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Hosting mega sports events is a key driver for sustainable development, particularly through fostering tourism marketing and planning. Although the 2022 FIFA World Cup has received considerable attention, as the first global event hosted in the Middle East and Arab world, studies that highlight and investigate the potential benefits of hosting such an event are rare. This study assessed the geographical accessibility to archaeological sites, monuments, and museums across Qatar, providing clear guidelines on how to represent the national cultural and historical heritage to global audiences during the 2022 mega event, to maximise socioeconomic revenue. Spatial data were assembled within GIS platforms to assess accessibility utilising geospatial techniques, such as the average nearest neighbour, near analysis, spatial autocorrelation (Moran 'I Index), and cost distance to estimate travel times. The analysis indicated that the archaeological sites are spatially clustered, predominantly concentrated in Doha and Al-Rayan municipalities, where historical and cultural landmarks are located. However, most castles, forts, and archaeological sites located outside the capital zone are less accessible, and at a long distance from hotels and residential areas. As the hosting stadiums are located along the northeast coast and within the most populous zone, the museums, towers, and cultural landscapes are easily accessible within a short travel time (less than 10 min). As a host community, planners and policymakers in Qatar may benefit from this research, as a spatial guideline to promote sustainable development of tourism, through facilitating accessibility to monuments and museums, as well as enriching the representation of the national tangible heritage to a global audience. • For host country, FIFA World Cup has crucial impacts on sustainable tourism. • Qatar World Cup 2022 offers a great opportunity to represent the national heritage. • Facilitating accessibility to monuments is a strategic pillar for the host country. • Geospatial techniques have been utilised to measure accessibility patterns. • Monuments within the capital zone are spatially clustered and easily accessed. [ABSTRACT FROM AUTHOR]

Tropical cyclones are a highly destructive natural hazard that can cause extensive damage to assets and loss of life. This is especially true for the many coastal cities and communities that lie in their paths. Despite their significance globally, research on post-cyclone recovery rates has generally been qualitative and, crucially, has lacked spatial definition. Here, we used freely available satellite nighttime light data to model spatially the rate of post-cyclone recovery and selected several spatial covariates (socioeconomic, environmental and topographical factors) to explain the rate of recovery. We fitted three types of regression model to characterize the relationship between rate of recovery and the selected covariates; one global model (linear regression) and two local models (geographically weighted regression, GWR, and multiscale geographically weighted regression, MGWR). Despite the rate of recovery being a challenging variable to predict, the two local models explained 42% (GWR) and 51% (MGWR) of the variation, compared to the global linear model which explained only 13% of the variation. Importantly, the local models revealed which covariates were explanatory at which places; information that could be crucial to policy-makers and local decision-makers in relation to disaster preparedness and recovery planning. [ABSTRACT FROM AUTHOR]

Oman is a major recipient of South Asian labor immigrants, and correspondingly, the demographic statistics reveal that the non-Omani populations primarily are constituted of the Asians. This research examines whether the largest immigrant groups (Indian, Bangladesh, Pakistani and Sri Lankan) in Oman form residential clusters according to their work skills (higher and lower skills). The residential geographies of South Asian workers at the subnational administrative boundaries have been investigated in the current study. Empirically, the study employs the use of Location Quotients (LQ) and Entropy Index within the GIS environment to spatially analyze the immigrant residential distributions based on their work skills. Interestingly, the findings of this research confirm the primary influence of geography on the residential patterns of the low-skilled immigrants. Initially, Al-Batnah governorates constituted the main destinations of low-skilled Bangladeshis. Further, the research also highlights significant clusters of Indian and Pakistani high-skilled immigrants in urban residential communities within the Muscat governorate. In addition, the South Asian are found to be a relatively constant stream of immigrants to inhabit the Omani urban areas, with employments in various public and private economic sectors (e.g. educational, health, manufacturing, finance, business etc.). This concentration pattern of low skilled immigrants is attributable to the need to labor shortages of native populations in certain jobs. The research results also indicate that the rural and suburban communities of Al-Batnah coastal plain not only have a greater number of low skilled immigrants but also display equitable distributions of the four South Asian groups in the residential settlements. For policy makers, it is imperative to understand the spatial patterns of low and high-skilled immigrants in Oman, which exert several geographic, economic, social and demographic implications. The significant role of immigration in development necessitates the identification of the predominant destinations for immigrants. It has been observed that the immigrants are crucial to several sectors of Omani urban economy, and urban areas with a large number of immigrants are more likely to grow and expand faster. In addition, the immigrants’ laborers offer potential economic benefits to the host country, which include fostering entrepreneurial activities, small businesses and reducing wages, especially in unskilled jobs. Despite the negative impacts of immigration (e.g. losing national cultural and creating socio-spatial segregation), the diverse and distinctive locational patterns of immigrant groups in Oman contribute crucially to the socioeconomic development and immigration policy. Yet understanding of the spatial dynamics of immigration in structuring the Omani regions and its influences remains very limited. Thus, further research accounting for the different spatial and attribute ancillary data is necessitated. [ABSTRACT FROM AUTHOR]

