Suchergebnisse

This paper suggests a new concept, 'spatial capability', for understanding how women's freedom of mobility is systematically constrained. Based on the capability approach, it demonstrates that women's adaptive daily culture causes individual agents voluntarily to limit their mobility. Through the orally expressed experiences of 42 married Korean immigrant women in Los Angeles, this research effectively argues how the marginalisation of the subjects in the migration decision, along with their limited daily mobility, constitutes their spatial capability. It also shows that their daily culture, including their gender roles and their Christianity, enhances their constraints by adapting the limits and ultimately becomes the limitation itself. Gendered mobility should be approached in terms of potential freedom and accompanied by a consideration of the dialectic relationship that exists between the social constraints and the individual agents.

Small states constitute a special analytical category in political science, political geography and the theory of international relations, even though the term is not universally agreed-upon, and its conceptualization depends on the aim of a particular study. Broadly speaking, small states are those countries that lack various kinds of resources and occupy a peripheral position in the system of international relations. There are many ways to conceptualize small states that take into account both quantitative and qualitative criteria, but it is with the relativistic approach that the context of the relationship between states and their dyadic links within the framework of the system of international relations is taken into account. Spatial statistical analysis and the factor of the neighborhood enable a relativistic conceptualization of small states more objectively and universally, since they take into account all dyadic connections between neighboring states. To achieve the aim of the study, composite indicator of national capability demonstrating "hard power" and spatial lag are employed to determine the range of states potentially experiencing significant influence from their immediate neighbors. The main limitations of the study is omittance of isolated island states that do not have immediate neighbors, whose potential influence is different, as well as lack of "soft power" factors, with the help of which small states occupy a certain niche in international relations. Such a method of conceptualization allows scholars to take a fresh look at the term «small states» for further research of this analytical category.

Migration is a form of spatial and social transplant from one local and national context to another. Migration trajectories often expose the underlying intersections of social relations and social hierarchies that underpin cultural and social national environments. Migrants who encounter those complex structural inequalities must learn to negotiate classed, gendered and racialised social relations and seek the most suitable social positions within new systems. This article builds on Amartya Sen's capability approach to conceptualise migrants' embeddedness in the framework of social inequalities and explores the relationship between individual choices, resources and entitlements. It points towards patterns of advantage and disadvantage that frame migrants' opportunities and draws tacit analytical, theoretical and methodological links that have the innovative potential for the study of migration. Building on the parallels between studies in the fields of social inequalities and migration, this article argues that Sen's analytical and conceptual approach provides innovative insights into migration experiences, and Sen's unique reasoning opens up new avenues for the discussion of migrants' social justice.

Regional economic differences occur in China; narrowing such differences requires solving the issues of regional innovation differences, which consist of innovation capability difference, innovation difference in structure and innovation efficiency difference. Additionally, regional innovation capability disparity is important to explain the imbalances between regional and international economic development. Thus, from the perspective of hi-tech industries, this study adopts the Theil index to analyse innovation capability difference and its evolution law in China and its five regions in 1998–2015 to explain the reasons for the differences in regional innovation capability on the spatial and factorial levels. Then convergence of innovation capability on different space scales is analysed by adopting the spatial panel of the β convergence model.

Although spatial analysis and Geographic Information Systems (GIS) have become prevalent in the social and policy sciences, GIS and its mapmaking capability remains an underutilized tool among the decision support tools available to policy makers. Using a case study of Medicaid expenditure changes in Ohio, the authors demonstrate how spatial analysis and display can incorporate useful weights for policy makers. Through the use of dependence indices based upon the distributions of the affected recipients and service providers to weight the expenditures, the link between the effects of policy changes and the spatial distributions of these populations becomes clearer. The article argues that policy makers can be given a more appropriate picture of the potential local implications of statewide policy changes through the use of weights. Because of the power of maps to so starkly display these distributions, the article concludes with a caution that such tools should be used ethically with considered judgment.

This paper analyzes factors that shape the technological capabilities of individual U.S. states and European countries, which are arguably comparable policy units. The analysis demonstrates convergence in technological capabilities from 2000 to 2007. The results indicate that social capabilities, such as a highly educated labor force, an egalitarian distribution of income, a participatory democracy and prevalence of public safety, condition the growth of technological capability. The analysis also considers other aspects of territorial dynamics, such as the possible effects of spatial agglomeration, urbanization economies, and differences in industrial specialization and knowledge spillovers from neighboring regions.

