11,326 research outputs found
Historical collaborative geocoding
The latest developments in digital have provided large data sets that can
increasingly easily be accessed and used. These data sets often contain
indirect localisation information, such as historical addresses. Historical
geocoding is the process of transforming the indirect localisation information
to direct localisation that can be placed on a map, which enables spatial
analysis and cross-referencing. Many efficient geocoders exist for current
addresses, but they do not deal with the temporal aspect and are based on a
strict hierarchy (..., city, street, house number) that is hard or impossible
to use with historical data. Indeed historical data are full of uncertainties
(temporal aspect, semantic aspect, spatial precision, confidence in historical
source, ...) that can not be resolved, as there is no way to go back in time to
check. We propose an open source, open data, extensible solution for geocoding
that is based on the building of gazetteers composed of geohistorical objects
extracted from historical topographical maps. Once the gazetteers are
available, geocoding an historical address is a matter of finding the
geohistorical object in the gazetteers that is the best match to the historical
address. The matching criteriae are customisable and include several dimensions
(fuzzy semantic, fuzzy temporal, scale, spatial precision ...). As the goal is
to facilitate historical work, we also propose web-based user interfaces that
help geocode (one address or batch mode) and display over current or historical
topographical maps, so that they can be checked and collaboratively edited. The
system is tested on Paris city for the 19-20th centuries, shows high returns
rate and is fast enough to be used interactively.Comment: WORKING PAPE
Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems
The modelling, analysis, and visualisation of dynamic geospatial phenomena
has been identified as a key developmental challenge for next-generation
Geographic Information Systems (GIS). In this context, the envisaged
paradigmatic extensions to contemporary foundational GIS technology raises
fundamental questions concerning the ontological, formal representational, and
(analytical) computational methods that would underlie their spatial
information theoretic underpinnings.
We present the conceptual overview and architecture for the development of
high-level semantic and qualitative analytical capabilities for dynamic
geospatial domains. Building on formal methods in the areas of commonsense
reasoning, qualitative reasoning, spatial and temporal representation and
reasoning, reasoning about actions and change, and computational models of
narrative, we identify concrete theoretical and practical challenges that
accrue in the context of formal reasoning about `space, events, actions, and
change'. With this as a basis, and within the backdrop of an illustrated
scenario involving the spatio-temporal dynamics of urban narratives, we address
specific problems and solutions techniques chiefly involving `qualitative
abstraction', `data integration and spatial consistency', and `practical
geospatial abduction'. From a broad topical viewpoint, we propose that
next-generation dynamic GIS technology demands a transdisciplinary scientific
perspective that brings together Geography, Artificial Intelligence, and
Cognitive Science.
Keywords: artificial intelligence; cognitive systems; human-computer
interaction; geographic information systems; spatio-temporal dynamics;
computational models of narrative; geospatial analysis; geospatial modelling;
ontology; qualitative spatial modelling and reasoning; spatial assistance
systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964);
Special Issue on: Geospatial Monitoring and Modelling of Environmental
Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press
Efficient Spatial Keyword Search in Trajectory Databases
An increasing amount of trajectory data is being annotated with text
descriptions to better capture the semantics associated with locations. The
fusion of spatial locations and text descriptions in trajectories engenders a
new type of top- queries that take into account both aspects. Each
trajectory in consideration consists of a sequence of geo-spatial locations
associated with text descriptions. Given a user location and a
keyword set , a top- query returns trajectories whose text
descriptions cover the keywords and that have the shortest match
distance. To the best of our knowledge, previous research on querying
trajectory databases has focused on trajectory data without any text
description, and no existing work has studied such kind of top- queries on
trajectories. This paper proposes one novel method for efficiently computing
top- trajectories. The method is developed based on a new hybrid index,
cell-keyword conscious B-tree, denoted by \cellbtree, which enables us to
exploit both text relevance and location proximity to facilitate efficient and
effective query processing. The results of our extensive empirical studies with
an implementation of the proposed algorithms on BerkeleyDB demonstrate that our
proposed methods are capable of achieving excellent performance and good
scalability.Comment: 12 page
Multi-Source Spatial Entity Linkage
Besides the traditional cartographic data sources, spatial information can
also be derived from location-based sources. However, even though different
location-based sources refer to the same physical world, each one has only
partial coverage of the spatial entities, describe them with different
attributes, and sometimes provide contradicting information. Hence, we
introduce the spatial entity linkage problem, which finds which pairs of
spatial entities belong to the same physical spatial entity. Our proposed
solution (QuadSky) starts with a time-efficient spatial blocking technique
(QuadFlex), compares pairwise the spatial entities in the same block, ranks the
pairs using Pareto optimality with the SkyRank algorithm, and finally,
classifies the pairs with our novel SkyEx-* family of algorithms that yield
0.85 precision and 0.85 recall for a manually labeled dataset of 1,500 pairs
and 0.87 precision and 0.6 recall for a semi-manually labeled dataset of
777,452 pairs. Moreover, we provide a theoretical guarantee and formalize the
SkyEx-FES algorithm that explores only 27% of the skylines without any loss in
F-measure. Furthermore, our fully unsupervised algorithm SkyEx-D approximates
the optimal result with an F-measure loss of just 0.01. Finally, QuadSky
provides the best trade-off between precision and recall, and the best
F-measure compared to the existing baselines and clustering techniques, and
approximates the results of supervised learning solutions
An IR-based Approach Towards Automated Integration of Geo-spatial Datasets in Map-based Software Systems
Data is arguably the most valuable asset of the modern world. In this era,
the success of any data-intensive solution relies on the quality of data that
drives it. Among vast amount of data that are captured, managed, and analyzed
everyday, geospatial data are one of the most interesting class of data that
hold geographical information of real-world phenomena and can be visualized as
digital maps. Geo-spatial data is the source of many enterprise solutions that
provide local information and insights. In order to increase the quality of
such solutions, companies continuously aggregate geospatial datasets from
various sources. However, lack of a global standard model for geospatial
datasets makes the task of merging and integrating datasets difficult and
error-prone. Traditionally, domain experts manually validate the data
integration process by merging new data sources and/or new versions of previous
data against conflicts and other requirement violations. However, this approach
is not scalable and is hinder toward rapid release, when dealing with
frequently changing big datasets. Thus more automated approaches with limited
interaction with domain experts is required. As a first step to tackle this
problem, in this paper, we leverage Information Retrieval (IR) and geospatial
search techniques to propose a systematic and automated conflict identification
approach. To evaluate our approach, we conduct a case study in which we measure
the accuracy of our approach in several real-world scenarios and we interview
with software developers at Localintel Inc. (our industry partner) to get their
feedbacks.Comment: ESEC/FSE 2019 - Industry trac
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