Monitoring the population change and urban growth of four major Pakistan cities through spatial analysis of open source data

Aslam, Rana Waqar; Hong, Su Young; Yaseen, Andaleeb

doi:10.1080/19475683.2023.2166989

Cited by 26 publications

(9 citation statements)

References 48 publications

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“…Noteworthy is the prominence of "Suburban or peri-urban grid cells" as the most populated class at 38.82%, as corroborated by recent research on urban dynamics (Jan and Iqbal, 2018). The study's data-driven approach to redefine urban areas integrates GIS, a methodology supported by previous works on urban delineation (Aslam, et al, 2023). By considering factors such as population density, infrastructure, and economic indicators, the research aligns with recommendations for a comprehensive understanding of urban landscapes (Haider, et al, 2010).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…These open data sources aid in capturing the geographical characteristics of urban areas with precision, allowing for detailed spatial analysis including population and land use patterns. This approach has been chosen for the past five years in comparable research studies recently conducted across the world (Kumar et al, 2017;Aslam et al, 2023). Therefore, in current time, GHSL-DUG technology is considered as crucial for delineating the arrangement of urban areas.…”

Section: Spatial Datamentioning

confidence: 99%

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Optimizing Urban Delimitations in Pakistan Through Exploratory Data Analytical Approach

Zia,

Shabbir,

Kazim

et al. 2024

ABBDM

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In Pakistan, the lack of a standardized definition for urban areas hampers effective urban planning and policy development. Thus, this research study employs data science by integrating the Geographic Information System (GIS) employed to find the weightage of the urban threshold that can redefine the urban areas in Pakistan. With diverse socio-economic and demographic factors considered, exploratory data analysis (EDA) incorporates population density, infrastructure development, economic indicators, and land use patterns to demark urban boundaries. As there is no consensus on what an “urban area” in Pakistan should be, this study takes a data-focused approach to determine the criteria that best capture the complexities of urbanization. It has been done through spatial analytics and statistical modeling in a data science framework. The integration of a data science framework enables to set-up threshold for development of urban landscape. The proposed redefinition of urban areas has implications for urban planning, resource allocation, and policy development, fostering a more accurate representation of the urban fabric in Pakistan. This study contributes to filling the existing gap in defining urban areas in Pakistan. It identifies new urban percentages that more accurately reflect the socio-economic and demographic realities of Pakistan's urban landscape. The findings shows that there are 36 percent of urban grid cells retrieved from the EDA approach. The outcomes provide a foundation for informed decision-making and policy interventions, aligning urban definitions with Pakistan's urbanization process's complex and dynamic nature. This research will also ensure a comprehensive understanding of urban landscapes in the face of diverse challenges particularly in developing countries.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Spatial Datamentioning

confidence: 99%

Optimizing Urban Delimitations in Pakistan Through Exploratory Data Analytical Approach

Zia,

Shabbir,

Kazim

et al. 2024

ABBDM

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“…Multi-temporal LandScan population data will be sourced for the specified years, namely 2000, 2010 and 2020. LandScan data offer global gridded population distribution information derived through advanced spatial modeling and data integration techniques, ensuring comprehensive and accurate coverage [62,63].…”

Section: Landscan Population Datamentioning

confidence: 99%

Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data

Aslam,

Shu,

Naz

et al. 2024

Remote Sensing

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Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection and risk mapping to examine moisture variability, land cover modifications, area changes and proximity-based threats over two decades. The random forest algorithm attained the highest accuracy (89.5%) for land cover classification based on rigorous k-fold cross-validation, with a training accuracy of 91.2% and a testing accuracy of 87.3%. This demonstrates the model’s effectiveness and robustness for wetland vulnerability modeling in the study area, showing 11% shrinkage in open water bodies since 2000. Inventory risk zoning revealed 30% of present-day wetland areas under moderate to high vulnerability. The cellular automata–Markov (CA–Markov) model predicted continued long-term declines driven by swelling anthropogenic pressures like the 29 million population growth surrounding Khinjhir Lake. The research demonstrates the effectiveness of integrating satellite data analytics, machine learning algorithms and spatial modeling to generate actionable insights into wetland vulnerability to guide conservation planning. The findings provide a robust baseline to inform policies aimed at ensuring the health and sustainable management and conservation of Khinjhir Lake wetlands in the face of escalating human and climatic pressures that threaten the ecological health and functioning of these vital ecosystems.

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“…Punjab is the most populated province of Pakistan. The population of Punjab increased from 20 million in 1951 to more than 110 million in 2017 [5]. Rapid urbanization, industrialization, unplanned consumption of groundwater, and lifestyle changes are increasing water demand [6].…”

Section: Introductionmentioning

confidence: 99%

Integrated Assessment and Geostatistical Evaluation of Groundwater Quality through Water Quality Indices

Naz,

Ahmad,

Aslam

et al. 2023

Water

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This study undertook an assessment of 24 physiochemical parameters at over 1094 sites to compute the water quality index (WQI) across the upper and central Punjab regions of Pakistan. Prior to the WQI calculation, an analytical hierarchy process (AHP) was employed to assign specific weights to each water quality parameter. The categorization of WQI into distinct classes was achieved by constructing a pairwise matrix based on their relative importance utilizing Saaty’s scale. Additionally, the groundwater quality status for irrigation and drinking purposes across various zones in the study area was delineated through the integration of WQI and geostatistical methodologies. The findings revealed discernible heavy metal issues in the Lahore division, with emerging microbiological contamination across the entire study region, potentially attributed to untreated industrial effluent discharge and inadequately managed sewerage systems. The computed indices for the Lahore, Sargodha, and Rawalpindi divisions fell within the marginal to unfit categories, indicating water quality concerns. In contrast, the indices for other divisions were in the medium class, suggesting suitability for drinking purposes. Scenario analysis for developing mitigation strategies indicated that primary treatment before wastewater disposal could rehabilitate 9% of the study area, followed by secondary (35%) and tertiary (41%) treatments. Microbiological contamination (27%) emerged as the predominant challenge for water supply agencies. Given the current trajectory of water quality deterioration, access to potable water is poised to become a significant public concern. Consequently, government agencies are urged to implement appropriate measures to enhance overall groundwater quality for sustainable development.

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Monitoring the population change and urban growth of four major Pakistan cities through spatial analysis of open source data

Cited by 26 publications

References 48 publications

Optimizing Urban Delimitations in Pakistan Through Exploratory Data Analytical Approach

Optimizing Urban Delimitations in Pakistan Through Exploratory Data Analytical Approach

Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data

Integrated Assessment and Geostatistical Evaluation of Groundwater Quality through Water Quality Indices

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