Geospatial Data Science Approaches for Transport Demand Modeling

2017 ◽  
pp. 177-204
Author(s):  
Zahra Navidi ◽  
Rahul Deb Das ◽  
Stephan Winter
Keyword(s):  
Data Science ◽  
Geospatial Data ◽  
Demand Modeling

Geospatial Data Science: A Transdisciplinary Approach

2017 ◽  
pp. 1-40
Author(s):  
Emre Eftelioglu ◽  
Reem Y. Ali ◽  
Xun Tang ◽  
Yiqun Xie ◽  
Yan Li ◽  
...  
Keyword(s):  
Data Science ◽  
Geospatial Data ◽  
Transdisciplinary Approach

scholarly journals Transdisciplinary Foundations of Geospatial Data Science

2017 ◽  
Vol 6 (12) ◽  
pp. 395 ◽  
Author(s):  
Yiqun Xie ◽  
Emre Eftelioglu ◽  
Reem Ali ◽  
Xun Tang ◽  
Yan Li ◽  
...  
Keyword(s):  
Data Science ◽  
Geospatial Data

scholarly journals Guerrilla Badges for Reproducible Geospatial Data Science (AGILE 2019 Short Paper)

2019 ◽  
Author(s):  
Daniel Nüst ◽  
Lukas Lohoff ◽  
Lasse Einfeldt ◽  
Nimrod Gavish ◽  
Marlena Götza ◽  
...  
Keyword(s):  
Data Science ◽  
Geospatial Data ◽  
Short Paper

CyberGIS and Geospatial Data Science for Advancing Geomorphology

2021 ◽  
Author(s):  
Shaowen Wang ◽  
Michael P. Bishop ◽  
Zhe Zhang ◽  
Brennan W. Young ◽  
Zewei Xu
Keyword(s):  
Data Science ◽  
Geospatial Data

scholarly journals Assessment and Benchmarking of Spatially Enabled RDF Stores for the Next Generation of Spatial Data Infrastructure

2019 ◽  
Vol 8 (7) ◽  
pp. 310 ◽  
Author(s):  
Weiming Huang ◽  
Syed Amir Raza ◽  
Oleg Mirzov ◽  
Lars Harrie
Keyword(s):  
Real World ◽  
Spatial Data ◽  
Linked Data ◽  
Large Scale ◽  
Data Science ◽  
Earth Science ◽  
Geospatial Data ◽  
Semantic Heterogeneity ◽  
Next Generation ◽  
Data Infrastructure

Geospatial information is indispensable for various real-world applications and is thus a prominent part of today’s data science landscape. Geospatial data is primarily maintained and disseminated through spatial data infrastructures (SDIs). However, current SDIs are facing challenges in terms of data integration and semantic heterogeneity because of their partially siloed data organization. In this context, linked data provides a promising means to unravel these challenges, and it is seen as one of the key factors moving SDIs toward the next generation. In this study, we investigate the technical environment of the support for geospatial linked data by assessing and benchmarking some popular and well-known spatially enabled RDF stores (RDF4J, GeoSPARQL-Jena, Virtuoso, Stardog, and GraphDB), with a focus on GeoSPARQL compliance and query performance. The tests were performed in two different scenarios. In the first scenario, geospatial data forms a part of a large-scale data infrastructure and is integrated with other types of data. In this scenario, we used ICOS Carbon Portal’s metadata—a real-world Earth Science linked data infrastructure. In the second scenario, we benchmarked the RDF stores in a dedicated SDI environment that contains purely geospatial data, and we used geospatial datasets with both crowd-sourced and authoritative data (the same test data used in a previous benchmark study, the Geographica benchmark). The assessment and benchmarking results demonstrate that the GeoSPARQL compliance of the RDF stores has encouragingly advanced in the last several years. The query performances are generally acceptable, and spatial indexing is imperative when handling a large number of geospatial objects. Nevertheless, query correctness remains a challenge for cross-database interoperability. In conclusion, the results indicate that the spatial capacity of the RDF stores has become increasingly mature, which could benefit the development of future SDIs.

