Probabilistic latent semantic analysis as a potential method for integrating spatial data concepts

In this paper we explore the use of Probabilistic Latent Semantic Analysis (PLSA) as a method for quantifying semantic differences between land cover classes. The results are promising, revealing ‘hidden’ or not easily discernible data concepts. PLSA provides a ‘bottom up’ approach to interoperability problems for users in the face of ‘top down’ solutions provided by formal ontologies. We note the potential for a meta-problem of how to interpret the concepts and the need for further research to reconcile the top-down and bottom-up approaches

Desperately seeking the IS in GIS

Geographical Information Systems (GIS) are now a widespread and important form of Information Technology (IT) use. In principle, Information Systems (IS) research is concerned with all forms of IT use. Yet despite this importance, GIS remains largely invisible in IS research. This paper illustrates this separation using bibliographic data drawn from both GIS and IS. It reviews discussion within IS as to the nature of the discipline and argues for a closer coupling between IS and GIS. It discusses Spatial Data Infrastructure (SDI), mobile computing and public participation GIS as examples of spatially related fields where further IS research would be beneficial

Exploring scientific collaborations in geographical information science (GIScience): A study of its co-authorship networks

Geographic Information Science (GIScience) as a discipline focuses on fundamental issues surrounding Geographic Information (Gl) and developments and applications of Geographical Information Technologies (GITechnologies). GIScience has accumulated a body of knowledge that can be easily exported and applied to other disciplines and assembled a wider multidisciplinary research community. Co-authorship networks are used to explore GIScience scientific collaborations during 1992-2002. Six different co-authorship networks were built from publication outlets comprising different sets of core and peripheral journals. The closer the periphery to the core, the more relevant the selected journals are to GIScience. Topological characteristics of all networks show similar networks despite the differences in sizes and the nature of the topics covered. However, networks with the peripheral journals closer to the core were more centralized around well-known scholars within the discipline. Furthermore, the network structures show a GIScience core linked to allied disciplines, especially to a highly clustered remote sensing research community. The core co-authorship network was geo-referenced using authors’ affiliation information. The results show that geographical proximity, language and cultural preferences play important roles. Countries known for their strong publishing patterns in other sciences such as England, USA and Canada were alos identified within GIScience domain. A growth of international collaboration among Scandinavian and European Countries was revealed. Results also show that China, India and Brazil have been increasing their international participation within the GIScience research community

Little Steps Towards Big Goals. Using Linked Data to Develop Next Generation Spatial Data Infrastructures (aka SDI 3.0)

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Society is moving at an increasing pace toward the next stage of the information society through linked data. Among the relevant developments in geographic information science, linked data approaches offer potential for improving SDI functionality [12]. Linked data uses Semantic Web technologies and makes it possible to link at a very granular level data resources of the web for a multitude of purposes. While the technological implementation in many ways is still in a phase of adolescence, vast amounts of data, including geographic information (GI) have been prepared, for example by the UK Ordinance Survey [8] and other governmental and non-governmental bodies. The overwhelming focus has been on producing RDF formatted data for linked data applications--the foundation for applications. In this short paper, we provide an overview of potentials of linked open data for SDI 3.0 developments. Through two exemplary use cases we illustrate specifically some first steps towards a more web-oriented and distributed approach to creating SDI architectures. The cases demonstrate applications based on the LOD4WFS Adapter, which opens the way for multi-perspective GI applications, created on-demand from multiple GI data resources. These applications automate geometry-based selections of data using spatial queries with the use of RCC8 and OGC Simple Features topological functions. Future work in this area includes adding semantic operators to refine GI processing with multiple ontologies

Decision Analysis with Geographically Varying Outcomes: Preference Models and Illustrative Applications

DRMI Working Paper SeriesThe series is intended to convey the preliminary results of [DRMI] ongoing research. The research described in these papers is preliminary and has not completed the usual review process for Institute publications. We welcome feedback from readers and encourage you to convey your comments and criticisms directly to the authors

