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The main objective of the study is to identify the vulnerable areas for river‐line and flash flood hazard and its mitigation through GIS Database Management System (DBMS) of geo‐hydrometeorological parameters. The Dabka watershed constitutes a part of the Kosi Basin in the Lesser Himalaya, India in district Nainital has been selected for the case illustration.

The Dabka DBMS is constituted of three GIS (Geographic Information System) modules, i.e. geo‐informatics (consists of geomorphology, soils, geology and land use pattern, slope analysis, drainage density and drainage frequency), weather informatics (consists of daily, monthly and annual weather data about temperature, rainfall, humidity and evaporation) and hydro‐informatics (consist of runoff, sediment delivery, and denudation). The geo‐informatics and weather informatics modules carried out by comprehensive field work and GIS mapping than both modules used to carry out hydro‐informatics module. Through the integration and superimposing of spatial data and attribute data with their GIS layers of all these modules prepared Flood Hazard Index (FHI) to identify the level of vulnerability for flood hazards and their socio‐economic and environmental risks.

The results suggest that geo‐environmentally most stressed areas of barren land (i.e. river‐beds, flood plain, denudational hills, sites of debris flow, gullies, landslide prone areas etc.) have extreme vulnerability for flood hazard due to high rate of runoff, sediment load delivery and denudation during rainy season (i.e. respectively 84.56 l/s/km², 78.60 t/km² and 1.21 mm/year) whereas in geo‐environmentally least stressed dense forest areas (i.e. oak, pine and mixed forests) have low vulnerability due to low rate of stream runoff, sediment load delivery and denudation (i.e. respectively 20.67 l/s/km², 19.50 t/km² and 0.20 mm/year). The other frazzled geo‐environment which also found high vulnerable for flood hazard and their risks is agricultural land areas due to high rate of stream runoff, sediment load delivery and denudation rates (i.e. respectively 53.15 l/s/km², 90.00 t/km² and 0.92 mm/year).

For hydro‐informatics module it is quite difficult to monitor water and sediment discharge data from each and every stream of the Himalayan terrain due the steep and rugged topography. It requires strategic planning and trained man power as well as sufficient funds; therefore representative micro‐watershed approach of varied ecosystem followed for a three years (2006‐2008) period.

The study will have great scientific relevance in the field of river‐line flood and flash flood hazard and its socio‐economic and environmental risks prevention and management in Himalaya and other mountainous terrain of the world.

This study generated primary data on hydro‐informatics and weather informatics to integrate with geo‐informatics data for flood hazard assessment and mitigation as constitutes a part of multidisciplinary project, Department of Science and Technology (D.S.T.) Government of India.

This research underlines the usefulness of Civil Rights Geographic Information Systems (CR-GIS) for understanding the social struggles and assessing the critical needs of the disempowered population of Alabama’s “Black Belt.” The social struggles have been persistent for decades in the Southern states, particularly in Alabama. Researchers have recognized the political and historical root causes and implications for these social struggles. The geographic region of Alabama’s Black Belt is significant because it became the epicenter of the Civil Rights struggle and still represents the vestiges of the social policy known as “Jim Crow.”

Although GIS has a great potential to explore social and political struggles, currently, it is not profoundly associated with Civil Rights studies. This research employs CR-GIS to illustrate the impact of the disfranchisement caused by biased geopolitics in three selected cases/issues: (1) gerrymandering and voting rights, (2) transportation, and (3) poverty in the State of Alabama. While there has been some progress in overcoming the social struggles in the Black Belt, there is a need for qualitative and quantitative analyses to understand persistent social, economic, and Civil Rights struggles in the region. GIS could be a valuable tool to understand and explore the social struggles in the disempowered communities of the “Black Belt” in Alabama. By incorporating the existing information and conducting ground truth studies, this research will lay the basic foundation for extended research by creating a policy template for empowering the disempowered for better social, economic, and political integration in the “Black Belt region.”

The study of spatial entities needs a model that is not only fully observable and controllable, but also computable. Euclidean topology on R² is a usually used tool for this study, but it has the following two weaknesses. First, there exists some phenomenon of human perception of the spatial entity that cannot be simulated by it. Second, its observation of the basic geometric properties (interior, exterior, boundary) of the spatial entity lacks computability so that the model based on it lacks computability and cannot be directly used to practical systems. Consequently, in this paper, we present another tool for studying spatial entities – raster quasi‐topology on R² and then compare the two tools.

Networks linking tourist attractions or organizations are a major focus of tourism research. Despite extensive research on tourism networks, academic research on the spatial structure and formation of wine tourism networks is limited. This study aims to investigate the spatial structure and factors influencing the development of a network among Ningxia wineries, an emerging wine tourism destination in China.

This study uses social network analysis to uncover “what” the spatial structure of wine tourism networks looks like. Sixteen in-depth interviews were conducted among key stakeholders to explain the “why” of such structural characteristics.

The results show that in an emerging wine tourism destination, popular tourist attractions enjoy high centrality and hold key positions in the wine tourism network. Small wineries exhibit high closeness centrality, and only one winery serves as a network broker. According to the stakeholders, the importance of network actors will increase as their economic and political importance increase, while small wineries that lack differentiation in the network may perish.

