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Hail damage is a leading cause of insured losses in Australia, but changes in this hazard have not been robustly quantified. Here, we provide a continental-scale analysis of changes in hail hazard in Australia. A hail proxy applied to reanalysis data shows that from 1979–2021 annual hail-prone days decreased over much of Australia but increased in some heavily populated areas. For example, the annual number of hail-prone days increased by ~40% around Sydney and Perth, the largest cities on Australia's east and west coasts, respectively. Changes in atmospheric instability have driven the trends. Radar observations, while covering shorter time spans and a more limited area than the reanalysis, corroborate the broad pattern of results. This study shows consistent hail-frequency trends in radar indicators and atmospheric environments and demonstrates substantial increases in hail frequency in major Australian cities where hail impacts are most significant. [ABSTRACT FROM AUTHOR]

Globally many urban areas, including the coastal megacity of Mumbai, have witnessed the increasing threat of heavy rainfall and flooding in recent decades. This paper provides the first results of a radar campaign that has been set up to study the precipitation systems over Mumbai in 2018. Results indicate that the coastal city experiences heavy rainfall spells during both the pre-monsoon and monsoon periods and the convective systems originate from the eastern and western parts of the region, respectively. The collocated observations of clouds and precipitation fields identify a distinct diurnal variation in rainfall occurrences with three peaks for the pre-monsoon period. No such features were evident during the monsoon months. The rainfall peaks correspond to an intense meso-convective environment as observed from the S-band doppler radar. The feedback of the urban convective environment during the pre-monsoon period and the offshore moisture supply to the city during the monsoon months are the likely contributing factors for these contrasting diurnal behaviour. The microphysical characteristics of rainfall show that the raindrops of diameter greater than 1.5 mm dominate the pre-monsoon months compared to the monsoon period. The convective urban environment characterized by higher localized CAPE aids strong thermals leading to smaller drops shifting aloft and allowing bigger drops to precipitate locally during the pre-monsoon season. [ABSTRACT FROM AUTHOR]

Peshawar is one of the most densely populated cities of Pakistan with high urbanization rate. The city overexploits groundwater resources for household and commercial usage which has caused land subsidence. Land subsidence has long been an issue in Peshawar due to insufficient groundwater removal. In this research, we employ the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technique with Sentinel-1 imaging data to observe the yearly land subsidence and generate accumulative time-series maps for the years (2018 to 2020) using the SAR PROcessing tool (SARPROZ). The PS-InSAR findings from two contiguous paths are combined by considering the variance over the overlapping area. The subsidence rates in the Peshawar are from −59 to 17 mm/yr. The results show that subsidence is −28.48 mm/yr in 2018, the subsidence reached −49.02 mm/yr in 2019, while in 2020, the subsidence reached −49.90 mm/yr. The findings indicate a notable rise in land subsidence between the years 2018 and 2020. Subsidence is predicted in the research region primarily due to excessive groundwater removal and soil consolidation induced by surficial loads. The correlation of land subsidence observations with groundwater levels and precipitation data revealed some relationships. Overall, the proposed method efficiently monitors, maps, and detects subsidence-prone areas. The utilization of land subsidence maps will enhance the efficiency of urban planning, construction of surface infrastructure, and the management of risks associated with subsidence. [ABSTRACT FROM AUTHOR]

The increased need for water resources in urban sprawls and intense droughts has forced more aggressive groundwater extraction resulting in numerous urban areas undergoing land subsidence. In most cases, only some large metropolitan areas have been well-characterized for subsidence. However, there is no existing country-wide assessment of urban areas, population, and households exposed to this process. This research showcases a methodology to systematically evaluate urban localities with land subsidence higher than − 2.8 cm/year throughout Mexico. We used Interferometric Synthetic Aperture Radar (InSAR) tools with a dataset of 4611 scenes from European Space Agency's Sentinel-1 A/B SAR sensors acquired from descending orbits from September 2018 through October 2019. This dataset was processed at a supercomputer using InSAR Scientific Computing Environment and the Miami InSAR Time Series software in Python software. The quality and calibration of the resulting velocity maps are assessed through a large-scale comparison with observations from 100 continuous GPS sites throughout Mexico. Our results show that an urban area of 3797 km2, 6.9 million households, and 17% of the total population in Mexico is exposed to subsidence velocities of faster than − 2.8 cm/year, in more than 853 urban localities within 29 land subsidence regions. We also confirm previous global potential estimations of subsidence occurrence in low relief areas over unconsolidated deposits and where groundwater aquifers are under stress. The presented research demonstrates the capabilities for surveying urban areas exposed to land subsidence at a country-scale level by combining Sentinel-1 velocities with spatial national census data. [ABSTRACT FROM AUTHOR]

Recently, global urbanization and industrialization have resulted in excessive groundwater exploitation, and the occurrence and damage of land subsidence to human life and property are increasing accordingly. The present study assessed the impact of tunnel excavation on the groundwater system and potential land subsidence in a Korean metropolitan area. A numerical model was established to predict groundwater level variations with tunnel excavation, and the mechanisms and cases of land subsidence worldwide were reviewed. The established model adequately represented the groundwater system in the study area. The tunnel excavation decreased the groundwater level along the tunnel line, and significant groundwater drawdown primarily occurred up to 6.1 m at locations with high permeability, leading to the temporary development of a large depression cone. The study area has favorable conditions for land subsidence, and curved tunnel excavation may induce ground disturbance, resulting in groundwater inflow and soil loss in the region. In addition, the risk of land subsidence is expected to persist owing to the lag time caused by the creep phenomenon, even though the groundwater level recovers. Efforts to effectively reduce land subsidence damage in urban areas are crucial, requiring measures such as controlling land subsidence occurrence and implementing prevention measures through early recognition, including artificial recharge of groundwater, underground cavity observation with ground penetrating radar, land subsidence observation with a borehole extensometer, and groundwater level monitoring systems. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) is a remote sensing tool used for monitoring urban areas affected by geological hazards. Here we analysed the effectiveness of stabilization works on a slow-moving landslide in Arcos de La Frontera (Cádiz, Spain) using a persistent scatterer interferometric approach. The works consisted on jet grouting of cement-based injections and were applied locally to stabilize the most damaged neighbourhood. We processed a large stack of Sentinel-1 SAR satellite acquisitions covering the period January, 2016, to March, 2023, and obtained surface velocity and displacement trends measured along the line of sight (LOS) of the satellite on both ascending and descending orbits. The results show a clear deceleration of the landslide head after mid-2018, suggesting the local stabilization works were effective after that time. Prior to mid-2018, the maximum LOS velocity of the landslide head was 2.2 cm/year in ascending orbit and 1.3 cm/year in the descending orbit, decreasing to 0.43 cm/year and 0.23 cm/year, respectively. The InSAR results were compared to in-situ monitoring data and revealed that the extent of the stabilization has influenced a much larger area beyond the zone of the local interventions. Overall, InSAR has proved a powerful and versatile tool to be implemented in operational geotechnical monitoring. [ABSTRACT FROM AUTHOR]

Land subsidence in cities along the northern coastline of Java has been at a worrying level. Monitoring efforts using geodetic data reveal that Jakarta, Pekalongan, Semarang, and Demak subside at least ~9x faster than the present-day rate of global sea level rise, which affects the cities' future urban viability. In this study, we publish a time series of the precise 3D displacements observed by twenty continuous Global Navigation Satellite System (GNSS) stations between 2010 and 2021. These are the first open-to-the-public and rigorously processed GNSS datasets that are useful for accurately quantifying land subsidence in the densely populated sinking cities in Java. The data also provides a way to tie other geodetic observations, such as Interferometric Synthetic Aperture Radar (InSAR), to a global reference frame in an attempt to build worldwide observations of coastal land subsidence. [ABSTRACT FROM AUTHOR]

