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Social and Behavioral Sciences Commons™
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- Keyword
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- Navigation (7)
- GIS (5)
- Geovisualization (5)
- OpenStreetMap (5)
- Uncertainty (5)
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- Big data (4)
- Data mining (4)
- Machine learning (4)
- Ontology (4)
- Spatial analysis (4)
- Spatial information (4)
- Trajectories (4)
- Visual analytics (4)
- Wayfinding (4)
- Clutter (3)
- Computational geometry (3)
- Context (3)
- Data quality (3)
- Euler circles (3)
- Geographic information retrieval (3)
- Granularity (3)
- Landmarks (3)
- Linear diagrams (3)
- Map generalization (3)
- OSM (3)
- Place (3)
- Region based diagrams (3)
- SDI (3)
- Set theory (3)
- Spatial big data (3)
Articles 1 - 30 of 144
Full-Text Articles in Social and Behavioral Sciences
Surface Network Extraction From High Resolution Digital Terrain Models, Eric Guilbert
Surface Network Extraction From High Resolution Digital Terrain Models, Eric Guilbert
Journal of Spatial Information Science
A surface network is a topological data structure formed by a set of thalwegs and ridges on a digital terrain model. Its computation relies on the detection of saddles on the terrain. Hence, computation methods must guarantee enough saddles are detected but also that no improper conflicts between ridges and thalwegs are created, leading to an inconsistent network. This paper presents a new approach that maximizes the number of saddles and ensures this topological consistency for high-resolution terrain models represented by a raster grid. The grid is triangulated in order to preserve saddles and to facilitate thalweg and ridge computation. …
Examining Satellite Images Market Stability Using The Records Theory: Evidence From French Spatial Data Infrastructures, Chadi Jabbour, Anis Hoayek, Pierre Maurel, Zaher Khraibani, Latifa Ghalayini
Examining Satellite Images Market Stability Using The Records Theory: Evidence From French Spatial Data Infrastructures, Chadi Jabbour, Anis Hoayek, Pierre Maurel, Zaher Khraibani, Latifa Ghalayini
Journal of Spatial Information Science
The spatial data infrastructures (SDIs) which constitute a direct link between spatial data users and the large Earth observation industry, have a leading role in establishing market opportunities in the space sector. The spatial information supplied through various forms of SDI platforms exhibits large increases in demand volatility. The users' demand is unpredictable and the market is vulnerable to high evolution shifts. We study the effect of extreme demands for a particular type of spatial information, the satellite images. Drawing on two French SDIs, GEOSUD and PEPS, we examine the shifts occurring on their platforms and assess the probability of …
Towards Detecting, Characterizing, And Rating Of Road Class Errors In Crowd-Sourced Road Network Databases, Johanna Guth, Sina Keller, Stefan Hinz, Stephan Winter
Towards Detecting, Characterizing, And Rating Of Road Class Errors In Crowd-Sourced Road Network Databases, Johanna Guth, Sina Keller, Stefan Hinz, Stephan Winter
Journal of Spatial Information Science
OpenStreetMap (OSM), with its global coverage and Open Database License, has recently gained popularity. Its quality is adequate for many applications, but since it is crowd-sourced, errors remain an issue. Errors in associated tags of the road network, for example, are impacting routing applications. Particularly road classification errors often lead to false assumptions about capacity, maximum speed, or road quality, possibly resulting in detours for routing applications. This study aims at finding potential classification errors automatically, which can then be checked and corrected by a human expert. We develop a novel approach to detect road classification errors in OSM by …
Service Quality Monitoring In Confined Spaces Through Mining Twitter Data, Mohammad Masoud Rahimi, Elham Naghizade, Mark Stevenson, Stephan Winter
Service Quality Monitoring In Confined Spaces Through Mining Twitter Data, Mohammad Masoud Rahimi, Elham Naghizade, Mark Stevenson, Stephan Winter
Journal of Spatial Information Science
Promoting public transport depends on adapting effective tools for concurrent monitoring of perceived service quality. Social media feeds, in general, provide an opportunity to ubiquitously look for service quality events, but when applied to confined geographic area such as a transport node, the sparsity of concurrent social media data leads to two major challenges. Both the limited number of social media messages--leading to biased machine-learning--and the capturing of bursty events in the study period considerably reduce the effectiveness of general event detection methods. In contrast to previous work and to face these challenges, this paper presents a hybrid solution based …
The Impact Of Urban Road Network Morphology On Pedestrian Wayfinding Behaviour, Debjit Bhowmick, Stephan Winter, Mark Stevenson, Peter Vortisch
The Impact Of Urban Road Network Morphology On Pedestrian Wayfinding Behaviour, Debjit Bhowmick, Stephan Winter, Mark Stevenson, Peter Vortisch
Journal of Spatial Information Science
