Water Cashier Offices Location Optimisation Using Machine Learning-Based Clustering Approaches: A Case Study of Ordu Altınordu

Kübra Selvi, Murat Tasyürek, Celal Öztürk

2025

Abstract

The location of water cashier offices is crucial in terms of both operational efficiency and citizens' easy access to payment points. This study aims to reduce the average distance covering the widest service area with the minimum number of cashier offices by using the K-Means and Hierarchical Clustering methods based on the geographical coordinates of independent sections in Altınordu district of Ordu province. Spatial analyses are playing an increasingly important role in urban planning, urban transformation and disaster management, and identifying regions with similar characteristics is of great value. The dataset contains the latitude and longitude information of the independent sections. The actual number of cashier offices in the Altınordu district of Ordu province is 3. The optimal number of clusters determined by the Elbow method was 5, while the optimal number of clusters found using dendrogram analysis was 8. In this context, clustering scenarios of 3, 5, and 8 were examined, and the performance of each algorithm was compared based on the average distance criterion. The analyses revealed that the K-Means algorithm provided the best average distance. The results demonstrate that the independent sections in Altınordu can be geographically clustered and that this clustering, taking into account settlement density and the current cashier distribution, can serve as a guide for cashier planning and resource allocation. This approach can guide the more effective placement of water cashier offices, thereby increasing service efficiency and accessibility for citizens.

Paper Citation

in Harvard Style

Selvi K., Tasyürek M. and Öztürk C. (2025). Water Cashier Offices Location Optimisation Using Machine Learning-Based Clustering Approaches: A Case Study of Ordu Altınordu. In Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS; ISBN 978-989-758-783-2, SciTePress, pages 278-286. DOI: 10.5220/0014363700004848

in Bibtex Style

@conference{iceeecs25,
author={Kübra Selvi and Murat Tasyürek and Celal Öztürk},
title={Water Cashier Offices Location Optimisation Using Machine Learning-Based Clustering Approaches: A Case Study of Ordu Altınordu},
booktitle={Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS},
year={2025},
pages={278-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014363700004848},
isbn={978-989-758-783-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS
TI - Water Cashier Offices Location Optimisation Using Machine Learning-Based Clustering Approaches: A Case Study of Ordu Altınordu
SN - 978-989-758-783-2
AU - Selvi K.
AU - Tasyürek M.
AU - Öztürk C.
PY - 2025
SP - 278
EP - 286
DO - 10.5220/0014363700004848
PB - SciTePress