Flash floods have been the most frequent, extensive, and dangerous type of worldwide natural crisis. Mountainous, urbanized, and arid land areas like Tabuk region of Saudi Arabia are highly vulnerable to flooding due to heavy and sudden rainstorms that affected the people, infrastructures, and ecological and biophysical environment. Immediately following such a disaster event, advanced techniques are very essential to delineate and assess the damage resulted from successful rescue and relief operations. In this article, the European Space Agency (ESA) freely accessed images were conducted from the earth observation synthetic aperture radar (SAR) sensor, the images were successfully practiced to delineate the deadly flood crisis took place in Tabuk City in January 2019. The Sentinel Application Platform for Sentinel-1 Toolbox (S1TBX) is used to support the display and analysis of the large archive of products from the ESA SAR mission. Level-1 ground range detected with high resolution of 20 m × 22 m image at 2019/01/30 T15:39:44 acquired and processed. A backscatter value of − 22.39 dub is selected to separate water from everything else. Only region of interest mask pixels are considered for the histogram analysis. Accordingly, the maximum threshold error is set to 0.255. RGB map created a binary mask of flooded, and non-flooded pixels show that a significant part of the area was in danger. The findings of the current methodology quantify the flooded surface area by almost 9 km2 covers mainly the agricultural practices and the urban areas of the city of Tabuk. This study recommends that the future master plan of the Tabuk area should consider similar flooding risks. Finally, most arid lands like Tabuk are covered by sand that has similar backscatter as flooded land conditions. Therefore, the influence of this backscatter in flood mapping works should be assessed and exclusion techniques must be included to increase accuracy. [ABSTRACT FROM AUTHOR]

Using more than three million Landsat satellite images, this research developed the first global impervious surface area (GISA) dataset from 1972 to 2019. Based on 120,777 independent and random reference sites from 270 cities all over the world, the omission error, commission error, and F-score of GISA are 5.16%, 0.82%, and 0.954, respectively. Compared to the existing global datasets, the merits of GISA include: (1) It provided the global ISA maps before the year of 1985, and showed the longest time span (1972–2019) and the highest accuracy (in terms of a large number of randomly selected and third-party validation sample sets); (2) it presented a new global ISA mapping method including a semi-automatic global sample collection, a locally adaptive classification strategy, and a spatio-temporal post-processing procedure; and (3) it extracted ISA from the whole global land area (not from an urban mask) and hence reduced the underestimation. Moreover, on the basis of GISA, the long time series global urban expansion pattern (GUEP) has been calculated for the first time, and the pattern of continents and representative countries were analyzed. The two new datasets (GISA and GUEP) produced in this study can contribute to further understanding on the human's utilization and reformation to nature during the past half century, and can be freely download from http://irsip.whu.edu.cn/resources/dataweb.php. [ABSTRACT FROM AUTHOR]

This paper presents the selected results of GPR (ground penetrating radar) and ERT (electrical resistivity tomography) surveys carried out on the sites in Poland where shallow karst forms were found in gypsum deposits. The aim of the surveys was the noninvasive detection of karst forms as well as weathered and fractured bedrock which may threaten the stability of the surface and, consequently, may cause damage to buildings, as well as overground and underground infrastructure. The geophysical surveys were conducted at a depth of only a few meters, i.e., to the depth of buildings foundations. GPR surveys were carried out in short-offset reflection profiling mode with standard orientation of the antennae set; however, on one site, different orientations of antennae were tested. During ERT surveys, different measurement arrays were applied in order to analyze which array was optimal for the detection of karst forms as well as weathered and fractured bedrock. Complex interpretation of geophysical surveys resulted in reduced ambiguity and revealed some regions, dangerous for surface stability. Due to the fact that gypsum deposits were investigated to the depth of maximum 10 m; therefore, hydrological processes were analyzed in the paper instead of hydrogeological processes. [ABSTRACT FROM AUTHOR]

Background: Sustainable rural development entails efforts to enhance the well-being of rural communities while safeguarding natural resources for future generations. The thorough examination of sustainable rural development is still scarce but nevertheless crucial, as it enables to reveal the various challenges and remaining potentials in rural areas, to identify key stakeholders and their respective roles in promoting sustainable rural development, and to determine the best practices for attaining set goals along the various sustainability dimensions considered. Germany has implemented a range of policies, initiatives, and programs to foster sustainable development, extending its efforts to also encompass rural areas. This study assesses the sustainability performance of rural municipalities in Germany, proposing a novel and comprehensive sustainability benchmarking system. For this purpose, the performance of selected rural municipalities along the ecological, social, economic, and technological dimensions is examined. Results: Based on the systematic implementation of methodological stages, a rigorous literature review process, a systematic indicator selection, and stringent filtering criteria, the selected indicators of our study cover environmental, energy, quality of life, economic, and technological aspects. These indicators enable to efficiently and effectively measure, compare, and evaluate the sustainability performance of rural municipalities. After normalization, weighting, and aggregation of the considered indicators, the performance is visualized in radar charts. Radar charts are handy for comparing a larger number of variables and displaying them in compact and comprehensible form in a single chart. Finally, the overall relative sustainability performance of the selected rural municipalities in Germany is compared based on an aggregated single score. The adopted methodology of aggregating indicators enables us to assess the sustainability performance of municipalities as well as to highlight variations among them. Conclusion: Most databases and sustainable development reports are updated only every couple of years and often do not report the performance of small rural municipalities but only larger (more urban) ones. By conducting a detailed analysis of these specific cases, we can identify key challenges and opportunities unique to rural communities and develop targeted strategies for a more sustainable development. Adding digitalization as a technological value indicator makes our approach more comprehensive than comparable others, and accounts for the important new sustainability dimension of societal transition. This study contributes to the existing literature by proposing a novel sustainability benchmarking system specifically tailored for rural areas. From a practical standpoint, the developed sustainability benchmarking system offers a practical tool for rural municipalities to assess and monitor their sustainability performance and to reflect on variations among them. The identified dimensions and indicators can guide the development of targeted strategies and interventions aimed at enhancing sustainability in rural communities. Finally, policymakers can utilize the benchmarking results to design policies, (incentive) programs, and initiatives that address the specific sustainability challenges faced by rural municipalities and account for limitations in the local potentials. [ABSTRACT FROM AUTHOR]

Pomarico is a very small town in Basilicata, South Italy, historically landslide prone. The latest main landslide started on 25th of January 2019, on the northern east side of the hill, rapidly evolving and causing, on January 29th, the collapse of the main road of the town as well as of the overlooked buildings and serious damages of several buildings of the town. This work introduces the main features of this landslide and the earlier results of the studies on this phenomenon. The main geological characteristics of the site and the morphological features of the landslide are presented. Moreover, the interpretation of the complex failure mechanism as well as an analysis of the singularity of the triggering rainfall event are shown. Finally, this work focuses on the terrestrial radar monitoring system, installed after the event, and its preliminary outcomes. [ABSTRACT FROM AUTHOR]