Exploring Uncertainty in Geodemographics with Interactive Graphics

Geodemographic classifiers characterise populations by categorising geographical areas according to the demographic and lifestyle characteristics of those who live within them. The dimension-reducing quality of such classifiers provides a simple and effective means of characterising population through a manageable set of categories, but inevitably hides heterogeneity, which varies within and between the demographic categories and geographical areas, sometimes systematically. This may have implications for their use, which is widespread in government and commerce for planning, marketing and related activities. We use novel interactive graphics to delve into OAC – a free and open geodemographic classifier that classifies the UK population in over 200,000 small geographical areas into 7 super-groups, 21 groups and 52 sub-groups. Our graphics provide access to the original 41 demographic variables used in the classification and the uncertainty associated with the classification of each geographical area on-demand. It also supports comparison geographically and by category. This serves the dual purpose of helping understand the classifier itself leading to its more informed use and providing a more comprehensive view of population in a comprehensible manner. We assess the impact of these interactive graphics on experienced OAC users who explored the details of the classification, its uncertainty and the nature of between – and within – class variation and then reflect on their experiences. Visualization of the complexities and subtleties of the classification proved to be a thought-provoking exercise both confirming and challenging users’ understanding of population, the OAC classifier and the way it is used in their organisations. Users identified three contexts for which the techniques were deemed useful in the context of local government, confirming the validity of the proposed methods

Geovisual analytics for spatial decision support: Setting the research agenda

This article summarizes the results of the workshop on Visualization, Analytics & Spatial Decision Support, which took place at the GIScience conference in September 2006. The discussions at the workshop and analysis of the state of the art have revealed a need in concerted cross‐disciplinary efforts to achieve substantial progress in supporting space‐related decision making. The size and complexity of real‐life problems together with their ill‐defined nature call for a true synergy between the power of computational techniques and the human capabilities to analyze, envision, reason, and deliberate. Existing methods and tools are yet far from enabling this synergy. Appropriate methods can only appear as a result of a focused research based on the achievements in the fields of geovisualization and information visualization, human‐computer interaction, geographic information science, operations research, data mining and machine learning, decision science, cognitive science, and other disciplines. The name ‘Geovisual Analytics for Spatial Decision Support’ suggested for this new research direction emphasizes the importance of visualization and interactive visual interfaces and the link with the emerging research discipline of Visual Analytics. This article, as well as the whole special issue, is meant to attract the attention of scientists with relevant expertise and interests to the major challenges requiring multidisciplinary efforts and to promote the establishment of a dedicated research community where an appropriate range of competences is combined with an appropriate breadth of thinking

A Data-driven, High-performance and Intelligent CyberInfrastructure to Advance Spatial Sciences

abstract: In the field of Geographic Information Science (GIScience), we have witnessed the unprecedented data deluge brought about by the rapid advancement of high-resolution data observing technologies. For example, with the advancement of Earth Observation (EO) technologies, a massive amount of EO data including remote sensing data and other sensor observation data about earthquake, climate, ocean, hydrology, volcano, glacier, etc., are being collected on a daily basis by a wide range of organizations. In addition to the observation data, human-generated data including microblogs, photos, consumption records, evaluations, unstructured webpages and other Volunteered Geographical Information (VGI) are incessantly generated and shared on the Internet. Meanwhile, the emerging cyberinfrastructure rapidly increases our capacity for handling such massive data with regard to data collection and management, data integration and interoperability, data transmission and visualization, high-performance computing, etc. Cyberinfrastructure (CI) consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high-performance networks to improve research productivity and enable breakthroughs that are not otherwise possible. The Geospatial CI (GCI, or CyberGIS), as the synthesis of CI and GIScience has inherent advantages in enabling computationally intensive spatial analysis and modeling (SAM) and collaborative geospatial problem solving and decision making. This dissertation is dedicated to addressing several critical issues and improving the performance of existing methodologies and systems in the field of CyberGIS. My dissertation will include three parts: The first part is focused on developing methodologies to help public researchers find appropriate open geo-spatial datasets from millions of records provided by thousands of organizations scattered around the world efficiently and effectively. Machine learning and semantic search methods will be utilized in this research. The second part develops an interoperable and replicable geoprocessing service by synthesizing the high-performance computing (HPC) environment, the core spatial statistic/analysis algorithms from the widely adopted open source python package – Python Spatial Analysis Library (PySAL), and rich datasets acquired from the first research. The third part is dedicated to studying optimization strategies for feature data transmission and visualization. This study is intended for solving the performance issue in large feature data transmission through the Internet and visualization on the client (browser) side. Taken together, the three parts constitute an endeavor towards the methodological improvement and implementation practice of the data-driven, high-performance and intelligent CI to advance spatial sciences.Dissertation/ThesisDoctoral Dissertation Geography 201

A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns