scholarly journals Analysing the Impact of Large Data Imports in OpenStreetMap

2021 ◽  
Vol 10 (8) ◽  
pp. 528
Author(s):  
Raphael Witt ◽  
Lukas Loos ◽  
Alexander Zipf
Keyword(s):  
Large Data ◽  
Heterogeneous Data ◽  
Geographical Information ◽  
Data Sets ◽  
Heterogeneous Data Source ◽  
External Data ◽  
Time Period ◽  
Global Mapping ◽  
Data Source ◽  
The Impact

OpenStreetMap (OSM) is a global mapping project which generates free geographical information through a community of volunteers. OSM is used in a variety of applications and for research purposes. However, it is also possible to import external data sets to OpenStreetMap. The opinions about these data imports are divergent among researchers and contributors, and the subject is constantly discussed. The question of whether importing data, especially large quantities, is adding value to OSM or compromising the progress of the project needs to be investigated more deeply. For this study, OSM’s historical data were used to compute metrics about the developments of the contributors and OSM data during large data imports which were for the Netherlands and India. Additionally, one time period per study area during which there was no large data import was investigated to compare results. For making statements about the impacts of large data imports in OSM, the metrics were analysed using different techniques (cross-correlation and changepoint detection). It was found that the contributor activity increased during large data imports. Additionally, contributors who were already active before a large import were more likely to contribute to OSM after said import than contributors who made their first contributions during the large data import. The results show the difficulty of interpreting a heterogeneous data source, such as OSM, and the complexity of the project. Limitations and challenges which were encountered are explained, and future directions for continuing in this field of research are given.

Development of interactive diagnostic tools and metrics for the socio-economic consequences of floods

2021 ◽  
Author(s):  
Annie-Claude Parent ◽  
Frédéric Fournier ◽  
François Anctil ◽  
Brian Morse ◽  
Jean-Philippe Baril-Boyer ◽  
...  
Keyword(s):  
Maximum Flow ◽  
Large Data ◽  
Heterogeneous Data ◽  
Economic Consequences ◽  
Disaster Mitigation ◽  
Diagnostic Tools ◽  
Data Sets ◽  
Residential Areas ◽  
Level Data ◽  
The Impact

<p>Spring floods have generated colossal damages to residential areas in the Province of Quebec, Canada, in 2017 and 2019. Government authorities need accurate modelling of the impact of theoretical floods in order to prioritize pre-disaster mitigation projects to reduce vulnerability. They also need accurate modelling of forecasted floods in order to direct emergency responses. </p><p>We present a governmental-academic collaboration that aims at modelling flood impact for both theoretical and forecasted flooding events over all populated river reaches of meridional Quebec. The project, funded by the ministère de la Sécurité publique du Québec (Quebec ministry in charge of public security), consists in developing a diagnostic tool and methods to assess the risk and impacts of flooding. Tools under development are intended to be used primarily by policy makers. </p><p>The project relies on water level data based on the hydrological regimes of nearly 25,000 km of rivers, on high-precision digital terrain models, and on a detailed database of building footprints and characterizations. It also relies on 24h and 48h forecasts of maximum flow for the subject rivers. The developed tools integrate large data sets and heterogeneous data sources and produce insightful metrics on the physical extent and costs of floods and on their impact on the population. The software also provides precise information about each building affected by rising water, including an estimated cost of the damages and impact on inhabitants.  </p>

Multi-Variable, High Order, Performance Models (2005C)

2005 ◽  
Author(s):  
David Japikse ◽  
Oleg Dubitsky ◽  
Kerry N. Oliphant ◽  
Robert J. Pelton ◽  
Daniel Maynes ◽  
...  
Keyword(s):  
Data Processing ◽  
Large Data ◽  
High Order ◽  
Large Data Sets ◽  
Data Sets ◽  
Performance Models ◽  
Statistical Accuracy ◽  
Evaluation Methodologies ◽  
New Models ◽  
The Impact

In the course of developing advanced data processing and advanced performance models, as presented in companion papers, a number of basic scientific and mathematical questions arose. This paper deals with questions such as uniqueness, convergence, statistical accuracy, training, and evaluation methodologies. The process of bringing together large data sets and utilizing them, with outside data supplementation, is considered in detail. After these questions are focused carefully, emphasis is placed on how the new models, based on highly refined data processing, can best be used in the design world. The impact of this work on designs of the future is discussed. It is expected that this methodology will assist designers to move beyond contemporary design practices.

scholarly journals Reporting time period matters: quantifying catch rates and exploring recall bias from fisher interviews in Thailand