While various sensors have been deployed to monitor vehicular flows, sensing pedestrian movement is still nascent. Yet walking is a significant mode of travel in many cities, especially those in Europe, Africa, and Asia. Understanding pedestrian volumes and flows is essential for designing safer and more attractive pedestrian infrastructure and for controlling periodic overcrowding. This study discusses a new approach to scale up urban sensing of people with the help of novel audio-based technology. It assesses the benefits and limitations of microphone-based sensors as compared to other forms of pedestrian sensing. A large-scale dataset called ASPED is presented, which includes high-quality audio recordings along with video recordings used for labeling the pedestrian count data. The baseline analyses highlight the promise of using audio sensors for pedestrian tracking, although algorithmic and technological improvements to make the sensors practically usable continue. This study also demonstrates how the data can be leveraged to predict pedestrian trajectories. Finally, it discusses the use cases and scenarios where audio-based pedestrian sensing can support better urban and transportation planning. [ABSTRACT FROM AUTHOR]

The consequences of sustainable and greener mobility have been demonstrated previous year by the pandemic situation. One significant benefit was to reduce the level of noise, dust and air pollution from the traffic in many European countries due to reduced mobility most of the population. Positive phenomena was also recorded in the Slovak Republic. Today's urban mobility offers new, more sustainable ways of transport for residents, especially app-based and shared-ride services, which have become increasingly widespread and very popular. Undoubtedly offer a level of convenience unseen before in the urban mobility systems throughout the world. Although new forms of transport are developed in most Slovak cities, their operation is not very rational and therefore individual car transport still dominates at the expense of sustainable ways of transport. In our paper, we analyze the traffic situation in the city of Nitra, as the large number of trips in the course of the peak-hours often results in severe traffic congestion. Multimodality is one of the solutions to improve the current traffic situation, as it allows urban residents to choose from a range of alternative travel options. The main aim of this paper is to determinate the possibilities of multimodal urban mobility in the city of Nitra in addition to analyze how the potential change in the variability of choice of means of transport affects the urban mobility behaviour. In order to meet stated objectives, a marketing research was conducted. The research findings point to the fact that the current urban infrastructure and overall possibilities are limited, the consequence of which is that travel behaviour of citizens does not demonstrate the features of sustainable urban mobility. Our results point to major differences in attitudes of Nitra´s residents from different urban and sub-urban areas. [ABSTRACT FROM AUTHOR]

Evaluation of urban environmental sustainability is an important prerequisite for scientific decision-making of urban sustainability. This paper proposed an evaluation method of urban environmental sustainability based on the integration of multi-improvement demands. Considering the interaction between the economic and social systems on the environmental system, this paper constructed an urban environmental sustainability evaluation indicator system from three dimensions: environmental foundation, environmental pollution, and environmental protection. Combined with the actual improvement demands of each city, a corresponding weighting method was constructed, and the weights of each city's individualized improvement demands were obtained through the analysis of indicator data. Further, in order to consider the fairness and guidance of the evaluation, the corresponding random simulation algorithm was matched for the individualized weights, and the evaluation value and the superiority probability in a stable state after multiple simulations were sought. Based on this, this paper conducted an empirical evaluation of urban environmental sustainability of the 14 cities in Liaoning Province, China. Through the demand-driven evaluation model, cities can obtain improvement suggestions that meet their demands while understanding their environmental sustainability level. It is convenient for top-down policy guidance and bottom-up practical exploration. [ABSTRACT FROM AUTHOR]

Understanding building morphology is crucial for accurately simulating interactions between urban structures and hydroclimate dynamics. Despite significant efforts to generate detailed global building morphology datasets, there is a lack of practical solutions using publicly accessible resources. In this work, we present GLAMOUR, a dataset derived from open-source Sentinel imagery that captures the average building height and footprint at a resolution of 0.0009° across urbanized areas worldwide. Validated in 18 cities, GLAMOUR exhibits superior accuracy with median root mean square errors of 7.5 m and 0.14 for building height and footprint estimations, indicating better overall performance against existing published datasets. The GLAMOUR dataset provides essential morphological information of 3D building structures and can be integrated with other datasets and tools for a wide range of applications including 3D building model generation and urban morphometric parameter derivation. These extended applications enable refined hydroclimate simulation and hazard assessment on a broader scale and offer valuable insights for researchers and policymakers in building sustainable and resilient urban environments prepared for future climate adaptation. [ABSTRACT FROM AUTHOR]

The notion of "spatial vulnerability" is present in most disaster studies with a strong geographical connotation and accordingly is adopted at all scales, including the urban. While enabling mapping and visualizing risk patterns at macroscales, this geocentric foundation fails to capture disaster risk dynamics associated with the urban spatial network—an element that plays a significant role in the everyday and emergency functioning of cities, enabling users' movement and interaction. Yet, urban vulnerability assessment overlooks this aspect and thus leaves urban disaster risk mechanisms partially unexplored. This study investigated the role of the network of urban public open spaces (UPOS) in the creation and progression of urban disaster risk in earthquake-prone settlements. Through a multimethod approach that integrates quantitative and qualitative methods and explores spatial configuration, planning policies, and practices of use of UPOS in everyday and emergency scenarios, our study demonstrated that UPOS configuration plays an active role in urban disaster risk. Urban public open spaces impact risk by influencing the exposure of pedestrians and their capacity for self-protection. The study further reconceptualized spatial vulnerability at the urban scale, as the fraction of vulnerability associated to the spatial network, highlighting the interplay of planning policies and spatial practices in its production and progression. Our findings make the notion of spatial vulnerability less ambiguous at the urban scale, by viewing the variable as an imbalance in capacities and exposure that generates spatially unsafe conditions. This refined conceptualization of spatial vulnerability becomes a lens for a more granular approach to urban disaster risk reduction and city planning by identifying and integrating sociospatial considerations. [ABSTRACT FROM AUTHOR]

At 5:38 am on the 24th June, 2017, a catastrophic rock avalanche destroyed the whole village of Xinmo, in Maoxian County, Sichuan Province, China. About 4.3 million m of rock detached from the crest of the mountain, gained momentum along a steep hillslope, entrained a large amount of pre-existing deposits, and hit the village at a velocity of 250 km/h. The impact produced a seismic shaking of ML = 2.3 magnitude. The sliding mass dammed the Songping gully with an accumulation body of 13 million m. The avalanche buried 64 houses; 10 people were killed and 73 were reported missing. The event raised great concerns both in China and worldwide. Extensive field investigation, satellite remote sensing, UAV aerial photography, and seismic analysis allowed to identify the main kinematic features, the dynamic process, and the triggering mechanism of the event. With the aid of ground-based synthetic aperture radar monitoring, the hazard deriving from potential further instabilities in the source area has been assessed. The preliminary results suggest that the landslide was triggered by the failure of a rock mass, which had been already weakened by the M 7.5 Diexi earthquake in 1933. Several major earthquakes since then, and the long-term effect of gravity and rainfall, contributed to the mass failure. The high elevation, slope angle, and vegetation cover in the source area hinder geological field investigation and make hazard assessment difficult. Nonetheless, monitoring and prevention of similar collapses in mountainous areas must be carried out to protect human lives and infrastructures. To this aim, the integrated use of modern high-precision observation technologies is strongly encouraged. [ABSTRACT FROM AUTHOR]

Growing urban population and the distinct strategies to accommodate them lead to diverse urban development patterns worldwide. While local evidence suggests the presence of urban signatures in rainfall anomalies, there is limited understanding of how rainfall responds to divergent urban development patterns worldwide. Here we unveil a divergence in the exposure to extreme rainfall for 1790 inland cities globally, attributable to their respective urban development patterns. Cities that experience compact development tend to witness larger increases in extreme rainfall frequency over downtown than their rural surroundings, while the anomalies in extreme rainfall frequency diminish for cities with dispersed development. Convection-permitting simulations further suggest compact urban footprints lead to more pronounced urban-rural thermal contrasts and aerodynamic disturbances. This is directly responsible for the divergent rainfall responses to urban development patterns. Our analyses offer significant insights pertaining to the priorities and potential of city-level efforts to mitigate the emerging climate-related hazards, particularly for countries experiencing rapid urbanization. Cities that experience compact development tend to witness more extreme rainfall over downtown than their rural surroundings, while the anomalies in extreme rainfall frequency diminish for cities with dispersed development patterns. [ABSTRACT FROM AUTHOR]

