Livability is a concept that assesses the quality of life and relative performance of different regions and communities, based on various qualitative and quantitative factors. The assessment of urban livability is a complex and multidimensional task, which is of utmost importance for informed and transparent policy and decision making. The present study aimed to develop a road map for cities’ ranking on the bases of their livability. We have used a case study of eight major cities of Punjab, Pakistan. Indicators of cities’ sustainability considered for the cities’ ranking were spatial planning and growth, individual wellbeing, urban economy, connectivity and infrastructure, quality of life, and the urban environment. In the first stage, after the consolidation of socio-economic, environmental, and other indicators, they are converted into dimension indices by taking geometric means. In the second stage, the Analytical Hierarchical Process (AHP) has been employed for assigning weights to each dimension into a composite index. The results of the study depict the disparities among cities through a multidimensional analysis. Lahore is ranked as first overall from the bottom. Thus, the study recommends that, despite the high standing of Lahore on the livability ladder, it must address the issue of connectivity and traffic congestion and per capita needs of the public infrastructure for a growing mega metropolitan city of over 10 million people. Similarly, Rawalpindi is ranked as the second-best city with regard to livability in Punjab. The main contributing factors for Rawalpindi city are ‘individual well-being’, ‘urban economy’, and ‘urban environment’. Moreover, the current study also suggests important policy implications for decision makers to highlight the areas that must be reconsidered for improvement in terms of the selected indicators and dimensions.

Landslide detection and mapping in remote and complex mountainous terrain is challenging, costly, and time-consuming for both disaster mitigation and land-use planning. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technologies, in this context, have emerged as powerful tools that enable the detection and tracking of subtle deformation signals associated with slow-moving landslides. In this chapter, slow-moving landslides in the remote and complex terrain of Pakistan using multi-orbit Sentinel-1 images acquired between January 2017 and December 2023 are mapped and investigated. We aim to map these landslides by testing and integrating three different point target detection criteria (i.e., spectral diversity, temporal variability, and phase stability) and processing them jointly for deformation modeling. We transformed average line-of-sight (LOS) deformation measurements (−60 to +60 mm/year) onto the steepest slope directions (vSLOPE), which were scanned through several GIS-based post-processing steps to identify and classify deformation hotspots, including landslides, subsidence, and erosion. Revealing the presence of approximately 2500 landslides of varying sizes up to 1 sq. km, the study also illustrates the potential of the tested method and Sentinel-1 data in detecting relatively low motion rates in remote and complex terrain that may contribute to updating the existing active landslide inventory in the region. Adhering to several factors, including precipitation as a speeding-up tool, the distribution analysis of the mapped landslides showed close relationships with distances to faults, drainages, and roads. The findings of this research have the potential to enhance regional-scale landslide risk management, and methodology can be applied to other landslide-prone regions worldwide.

Population measurement and its spatial distribution are required to understand the urban pattern of any city. We have used a methodology for measuring urban spatial morphology, infill, outlying expansion, and compactness over a decade. The aims of this research are related to data-driven categorization often large cities of Punjab and its correlation with urban attributes by identifying relative disparities in expansion and vertical growths; instead of city portfolios exerted and/or perceived by various groups of relevant urban stakeholders. Urban extents and sprawls were identified by using Geospatial data and techniques by applying supervised parameter optimization. The analytical tool was employed to highlight and better understand the relative (hierarchical) position of selected cities of Punjab, Pakistan. The results depict that the total urban area of selected cities has increased in ten years (2008-2018) from 914 km2 to 1649 km2. The cities’ saturation level helped to identify highly densified and the cities where the potential of infill is available. This study provides an effective and efficient way to monitor and evaluate the morphology of the cities and addresses the issues of mismanaged urban sprawl and decreasing densities.