Data integration for quantitative analysis of sustainability

The development of an integrated data repository for urban sustainability analysis is presented; it aims to enhance quantitative analysis of urban sustainability on real statistical data with relevant geographic references. The method is complementary to qualitative and quantitative analyses of sample data, which are the main approaches adopted in conventional urban sustainability analysis. The paper highlights the background and importance of using statistical data for urban planning and analysis. Selected statistics sources and various geospatial objects relevant to the geo-references of the statistical data are reviewed. The conceptual model of the integrated data repository is described and the logical integration of statistics from different sources is illustrated. The detailed geospatial conceptual model shows the geospatial classes, main attributes and relationships between these geospatial classes. The paper details the procedure of setting up a server-based database to host both geospatial and non-geospatial data, and uses PostGIS query to interrogate geospatial information in the database, including how to convert the database tables from other formats. A geographic information system (GIS) is introduced as a user-side tool to reveal geospatial and non-geospatial data. Case studies of application of the database for urban sustainability analysis are described. Data maps generated for these case studies can assist planning and design professionals in analysing some urban sustainability issues. Potential applications in various sustainability analyses, barriers and future improvements are also discussed

Geospatial Data Management Research: Progress and Future Directions

Without geospatial data management, today´s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIM/GIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D/4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geo-data science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the geo-related sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Data-streaming libraries and “in-situ” geo-computing on objects executed directly on the sensors will revolutionize geo-information science and bridge geo-computing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objects’ trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis

Village landscape from near and far : Participatory geospatial methods to integrate local knowledge into formal land use planning in Tanzania

In this dissertation, I explore the use of participatory geospatial methods in improving local knowledge integration and inclusiveness in formal land use planning in Tanzania. Communities and governments in the Global South are tackling multiple challenges to human well-being and ecological integrity. Participatory land use planning that combines knowledge sources and balances needs and values of different land users has been promoted as a prerequisite for addressing these challenges in a socio-ecologically sustainable way. Land use planning practice requires tools such as participatory geospatial methods that capture local spatial knowledge (LSK) on human-environment relationships in forms that are compatible with spatial planning standards. There exists, however, little evidence on the usability and impact of these methods in formal planning processes. For my research, Tanzania offers an exciting opportunity to examine the use of these methods due to its decentralized land and natural resource policies and rapid digitalization of planning processes. Through case studies I study what LSK reveals about land use and land use management-related decision-making, how existing Tanzanian land use planning policies and practices integrate LSK into planning decision-making, and I then codevelop participatory geospatial methods capable of integrating LSK into formal land use planning and study their benefits and adoption potential. Finally, I reflect on the limitations of the geospatial methods in representing LSK and diverse perspectives in these processes. The research is based on transdisciplinary and mixed methods approach. I frame my assessments of existing policies and practices with literature-based criteria, which I develop for each study. In each study, I also collaborate with Tanzanian practitioners at various research phases and develop a practitioners’ manual to guide the use of our participatory geospatial method. I combine interviews, group discussions, surveys, participatory mapping exercises and observations to study the relationship between people, their land uses and environment (Article I) and people’s experiences in planning processes (Articles II, III and IV). The findings show that local knowledge is instrumental in identifying land use patterns in the landscape and in explaining the rationale behind local land use, its dynamics and forest-farmland conversion (Article I). The analysis of existing policies and practices reveals that despite the emphasis on inclusive and active participation of local communities, the planning practice does not capture LSK in a georeferenced form or use geospatial tools to facilitate planning deliberation (Articles II and III). Subsequently the plans fail to recognize local priorities and complexities of land use. The codeveloped participatory geospatial method was observed to increase the quality of spatial data in which LSK is captured, and support learning, deliberation and spatial understanding among participants and practitioners during the formal planning process (Articles III and IV). The planning practitioners identified several benefits of the geospatial methods to their work, which is a strong incentive for adoption (Article IV). Wider adoption, however, requires efforts in geospatial education and on-the-job training to practitioners, as well as general commitment to participatory processes at all administrative levels. While the participatory geospatial methods improve integration of LSK into planning decision-making, future research and method development should focus on recognizing the diversity of local spatial knowledge and community priorities. Here self-determination of what LSK is collected and how it is visualized and used plays an important role

