Using the Manhattan Distance Matrix to Estimate a Spatial Negative Binomial Regression Model
DOI:
https://doi.org/10.31272/jae.i146.1317Keywords:
Negative binomial regression model, Maximum possibility function, Moran's coefficient, Manhattan distance matrixAbstract
A spatial negative binomial regression model was applied to analyse the number of traffic accidents in the Iraqi governorates according to the different types of roads. In light of changes in the weather conditions for the year 2022, the maximum likelihood method was used to estimate the model and the weight matrix based on the distance to Manhattan, and it was reached. There is a direct relationship between the number of traffic accidents and the weather conditions, including temperatures, the amount of rain falling, and the amount of dust in the air.
Downloads
References
[1] Akkar, A. A. (2021). Estimation the spatial Durban regression model for anemia patients sample in some region of Al-Karkh/Baghdad. Journal of Administration and Economics, (128), 275–293. https://doi.org/10.31272/jae.i128.86
[2] Anselin, L., & Bera, A. K. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics. In D. E. A. Giles, Handbook of Applied Economic Statistics (pp. 237–290). Marcel Dekker.
[3] Anselin, L. (2010). Thirty years of spatial econometrics. Papers in Regional Science, 89(1), 3–26. https://doi.org/10.1111/j.1435-5957.2009.00263.x DOI: https://doi.org/10.1111/j.1435-5957.2010.00279.x
[4] Anselin, L. (2022). Spatial econometrics. In S. J. Rey & R. S. Franklin (Eds.), Handbook of spatial analysis in the social sciences (pp. 101–122). Edward Elgar Publishing.
[5] Apparicio, P., Abdelmajid, M., Riva, M., & Shearmur, R. (2008). Comparing alternative approaches to measuring the geographical accessibility of urban health services: Distance types and aggregation-error issues. International Journal of Health Geographics, 7, 1–14. https://doi.org/10.1186/1476-072X-7-1 DOI: https://doi.org/10.1186/1476-072X-7-7
[6] BjØrnstad, O. N., & Falck, W. (2001). Nonparametric spatial covariance functions: estimation and testing. Environmental and Ecological Statistics, 8(1), 53–70. https://doi.org/10.1023/A:1017551000673 DOI: https://doi.org/10.1023/A:1009601932481
[7] Buddhavarapu, P., Bansal, P., & Prozzi, J. A. (2021). A new spatial count data model with time-varying parameters. Transportation Research Part B: Methodological, 150, 566–586. https://doi.org/10.1016/j.trb.2021.06.014 DOI: https://doi.org/10.1016/j.trb.2021.06.015
[8] Glaser, S. (2017). A review of spatial econometric models for count data (Hohenheim Discussion Papers in Business, Economics and Social Sciences No. 19-2017). University of Hohenheim, Faculty of Business, Economics and Social Sciences.
[9] Hilbe, J. M. (2011). Negative binomial regression (2nd Ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511843076 DOI: https://doi.org/10.1017/CBO9780511973420
[10] Ibrahim, W. S., Majeedb, G. H., & Hussain, W. J. (2021). Comparison and estimation of a Spatial Autoregressive (SAR) model for cancer in Baghdad Regions. International Journal of Agricultural and Statistical Sciences, 17(1), 1921–1927. https://doi.org/10.35950/cbej.v28i114.5413 DOI: https://doi.org/10.35950/cbej.v28i114.5413
[11] Ibrahim, W. S., Hussein, W. J., & Anbar, J. A. (2022). Spatial Adjacency Matrices and Their Different Types. Journal of Administration and Economics, (132), 310–316.
[12] Ibrahim, W. S., & Mousa, N. S. (2022). Estimation of the general spatial regression model (SAC) by the maximum likelihood method. International Journal of Nonlinear Analysis and Applications, 13(1), 2947–2957.
[13] Musa, N. S., & Ibrahim, W. S. (2022). Estimation of the Spatial Autoregressive Combined (SAC) Model Parameters by Maximum Likelihood (MLE) Method for the Queen Contiguity Matrix using Simulation. Journal of the College of Basic Education, 28(114), 344–353. DOI: https://doi.org/10.35950/cbej.v28i114.5413
[14] Saad, S. O. (2022). Comparison between Maximum Likelihood and Bayesian Methods in Estimating the Spatial Autoregressive Model with a Practical Application [Master's Thesis, Al-Mustansiriya University].
[15] Toma, A. F. (2021). Selecting the Best Regression Model Estimator for the Negative Binomial Distribution with a Practical Application [Master's Thesis, University of Karbala].
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Basma Muhammad Lafta Mariyoush, Aseel Abdul Razzak Rasheed
This work is licensed under a Creative Commons Attribution 4.0 International License.
The journal of Administration & Economics is an open- access journal that all contents are free of charge. Articles of this journal are licensed under the terms of the Creative Commons Attribution International Public License CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode) that licensees are unrestrictly allowedto search, download, share, distribute, print, or link to the full text of the articles, crawl them for indexing and reproduce any medium of the articles provided that they give the author(s) proper credits (citation). The journal allows the author(s) to retain the copyright of their published article.
Creative Commons-Attribution (BY) 