Air quality in urban areas is mainly controlled by emission sources and dispersion conditions. The mixing layer height (MLH) within which the pollutants are dispersed plays an important role in air pollution assessment. In this study, the MLH was determined based on the daily radon flux obtained from the European radon flux map and outdoor radon concentration measured in Bratislava, Slovakia. The radon-based MLH was compared with the boundary layer height obtained from the ERA-5 reanalysis dataset, showing good agreement. Using the Sturges grouping method and regression analysis, radon-based MLH showed significant correlations with the concentration of various atmospheric pollutants. [ABSTRACT FROM AUTHOR]

The transformation of cities into smarter and more efficient environments relies on proactive and timely detection and maintenance of city-wide infrastructure, including roadside infrastructure such as road signs and the cleaning of illegally dumped rubbish. Currently, these maintenance tasks rely predominantly on citizen reports or on-site checks by council staff. However, this approach has been shown to be time-consuming and highly costly, resulting in significant delays that negatively impact communities. This paper presents AIoT-CitySense, an AI and IoT-driven city-scale sensing framework, developed and piloted in collaboration with a local government in Australia. AIoT-CitySense has been designed to address the unique requirements of roadside infrastructure maintenance within the local government municipality. A tailored solution of AIoT-CitySense has been deployed on existing waste service trucks that cover a road network of approximately 100 kms in the municipality. Our analysis shows that proactive detection for roadside infrastructure maintenance using our solution reached an impressive 85%, surpassing the timeframes associated with manual reporting processes. AIoT-CitySense can potentially transform various domains, such as efficient detection of potholes and precise line marking for pedestrians. This paper exemplifies the power of leveraging city-wide data using AI and IoT technologies to drive tangible changes and improve the quality of city life. [ABSTRACT FROM AUTHOR]

Urban flood models that use Digital Elevation Models (DEMs) to simulate extent and depth of flood inundation rely on the accuracy of DEMs for predicting flood events. Despite recent advances in developing vegetation corrected DEMs, the effect of building height and density errors in global DEMs in urban areas are still poorly understood, and their correction remains a challenge. In this research we developed a methodology for building error correction that can be applied to any other case study, where building density data and a local reference DEM data are available. This methodology was applied to Nairobi, Kenya using six global DEMs (SRTM, MERIT, ALOS, NASADEM, TanDEM-X 12 m, and TanDEM-X 90 m DEM). Our results show building error at highest building density varying between 1.25 m and 5.07 m for the DEMs used, with the MERIT DEM showing the smallest vertical height error from the reference DEM. The six DEMs were corrected by deriving a linear relationship between building density and DEM error. Our findings show that the removal of building density error resulted in the improvement of the vertical height accuracy of the global DEMs of up to 45% for MERIT and 40% for ALOS. This methodology was also applied to the Central Business District (CBD) area of Nairobi, characterized by taller buildings and high building density. The error parameters in the CBD area resulted to be between 15 to 45% higher than those of the Nairobi city wide area for the six global DEMs, thus providing further insights into the contribution of building heights to errors in global DEMs. Building height data is still unavailable on a global scale and our results show that global DEMs can be usefully corrected for building density errors in urban areas, even where specific building height data are not available. [ABSTRACT FROM AUTHOR]

Flooding is a common phenomenon in the urban areas located close to the seacoast. In India, there is a recurrence of floods in the coastal cities of Mumbai and Chennai. In this study, we have tried to predict the flood occurrence in Mumbai using various machine learning techniques such as Logistic Regression, Support Vector Machines, K-Nearest Neighbour, Random Forest and Gradient Boosting methods. Historical flooding events and the value of flood contributing factors are used as inputs for the model. These input data are converted to raster layers with help of GIS tools. Our dependent variable would be a one-hot encoded vector stating whether or not it was flooded with those conditions. We have also tried to incorporate feature importance in our model. The training testing accuracy is the highest for K-Nearest Neighbour (KNN = 84%) and is followed by Random Forest Classification (RF = 81%). Using our model, we have identified the flood locations in Mumbai for August-2020 and found that out of 313 flood locations, KNN was able to correctly identify 133 flood locations whereas Random Forest Model identified 157 locations correctly. For better after-effects of flood forecast models, similar to hybridization, information deterioration, calculation group, and model streamlining are represented as the best methods for the improvement of ML procedures. Thus, our model can be used for the prediction of the locations where the chances of flood occurrence are highly likely and can accordingly plan for flood mitigation and relief measures. [ABSTRACT FROM AUTHOR]

Forecasting precipitation is a crucial input to hydrological models and hydrological event management. Accurate forecasts minimize the impact of extreme events on communities and infrastructure by providing timely and reliable information. In this study, six artificial intelligent hybrid models are developed to predict daily rainfall in urban areas by combining the firefly optimization algorithm (FA), invasive weed optimization algorithm (IWO), genetic particle swarm optimization algorithm (GAPSO), neural network (ANN), group method of data handling (GMDH), and wavelet transformation. Optimization algorithms increase forecasting accuracy by controlling all stages. A variety of criteria are used for validating the models, including correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), critical success index (CSI), probability of detection (POD), and false alarm ratio (FAR). The proposed models are also evaluated in an urban area in Ahvaz, Iran. The GAPSO-Wavelet-ANN model is superior to other models for predicting daily rainfall, with an RMSE of 1.42 mm and an R of 0.9715. [ABSTRACT FROM AUTHOR]

InSAR time series analysis has become a major tool for nationwide land deformation monitoring. Sentinel-1 SAR data have enabled us to measure and monitor ground deformation globally with high accuracy and resolution through InSAR time series analysis, due to its constant and frequent global coverage and open data policy since 2014. Although several datasets from previous SAR satellites were available before Sentinel-1, such comprehensive deformation monitoring was not performed due to several limitations such as data quality, analysis technique, data policy, and processing capacity at that time. However, since a large amount of ALOS InSAR products and an open-source InSAR time series analysis tool LiCSBAS have become openly and freely available, we can easily derive the deformation from 2006 to 2011 by using them. In this study, we detected the deformation time series and velocity in all major urban areas in Japan from 2006 to 2011 and compared the results with the deformation from 2014 to 2020 detected by Sentinel-1 data. The two deformation datasets with different time periods revealed various 15-year deformation histories, such as long-term constant subsidence in Tomakomai and Niigata, changes in deformation areas and/or velocities in Hirosaki, Kujyukuri, Kanazawa, and Matsushiro, and appearance or disappearance of deformation in Joso, Yoyogi, and Kyoto. Future abundant and continuous SAR data acquisitions will reveal more long-term deformation transitions and help to understand the details of the mechanisms. [ABSTRACT FROM AUTHOR]

