Harish Karnatak

An automated and optimized approach for online spatial biodiversity model: a case study of OGC web processing service

Abstract An online spatial biodiversity model (SBM) for optimized and automated spatial modelling and analysis of geospatial data is proposed, which is based on web processing service (WPS) and web service orchestration (WSO) in parallel computing environment. The developed model integrates distributed geospatial data in geoscientific processing workflow to compute the algorithms of spatial landscape indices over the web using free and open source software. A case study for Uttarakhand state of India demonstrates the model outputs such as spatial biodiversity disturbance index (SBDI) and spatial biological richness index (SBRI). In order to optimize and automate, an interactive web interface is developed using participatory GIS approaches for implementing fuzzy AHP. In addition, sensitivity analysis and geosimulation experiments are also performed under distributed GIS environment. Results suggest that parallel algorithms in SBM execute faster than sequential algorithms and validation of SBRI with biological diversity shows significant correlation by indicating high R2 values.

Performance study of various spatial indexes on 3D geo-data in Geo-RDBMS

In the emerging era of information and communication technologies, geotechnology is one of the fastest growing fields. Geo-RDBMS is very important and evolving aspect for GIS, as it can manage large volume of spatial data inside RDBMS. The utilization of RDBMS for geospatial data was one of the important focuses of GIS professionals in last decades to store and manage 2D geo-data. However, the support for 3D geo-data inside RDBMS is still limited and is a challenging task for RDBMS providers. In this study, data organization and performance assessment of 3D geo-data inside RDBMS are carried out. In this process, various file-based 3D data models such as CityGML, COLLADA and KML are migrated to geo-RDBMS to bring entire 3D geo-data in common platform. Various spatial indexing techniques viz. R-Tree, B-Tree, GiST, etc. are applied on these 3D data models and best indexing techniques are studied for 3D GIS operations.

Himalayan Spatial Biodiversity Information System

Spatial distribution of environmental resource and its management issues are determined by complex processes and relationships. It involves several interrelating elements with many attributes and a dynamic behavior that required advanced spatial analytical capabilities in the GIS software. The technological solutions required to analyze the system include spatially distributed simulation and optimization models, interactive information system, decision support tools, and expert systems based on geospatial technologies. The primary paradigm of a GIS is the map, an inherently static concept of limited attributes. While modern GIS extends the scope of what can be done within this paradigm toward digital cartography considerably, elaborate applications can be built within existing GIS systems and powerful and flexible tool that involves spatial elements can be developed for different environmental applications. The Eastern Himalayan region is known as one of the global biodiversity hotspots. It includes several Global 200 eco-regions, two Endemic Bird Areas, and several centers for plant diversity. The high biological diversity of the Himalaya is mainly due to the multiple biogeographic origins. The climate variability as a result of being associated with the huge, complex, and steep terrain also gives the Himalayan region a plethora of habitats for the occurrence of the biodiversity hotspot in the region. Apart from being a storehouse of natural resources, the Himalaya is also prone to innumerable natural and anthropogenically induced disasters. This is evident by the recurrent calamities like Kedarnath tragedy, which results in huge loss of life and property.

Spatial landscape model to characterize biological diversity using R statistical computing environment

Due to urbanization and population growth, the degradation of natural forests and associated biodiversity are now widely recognized as a global environmental concern. Hence, there is an urgent need for rapid assessment and monitoring of biodiversity on priority using state-of-art tools and technologies. The main purpose of this research article is to develop and implement a new methodological approach to characterize biological diversity using spatial model developed during the study viz. Spatial Biodiversity Model (SBM). The developed model is scale, resolution and location independent solution for spatial biodiversity richness modelling. The platform-independent computation model is based on parallel computation. The biodiversity model based on open-source software has been implemented on R statistical computing platform. It provides information on high disturbance and high biological richness areas through different landscape indices and site specific information (e.g. forest fragmentation (FR), disturbance index (DI) etc.). The model has been developed based on the case study of Indian landscape; however it can be implemented in any part of the world. As a case study, SBM has been tested for Uttarakhand state in India. Inputs for landscape ecology are derived through multi-criteria decision making (MCDM) techniques in an interactive command line environment. MCDM with sensitivity analysis in spatial domain has been carried out to illustrate the model stability and robustness. Furthermore, spatial regression analysis has been made for the validation of the output.