Local governments can implement the suggested measures for improving network connections, and wineries are advised to find suitable positions to improve the experiences of tourists.

This study pioneers the identification of the distinct structure and factors influencing the network of an emerging wine tourism destination, thus enriching the understanding of the interplay and roles of different actors.

Practical needs in geographical information systems (GIS) have led to the investigation of formal, sound and computational methods for spatial analysis. Since models based on topology of R² have a serious problem of incapability of being applied directly for practical computations, we have noticed that models developed on the raster space can overcome this problem. Because some models based on vector spaces have been effectively used in practical applications, we then introduce the idea of using the raster space as our platform to study spatial entities of vector spaces. In this paper, we use raster spaces to study not only morphological changes of spatial entities of vector spaces, but also equal relations and connectedness of spatial entities of vector spaces. Based on the discovery that all these concepts contain relativity, we then introduce several new concepts, such as observable equivalence, strong connectedness, and weak connectedness. Additionally, we present a possible method of employing raster spaces to study spatial relations of spatial entities of vector spaces. Since the traditional raster spaces could not be used directly, we first construct a new model, called pansystems model, for the concept of raster spaces, then develop a procedure to convert a representation of a spatial entity in vector spaces to that of the spatial entity in a raster space. Such conversions are called approximation mappings.

As climate change shocks and stresses increasingly affect urban areas in developing countries, resilience is imperative for the purposes of preparation, recovery and adaptation. This study aims to investigate demographic characteristics and social networks that influence the household capacity to prepare, recover and adapt when faced with prolonged droughts or erratic rainfall events in Mbale municipality in Eastern Uganda.

A cross-sectional research design was used to elicit subjective opinions. Previous studies indicate the importance of subjective approaches for measuring social resilience but their use has not been well explored in the context of quantifying urban resilience to climate change shocks and stresses. This study uses 389 structured household interviews to capture demographic characteristics, social networks and resilience capacities. Descriptive and inferential statistics were used for analysis.

The ability of low-income households to meet their daily expenditure needs, household size, and networks with relatives and non government organizations (NGOs) were significant determinants of preparedness, recovery and adaptation to prolonged droughts or erratic rainfall events.

The results imply that policymakers and practitioners have an important role vis-à-vis encouraging activities that boost the ability of households to meet their daily expenditure needs, promoting small household size and reinforcing social networks that enhance household resilience.

Even the low-income households are substantially more likely to prepare for and recover from prolonged droughts or erratic rainfall events if they can meet their daily expenditure needs. This finding is noteworthy because the poorest in society are generally the most vulnerable to hazards.

The purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic issues. Since air pollution poses a severe health threat, city residents should have a right to know about the (invisible) hazards they are exposed to.

Within spatial-temporal modeling of air pollutants in Berlin, Germany, three interpolation techniques are tested. The most suitable one is selected to create seasonal maps for 2018 and 2021 with pollution concentrations for particulate matter values and nitrogen dioxide for each 1,000 m² cell within the administrative boundaries. Based on the evaluated pollution particulate matter values, which are used as additional variables for semi-parametric regressions the impact of the air quality on rents is estimated.

The findings reveal a compelling association between air quality and the economic aspect of the residential real estate market, with noteworthy implications for both tenants and property investors. The relationship between air pollution variables and rents is statistically significant. However, there is only a “willingness-to- pay” for low particulate matter values, but not for nitrogen dioxide concentrations. With good air quality, residents in Berlin are willing to pay a higher rent (3%).

These results suggest that a “marginal willingness-to-pay” occurs in a German city. The research underscores the multifaceted impact of air quality on the residential rental market in Berlin. The evidence supports the notion that a cleaner environment not only benefits human health and the planet but also contributes significantly to the economic bottom line of property investors.

The paper has a unique data engineering approach. It collects spatiotemporal data from network of state-certified measuring sites to create an index of air pollution. This spatial information is merged with residential listings. Afterward non-linear regression models are estimated.

Despite vast research on outcome‐based education (OBE), there is little consensus among academics regarding its impact, with both positive and negative comments emerging from the literature. The Hong Kong Polytechnic University (PolyU) has taken the view that OBE is conducive to the enhancement of student core competencies such as global outlook, critical and creative thinking, social and national responsibility, lifelong learning, entrepreneurship and leadership. The aim of this study was to investigate how a newly developed curriculum, based on the OBE philosophy, impacted on students’ core competencies in an engineering/surveying discipline of PolyU. Qualitative interviews were conducted to establish that OBE was being properly implemented prior to a quantitative examination of its impact on students. Only specific positive learning behaviours were identified among students experiencing OBE. Their core competencies, however, were not statistically proven to be greater than those of students with little or no exposure to OBE. Nevertheless, this did not offer unequivocal evidence to either prove or disprove the effectiveness of OBE, and numerous factors other than the OBE philosophy itself were considered possible causes. It also appeared that some teaching staff lacked faith and knowledge in the implementation of OBE which implicates a need for rectification of the situation.

Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.