Land degradation and soil erosion are becoming increasingly problematic in Africa's rapidly developing urban areas, particularly in Major Port Cities. Uncontrolled expansion and human pressures are hindering planning, adaptation, and conservation efforts. To understand the extent of these issues, this study combined morphometric analysis, soil loss calculation, field monitoring, and remote sensing and GIS tools to assess soil erosion in the Metropolis of Tangier (Morocco) located at the confluence of the Mediterranean Sea and the Atlantic Ocean at the Strait of Gibraltar. The study relied on data from 13 rain gauge stations, official reports, and remote sensing acquisitions, as well as field observations. Results showed an average soil erosion rate of 24.2 t/ha/year, equivalent to an annual soil loss of 588,051 t/year. This high rate was largely due to areas with a high erosion risk (99.8%), covering only 8.3% of the territory, which were characterized by recently burned topsoil, fallow land, and steep slopes. These areas included both uncontrolled neighbourhoods and areas for planned urban and industrial expansion, posing a threat to the landscape's sustainability and socio-economic prospects. The morphometric analysis revealed its high vulnerability to erosion and degradation, with the highest soil loss rates observed in the eastern and western regions. The study also found that flash floods caused by hydroclimatic hazards can lead to significant damage to infrastructure and equipment, particularly in western sub-basins and mountainous regions. In conclusion, the use of remote sensing and GIS technologies provided valuable insights into the physical characteristics and vulnerability of the Tangier Metropolis to land degradation and soil erosion. These findings emphasize the need for effective land management practices and conservation measures to mitigate the impacts of land degradation and soil erosion in the face of climate change. This information is crucial for decision-makers and stakeholders to develop strategies to address these pressing issues. [ABSTRACT FROM AUTHOR]

The urban community faces a significant obstacle in effectively utilising Earth Observation (EO) intelligence, particularly the Copernicus EO program of the European Union, to address the multifaceted aspects of urban sustainability and bolster urban resilience in the face of climate change challenges. In this context, here we present the efforts of the CURE project, which received funding under the European Union's Horizon 2020 Research and Innovation Framework Programme, to leverage the Copernicus Core Services (CCS) in supporting urban resilience. CURE provides spatially disaggregated environmental intelligence at a local scale, demonstrating that CCS can facilitate urban planning and management strategies to improve the resilience of cities. With a strong emphasis on stakeholder engagement, CURE has identified eleven cross-cutting applications between CCS that correspond to the major dimensions of urban sustainability and align with user needs. These applications have been integrated into a cloud-based platform known as DIAS (Data and Information Access Services), which is capable of delivering reliable, usable and relevant intelligence to support the development of downstream services towards enhancing resilience planning of cities throughout Europe. [ABSTRACT FROM AUTHOR]

Deprived settlements, usually referred to as slums, are often located in hazardous areas. However, there have been very few studies to examine this notion. In this study, we leverage the advancements in open geospatial data, earth observation (EO), and machine learning to create a multi-hazard susceptibility index and a transferrable disaster risk approach to be adapted in low- and middle-income country (LMIC) cities, with low-cost methods. Specifically, we identify multi-hazards in Nairobi's selected case study area and construct a susceptibility index. Then, we test the predictability of deprived settlements using the multi-hazard susceptibility index in comparison with EO texture-based methods. Lastly, we survey 100 households in two deprived settlements (typical and atypical slums) in Nairobi and use the survey outcomes to validate the multi-hazard susceptibility index. To test the assumption that deprived areas are dominantly located in areas with higher susceptibility to multiple hazards, we contrast morphologically identified deprived settlements with non-deprived settlements. We find that deprived settlements are generally more exposed to hazards. However, there are variations between central and peripheral settlements. In testing the predictability of deprivation using multi-hazards, the multi-hazard-based model performs better for deprived settlements than for other classes. In contrast, the texture-based model is better at classifying all types of morphological settlements. Lastly, by contrasting the survey outcomes to the household interviews, we conclude that proxies used for the multi-hazard susceptibility index adequately capture the hazards. However, more localized proxies can be used to improve the index performance. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive assessment of a dynamical downscaling of ERA5 Reanalysis recently performed over Italy through the COSMO-CLM model at a convection-permitting scale (0.02°) over the period 1989–2020. Results are analysed against several independent observational datasets and reanalysis products. The capability of the downscaling to realistically represent the climatology for 2 m temperature and precipitation is analysed over the whole peninsula and subdomains. Hourly precipitation patterns, orography effects, and urban climate dynamics are also investigated, highlighting the weaknesses and strengths of the convection-permitting model. In particular, gains in performances are achieved in mountainous areas where the climate characteristics are correctly represented, as are the hourly precipitation characteristics. Losses in performances occur in coastal and flat areas of the Italian peninsula, where the convection-permitting model performance does not seem to be satisfactory, as opposed to complex orographic areas. The adopted urban parameterisation is demonstrated to simulate heat detection for two Italian cities: Rome and Milan. Finally, a subset of extreme climate indicators is evaluated, finding: (i) a region-dependent response, (ii) a notable performance of the convection-permitting model over mountainous areas and (iii) discrepancies in the South, Central and Insular subdomains. Climate indicators detect extreme events at a detailed scale, becoming an important tool for turning climate data into information. [ABSTRACT FROM AUTHOR]

Groundwater overdraft gives rise to multiple adverse impacts including land subsidence and permanent groundwater storage loss. Existing methods are unable to characterize groundwater storage loss at the global scale with sufficient resolution to be relevant for local studies. Here we explore the interrelation between groundwater stress, aquifer depletion, and land subsidence using remote sensing and model-based datasets with a machine learning approach. The developed model predicts global land subsidence magnitude at high spatial resolution (~2 km), provides a first-order estimate of aquifer storage loss due to consolidation of ~17 km3/year globally, and quantifies key drivers of subsidence. Roughly 73% of the mapped subsidence occurs over cropland and urban areas, highlighting the need for sustainable groundwater management practices over these areas. The results of this study aid in assessing the spatial extents of subsidence in known subsiding areas, and in locating unknown groundwater stressed regions. Groundwater overdraft can lead to land subsidence and groundwater storage loss. Here, the authors develop a machine learning-based method to map subsidence globally, explore subsidence drivers, and identify regions under high groundwater stress. [ABSTRACT FROM AUTHOR]

Covid-19 significantly impacted the South African economy, particularly through geographical flexibility in workspaces, made possible by improvements in smart office technology. The ability to participate in hybrid forms of work (and 'work from home') has increased patterns of decentralised living, moving away from larger cities towards peripheral suburban locations and smaller towns, usually with the aim of improving living conditions. This trend has been described as 'semigration', and towns in the Western Cape seem to be the prime locations. The aim of this study is to investigate the extent and characteristics of semigration to Hermanus, in the Western Cape of South Africa. The data for this study was collected from two companies (Lightstone and Property24). Sampled data from Lightstone was analysed through frequency counts to provide a trend analysis of semigration to Hermanus. Property24's open-source data was analysed through frequencies, crosstabulations, and various spatial statistical techniques in ArcMap. Results indicate that Covid-19 slowed the pace of property purchases in Hermanus but did not necessarily increase this pace drastically in comparison to previous years. This offers only some anecdotal evidence of potential semigration but does not necessarily confirm it. Geographic shifts were however observed in locations where properties were purchased, while property prices increased in certain suburbs but decreased in others. These variations, together with changing characteristics of semigrants (i.e., their origin locations, wealth segments, and age groups) could suggest changing demographic patterns associated with the semigration phenomenon. Significant impacts on municipal policies and strategies were identified and suggestions made, along with recommendations for further research. [ABSTRACT FROM AUTHOR]