2018 ◽  
Vol 75 (12) ◽  
pp. 2114-2122 ◽  
Author(s):  
Lindsay Aylesworth ◽  
Ting-Chun Kuo
Keyword(s):  
Fisheries Management ◽  
Data Sets ◽  
Multiple Time ◽  
Recall Bias ◽  
External Data ◽  
Time Period ◽  
The Status ◽  
The Mean ◽  
Catch Rates ◽  
Reported Data

Catch rates reported by fishers are commonly used to understand the status of a fishery, but the reliability of fisher-reported data is affected by how they recall such information. Recalling catch may be influenced by the choice of reporting time period. Using interview data from fishers in Thailand, we investigated (1) how the time period for which fishers report their catch rates (e.g., per day or month) correlates with annual catch estimates and (2) the potential of recall bias when fishers reported multiple catch rates. We found that the annual catch estimates of fishers who reported on a shorter time period (haul, day) were significantly higher than those reported on a longer time period (month, year). This trend held true when individual fishers reported over multiple time periods, suggesting recall bias. By comparing fisher reports with external data sets, we identified that the mean across all reports was most similar to other data sources, rather than any time period. Our research has strong implications in using fishers’ knowledge for fisheries management.

University Heterogeneous Data Source Integration Middleware Design Based on XML

2014 ◽  
Vol 543-547 ◽  
pp. 2937-2940
Author(s):  
Xiao Xiao Liang ◽  
Shun Min Wang ◽  
Chong Gang Wei ◽  
Chuang Shen
Keyword(s):  
Data Integration ◽  
Heterogeneous Data ◽  
Heterogeneous Data Source ◽  
Heterogeneous Data Integration ◽  
Data Source Integration ◽  
Data Source ◽  
The University

According to the distribution, autonomy and heterogeneity of the university database, we designed the structure, main arithmetic, query distribution device, result processor and wrapper of the university heterogeneous data integration middle ware by using Java, XML and middle ware. We emphasized on introducing the designation of query distribution device, result processor and wrapper.

Integrating Heterogeneous Data Source Using Ontology

2009 ◽  
Vol 4 (8) ◽  
Author(s):  
Jinpeng Wang ◽  
Jianjiang Lu ◽  
Yafei Zhang ◽  
Zhuang Miao ◽  
Bo Zhou
Keyword(s):  
Heterogeneous Data ◽  
Heterogeneous Data Source ◽  
Data Source

Visual Communication in Times of Crisis: The Fukushima Nuclear Accident

Leonardo ◽  
2012 ◽  
Vol 45 (2) ◽  
pp. 113-118 ◽  
Author(s):  
Rama C. Hoetzlein
Keyword(s):  
News Media ◽  
Visual Communication ◽  
Large Data ◽  
Nuclear Accident ◽  
Large Data Sets ◽  
Fukushima Nuclear Accident ◽  
Data Sets ◽  
On Line ◽  
The Impact ◽  
Information Aesthetics

This paper follows the development of visual communication through information visualization in the wake of the Fukushima nuclear accident in Japan. While information aesthetics are often applied to large data sets retrospectively, the author developed new works concurrently with an ongoing crisis to examine the impact and social aspects of visual communication while events continued to unfold. The resulting work, Fukushima Nuclear Accident—Radiation Comparison Map, is a reflection of rapidly acquired data, collaborative on-line analysis and reflective criticism of contemporary news media, resolved into a coherent picture through the participation of an on-line community.

scholarly journals Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2673
Author(s):  
Chonghuan Xu ◽  
Dongsheng Liu ◽  
Xinyao Mei
Keyword(s):  
Estimation Method ◽  
Geographical Location ◽  
Heterogeneous Data ◽  
User Preferences ◽  
Data Sets ◽  
User Characteristics ◽  
Model Studies ◽  
Poi Recommendation ◽  
Temporal Factors ◽  
The Impact

The advent of mobile scenario-based consumption popularizes and gradually maturates the application of point of interest (POI) recommendation services based on geographical location. However, the insufficient fusion of heterogeneous data in the current POI recommendation services leads to poor recommendation quality. In this paper, we propose a novel hybrid POI recommendation model (NHRM) based on user characteristics and spatial-temporal factors to enhance the recommendation effect. The proposed model contains three sub-models. The first model considers user preferences, forgetting characteristics, user influence, and trajectories. The second model studies the impact of the correlation between the locations of POIs and calculates the check-in probability of POI with the two-dimensional kernel density estimation method. The third model analyzes the influence of category of POI. Consequently, the above results were combined and top-K POIs were recommended to target users. The experimental results on Yelp and Meituan data sets showed that the recommendation performance of our method is superior to some other methods, and the problems of cold-start and data sparsity are alleviated to a certain extent.

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