. "Regression, Geographically Weighted." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1107.

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Wheeler, David C. "Geographically Weighted Regression." In Handbook of Regional Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-23430-9_77.

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Que, Xiang, and Shaoqiang Su. "Geographically Weighted Regression." In Encyclopedia of Mathematical Geosciences. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-85040-1_141.

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Que, Xiang, and Shaoqiang Su. "Geographically Weighted Regression." In Encyclopedia of Mathematical Geosciences. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-26050-7_141-1.

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Wheeler, David C. "Geographically Weighted Regression." In Handbook of Regional Science. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-60723-7_77.

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Conference papers on the topic "Geographically Weighted Panel Regression"

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Zhang, Yanjiao. "Sustainability Evaluation of Rural Development using Geographically Weighted Ridge Regression." In 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2025. https://doi.org/10.1109/icdcece65353.2025.11035695.

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Senen, Adri, Jasrul Jamani Jamian, Tri Wahyu Oktaviana Putri, Eko Supriyanto, and Dwi Anggaini. "Enhancing Spatial Load Forecasting Using Geographically Weighted Regression Based on Geographic Information Systems." In 2024 6th International Conference on Power Engineering and Renewable Energy (ICPERE). IEEE, 2024. https://doi.org/10.1109/icpere63447.2024.10845608.

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Gong, XiangFei. "Analyzing the Spatial Autocorrelation of Logistics Nodes Using the Geographically Weighted Regression Model." In 2024 3rd International Conference on Smart City Challenges & Outcomes for Urban Transformation (SCOUT). IEEE, 2024. https://doi.org/10.1109/scout64349.2024.00017.

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Zhang, Miao, YuCai Li, and WenFei Shen. "Research on the analysis of three-dimensional spatial pattern based on multiscale geographically weighted regression." In Sixth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2024), edited by Zhiliang Qin, Jun Chen, and Huaichun Wu. SPIE, 2025. https://doi.org/10.1117/12.3057517.

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Sa’adi, Zulfaqar, Nor Eliza Alias, Zulkifli Yusop, et al. "Examining the NDVI-Rainfall Relationship Under High Enso Event Influence Using Geographically Weighted Regression in Peninsular Malaysia." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642469.

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Lin, Zhipeng. "ML Estimation of Spatial Panel Data Geographically Weighted Regression Model." In 2011 International Conference on Management and Service Science (MASS 2011). IEEE, 2011. http://dx.doi.org/10.1109/icmss.2011.5999234.

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Mondiana, Yani Quarta, Henny Pramoedyo, Atiek Iriany, and Marjono Marjono. "Fixed effect geographically weighted panel regression: A comparison of Kernel Gaussian and Bi-Square for modeling sugarcane yield in East Java." In THE 4TH INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS (ICOMATHAPP) 2023: Mathematics and its Applications on Society 5.0: Challenges and Opportunities. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0234584.

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Juan Luo. "Parameter estimation in geographically weighted regression." In 2009 17th International Conference on Geoinformatics. IEEE, 2009. http://dx.doi.org/10.1109/geoinformatics.2009.5292988.

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Hung Tien Tran, Hiep Tuan Nguyen, and Viet-Trung Tran. "Large-scale geographically weighted regression on Spark." In 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2016. http://dx.doi.org/10.1109/kse.2016.7758041.

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Timofeev, Vladimir S., Vladislav Yu Shchekoldin, and Anastasiia Yu Timofeeva. "Geographically weighted regression: Fitting to spatial location." In 2016 13th International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE). IEEE, 2016. http://dx.doi.org/10.1109/apeie.2016.7806489.

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Reports on the topic "Geographically Weighted Panel Regression"

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Mathew, Sonu, Srinivas S. Pulugurtha, and Sarvani Duvvuri. Modeling and Predicting Geospatial Teen Crash Frequency. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2119.

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This research project 1) evaluates the effect of road network, demographic, and land use characteristics on road crashes involving teen drivers, and, 2) develops and compares the predictability of local and global regression models in estimating teen crash frequency. The team considered data for 201 spatially distributed road segments in Mecklenburg County, North Carolina, USA for the evaluation and obtained data related to teen crashes from the Highway Safety Information System (HSIS) database. The team extracted demographic and land use characteristics using two different buffer widths (0.25

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