A novel computational knowledge-base framework for visualization and quantification of geospatial metadata in spatial data infrastructures

Advances in Metadata research have been instrumental in predictions and ‘fitness-of-use evaluation’ for the effective Decision-making process. For the past two decades, the model has been developed to provide visual assistance for assessing the quality information in metadata and quantifying the degree of metadata population. Still, there is a need to develop a framework that can be generic to adopt all the standards available for Geospatial Metadata. The computational analysis of metadata for specific applications remains uncharted for investigations and studies. This work proposes a computational framework for Geospatial Metadata by integrating TopicMaps and Hypergraphs (HXTM) based on the elements and their dependency relationships. A purpose-built dataset extracted from schemas of various standardisation organisations and existing knowledge in the discipline is utilised to model the framework and thereby evaluate ranking strategies. Hypergraph-Helly Property based Weight-Assignment Algorithm (HHWA) have been proposed for HXTM framework to calculate Stable weights for Metadata Elements. Recursive use of Helly-property ensures predominant elements, while Rank Order Centroid (ROC) method is used to compute standard weights. A real corpus using case studies from FGDC’s Standard for Geospatial Metadata, INSPIRE Metadata Standards, and ISRO Metadata Content Standard (NSDI 2.0) is used to validate the proposed framework. The observations show that the Information Gain (Entropy) of the proposed model along with the algorithm proves to be computationally smart for quantification purposes and visualises the strength of Metadata Elements for all applications. A prototype tool, ‘MetDEVViz- MetaData Editor, Validator & Visualization’ is designed to exploit the benefits of the proposed algorithm for the case studies that acts as a web service to provide a user interface for editing, validating and visualizing metadata elements.

Geo-enabled e-Democracy Tools and Services for Smart Cities

In the recent past, an overwhelming growth in geo-enabled open source data and tools through web services and data repositories is witnessed. Internet technology has significantly enhanced the utility of geo-enabled data and applications by making them more accessible to a wider range of users, planners and decision makers through geoportals, mobile Apps and Cloud GIS. The Cloud Computing Architecture (CCA), Internet of Things (IoT) and Service Oriented Architecture (SOA) represent new technological development which allow them to send and receive data without requiring user interaction and enhance interoperability in data and information services. The geospatial information available through geoportals and online data repositories have immense scope for its utilisation in smart city planning with many success stories world over. Geo-enabled data and tools can go a long way in creating a range of smart city applications where citizen participation is one of the key objectives. These tools and services have immense application potential for public participation, grievance management and to address many more aspects of e-democracy and e-governance such as Tourism GIS, Municipal GIS and so on. These citizen-centric Apps and web services facilitate faster information dissemination and improve the efficiency and management of infrastructure, which is essential to enhance the quality of life of urban dwellers and one of the key objectives of the smart city movement. In India, the “Bhuvan” geoportal developed by the Indian Space Research Organisation (ISRO) provides a milieu of data sets which can be used for building smart city applications. Bhuvan portal hosts high-resolution data (~1 m resolution) of more than 350 Indian cities till date and planning to cover other cities in near future. It also offers thematic maps useful for Master Plan formulation for 152 towns prepared under National Urban Information System (NUIS). Effort is on to use high-resolution satellite images for the overlay and fine-tuning of Urban Framework Survey. It also hosts many other data sets, e.g., land use/land cover, road network, soil, geomorphology, etc. which can be used to plan and manage the smart cities effectively.