- freely available
ISPRS Int. J. Geo-Inf. 2015, 4(2), 661-676; https://doi.org/10.3390/ijgi4020661
2. Literature Review
2.1. Borderlands Data Acquisition
2.2. Theme Spatial Analysis and Visualization
2.3. Borderlands Monitoring and Management
3. Challenges in GIS-Based Borderlands Modeling and Understanding
3.1. Integrated Borderlands Data Modeling
- Multi-scale: since the geographical extent of borderlands does not follow an absolute area, there are significant differences of natural phenomena and human activities in different geographical scales (national-, regional-, and sub-regional scale). Borderlands data modeling should represent spatial information and related properties in various scales . Non-spatial data should be organized hierarchically. Due to the data accessibility and availability, upscaling or downscaling processes are necessary under specific conditions. For example, a downscaling method should be applied to produce population density grids from the country- or province-level census data . As the border regions are much larger than the boundary strip areas, a multi-scale and multi-resolution data modeling strategy is becoming necessary to meet different user requirements or priorities from their specific applications.
- Dynamic interaction: the natural process (e.g., seasonal variation of water resources of international rivers) and anthropological activities (e.g., migration over borders) are highly dynamic and interactive over time and space. The dynamic interaction between humankind and environment (e.g., resulting in land use/cover changes) and between different peoples (e.g., migration, trade, cultural exchange, conflicts, etc.) raises difficulties in data acquisition and modeling, which should support the analysis of drivers, development and impacts of such dynamics and interactions. Some regression models were developed to represent the interaction between build-up land and population density  and to simulate interaction between carbon footprint and environment . A focused web crawler has been developed and used to collect the dynamic borderlands situation information , and to derive those news reports about the borderland events to dynamically create borderland-situation charts, both in spatial- and time-series.
- Harmonization of dataset: borderlands data collection and processing can be achieved through the utilization of earth observation, crowdsourcing information, and conversion and harmonization of existing open data sets at global, regional and national scales. However, many existing social-economic and geo-political data often lack a clear spatial context referent, and the specific units and boundaries are often not the same . The integration of all the available data sets for consistent and reliable borderlands data sets remains one of the most difficult tasks. New technical standards and data processing methods need to be investigated.
3.2. Comprehensive Borderlands Analysis
- GIS-based spatial analysis: The geographical location and other geographical conditions have significant impacts or effects on the neighboring environment and borderlands policy. With advanced GIS-based spatial analysis, it is possible to compute their effects or evaluate the impacts, such as the differences of accessibility with or without geographical obstacles , spatial interaction among various (political, economic, or cultural) neighboring units , spatial heterogeneity of landscape, and neighborhood . There are a number of GIS spatial analysis methods available, such as multi-criteria decision analysis , spatial relation computation , etc. For instance, neighboring countries share common boundaries or have other kinds of adjacent relations. A Voronoi-based k-order relation model may be used for a quantitative and qualitative analysis modeled .
- Multidisciplinary expert knowledge: Some borderlands phenomena and affairs require a synergetic analysis of both geographic condition and other socio-economic, cultural and environmental factors. For instance, international or regional emergency rescue and peacekeeping activities are based on geopolitical risk analysis and forecasting. The security and stability status, potential for cooperation and possible conflicts are among other subjects of synergetic analysis. This requires an integration of multi-disciplinary expert knowledge from academia, government sectors, and international organizations. Effort has been made to model geo-political influence among nations using a set of indicators and multi-variables estimate method . The geo-political influence of China and the United States (US) in South Asia during 2007–2012 was also modeled . The geo-advantages of border-cities in cross-border industrial and enterprise cooperation were studied to reveal a geo-political and geo-economic mode for border-cities . The framework for analyzing re-scaling processes was proposed and applied to a case study of the Dutch-German EUREGIO cross-border region .
3.3. Collaborative Borderland Geospatial Service
- Distributed borderland data sharing: The first objective of such a collaborative borderland geospatial service platform is to connect all the borderlands related open data sources scattered around the world and to provide “one stop” information sharing. This will enable researchers and users to have an easy access to historical and up-to-date borderlands data and to share their own data with the others. One of the key challenges is how to ensure a continuous updating of these borderlands data sets. While earth observation approaches can be used for spatial change detection , some other situation changes can be collected using topic-specific web crawlers from the huge source of information contained in the Internet .
- Borderlands geo-processing services: While data sharing is a lower level of collaborative geospatial services, the analytical models or paradigms of data processing and analysis from different borderland research groups and relevant scientific communities can be shared through geo-processing services. Visualization and mapping service can be one of the geo-processing services that will facilitate the interactive data exploration and efficient presentation of analysis results. A high performance of geo-computation infrastructure is required to achieve this goal .
4. A Research Agenda
4.1. Classification and Representation of Borderland Information
4.2. Derivation of Neighborhood Information
4.3. Development of Synergetic Analysis
4.4. Design and Development of a Geo-Portal for Borderlands Studies
Conflicts of Interest
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