Globally, natural hazards have become more destructive in recent times because of rapid urban development and exposure. Consequently, significant human life loss, the damage to property and infrastructure, and the collapse of the environment directed the attention of geoscientists to control the consequences and risk management in relation to geo-hazards. In this research, an effort was made to produce a compound map, geo-visualizing the susceptibility of multi-hazards, to select suitable sites for sustainable future development and other economic activities in the region. Muzaffarabad District was chosen as a case research area due to the high magnitude of hydro-meteorological and geological hazards. On the one hand, both selected geo-hazard inventories were developed using the field survey and remote sensing data. The subjective and objective weight of all the causative factors and their classes were calculated using the assembled geospatial techniques, such as the Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) in the Geographic Information System (GIS). The results reveal that the most suitable areas are distributed in the southern and northwestern parts, which can be used for future sustainable development and other economic activities. In contrast, the eastern and western regions, including Muzaffarabad City, are within high and very susceptibility zones. Finally, more than 50% of the land area is located in very low and low susceptibility zones. The validation of the proposed model was checked by using three different techniques: the Receiver Operative Characteristic (ROC) curve, Seed Cell Area Index (SCAI), and Frequency Ratio (FR). Both ROCs, the Success Rate Curve (SRC) and the Predictive Rate Curve (PRC), showed the goodness of fit for both the selected geo-hazards: landslides (81.3%) and floods (93.2%), at 80.1% and 91.7%, respectively. All the validation techniques showed good fitness for both the individual and multi-hazard maps. The proposed model sets a baseline for policy implementation for all the stakeholders to minimize the risk and sustainable future development in areas of high frequent geo-hazards.

The Indus River is Asia’s longest river, having its origin in the Tibet Mountain northwest of Pakistan. Routed from northern Gilgit and flowing to the plains, the river passes through several provinces and is connected by numerous small and large tributaries. The river was formed tectonically due to the collusion of the Indian and Eurasian plates, which is referred to as the Indus suture Plains zone (ISPZ). The geological setting of the study area is mainly composed of igneous and metamorphic rocks. The river passed through a variety of climatic zones and areas, although the predominant climate is subtropic arid and sub arid to subequatorial. Locally and globally, anthropogenic activities such as building, dams, and water canals for irrigation purposes, mining exploration, and industries and factories all affected the physical and chemical behaviors of the sediments in various rivers. The main effect of human activities is the reworking of weathered soil smectite, a chemical weathering indicator that rises in the offshore record about 5000 years ago. This material indicates increased transport of stronger chemically weathered material, which may result from agriculture-induced erosion of older soil. However, we also see evidence for the incision of large rivers into the floodplain, which is also driving the reworking of this type of material, so the signal may be a combination of the two. Sediments undergo significant changes in form and size due to clashing with one another in the high-charge river.

No abstract

No abstract

The sustainable development of collection and delivery points and urban street network is an important consideration of logistic planners. Urban street networks have a significant impact on collection and delivery points’ location, but the spatial relationship between the centrality of urban street network and collection and delivery points has not been studied using spatial design network analysis. In a multiple centrality assessment model, we used point of interest and street network data to evaluate the location of two types of collection and delivery points and the centrality of streets in Nanjing city, based on four indicators: closeness, betweenness, severance, and efficiency. Then, kernel density estimation and spatial autocorrelation are used to study spatial patterns of distribution and centrality coupling effects of urban street network and collection and delivery points. The results show that the centrality of Nanjing streets has a big influence on the location of the collection and delivery points, and the directions of different types of centrality also vary. The location of the Cainiao Stations are largely related to closeness, followed by betweenness, severance, and efficiency. China Post Stations and street centrality have a weak correlation between efficiency and severance, but no correlation between closeness and betweenness. Our results can help logistics enterprises and urban planners to develop collection and delivery points’ network based on the urban street network.