During wayfinding pedestrians do not always choose the shortest available route. Instead, route choices are guided by several well-known wayfinding strategies or heuristics. These heuristics minimize cognitive effort and usually lead to satisfactory route choices. Our previous study evaluated the costs of four well-known pedestrian wayfinding heuristics and their variation across nine network morphologies. It was observed that the variation in the cost of these wayfinding heuristics increased with an increase in the irregularity of the network, indicating that people may opt for more diverse heuristics while walking through relatively regular networks, and may prefer specific heuristics in the relatively …
How Does Socio-Economic And Demographic Dissimilarity Determine Physical And Virtual Segregation?, Michael Dorman, Tal Svoray, Itai Kloog
How Does Socio-Economic And Demographic Dissimilarity Determine Physical And Virtual Segregation?, Michael Dorman, Tal Svoray, Itai Kloog
Journal of Spatial Information Science
It is established that socio-economic and demographic dissimilarities between populations are determinants of spatial segregation. However, the understanding of how such dissimilarities translate into actual segregation is limited. We propose a novel network-analysis approach to comprehensively study the determinants of communicative and mobility-related spatial segregation, using geo-tagged Twitter data. We constructed weighted spatial networks representing tie strength between geographical areas, then modeled tie formation as a function of socio-economic and demographic dissimilarity between areas. Physical and virtual tie formation were affected by income, age, and race differences, although these effects were smaller by an order of magnitude than the geographical …
Geocomputation 2019 Special Feature, Antoni Moore, Mark Gahegan
Geocomputation 2019 Special Feature, Antoni Moore, Mark Gahegan
Journal of Spatial Information Science
No abstract provided.
Modelling Orebody Structures: Block Merging Algorithms And Block Model Spatial Restructuring Strategies Given Mesh Surfaces Of Geological Boundaries, Raymond Leung
Journal of Spatial Information Science
This paper describes a framework for capturing geological structures in a 3D block model and improving its spatial fidelity, including the correction of stratigraphic, mineralisation and other types of boundaries, given new mesh surfaces. Using surfaces that represent geological boundaries, the objectives are to identify areas where refinement is needed, increase spatial resolution to minimise surface approximation error, reduce redundancy to increase the compactness of the model and identify the geological domain on a block-by-block basis. These objectives are fulfilled by four system components which perform block-surface overlap detection, spatial structure decomposition, sub-blocks consolidation and block tagging, respectively. The main …
Big Issues For Big Data: Challenges For Critical Spatial Data Analytics, Chris Brunsdon, Alexis Comber
Big Issues For Big Data: Challenges For Critical Spatial Data Analytics, Chris Brunsdon, Alexis Comber
Journal of Spatial Information Science
In this paper we consider some of the issues of working with big data and big spatial data and highlight the need for an open and critical framework. We focus on a set of challenges underlying the collection and analysis of big data. In particular, we consider 1) inference when working with usually biased big data, challenging the assumed inferential superiority of data with observations, n, approaching N, the population n -> N. We also emphasise 2) the need for analyses that answer questions of practical significance or with greater emphasis on the size of the effect, rather than the …
Route Schematization With Landmarks, Marcelo De Lima Galvao, Jakub Krukar, Martin Noellenburg, Angela Schwering
Route Schematization With Landmarks, Marcelo De Lima Galvao, Jakub Krukar, Martin Noellenburg, Angela Schwering
Journal of Spatial Information Science
Predominant navigation applications make use of a turn-by-turn instructions approach and are mostly supported by small screen devices. This combination does little to improve users' orientation or spatial knowledge acquisition. Considering this limitation, we propose a route schematization method aimed for small screen devices to facilitate the readability of route information and survey knowledge acquisition. Current schematization methods focus on the route path and ignore context information, specially polygonal landmarks (such as lakes, parks, and regions), which is crucial for promoting orientation. Our schematization method, in addition to the route path, takes as input: adjacent streets, point-like landmarks, and polygonal …
Local Modelling: One Size Does Not Fit All, A. Stewart Fotheringham
Local Modelling: One Size Does Not Fit All, A. Stewart Fotheringham
Journal of Spatial Information Science
This editorial piece considers what happens when we abandon the concept that models of social processes have global application in favor of a local approach in which context or the influence of 'place' has an important role. A brief history of this local approach to statistical modelling is given, followed by a consideration of its ramifications for understanding societal issues. The piece concludes with futures challenges and prospects in this area.
