| Title: | High Order Spatial Matrix |
|---|---|
| Description: | Automatically displays the order and spatial weighting matrix of the distance between locations. This concept was derived from the research of Mubarak, Aslanargun, and Siklar (2021) <doi:10.52403/ijrr.20211150> and Mubarak, Aslanargun, and Siklar (2022) <doi:10.17654/0972361722052>. Distance data between locations can be imported from 'Ms. Excel', 'maps' package or created in 'R' programming directly. This package also provides 5 simulations of distances between locations derived from fictitious data, the 'maps' package, and from research by Mubarak, Aslanargun, and Siklar (2022) <doi:10.29244/ijsa.v6i1p90-100>. |
| Authors: | Fadhlul Mubarak [aut, cre], Sukru Acitas [aut], Atilla Aslanargun [aut], Ilyas Siklar [aut], Vinny Yuliani Sundara [aut] |
| Maintainer: | Fadhlul Mubarak <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2025-02-08 04:23:40 UTC |
| Source: | https://github.com/mubarakfadhlul/hosm |
Creates high order spatial matrix of the distance between locations
hosm(data)hosm(data)
data |
dataframes from distances between locations |
A list the order and spatial weighting matrix of the distance between locations
Mubarak, F., Aslanargun, A., & Sıklar, I. (2022). GSTARIMA Model with Missing Value for Forecasting Gold Price. Indonesian Journal of Statistics and Its Applications, 6(1), 90–100. https://doi.org/10.29244/ijsa.v6i1p90-100
Mubarak, F., Aslanargun, A., & Sıklar, I. (2021). High order spatial weighting matrix using Google Trends. Int J Res Rev, 8(11), 388–396. https://doi.org/10.52403/ijrr.20211150
Mubarak, F., Aslanargun, A., & Sıklar, İ. (2022). Higher-order spatial classification using Google trends data during covid-19. Adv. Appl. Stat., 78, 93–103. https://doi.org/10.17654/0972361722052
hosm(simulation1) hosm(simulation2) hosm(simulation3) hosm(simulation4) hosm(simulation5)hosm(simulation1) hosm(simulation2) hosm(simulation3) hosm(simulation4) hosm(simulation5)
Simulation 1 for High Order Spatial Matrix
simulation1simulation1
A data frame with 4 locations:
Name of Location
1st Location
2nd Location
3rd Location
4th Location
data(simulation1)data(simulation1)
Simulation 2 for High Order Spatial Matrix
simulation2simulation2
A data frame with 5 locations:
Name of Location
'Amman City in Jordan
Abu Dhabi City in United Arab Emirates
Abuja City in Nigeria
Accra City in Ghana
Adamstown City in Pitcairn
data(simulation2)data(simulation2)
Simulation 3 for High Order Spatial Matrix
simulation3simulation3
A data frame with 5 locations:
Name of Location
Yaren City in Nauru
Yerevan City in Armenia
Zagreb City in Croatia
al-'Ayun City in Western Sahara
al-Kuwayt in (Kuwait)
data(simulation3)data(simulation3)
Simulation 4 for High Order Spatial Matrix
simulation4simulation4
A data frame with 4 locations:
Name of Location
Ankara City in Turkey
Jakarta City in Indonesia
London City in UK
Washington in USA
data(simulation4)data(simulation4)
Simulation 5 for High Order Spatial Matrix
simulation5simulation5
A data frame with 4 locations:
Name of Location
Banda Aceh City in Indonesia
Edison City in USA
Hakkari City in Turkey
London City in UK
data(simulation5)data(simulation5)