Geospatial Data Science: A Transdisciplinary Approach

2017 ◽  
pp. 1-40
Author(s):  
Emre Eftelioglu ◽  
Reem Y. Ali ◽  
Xun Tang ◽  
Yiqun Xie ◽  
Yan Li ◽  
...  
Keyword(s):  
Data Science ◽  
Geospatial Data ◽  
Transdisciplinary Approach

scholarly journals A Multiple Subspaces-Based Model: Interpreting Urban Functional Regions with Big Geospatial Data

2021 ◽  
Vol 10 (2) ◽  
pp. 66
Author(s):  
Jiawei Zhu ◽  
Chao Tao ◽  
Xin Lin ◽  
Jian Peng ◽  
Haozhe Huang ◽  
...  
Keyword(s):  
Data Science ◽  
Information Science ◽  
Dimensional Space ◽  
Human Mobility ◽  
Activity Patterns ◽  
Real Data ◽  
Geospatial Data ◽  
Geographical Information ◽  
Urban Spatial Structure ◽  
Functional Regions

Analyzing the urban spatial structure of a city is a core topic within urban geographical information science that has the ability to assist urban planning, site selection, location recommendation, etc. Among previous studies, comprehending the functionality of places is a central topic and corresponds to understanding how people use places. With the help of big geospatial data which contain affluent information about human mobility and activity, we propose a novel multiple subspaces-based model to interpret the urban functional regions. This model is based on the assumption that the temporal activity patterns of places lie in a high-dimensional space and can be represented by a union of low-dimensional subspaces. These subspaces are obtained through finding sparse representations using the data science method known as sparse subspace clustering (SSC). The paper details how to use this method in the context of detecting functional regions. With these subspaces, we can detect the functionality of urban regions in a designated study area and further explore the characteristics of functional regions. We conducted experiments using real data in Shanghai. The experimental results and outperformance of our proposed model against the single subspace-based method prove the efficacy and feasibility of our model.

scholarly journals NEW SOURCE OF GEOSPATIAL DATA: CROWDSENSING BY ASSISTED AND AUTONOMOUS VEHICLE TECHNOLOGIES

2018 ◽  
Vol XLII-4/W8 ◽  
pp. 211-216 ◽  
Author(s):  
C. K. Toth ◽  
Z. Koppanyi ◽  
M. G. Lenzano
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Data Science ◽  
Information Dissemination ◽  
Autonomous Vehicle ◽  
Weather Conditions ◽  
Traffic Monitoring ◽  
Geospatial Data ◽  
Object Space ◽  
Vehicle Technologies

<p><strong>Abstract.</strong> The ongoing proliferation of remote sensing technologies in the consumer market has been rapidly reshaping the geospatial data acquisition world, and subsequently, the data processing as well as information dissemination processes. Smartphones have clearly established themselves as the primary crowdsourced data generators recently, and provide an incredible volume of remote sensed data with fairly good georeferencing. Besides the potential to map the environment of the smartphone users, they provide information to monitor the dynamic content of the object space. For example, real-time traffic monitoring is one of the most known and widely used real-time crowdsensed application, where the smartphones in vehicles jointly contribute to an unprecedentedly accurate traffic flow estimation. Now we are witnessing another milestone to happen, as driverless vehicle technologies will become another major source of crowdsensed data. Due to safety concerns, the requirements for sensing are higher, as the vehicles should sense other vehicles and the road infrastructure under any condition, not just daylight in favorable weather conditions, and at very fast speed. Furthermore, the sensing is based on using redundant and complementary sensor streams to achieve a robust object space reconstruction, needed to avoid collisions and maintain normal travel patterns. At this point, the remote sensed data in assisted and autonomous vehicles are discarded, or partially recorded for R&amp;amp;D purposes. However, in the long run, as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies mature, recording data will become a common place, and will provide an excellent source of geospatial information for road mapping, traffic monitoring, etc. This paper reviews the key characteristics of crowdsourced vehicle data based on experimental data, and then the processing aspects, including the Data Science and Deep Learning components.</p>

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