The Hindu Kush Himalaya (HKH) region is a major source of natural resources, like fresh water, for communities throughout south Asia. Monitoring the spatial patterns of climate variables throughout the region can help better understand and predict the future of this critical resource. In situ daily snow depth measurements from 1999 to 2019 were utilized to detect spatial patterns of the long-term trends in snow depths across the HKH region. The geospatial analysis included the use of emerging hot spots analysis. The results of our analysis revealed three broad regional clusters of long-term trends, including western, central, and eastern regions. Both eastern and western regions displayed declining snow depths, whereas the central region experienced areas of increasing snow depths. We also examined the localized long-term trends of snow depth around mega cities in the region, to delineate the role of urbanization on snow cover. The local level trends around the mega cities showed variable trends, determined by elevation and local pollution levels. [ABSTRACT FROM AUTHOR]

In recent years, an emerging strand of research has focused on the role arrival neighbourhoods play for newcomers finding their footing in a new urban context. However, little is known about the underlying factors and drivers influencing their function(ing). This concerns in particular the role of the local housing market and its players in shaping their emergence and development. The paper deals with the question of how arrival neighbourhoods are (co-)produced by housing market players and how the latter are embedded in local governance structures. Looking at three German arrival neighbourhoods, the article illustrates how they are co-produced by ownership structures and the allocation practices of different housing market players. However, the strengthening of an arrival neighbourhood´s function not only depends on ownership structures but also on the capacities of municipal housing providers and civil society organisations, their strategic goals and the will for (concerted) action. Our findings show that arrival neighbourhoods can take on an important citywide function, enabling newcomers to gain a foothold in the city if three criteria are met: they are accessible/affordable for low-income groups, are equipped with infrastructures for newcomers, and are permeable with regard to residents' relocation to other neighbourhoods. [ABSTRACT FROM AUTHOR]

Several factors overlap in making urban heritage conservation vulnerable in terms of long-term sustainability. The purpose of this study is to offer insights into the dynamic role that heritage governance plays in the current sustainability debate. This purpose is achieved by investigating the shift from a 'governing for culture' approach to a 'governing through culture' approach in heritage conservation. Subsequently, a case is built for a circularity-based conservation strategy applicable to the governance of historic cities. Different indicators of the circular governance approach are considered, and useful data are collected in comparative form. The cross-matching relationship between the factors is then evaluated by employing the analytic hierarchy process (AHP) on the collected data. As a test case, the conservation strategy of the Medina of Tunis is presented. For a more general conservation model, case-specific data are acquired. Finally, the same framework is applied to compare the case-dependent and case-independent data to define an integrated conservation framework. The obtained results show that the knowledge and data exchange factor, carries the highest significance. This result translates into heritage-led urban regeneration through knowledge sharing and the effective redistribution of cultural activities in historic city centres. [ABSTRACT FROM AUTHOR]

Measuring disaster resilience from the perspective of long-term recovery ability is important for the planning and construction of urban sustainability, whereas short-term resilient recovery can better reflect a city's ability to recover quickly after a disaster occurs. This study proposes an analytical framework for urban disaster recovery and resilience based on social media data that can analyze short-term disaster recovery and assess disaster resilience from the perspectives of infrastructure and people's psychological states. We consider the downpour in Henan, China, in July 2021. The results show that (1) social media data can effectively reflect short-term disaster recovery, (2) disaster resilience can be assessed using social media data combined with rainfall and damage data, and (3) the framework can quantitatively reflect the differences in disaster recovery and resilience across regions. The findings can facilitate better decision-making in disaster emergency management for precise and effective post-disaster reconstruction and psychological intervention, and provide references for cities to improve disaster resilience. [ABSTRACT FROM AUTHOR]

The determination of seismic risk in urban settlements has received increasing attention in the scientific community during the last decades since it allows to identify the most vulnerable portions of urban areas and therefore to plan appropriate strategies for seismic risk reduction. In order to accurately evaluate the seismic risk of urban settlements it should be necessary to estimate in detail the seismic vulnerability of all the existing buildings in the considered area. This task could be very cumbersome due to both the great number of information needed to accurately characterize each building and the huge related computational effort. Several simplified methods for the assessment of the seismic vulnerability of existing buildings have been therefore presented in the literature. In order to estimate the occurrence of damage in buildings due to possible seismic phenomena, the published studies usually refer to response spectra evaluated according to seismic events expected in the territory with assumed probabilities. In the present paper seismic events are instead simulated using a modified Olami–Feder–Christensen (OFC) model, within the framework of self-organized criticality. The proposed methodology takes into account some geological parameters in the evaluation of the seismic intensities perceived by each single building, extending the approach presented in a previous study of some of the authors. Here, a large territory in the Sicilian oriental coast, the metropolitan area of Catania, which includes several urbanized zones with different features, has been considered as a new case study. Applications of the procedure are presented first with reference to seismic sequences of variable intensity, whose occurrence is rather frequent in seismic territories, showing that the damage can be progressively accumulated in the buildings and may lead to their collapse even when the intensities of each single event are moderate. Moreover, statistically significant simulations of single major seismic events, equivalent to a given sequence in terms of produced damages on buildings, are also performed. The latter match well with a novel a-priori risk index, introduced with the aim of characterizing the seismic risk of each single municipality in the considered metropolitan area. The proposed procedure can be applied to any large urbanized territory and, allowing to identify the most vulnerable areas, can represent a useful tool to prioritize the allocation of funds. This could be a novelty for risk policies in many countries in which public subsidies are currently assigned on a case-by-case basis, taking into account only hazard and vulnerability. The use of an a-priori risk index in the allocation process will allow to take into due account the relevant role of exposure. [ABSTRACT FROM AUTHOR]

The impact of global warming presents an increased risk to the world's shorelines. The Intergovernmental Panel on Climate Change (IPCC) reported that the twenty-first century experienced a severe global mean sea-level rise due to human-induced climate change. Therefore, coastal planners require reasonably accurate estimates of the rate of sea-level rise and the potential impacts, including extreme sea-level changes, floods, and shoreline erosion. Also, land loss as a result of disturbance of shoreline is of interest as it damages properties and infrastructure. Using a nonlinear autoregressive network with an exogenous input (NARX) model, this study attempted to simulate (1991 to 2012) and predict (2013–2020) sea-level change along Merang kechil to Kuala Marang in Terengganu state shoreline areas. The simulation results show a rising trend with a maximum rate of 28.73 mm/year and an average of about 8.81 mm/year. In comparison, the prediction results show a rising sea level with a maximum rate of 79.26 mm/year and an average of about 25.34 mm/year. The database generated from this study can be used to inform shoreline defense strategies adapting to sea-level rise, flood, and erosion. Scientists can forecast sea-level increases beyond 2020 using simulated sea-level data up to 2020 and apply it for future research. The data also helps decision-makers choose measures for vulnerable shoreline settlements to adapt to sea-level rise. Notably, the data will provide essential information for policy development and implementation to facilitate operational decision-making processes for coastal cities. [ABSTRACT FROM AUTHOR]

Floods have caused socio-economic and environmental damage globally and, thus, require research. Several factors influence flooding events, such as extreme rainfall, physical characteristics, and local anthropogenic factors; therefore, such factors are essential for mapping flood risk areas and enabling measures that mitigate the damage they cause. This study aimed to map and analyze regions susceptible to flood risk in three different study areas belonging to the same Atlantic Forest biome, in which flood disasters are recurrent. Due to the presence of numerous factors, a multicriteria analysis using the Analytical Hierarchical Process was conducted. First, a geospatial database was composed of layers of elevation, slope, drainage distance, soil drainage, soil hydrological group, precipitation, relief, and land use and cover. Flood risk maps for the study area were then generated, and patterns in the study areas were verified, with the greatest influence being exerted by intense precipitation on consecutive days, elevation at the edges of the channel with low altimetric variation and a flat combination, densely built areas close to the banks of the main river, and an expressive water mass in the main watercourse. The results demonstrate that these characteristics together can indicate the occurrence of flooding events. [ABSTRACT FROM AUTHOR]