Abstract: GPS coordinates are increasingly available as spatial references on population surveys in the developing world, where high-resolution address and street mapping are absent. This potentially offers opportunities to enhance national census data by spatial linkage with survey sources. The paper explores the use of GPS-referenced Demographic and Health Survey (DHS) data in combination with census data in Egypt and identifies errors in coordinate referencing. The study develops a practical approach to the measurement of spatial uncertainty in this situation and assessment of its impact on data linkage. The analysis specifically addresses the analytical implications at three different spatial scales and is internationally relevant to the handling of GPS-referenced DHS data in GIS. [Copyright &y& Elsevier]

Urban green spaces are a crucial component in regards to the quality of life, ecosystem balance and recreational services of populations, particularly in arid and semi-arid regions. This study aimed to explore spatial patterns of accessibility to public parks on a neighbourhood scale in the Sohar Wilayat, Oman. Utilising GIS techniques and landscape metrics, we investigated the spatial variations of green patches relevant to other land use types predominantly residential buildings in each local area. The entropy index, the landscape shape index (LSI) and the area-weighted mean shape index (AWMSI) were calculated to analyse landscape spatial patterns of urban green spaces across the study area. The results of this study indicated that the large numbers of green space patches in the majority of neighbourhoods were associated with large population size. In measuring spatial accessibility to public parks, the central neighbourhoods were characterised by low scores and long distances from green spaces, while neighbourhoods in the south and north showed short distances and high scores for residents' accessibility to the nearest park. High rates of fragmentation and irregular shapes, particularly within marginal and inner neighbourhoods, can be attributed to rapid urbanisation and sprawl, which has extensively transformed urban green spaces and vacant land into dwellings. Our findings suggest that a spatial quantification and identification of green space distribution patterns and the accessibility of public parks could provide decision-makers and municipality planners with invaluable guidelines for allocating green parks and recreational amenities equitably and efficiently to urban residents. ⁃ Green areas are important structure of any arid or semi-arid cities. ⁃ This study analyses accessibility and provision of parks and green spaces in Sohar wilayat, Oman. ⁃ Several spatial metrics were employed to assess distribution patterns of green richness. ⁃ Various neighborhoods lack sufficient green spaces and were characterized by low accessibility. ⁃ The findings are of great importance to sustain ecological services in arid urban places. [ABSTRACT FROM AUTHOR]

Globally, an increasing and more dispersed population, as well as climate change, have led to growing impacts of environmental hazards, particularly across areas prone to extreme weather events such as tropical cyclones. Tropical cyclones frequently cause fatalities, damage to infrastructure, and disruption to economic activities. The north and northeast regions of Oman, particularly the Oman seacoast, are prone to the storm surges, windstorms and extreme precipitation events associated with these tropical storms. However, integrated spatial risk assessments, for the purpose of mapping cyclone risk at subnational geographic scales, have not yet been developed in this area. Here we evaluate and map cyclone risk using four independent components of risk: hazard, exposure, vulnerability and mitigation capacity. An integrated risk index was calculated using a geographical information system (GIS) and an analytical hierarchical process (AHP) technique, based on a geodatabase including 17 variables (i.e., GIS data layers) and criteria, with rank and weight scores for each criterion. The resulting risk assessment reveals the spatial variation in cyclone risk across the study area and highlights how this variation is controlled by variations in physical hazard, exposure, vulnerability and emergency preparedness. The risk maps reveal that, despite their perceived adaptive capacity for disaster mitigation, the population and assets in low-lying lands situated near the coastline in the east of Muscat, as well as the Al-Batnah south governorates, are at high risk due to cyclones. Furthermore, the coastal zones of the urban Wilayats of the Muscat governorate were also found to be at high, to very high, risk. This study has several policy implications and can provide effective guidelines for natural hazard preparedness and mitigation across the northern coasts of Oman. [Display omitted] • Globally, tropical cyclones as extreme climate events have devastating effects on environmental and socioeconomic systems. • Northeast coastal areas in Oman are exposed and susceptible to cyclone risks. • In this study, a spatial simulation process has been conducted utilising integrated GIS and AHP techniques. • Low-lying lands situated near the coastline of the study area are highly vulnerable and exposed to cyclone risks. • The findings of this study provide indispensable spatial strategies of disaster preparedness and cyclone risk mitigation. [ABSTRACT FROM AUTHOR]

