Understanding the spatial growth of cities is crucial for proactive planning and sustainable urbanization. The largest and most densely inhabited megapolis of Pakistan, Karachi, has experienced massive spatial growth not only in the core areas of the city, but also in the city’s suburbs and outskirts over the past decades. In this study, the land use/land cover (LULC) in Karachi was classified using Landsat data and the random forest algorithm from the Google Earth Engine cloud platform for the years 1990, 2000, 2010, and 2020. Land use/land cover classification maps as well as an urban sprawl matrix technique were used to analyze the geographical patterns and trends of urban sprawl. Six urban classes, namely, the primary urban core, secondary urban core, sub-urban fringe, scatter settlement, urban open space, and non-urban area, were determined for the exploration of urban landscape changes. Future scenarios of LULC for 2030 were predicted using a CA–Markov model. The study found that the built-up area had expanded in a considerably unpredictable manner, primarily at the expense of agricultural land. The increase in mangroves and grassland and shrub land proved the effectiveness of afforestation programs in improving vegetation coverage in the study area. The investigation of urban landscape alteration revealed that the primary urban core expanded from the core districts, namely, the Central, South, and East districts, and a new urban secondary core emerged in Malir in 2020. The CA–Markov model showed that the total urban built-up area could potentially increase from 584.78 km² in 2020 to 652.59 km² in 2030. The integrated method combining remote sensing, GIS, and an urban sprawl matrix has proven invaluable for the investigation of urban sprawl in a rapidly growing city.

The geographical concentration of criminal violence is closely associated with the social, demographic, and economic structural characteristics of neighborhoods. However, few studies have investigated homicide patterns and their relationships with neighborhoods in South Asian cities. In this study, the spatial and temporal patterns of homicide incidences in Karachi from 2009 to 2018 were analyzed using the local indicators of spatial association (LISA) method. Generalized linear modeling (GLM) and geographically weighted Poisson regression (GWPR) methods were implemented to examine the relationship between influential factors and the number of homicides during the 2009–2018 period. The results demonstrate that the homicide hotspot or clustered areas with high homicide counts expanded from 2009 to 2013 and decreased from 2013 to 2018. The number of homicides in the 2017–2018 period had a positive relationship with the percentage of the population speaking Balochi. The unplanned areas with low-density residential land use were associated with low homicide counts, and the areas patrolled by police forces had a significant negative relationship with the occurrence of homicide. The GWPR models effectively characterized the varying relationships between homicide and explanatory variables across the study area. The spatio-temporal analysis methods can be adapted to explore violent crime in other cities with a similar social context.

The groundwater resources have always been explored for its utilization in domestic and agriculture purposes. In this study, Winder area in Balochistan which is along the coast of Offshore Indus Basin has been explored using electrical resistivity me...

Climate change has made researchers realize there is a need to focus on crops consumed staple, including fruits. Uthal is the districts headquarter of Lasbela and has long been known for its valuable banana production. Due to scarcity of rainfall, aquifers are not recharging significantly, causing slow decrease in the Musa acuminate (common name banana) crop. Banana is a significant source of earnings for local cultivators. The primary objective of the present study is the fluctuation assessment of banana cultivation of Uthal. The methodology has been designed in three steps. The first was ground-truthing with the help of GPS survey of banana cultivation areas, the second, classification of greenery and banana crops based on Landsat 8 images via EVI and NDVI remote sensing tools. The third and last step was combined both data along with government published data for the accuracy assessment of banana cultivation areas. As per extracted results from NDVI, the share of banana total cultivated land of the study area has declined concerning the overall cultivated land of the study area which reduced from 1115 to 879 ha between 2013 and 2022. Results identified that the EVI-NDVI-GPS model is more significant for monitoring agricultural activities. The present study is an advanced contribution to the scientific field of Banana cultivation in Uthal.