In Thailand, floods are occurring more frequently due to climate change, and recent economic development and population growth may have altered the way in which people interact with floods, including migration to other regions. In this study, we analyzed the relationship between flooding and population distribution across Thailand from 2005 to 2019 to improve measures for minimizing flood damage. We used population distribution point data from 2009 to 2019 produced by Oak Ridge National Laboratory to analyze trends in population movement and distribution, by examining whether population sizes were greater than, less than, or equal to estimated values in regions throughout Thailand. The results suggest that floods in 2011 and 2017 caused temporary migration to areas that were not inundated or to the metropolitan Bangkok area. Flood responses changed after the 2011 floods, which have been described as the worst flood in Thai history. Next, we examined the relationship between the number of regions with lower than estimated population and flood data for the previous year including precipitation, inundated area, and deaths caused by flooding. Inundation area had a significant impact on population decline, with correlation coefficients of 0.426 and 0.501 for the north and northeast, respectively. The number of deaths caused by flooding in a given year also led to a population decline in the following year. However, precipitation did not exhibit the same trend. Therefore, population demographics after floods have shown regional characteristics in recent years, with Thai people shifting from a flood-tolerance lifestyle to a flood-avoidance lifestyle, mainly in local urban areas and the metropolitan Bangkok area. [ABSTRACT FROM AUTHOR]

Focused on city-scale automation, and using self-driving cars (SDCs) as a case study, this article reflects on the role of AI—and in particular, computer vision systems used for mapping and navigation—as a catalyst for urban transformation. Urban research commonly presents AI and cities as having a one-way cause-and-effect relationship, giving undue weight to AI's impact on cities and overlooking the role of cities in shaping AI. Working at the intersection of data science and social research, this paper aims to counter this trend by exploring the reverse perspective: how do cities affect the development, and expose the present limits, of SDCs? The contribution of this paper is threefold. First, by comparing urban and nonurban environments and thoroughly examining the relationship between computer vision and city-specific sociality and form, it defines machine autonomy/automation as a function of the sociotechnical milieu in which an AI system operates. Second, and related, the paper problematizes the notion of SDCs as autonomous technologies and the role it plays in envisioning contending policy arrangements and technical solutions for achieving full driving automation. Finally, the article offers insight into a materialist and spatialized understanding of AI—namely, not as an abstract quality susceptible to replication within discrete machines, but rather as a distributed property emerging through embodied interactions among a multiplicity of agents (human, non-human, and technological) within/with their environments. [ABSTRACT FROM AUTHOR]

Homo Viator [traveling Man] refers in this article to the mobile individuals of the 2020s, gradually exposed to EVs (electric vehicles) and AVs (autonomous vehicles), as electricity-based and Internet-dependent personal road transport, respectively. This article examines the similarities and differences between the electrification and Internetizing of terrestrial personal mobilities and assesses the significance of these two trends for physically moving individuals, mobile society, and mobility-based urban space. EVs are already increasingly adopted, whereas AVs are still mostly being tested. Internet-based communications have already become universally adopted. Electricity and the Internet differ from each other, notably as far as the car industry is concerned, by their roles, modes of production, transmission channels, and storage. For individuals, EVs and AVs differ from each other in several ways: The degree of personal operations and the required accounting and licensing; the ability to move human-made products electronically; ergonomic aspects; travel and communications as an experience; and interactions with fellow individuals. From a societal perspective, the universal use of electricity and the Internet for road transport will require strict security assurance for their production and transmission. In addition, the importance of the relevant communications and electricity professions will grow. On yet another end, the ability to work during car riding may blur the buffer time between work and home. Spatially, EV-based cities will be quieter and cleaner ones, whereas AV-based cities will be characterized by removing traffic lights and road signs, coupled with the availability of more parking spaces. [ABSTRACT FROM AUTHOR]

This research analyzed interventions of State Fire Service (SFS) units in the Wielkopolska region of Poland that were triggered by extreme precipitation for the period 2010–2021. Our results demonstrated that the most populated and urbanized towns in the Wielkopolska (Greater Poland, west of Warsaw) region are at the most risk in the event of extreme precipitation occurrence as measured by the total number of interventions made by the SFS. The number of SFS unit interventions in towns, standardized to 10,000 inhabitants, indicates that the highest proportional volume of interventions also occurred in smaller towns. In the rural municipalities the number of SFS unit interventions increases along with higher population density and proportion of infrastructure areas. As analyzed in this study, the 12 years from 2010 to 2021 were characterized by a higher number of days with heavy precipitation, for example, 20, 30, 40, and 50 mm, in comparison to the previous periods 1961–2010 and 1981–2010. Intervention databases collected by emergency services are a valuable source of information for hazard mapping. Based on those and other available data, a statistical model was created and factors influencing the local and regional occurrence of interventions were determined. Increasing suburbanization, the rising proportion of impermeable surfaces, and the impact of climate change are of considerable importance in urban flood risk. It is necessary to help municipalities develop abilities to absorb larger amounts of rainwater. [ABSTRACT FROM AUTHOR]

Land-use decision making in densely populated cities is challenged to cover the urban demand for water and food while maintaining the integrity of ecosystems that regulate the supply of these ecosystem services, beyond urban limits. Land-use decision making can be informed using spatial analysis of ecosystem services supply and demand, shedding light on potential mismatches, surpluses, deficits, and unsustainable uses of ecosystems. The integration of such analysis in land-use decision making, however, can be challenged by the complexity of the hierarchical and inflexible planning models used in various countries. We explored how the analysis of potential mismatches, surpluses, and deficits in the supply and demand of water and fertile soil can be spatially assessed for urban areas, and how such information can be used to inform land-use decision making. We used official data and geoprocessing methods to map water -supply, demand, and risk for water scarcity-, and fertile supply –supply, demand, and risk for food insecurity-, for Bogota and Villavicencio. We showed a high risk for water scarcity in Bogota, and an increasing competition for fertile soil between urbanization and agriculture. We recommended a cooperative management strategy of ecosystems and services considering that both cities shared river basins and natural reserves, in addition to their strike differences in population size. Inter-jurisdictional and inter-municipal cooperation, in addition to strong governance, are necessary to handle mismatches of urban ecosystem services supply and demand, and a potential solution to solve conflicts related to access, availability, and jurisdiction of ecosystem services in urban areas.. [ABSTRACT FROM AUTHOR]