• The effects of sociodemographic determinants on COVID-19 incidence were spatially modelled. • 4 out of 12 sociodemographic variables were influential predictors of COVID-19 incidence rates. • MGWR model explained 71 % of the spatial variations of COVID-19 incidence rate. • Spatial modelling of COVID-19 can be used to guide vital preventative and mitigation measures. The current COVID-19 pandemic is evolving rapidly into one of the most devastating public health crises in recent history. By mid-July 2020, reported cases exceeded 13 million worldwide, with at least 575,000 deaths and 7.33 million people recovered. In Oman, over 61,200 confirmed cases have been reported with an infection rate of 1.3. Spatial modeling of disease transmission is important to guide the response to the epidemic at the subnational level. Sociodemographic and healthcare factors such as age structure, population density, long-term illness, hospital beds and nurse practitioners can be used to explain and predict the spatial transmission of COVID-19. Therefore, this research aimed to examine whether the relationships between the incidence rates and these covariates vary spatially across Oman. Global Ordinary Least Squares (OLS), spatial lag and spatial error regression models (SLM, SEM), as well as two distinct local regression models (Geographically Weighted Regression (GWR) and multiscale geographically weighted regression MGWR), were applied to explore the spatially non-stationary relationships. As the relationships between these covariates and COVID-19 incidence rates vary geographically, the local models were able to express the non-stationary relationships among variables. Furthermore, among the eleven selected regressors, elderly population aged 65 and above, population density, hospital beds, and diabetes rates were found to be statistically significant determinants of COVID-19 incidence rates. In conclusion, spatial information derived from this modeling provides valuable insights regarding the spatially varying relationship of COVID-19 infection with these possible drivers to help establish preventative measures to reduce the community incidence rate. [ABSTRACT FROM AUTHOR]

• During the last decade, an obvious shift in the urban landscape has taken place in Oman. • The mountainous settlements has changed from rural to predominately urban landscape. • The integration of GIS techniques with the CA-Markov model successfully simulated spatiotemporal of LULC changes. • The urban expansion was initially associated with the increased immigration to urban centres. • Urban growth in the mountainous cities of Oman has significantly influenced vegetation cover. As a result of the socioeconomic transformation, the rapid urban expansion of cities and towns in the Gulf Cooperation Council (GCC) states has predominately led to tremendous pressure on the limited natural resources and loss of productive lands. Indeed, the spatial patterns of urbanisation and their impacts on mountain resources and environment have received little attention, particularly in Oman. Predicting urban growth in the mountainous cities has the potential to better understand the interaction between the spatial growth patterns and the mountain topography. This study aims to analyse spatiotemporal dynamics of land use/land cover (LULC) (2008–2018) and simulate urban expansion (2008–2038) in Nizwa city, Al Dakhliyah governorate, Oman. Cellular Automata (CA)-Markov and geospatial techniques were utilised to assess and project urban growth and land cover changes. The analysis was based on three maps of LULC at equal intervals derived from satellite imageries: Landsat TM for 1998, 2008 and 2018, along with topographic spatial layers (elevation, aspects, and terrain slopes) derived from the ASTER digital elevation model. In addition, other spatial parameters (population density, proximity to urban centres, and proximity to major roads,) were incorporated in the simulation process. The findings revealed that the actual LULC change during 2008–2018 was 12,014 ha of net urban growth (418.5 % change), while the simulated change was expected to be 14,985 ha by 2028, with a total of 37,465 ha increase in the built-up area and urban growth by 2038. Although the topographic variability will control LULC changes, the urban expansion overly will occupy the arable land across the valleys along with the flat areas. During the next two decades, the built-up areas will dominant, with a large percentage of vacant land (net loss 12,813 ha) and vegetation cover (net loss 35 ha) will be gradually converted into residential land use. The output of the simulations in this research could serve not only as spatial guidelines for monitoring future trends of LULC dynamics, but also address the threats and deteriorates of urban sustainability in the Omani mountainous cities. Furthermore, identifying bare soils and vegetation areas that are susceptible to urbanisation is of value for the national strategy of future urban planning in Oman. [ABSTRACT FROM AUTHOR]

Tropical cyclones are among the most destructive natural phenomena in the world. The effects of cyclones on coastal areas include the loss of lives, property damage, and infrastructure destruction. As a cyclone-prone area, Dhofar Governorate in Oman is regularly hit by tropical cyclones, of which the last was Mekunu in May 2018. In southern Oman, a vast majority of the population and infrastructure is concentrated along the coast of the Arabian Sea, and an explicit spatial assessment is essential to create the maps of risk indices and to identify the areas of relative high cyclone risks. In this research, we aimed to develop a geospatial modelling approach to quantify the spatial variations of cyclone risk impacts across all the administrative zones of Dhofar Governorate. Three major components, namely vulnerability and exposure, hazard, and mitigation, consisting of 14 spatial criteria were incorporated in the analysis at the local scale. A spatial layer was generated for each criterion as well as a calculated weighted score using the Analytic Hierarchy Process (AHP) and Geographical Information Systems (GIS) overlay techniques. An individual map for each risk component was produced, and the risk index was calculated on the basis of the vulnerability, hazard, and capacity indices. The findings indicated that vulnerable populations and highly exposed areas to severe cyclone impacts were distributed along the coastlines of the southern (Salalah, Taqah, and Mirbat) and southwest (Rakhyut and Dalkut) wilayats. The present study has potential and valuable policy implications for planners and decision makers, as well as serves as a robust baseline for developing national risk mitigation strategies which aim to diminish and absorb cyclone disaster impacts. [ABSTRACT FROM AUTHOR]