Tourism Dynamic Assessment Using Geospatial Approach in Bageshwar District, Uttarakhand

Dynamics of tourism is a complex thing requires geospatial approach for effective planning and assessment. Geospatial technique for visualization is an innovation and emerging discipline and a valuable tool to recognized the dynamic assessment of tourism, by integrating geospatial data as well as non spatial data in the form of a map display as an outcome. Geospatial visualization approach necessitates the planning which is based on the needs, goals, formulation, monitoring and evaluation of an action plan or programmes. Present study practices a geovisualization of dynamic assessment of tourism in Bageshwar district by integration of spatial and non-spatial data and also explores the suggestions to develop the tourism industry. The study investigated a case study in Bageshwar district. Study area has extensive potentialities for expansion of tourism industry, so in the region there is need to make efforts in this direction and make region more attractive and suitable for tourist as well as region itself. In the present study, an approach has been described the socio-economic development and growth analysis in the study area on account of tourism by linking the geospatial data (spatial & non-spatial). It can be done through the tourist growth. Growth analysis is done through attribute data, as domestic and international tourist arrival data calculated by compound annual growth rate (CAGR) and coefficient of variation that correlates with geospatial data using GIS as tool. The present study presents the tourism development in context of impact aspect and geovisualization done by tourism growth and tourist variation map. The finding reveals that the growth of tourist arrival increased to 3.54 percent during the year 2000 to 2010. The growth of kausani, Baijnath and Bageshwar tourist places of Bageshwar district also shows increment in the annual growth

A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

abstract: Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making processes. In this paper, we report on our efforts to design and develop a geospatial cyberinfrastructure (GCI) for urban economic analysis and simulation. This GCI provides an operational graphic user interface, built upon a service-oriented architecture to allow (1) widespread sharing and seamless integration of distributed geospatial data; (2) an effective way to address the uncertainty and positional errors encountered in fusing data from diverse sources; (3) the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4) capturing and representing provenance of geospatial data to trace its flow in the modeling task. The Greater Los Angeles Region serves as the test bed. We expect this work to contribute to effective spatial policy analysis and decision-making through the adoption of advanced GCI and to broaden the application coverage of GCI to include urban economic simulations

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

Multi-Paradigm Reasoning for Access to Heterogeneous GIS

Accessing and querying geographical data in a uniform way has become easier in recent years. Emerging standards like WFS turn the web into a geospatial web services enabled place. Mediation architectures like VirGIS overcome syntactical and semantical heterogeneity between several distributed sources. On mobile devices, however, this kind of solution is not suitable, due to limitations, mostly regarding bandwidth, computation power, and available storage space. The aim of this paper is to present a solution for providing powerful reasoning mechanisms accessible from mobile applications and involving data from several heterogeneous sources. By adapting contents to time and location, mobile web information systems can not only increase the value and suitability of the service itself, but can substantially reduce the amount of data delivered to users. Because many problems pertain to infrastructures and transportation in general and to way finding in particular, one cornerstone of the architecture is higher level reasoning on graph networks with the Multi-Paradigm Location Language MPLL. A mediation architecture is used as a “graph provider” in order to transfer the load of computation to the best suited component – graph construction and transformation for example being heavy on resources. Reasoning in general can be conducted either near the “source” or near the end user, depending on the specific use case. The concepts underlying the proposal described in this paper are illustrated by a typical and concrete scenario for web applications

Building National Forest and Land-Use Information Systems: Lessons from Cameroon, Indonesia, and Peru

This working paper examines the institutional, human resources, and financial capacities of three countries that have developed a forest and land-use information system, and highlights common enabling factors and challenges