Indigeneity And Spatial Information Science, Matt Duckham, Serene Ho
Indigeneity And Spatial Information Science, Matt Duckham, Serene Ho
Journal of Spatial Information Science
Spatial information science has given rise to a set of concepts, tools, and techniques for understanding our geographic world. In turn, the technologies built on this body of knowledge embed certain ways of knowing." This vision paper traces the roots and impacts of those embeddings and explores how they can sometimes be inherently at odds with or completely subvert Indigenous Peoples' ways of knowing. However advancements in spatial information science offer opportunities for innovation whilst working towards reconciliation. We highlight as examples four active research topics in the field to support a call to action for greater inclusion of Indigenous …
Inferring Movement Patterns From Geometric Similarity, Maike Buchin, Carola Wenk
Inferring Movement Patterns From Geometric Similarity, Maike Buchin, Carola Wenk
Journal of Spatial Information Science
Spatial movement data nowadays is becoming ubiquitously available, including data of animals, vehicles and people. This data allows us to analyze the underlying movement. In particular, it allows us to infer movement patterns, such as recurring places and routes. Many methods to do so rely on the notion of similarity of places or routes. Here we briefly survey how research on this has developed in the past 15 years and outline challenges for future work.
Why Are Events Important And How To Compute Them In Geospatial Research?, May Yuan
Why Are Events Important And How To Compute Them In Geospatial Research?, May Yuan
Journal of Spatial Information Science
Geospatial research has long centered around objects. While attention to events is growing rapidly, events remain objectified in spatial databases. This paper aims to highlight the importance of events in scientific inquiries and overview general event-based approaches to data modeling and computing. As machine learning algorithms and big data become popular in geospatial research, many studies appear to be the products of convenience with readily adaptable data and codes rather than curiosity. By asking why events are important and how to compute events in geospatial research, the author intends to provoke thinking into the rationale and conceptual basis of event-based …
Integrated Science Of Movement, Urska Demsar, Jed A. Long, Katarzyna Sila-Nowicka
Integrated Science Of Movement, Urska Demsar, Jed A. Long, Katarzyna Sila-Nowicka
Journal of Spatial Information Science
Recent technological advances in movement data acquisition have enabled researchers in many disciplines to study movement at increasingly detailed spatial and temporal scales. Yet there is little overlap in the sharing of methods and models between disciplines, despite similar research objectives and data models. Attempts to bridge this gap are leading towards the establishment of an overarching interdisciplinary science, termed the Integrated Science of Movement. Here we present opportunities and challenges of this process and outline the crucial role that GIScience as a discipline with a focus on space, place, and time can play in the integrated science of movement.
