Spatial Web Portal for Building Spatial Data Infrastructure

Yang, Chaowei; Cao, Y. Charles; Evans, John; Kafatos, Menas; Bambacus, Myra

doi:10.1080/10824000609480616

Cited by 4 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ability to integrate multi-source mobility data also helps tackle the veracity challenge, as fusing multiple mobility sources can provide a more comprehensive picture of human mobility [ 27 ]. The shareability and usability of the big movement data are facilitated with a spatial web portal [ 42 ] allowing for interactive query, visualization, and download of ODT flows. RESTful Application Programming Interfaces (ODT Flow REST APIs) are developed to allow users to query and access the data in a flexible programmatical way using scientific workflow and Jupyter notebook, to list a few.…”

Section: Methodsmentioning

confidence: 99%

ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility

Huang

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users to explore multi-source mobility datasets with user-defined spatiotemporal scales. To promote reproducibility and replicability, we further develop ODT Flow REST APIs that provide researchers with the flexibility to access the data programmatically via workflows, codes, and programs. Demonstrations are provided to illustrate the potential of the APIs integrating with scientific workflows and with the Jupyter Notebook environment. We believe the platform coupled with the derived multi-scale mobility data can assist human mobility monitoring and analysis during disaster events such as the ongoing COVID-19 pandemic and benefit both scientific communities and the general public in understanding human mobility dynamics.

show abstract

Section: Methodsmentioning

confidence: 99%

ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility

Huang

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Although the stability of the satellite images markets cannot be dissociated from previously mentioned factors, the literature examining the technical issues related to the expansion of the EO markets [1,55] has not addressed clearly how the users' demand can reveal part of the market risk [20,22]. Studying the users' demand occurring via the SDI platforms still presents many complications [44,57,66,71]. Usually, the time series of the satellite images demand are non-iid, and assumptions about the type of distribution are complex.…”

Section: Introductionmentioning

confidence: 99%

Examining satellite images market stability using the Records theory: Evidence from French spatial data infrastructures

Jabbour

Hoayek

Maurel

et al. 2021

JOSIS

View full text Add to dashboard Cite

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 witnessing a spike/drop in the short term of different satellite imagery schemes: the high resolution images through GEOSUD; the Landsat (U.S.), Sentinel (Europe) and SPOT (France) images through PEPS. We analyze the market stability through the two SDIs and evaluate the probability of future records by using the Records theory. The results show that the high resolution images demand through GEOSUD, for which the classical i.i.d. model fits the most, is stable. Moreover, the Yang-Nevzorov model fits to the Landsat data, due to more records concentrated beyond the first observations. The Landsat demand is the less stable out of the other three satellite images series, and the probability of having a record in the coming years is the highest. While the use of Records theory drops mathematical constraints, it offers an alternative solution to the non-applicability of the machine learning techniques and long-term memory models.

show abstract

“…In order to effectively share the datasets, we need to design the spatial data management infrastructure compatible with the existing standards. Many researches in the geographic information science has explored the efficiency of geospatial web services, which will provide great insights for building the data management system (Yang et al, 2006;Li et al, 2015). Academic libraries has also explored to manage geospatial data via their institutional data repositories (Newton, Miller and Bracke, 2010;Durante and Hardy, 2015).…”

Section: Introductionmentioning

confidence: 99%

One store has all? - the backend story of managing geospatial information toward an easy discovery

Kong

2019

View full text Add to dashboard Cite

Geospatial data includes many formats, varying from historical paper maps, to digital information collected by various sensors. Many libraries have started the efforts to build a geospatial data portal to connect users with the various information. For example, GeoBlacklight and OpenGeoportal are two open-source projects that initiated from academic institutions which have been adopted by many universities and libraries for geospatial data discovery. While several recent studies have focused on the metadata, usability and data collection perspectives of geospatial data portals, not many have explored the backend stories about data management to support the data discovery platform. The objective of this paper is to provide a summary about geospatial data management strategies involved in the geospatial data portal development by reviewing case studies. These data management strategies include managing the historical paper maps, scanned maps, aerial photos, research generated geospatial information, and web map services. This paper focuses on the data organization, storage, cyberinfrustracture configuration, preservation and sharing perspectives of these efforts with the goal to provide a range of options or best management practices for information managers when curating geospatial data in their own institutions.

show abstract

Spatial Web Portal for Building Spatial Data Infrastructure

Cited by 4 publications

References 4 publications

ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility

ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility

Examining satellite images market stability using the Records theory: Evidence from French spatial data infrastructures

One store has all? - the backend story of managing geospatial information toward an easy discovery

Contact Info

Product

Resources

About