Innovative GIS techniques for identifying optimal service center locations in Islamabad a PTCL case study

Deleted Journal, Journal Year: 2025, Volume and Issue: 2(1)

Published: Jan. 15, 2025

In the ever-competitive telecommunications landscape, acquiring new customers hinges on identifying their precise locations. This study investigates how Pakistan's leading provider, PTCL, employed an innovative geo-marketing approach to pinpoint optimal locations for customer service centers in Islamabad. The integrates advanced GIS-based spatial analysis with statistical techniques, such as geographically weighted regression (GWR), achieving adjusted R2 of over 0.75 predicting potential density. Unique this work is incorporation multidimensional datasets, including demographic, economic, and competitive factors, coupled tailored site selection criteria optimize accessibility, visibility, resource allocation. By leveraging novel approach, PTCL enhanced targeting efficiency strategic decision-making, gaining a stronger market position. highlights transformative data-driven emerging markets, providing actionable insights companies aiming acquisition strategies.

Language: Английский

---

Decadal Dynamics of Rangeland Cover Using Remote Sensing and Machine Learning Approach

Rangeland Ecology & Management, Journal Year: 2025, Volume and Issue: 100, P. 1 - 13

Published: Feb. 14, 2025

Language: Английский

---

Spatio-temporal analysis of urban expansion and land use dynamics using google earth engine and predictive models

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 27, 2025

Urban expansion and changes in land use/land cover (LULC) have intensified recent decades due to human activity, influencing ecological developmental landscapes. This study investigated historical projected LULC urban growth patterns the districts of Multan Sargodha, Pakistan, using Landsat satellite imagery, cloud computing, predictive modelling from 1990 2030. The analysis images was grouped into four time periods (1990–2000, 2000–2010, 2010–2020, 2020–2030). Google Earth Engine cloud-based platform facilitated classification 5 ETM (1990, 2000, 2010) 8 OLI (2020) Random Forest model. A simulation model integrating Cellular Automata an Artificial Neural Network Multilayer Perceptron MOLUSCE plugin QGIS employed forecast resulting maps showed consistently high accuracy levels exceeding 92% for both across all periods. revealed that Multan's built-up area increased 240.56 km2 (6.58%) 440.30 (12.04%) 2020, while Sargodha experienced more dramatic 730.91 (12.69%) 1,029.07 (17.83%). Vegetation remained dominant but significant variations, particularly peri-urban areas. By 2030, is stabilize at 433.22 km2, primarily expanding southeastern direction. expected reach 1,404.97 showing balanced multi-directional toward northeast north. presents effective analytical method processing, GIS, change modeling evaluate spatiotemporal changes. approach successfully identified main transformations trends areas highlighting potential urbanization zones where opportunities exist developing planned managed settlements.

Language: Английский

---

Monitoring of Fire-Affected Buildings and Air-Quality Assessment: A Remote-Sensing Study Using Ground-Based interferometric Radar and Google-Earth-Engine

Rangeland Ecology & Management, Journal Year: 2025, Volume and Issue: 101, P. 28 - 42

Published: May 2, 2025

Language: Английский

---

Evaluating the impact of urbanization patterns on LST and UHI effect in Afghanistan’s Cities: a machine learning approach for sustainable urban planning

Environment Development and Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: May 5, 2025

Language: Английский

---