From Spatial To Platial - The Role And Future Of Immersive Technologies In The Spatial Sciences, Alexander Klippel
From Spatial To Platial - The Role And Future Of Immersive Technologies In The Spatial Sciences, Alexander Klippel
Journal of Spatial Information Science
Immersive technologies such as virtual and augmented reality have been part of the technology mindset in computer and geospatial sciences early on. The promise of delivering realistic experiences to the human senses that are not bound by physical reality has inspired generations of scientists and entrepreneurs alike. However, the vision for immersive experiences has been in stark contrast to the ability to deliver at the technology end; the community has battled nuisances such as cybersickness, tethers, and the uncanny valley for the last decades. With the 'final wave' of immersive technologies, we are now able to fulfill a long-held promise …
Thinking Spatial, Mohamed F. Mokbel
Thinking Spatial, Mohamed F. Mokbel
Journal of Spatial Information Science
The systems community in both academia and industry has tremendous success in building widely used general purpose systems for various types of data and applications. Examples include database systems, big data systems, data streaming systems, and machine learning systems. The vast majority of these systems are ill equipped in terms of supporting spatial data. The main reason is that system builders mostly think of spatial data as just one more type of data. Any spatial support can be considered as an afterthought problem that can be supported via on-top functions or spatial cartridges that can be added to the already …
Cartographic Generalization, Monika Sester
Cartographic Generalization, Monika Sester
Journal of Spatial Information Science
This short paper gives a subjective view on cartographic generalization, its achievements in the past, and the challenges it faces in the future.
Josis' 10th Anniversary Special Feature: Part Two, Benjamin Adams, Somayeh Dodge, Ross Purves
Josis' 10th Anniversary Special Feature: Part Two, Benjamin Adams, Somayeh Dodge, Ross Purves
Journal of Spatial Information Science
No abstract provided.
Josis' 10th Anniversary Special Feature, Benjamin Adams, Somayeh Dodge, Ross Purves
Josis' 10th Anniversary Special Feature, Benjamin Adams, Somayeh Dodge, Ross Purves
Journal of Spatial Information Science
No abstract provided.
Grand Challenges For The Spatial Information Community, Leye Wang, Ouri Wolfson
Grand Challenges For The Spatial Information Community, Leye Wang, Ouri Wolfson
Journal of Spatial Information Science
The spatial information (SI) community has an opportunity to address major societal and scientific problems including public health, climate change, air pollution, transportation, and others. Beyond the significant contributions made by the SI community, more can be done by focusing the efforts of the community, and generalizing them. Focus can be achieved by an IMAGENET-like spatial information database and competition. Generalization can be achieved by solving spatio-temporal information problems in disciplines such as neuroscience, chemistry, biology, astronomy, and engineering.
Ontologies For Geospatial Information: Progress And Challenges Ahead, Christophe Claramunt
Ontologies For Geospatial Information: Progress And Challenges Ahead, Christophe Claramunt
Journal of Spatial Information Science
Over the past 50 years or so the representation of spatial information within computerized systems has been widely addressed and developed in order to provide suitable data manipulation, analysis, and visualisation mechanisms. The range of applications is unlimited and nowadays impacts almost all sciences and practices. However, current conceptualisations and numerical representations of geospatial information still require the development of richer abstract models that match the complexity of spatial and temporal information. Geospatial ontologies are promising modelling alternatives that might favour the implementation and sharing of geographical information. The objective of this vision paper is to provide a short introduction …
Mining Urban Perceptions From Social Media Data, Yu Liu, Yihong Yuan, Fan Zhang
Mining Urban Perceptions From Social Media Data, Yu Liu, Yihong Yuan, Fan Zhang
Journal of Spatial Information Science
This vision paper summaries the methods of using social media data (SMD) to measure urban perceptions. We highlight two major types of data sources (i.e., texts and imagery) and two corresponding techniques (i.e., natural language processing and computer vision). Recognizing the data quality issues of SMD, we propose three criteria for improving the reliability of SMD-based studies. In addition, integrating multi-source data is a promising approach to mitigating the data quality problems.
How Well Do We Really Know The World? Uncertainty In Giscience, Michael F. Goodchild
How Well Do We Really Know The World? Uncertainty In Giscience, Michael F. Goodchild
Journal of Spatial Information Science
There are many reasons why geospatial data are not geography, but merely representations of it. Thus geospatial data will always leave their user uncertain about the true nature of the world. Over the past three decades uncertainty has become the focus of significant research in GIScience. This paper reviews the reasons for uncertainty, its various dimensions from measurement to modeling, visualization, and propagation. The later sections of the paper explore the implications of current trends, specifically data science, new data sources, and replicability, and the new questions these are posing for GIScience research in the coming years.