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Among the metro cities in India, Kolkata is chosen incidentally for the present study. The study aims at clustering of pre-monsoon days of the urban area, Kolkata (22º32′N, 88º20′E) (India) in two groups (thunderstorm days denoted by TS and non-thunderstorm days denoted by NTS in the literature) using hard k-mean technique, backward selection procedure and fuzzy c-mean algorithm (FCM). Various thermodynamic and dynamic parameters that are already identified by several scientists as responsible for thunderstorm formation have been considered here for different atmospheric layers up to 500 hPa. The study is performed in two stages: In the first stage, the hard k-mean technique is applied to cluster the days of a semi-supervised dataset in the two categories mentioned above. Then the backward selection procedure is used to find the best possible combinations of the theoretically influential atmospheric parameters considered in the study, that plays the dominant role in the categorization on the basis of performance score (PC). In the second stage of the work, fuzzy c-mean algorithm is applied to the same semi-supervised dataset of parameters to clarify the results obtained in the first stage. This study is performed separately for the morning (0000 UTC) and afternoon (1200 UTC) atmosphere as it is already revealed that there is a structural difference between the morning and afternoon atmosphere of Kolkata, India. In the first stage with the (thunderstorm and non-thunderstorm) dates of 0000 UTC (morning) data reveals that the combination of maximum vertical velocity and P–PLCL at 1000 hPa level performs better in detecting the pre-monsoon thunderstorm days, whereas with those at 1200 UTC (afternoon) data showed vertical wind speed shear for (1000–850) hPa layer, maximum vertical velocity, P–PLCL at 1000 hPa level and (θes–θe) at 850 hPa level dominate better in detecting thunderstorm days. It is interesting to note that these findings are supported by FCM in the second stage for both morning and afternoon atmosphere. Research highlights: The study in the first stage reveals that the hard k-means clustering technique with two different metrics, Euclidean distance produces more stable results than Manhattan distances. In the second stage the fuzzy c-means algorithm helps to clarify the reasons behind the results obtained in the first stage. In this study proportion correct, HK skill score and backward selection procedure indicates that combination of maximum vertical velocity and P–PLCL furnish better results in morning whereas in afternoon along with the above two parameters, vertical wind speed shear (1000–850 hPa) and (θes–θe) at 850 hPa also gives better results for the categorization of pre-monsoon days of Kolkata (India), in two groups, thunderstorm and non-thunderstorm. [ABSTRACT FROM AUTHOR]

After roughly 20 years since the emergence of urban scholarship in same-sex sexualities in South Africa, it is worthwhile considering how some of the concerns that originally animated that scholarship have evolved and also how such concerns are today reflected differently away from primary cities (where much earlier research was conducted). In this commentary, I explore the unique history of Stellenbosch, a university town/secondary city 50 km away from Cape Town. Stellenbosch's own unique history of—and recent developments with regard to—public (male) same-sex expression help set into relief earlier scholarship and also points towards some future research questions that may also be applicable elsewhere on the African continent. While, as made clear, Stellenbosch is in some key instances unique in terms of its sexualized and raced history both in South Africa and the wider continent, its position as what we might increasingly want to frame as a secondary city, its particular racial composition, and also its changing spaces of socio-sexual interaction since the COVID-19 pandemic gesture towards key areas of potentially generative wider research interest. [ABSTRACT FROM AUTHOR]

Understanding the spatial structure of a city is essential for formulating a spatial strategy for that city. In this study, we propose a method for analyzing the functional spatial structure of cities based on satellite remote sensing data. In this method, we first assume that urban functions consist of residential and central functions, and that these functions are measured by trip attraction by purpose. Next, we develop a model to explain trip attraction using remote sensing data, and estimate trip attraction on a grid basis. Using the estimated trip attraction, we created a contour tree to identify the spatial extent of the city and the hierarchical structure of the central functions of the city. As a result of applying this method to the Tokyo metropolitan area, we found that (1) our method reproduced 84% of urban areas and 94% of non-urban areas defined by the government, (2) our method extracted 848 urban centers, and their size distribution followed a Pareto distribution, and (3) the top-ranking urban centers were consistent with the districts defined in the master plans for the metropolitan area. Based on the results, we discussed the applicability of our method to urban structure analysis. [ABSTRACT FROM AUTHOR]

Currently, air quality has become central to global environmental policymaking. As a typical mountain megacity in the Cheng-Yu region, the air pollution in Chongqing is unique and sensitive. This study aims to comprehensively investigate the long-term annual, seasonal, and monthly variation characteristics of six major pollutants and seven meteorological parameters. The emission distribution of major pollutants is also discussed. The relationship between pollutants and the multi-scale meteorological conditions was explored. The results indicate that particulate matter (PM), SO2 and NO2 showed a "U-shaped" variation, while O3 showed an "inverted U-shaped" seasonal variation. Industrial emissions accounted for 81.84%, 58% and 80.10% of the total SO2, NOx and dust pollution emissions, respectively. The correlation between PM2.5 and PM10 was strong (R = 0.98). In addition, PM only showed a significant negative correlation with O3. On the contrary, PM showed a significant positive correlation with other gaseous pollutants (SO2, NO2, CO). O3 is only negatively correlated with relative humidity and atmospheric pressure. These findings provide an accurate and effective countermeasure for the coordinated management of air pollution in Cheng-Yu region and the formulation of the regional carbon peaking roadmap. Furthermore, it can improve the prediction accuracy of air pollution under multi-scale meteorological factors, promote effective emission reduction paths and policies in the region, and provide references for related epidemiological research. [ABSTRACT FROM AUTHOR]

Urban flooding has become a widespread issue across the world over the last few decades, making it crucial for cities to prioritise the flood risk mitigation. Kolkata has been selected as the study area as this city is densely urbanised and waterlogging situation is frequent here. The main goal of this paper is to apply the Integrated Valuation of Ecosystem Services and Tradeoffs-Urban Flood Risk Mitigation (InVEST-UFRM) model for the city Kolkata to understand the flood-like situation after consecutive rainfall for 2 h with different rainfall depths and find possible mitigation measures. The result finds Kolkata is covered up with 75% impervious surface where more than 80% of the rain water transforms into runoff after 2 h of rainfall and northern portion of Kolkata is extremely vulnerable to flooding. About 47% flood volume increases by 71% with a change in rainfall depth. Open green spaces have the highest potentiality to retain rainfall. Average runoff retention capacity of watersheds reduces about 10% with increase in 50% increase in rainfall. Economic damage costs about 10 lakh rupees for the most vulnerable area of 5.45 km2 from the study area with a rainfall depth of 47 mm in 2 h. Flood risk related mitigation measures that should be followed for the study area with three perspectives like precipitation parameter, excess runoff reduction and runoff retention measures. Application of this model for urban flood mitigation is comparatively easy to use and more approachable for any urban area. [ABSTRACT FROM AUTHOR]

Observing the state and changes of land cover (LC) is critical for assessing the status and changes of ecosystems such as urban development. Remote sensing can extract useful data from a large region regularly and at the same time. The present study has used Landsat 5 and 8 images to derive LC classification and Land Surface Temperature (LST) maps of Reykjavik city for four years 1987, 1995, 2006, and 2019. Satellite images were classified into four major land-cover classes using the Artificial Neural Network classification (ANN) algorithm. Then, calculations including the LST and the distance from the water were calculated for each study period. Finally, using the Markov chain, the prediction of land cover classes and LST classes for 2020 was obtained. The results have shown an average of 21.5% (8.2 km2), 71% (32.9 km2), -62.9% (43.7 km2), and 3.4% (2.6 km2) growth for Urban Areas (UA), Vegetation Land (VL), Bare Lands (BL), and Water Lands (WL) of Reykjavik evaluated LC classes. Reykjavik LC forecasting shows that UA will have had a 38.5% (8.2 km2), VL a -43% (34.1 km2), BL a 14.7% (3.8 km2), and WL a 14.8% (11.9 km2) by 2030. The mean of LST in this area has risen by 67.5% (7.7 °C) during this period. This is also supported by the findings of the analysis of the NDVI and LST connection. According to the research, the LST and the NDVI value of 0.1 to 0.7 have a positive relationship in this area. However, there is a negative correlation between LST with NDVI values of 0.8 and 0.9. The results showed that the mean of LST was also influenced by the distance from WL water bodies. The LST has grown by a mean of 0.7 of correlation as the distance from WL has increased. The results of this research will be useful in order to get an overview of the past and present situation and plan for the control of heat islands and land cover changes in Reykjavik. [ABSTRACT FROM AUTHOR]