International Journal of Advanced Research in Artificial Intelligence(IJARAI), 4(4), 2015 ; A review of some latest achievements in the area of military robotics is given, with main demands to management of advanced unmanned systems formulated. The developed Spatial Grasp Technology, SGT, capable of satisfying these demands will be briefed. Directly operating with physical, virtual, and executive spaces, as well as their combinations, SGT uses high-level holistic mission scenarios that self-navigate and cover the whole systems in a super-virus mode. This brings top operations, data, decision logic, and overall command and control to the distributed resources at run time, providing flexibility, ubiquity, and capability of self-recovery in solving complex problems, especially those requiring quick reaction on unpredictable situations. Exemplary scenarios of tasking and managing robotic collectives at different conceptual levels in a special language will be presented. SGT can effectively support gradual transition to automated up to fully robotic systems under the unified command and control. ; http://thesai.org/Downloads/IJARAI/Volume4No4/Paper_2-Military_Robotics_Latest_Trends_and_Spatial_Grasp_Solutions.pdf

Context Early surveillance and the detection of incursions of species of biosecurity concern are a crucial component of an effective biosecurity system. Citizen science represents an opportunity to engage communities in biosecurity, and to provide mechanisms for citizen scientists to contribute to both monitoring the spread of species already present in country, and reporting new incursions. Aims To present an example of how citizen science is being used for environmental biosecurity surveillance in Australia and showcase the value of large data services such as the Atlas of Living Australia (ALA), as a connector between citizen science and management. Methods We detail how the alert email system was set up, using a bespoke solution implemented in the R programming language. The system works by querying the ALA database for species that match lists provided by management authorities. Alerts can be sent out at national, state/territory and local government scales, as well as defined spatial areas such as national park estates. Key results Twelve months in, the top source for alerts comes from iNaturalist (a popular global biodiversity citizen-science platform), with other contributions from a set of biodiversity-reporting applications. Over a 12-month period, the alerts service has provided notifications for over 150 species, including the first public record of an invasive species in Australia. Conclusions Systems such as the Biosecurity Alerts Service, provide impact through the connection between communities and decision-making. Implications Our findings showed how the advancement of citizen science is interconnected with the advancement of research infrastructure and will ultimately lead to greater scientific and management value of citizen-science data.

Widely used across disciplines such as natural resources, social sciences, public health, humanities, and economics, spatial data is an important component in many studies and has promoted interdisciplinary research development. Though an institutional data repository provides a great solution for data curation, preservation, and sharing, it usually lacks the spatial visualization capability, which limits the use of spatial data to professionals. To increase the impact of research-generated spatial data and truly turn them into digital maps for a broader user base, we have designed and developed the workflow and cyberinfrastructure to extend the current capability of our institutional data repository by visualizing the spatial data on the web. In this project, we added a GIS server to the original institutional data repository cyberinfrastructure, which enables web map services. Then, through a web mapping API, we visualized the spatial data as an interactive web map and embedded in the data repository web page. From the user's perspective, researchers can still identify, cite and reuse the dataset by downloading the data and metadata and the DOI offered by the data repository. General information users can also browse the web maps to find location-based information. In addition, these data was ingested into the spatial data portal to increase the discoverability for spatial information users. Initial usage statistics suggest that this cyberinfrastructure has greatly improved the spatial data usage and extended the institutional data repository to facilitate spatial data sharing.

AbstractManaging navigational safety is a key responsibility of coastal states. Predicting and measuring these risks has a high complexity due to their infrequent occurrence, multitude of causes, and large study areas. As a result, maritime risk models are generally limited in scale to small regions, generalized across diverse environments, or rely on the use of expert judgement. Therefore, such an approach has limited scalability and may incorrectly characterize the risk. Within this article a novel method for undertaking spatial modeling of maritime risk is proposed through machine learning. This enables navigational safety to be characterized while leveraging the significant volumes of relevant data available. The method comprises two key components: aggregation of historical accident data, vessel traffic, and other exploratory features into a spatial grid; and the implementation of several classification algorithms that predicts annual accident occurrence for various vessel types. This approach is applied to characterize the risk of collisions and groundings in the United Kingdom. The results vary between hazard types and vessel types but show remarkable capability at characterizing maritime risk, with accuracies and area under curve scores in excess of 90% in most implementations. Furthermore, the ensemble tree‐based algorithms of XGBoost and Random Forest consistently outperformed other machine learning algorithms that were tested. The resultant potential risk maps provide decisionmakers with actionable intelligence in order to target risk mitigation measures in regions with the greatest requirement.