2,947 research outputs found
GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding
Humans subconsciously engage in geospatial reasoning when reading articles.
We recognize place names and their spatial relations in text and mentally
associate them with their physical locations on Earth. Although pretrained
language models can mimic this cognitive process using linguistic context, they
do not utilize valuable geospatial information in large, widely available
geographical databases, e.g., OpenStreetMap. This paper introduces GeoLM, a
geospatially grounded language model that enhances the understanding of
geo-entities in natural language. GeoLM leverages geo-entity mentions as
anchors to connect linguistic information in text corpora with geospatial
information extracted from geographical databases. GeoLM connects the two types
of context through contrastive learning and masked language modeling. It also
incorporates a spatial coordinate embedding mechanism to encode distance and
direction relations to capture geospatial context. In the experiment, we
demonstrate that GeoLM exhibits promising capabilities in supporting toponym
recognition, toponym linking, relation extraction, and geo-entity typing, which
bridge the gap between natural language processing and geospatial sciences. The
code is publicly available at https://github.com/knowledge-computing/geolm.Comment: Accepted to EMNLP23 mai
A COMPREHENSIVE GEOSPATIAL KNOWLEDGE DISCOVERY FRAMEWORK FOR SPATIAL ASSOCIATION RULE MINING
Continuous advances in modern data collection techniques help spatial scientists gain access to massive and high-resolution spatial and spatio-temporal data. Thus there is an urgent need to develop effective and efficient methods seeking to find unknown and useful information embedded in big-data datasets of unprecedentedly large size (e.g., millions of observations), high dimensionality (e.g., hundreds of variables), and complexity (e.g., heterogeneous data sources, space–time dynamics, multivariate connections, explicit and implicit spatial relations and interactions). Responding to this line of development, this research focuses on the utilization of the association rule (AR) mining technique for a geospatial knowledge discovery process.
Prior attempts have sidestepped the complexity of the spatial dependence structure embedded in the studied phenomenon. Thus, adopting association rule mining in spatial analysis is rather problematic. Interestingly, a very similar predicament afflicts spatial regression analysis with a spatial weight matrix that would be assigned a priori, without validation on the specific domain of application. Besides, a dependable geospatial knowledge discovery process necessitates algorithms supporting automatic and robust but accurate procedures for the evaluation of mined results. Surprisingly, this has received little attention in the context of spatial association rule mining.
To remedy the existing deficiencies mentioned above, the foremost goal for this research is to construct a comprehensive geospatial knowledge discovery framework using spatial association rule mining for the detection of spatial patterns embedded in geospatial databases and to demonstrate its application within the domain of crime analysis. It is the first attempt at delivering a complete geo-spatial knowledge discovery framework using spatial association rule mining
Geospatial big data and cartography : research challenges and opportunities for making maps that matter
Geospatial big data present a new set of challenges and opportunities for cartographic researchers in technical, methodological, and artistic realms. New computational and technical paradigms for cartography are accompanying the rise of geospatial big data. Additionally, the art and science of cartography needs to focus its contemporary efforts on work that connects to outside disciplines and is grounded in problems that are important to humankind and its sustainability. Following the development of position papers and a collaborative workshop to craft consensus around key topics, this article presents a new cartographic research agenda focused on making maps that matter using geospatial big data. This agenda provides both long-term challenges that require significant attention as well as short-term opportunities that we believe could be addressed in more concentrated studies.PostprintPeer reviewe
Making Sense of Document Collections with Map-Based Visualizations
As map-based visualizations of documents become more ubiquitous, there is a greater need for them to support intellectual and creative high-level cognitive activities with collections of non-cartographic materials -- documents. This dissertation concerns the conceptualization of map-based visualizations as tools for sensemaking and collection understanding. As such, map-based visualizations would help people use georeferenced documents to develop understanding, gain insight, discover knowledge, and construct meaning. This dissertation explores the role of graphical representations (such as maps, Kohonen maps, pie charts, and other) and interactions with them for developing map-based visualizations capable of facilitating sensemaking activities such as collection understanding. While graphical representations make document collections more perceptually and cognitively accessible, interactions allow users to adapt representations to users’ contextual needs. By interacting with representations of documents or collections and being able to construct representations of their own, people are better able to make sense of information, comprehend complex structures, and integrate new information into their existing mental models. In sum, representations and interactions may reduce cognitive load and consequently expedite the overall time necessary for completion of sensemaking activities, which typically take much time to accomplish. The dissertation proceeds in three phases. The first phase develops a conceptual framework for translating ontological properties of collections to representations and for supporting visual tasks by means of graphical representations. The second phase concerns the cognitive benefits of interaction. It conceptualizes how interactions can help people during complex sensemaking activities. Although the interactions are explained on the example of a prototype built with Google Maps, they are independent iv of Google Maps and can be applicable to various other technologies. The third phase evaluates the utility, analytical capabilities and usability of the additional representations when users interact with a visualization prototype – VIsual COLlection EXplorer. The findings suggest that additional representations can enhance understanding of map-based visualizations of library collections: specifically, they can allow users to see trends, gaps, and patterns in ontological properties of collections
Information Visualization (iV): Notes about the 9th IV ’05 International Conference, London, England
This review tells about the International Conference on Information Visualization that is held annually in London, England. Themes selected from the Conference Proceedings are focused on theoretical concepts, semantic approach to visualization, digital art, and involve 2D, 3D, interactive and virtual reality tools and applications. The focal point of the iV 05 Conference was the progress in information and knowledge visualization, visual data mining, multimodal interfaces, multimedia, web graphics, graph theory application, augmented and virtual reality, semantic web visualization, HCI, digital art, among many other areas such as information visualization in geology, medicine, industry and education
Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010
This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb.
UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010.
The overarching theme this year was “Global Challenges”, with specific focus on the following themes:
* Crime and Place
* Environmental Change
* Intelligent Transport
* Public Health and Epidemiology
* Simulation and Modelling
* London as a global city
* The geoweb and neo-geography
* Open GIS and Volunteered Geographic Information
* Human-Computer Interaction and GIS
Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond
V2V: Vector Embedding of a Graph and Applications
We present V2V, a method for embedding each vertex in a graph as a vector in a fixed dimensional space. Inspired by methods for word embedding such as word2vec, a vertex embedding is computed through enumerating random walks in the graph, and using the resulting vertex sequences to provide the context for each vertex. This embedding allows one to use well-developed techniques from machine learning to solve graph problems such as community detection, graph visualization, and vertex label prediction. We evaluate embeddings produced by V2V through comparing results obtained using V2V with results obtained through a direct application of a graph algorithm, for community detection. Our results show that V2V provides interesting trade-offs among computation time and accuracy
Three-dimensional hydrodynamic models coupled with GIS-based neuro-fuzzy classification for assessing environmental vulnerability of marine cage aquaculture
There is considerable opportunity to develop new modelling techniques within a
Geographic Information Systems (GIS) framework for the development of sustainable
marine cage culture. However, the spatial data sets are often uncertain and incomplete,
therefore new spatial models employing “soft computing” methods such as fuzzy logic
may be more suitable.
The aim of this study is to develop a model using Neuro-fuzzy techniques in a 3D GIS
(Arc View 3.2) to predict coastal environmental vulnerability for Atlantic salmon cage
aquaculture. A 3D hydrodynamic model (3DMOHID) coupled to a particle-tracking
model is applied to study the circulation patterns, dispersion processes and residence
time in Mulroy Bay, Co. Donegal Ireland, an Irish fjard (shallow fjordic system), an
area of restricted exchange, geometrically complicated with important aquaculture
activities.
The hydrodynamic model was calibrated and validated by comparison with sea surface
and water flow measurements. The model provided spatial and temporal information on
circulation, renewal time, helping to determine the influence of winds on circulation
patterns and in particular the assessment of the hydrographic conditions with a strong
influence on the management of fish cage culture.
The particle-tracking model was used to study the transport and flushing processes.
Instantaneous massive releases of particles from key boxes are modelled to analyse the
ocean-fjord exchange characteristics and, by emulating discharge from finfish cages, to
show the behaviour of waste in terms of water circulation and water exchange.
In this study the results from the hydrodynamic model have been incorporated into GIS
to provide an easy-to-use graphical user interface for 2D (maps), 3D and temporal
visualization (animations), for interrogation of results.
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Data on the physical environment and aquaculture suitability were derived from a 3-
dimensional hydrodynamic model and GIS for incorporation into the final model
framework and included mean and maximum current velocities, current flow quiescence
time, water column stratification, sediment granulometry, particulate waste dispersion
distance, oxygen depletion, water depth, coastal protection zones, and slope.
The Neuro-fuzzy classification model NEFCLASS–J, was used to develop learning
algorithms to create the structure (rule base) and the parameters (fuzzy sets) of a fuzzy
classifier from a set of classified training data. A total of 42 training sites were sampled
using stratified random sampling from the GIS raster data layers, and the vulnerability
categories for each were manually classified into four categories based on the opinions
of experts with field experience and specific knowledge of the environmental problems
investigated.
The final products, GIS/based Neuro Fuzzy maps were achieved by combining modeled
and real environmental parameters relevant to marine fin fish Aquaculture.
Environmental vulnerability models, based on Neuro-fuzzy techniques, showed
sensitivity to the membership shapes of the fuzzy sets, the nature of the weightings
applied to the model rules, and validation techniques used during the learning and
validation process. The accuracy of the final classifier selected was R=85.71%,
(estimated error value of ±16.5% from Cross Validation, N=10) with a Kappa
coefficient of agreement of 81%. Unclassified cells in the whole spatial domain (of
1623 GIS cells) ranged from 0% to 24.18 %.
A statistical comparison between vulnerability scores and a significant product of
aquaculture waste (nitrogen concentrations in sediment under the salmon cages) showed
that the final model gave a good correlation between predicted environmental
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vulnerability and sediment nitrogen levels, highlighting a number of areas with variable
sensitivity to aquaculture.
Further evaluation and analysis of the quality of the classification was achieved and the
applicability of separability indexes was also studied. The inter-class separability
estimations were performed on two different training data sets to assess the difficulty of
the class separation problem under investigation. The Neuro-fuzzy classifier for a
supervised and hard classification of coastal environmental vulnerability has
demonstrated an ability to derive an accurate and reliable classification into areas of
different levels of environmental vulnerability using a minimal number of training sets.
The output will be an environmental spatial model for application in coastal areas
intended to facilitate policy decision and to allow input into wider ranging spatial
modelling projects, such as coastal zone management systems and effective
environmental management of fish cage aquaculture
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