What Spatial Environments Mean, Thora Tenbrink
What Spatial Environments Mean, Thora Tenbrink
Journal of Spatial Information Science
Language is one of the most prominent means of representing human thought. Spatial cognition research has made use of this fact for decades, exploring how humans perceive and understand their spatial environments through language analysis. So far, this research has mainly focused on generic cognitive aspects underlying everyday purposes such as knowing where objects are, how they relate to each other, and how to find one's way to a familiar or unfamiliar location. However, human concepts about space can be threatened by change, as the environment changes. Across the globe, people become increasingly aware of climate-change related threats to their …
Movement Analytics For Sustainable Mobility, Harvey J. Miller
Movement Analytics For Sustainable Mobility, Harvey J. Miller
Journal of Spatial Information Science
Mobility is central to urbanity, and urbanity is central to our common future as the world's population crowds into urban areas. This is creating a global urban mobility crisis due to the unsustainability of our 20th century transportation systems for an urban world. Fortunately, the science and planning of urban mobility is transforming away from infrastructure as the solution towards a sustainable mobility paradigm that manages rather than encourages travel, diminishes mobility and accessibility inequities, and reduces the harms of mobility to people and environments. In this essay, I discuss the contributions over the past decade of movement analytics to …
Geoai: Where Machine Learning And Big Data Converge In Giscience, Wenwen Li
Geoai: Where Machine Learning And Big Data Converge In Giscience, Wenwen Li
Journal of Spatial Information Science
In this paper GeoAI is introduced as an emergent spatial analytical framework for data-intensive GIScience. As the new fuel of geospatial research, GeoAI leverages recent breakthroughs in machine learning and advanced computing to achieve scalable processing and intelligent analysis of geospatial big data. The three-pillar view of GeoAI, its two methodological threads (data-driven and knowledge-driven), as well as their geospatial applications are highlighted. The paper concludes with discussion of remaining challenges and future research directions of GeoAI.
Spatio-Temporal Visual Analytics: A Vision For 2020s, Natalia Andrienko, Gennady Andrienko
Spatio-Temporal Visual Analytics: A Vision For 2020s, Natalia Andrienko, Gennady Andrienko
Journal of Spatial Information Science
Visual analytics is a research discipline that is based on acknowledging the power and the necessity of the human vision, understanding, and reasoning in data analysis and problem solving. Visual analytics develops methods, analytical workflows, and software tools for analysing data of various types, particularly, spatio-temporal data, which can describe the processes going on in the environment, society, and economy. We briefly overview the achievements of the visual analytics research concerning spatio-temporal data analysis and discuss the major open problems.
Volunteered And Crowdsourced Geographic Information: The Openstreetmap Project, Michela Bertolotto, Gavin Mcardle, Bianca Schoen-Phelan
Volunteered And Crowdsourced Geographic Information: The Openstreetmap Project, Michela Bertolotto, Gavin Mcardle, Bianca Schoen-Phelan
Journal of Spatial Information Science
Advancements in technology over the last two decades have changed how spatial data are created and used. In particular, in the last decade, volunteered geographic information (VGI), i.e., the crowdsourcing of geographic information, has revolutionized the spatial domain by shifting the map-making process from the hands of experts to those of any willing contributor. Started in 2004, OpenStreetMap (OSM) is the pinnacle of VGI due to the large number of volunteers involved and the volume of spatial data generated. While the original objective of OSM was to create a free map of the world, its uses have shown how the …
Spatial Data Science For Sustainable Mobility, Martin Raubal
Spatial Data Science For Sustainable Mobility, Martin Raubal
Journal of Spatial Information Science
The constant rise of urban mobility and transport has led to a dramatic increase in greenhouse gas emissions. In order to ensure livable environments for future generations and counteract climate change, it will be necessary to reduce our future CO2 footprint. Spatial data science contributes to this effort in major ways, also fuelled by recent progress regarding the availability of spatial big data, computational methods and geospatial technologies. This paper demonstrates important contributions from Spatial data science to mobility pattern analysis and prediction, context integration, and the employment of geospatial technologies for changing people's mobility behavior. Among the interdisciplinary research …