scholarly journals The Random Forest-Based Method of Fine-Resolution Population Spatialization by Using the International Space Station Nighttime Photography and Social Sensing Data

2018 ◽  
Vol 10 (10) ◽  
pp. 1650 ◽  
Author(s):  
Kangning Li ◽  
Yunhao Chen ◽  
Ying Li
Keyword(s):  
Random Forest ◽  
International Space Station ◽  
Population Distribution ◽  
Space Station ◽  
Small Scale ◽  
International Space ◽  
Point Of Interest ◽  
Scale Population ◽  
Fine Resolution ◽  
Population Mapping

Despite the importance of high-resolution population distribution in urban planning, disaster prevention and response, region economic development, and improvement of urban habitant environment, traditional urban investigations mainly focused on large-scale population spatialization by using coarse-resolution nighttime light (NTL) while few efforts were made to fine-resolution population mapping. To address problems of generating small-scale population distribution, this paper proposed a method based on the Random Forest Regression model to spatialize a 25 m population from the International Space Station (ISS) photography and urban function zones generated from social sensing data—point-of-interest (POI). There were three main steps, namely HSL (hue saturation lightness) transformation and saturation calibration of ISS, generating functional-zone maps based on point-of-interest, and spatializing population based on the Random Forest model. After accuracy assessments by comparing with WorldPop, the proposed method was validated as a qualified method to generate fine-resolution population spatial maps. In the discussion, this paper suggested that without help of auxiliary data, NTL cannot be directly employed as a population indicator at small scale. The Variable Importance Measure of the RF model confirmed the correlation between features and population and further demonstrated that urban functions performed better than LULC (Land Use and Land Cover) in small-scale population mapping. Urban height was also shown to improve the performance of population disaggregation due to its compensation of building volume. To sum up, this proposed method showed great potential to disaggregate fine-resolution population and other urban socio-economic attributes.

scholarly journals Fine-Resolution Population Mapping from International Space Station Nighttime Photography and Multisource Social Sensing Data Based on Similarity Matching

2019 ◽  
Vol 11 (16) ◽  
pp. 1900 ◽  
Author(s):  
Luyao Wang ◽  
Hong Fan ◽  
Yankun Wang
Keyword(s):  
Social Media ◽  
Population Distribution ◽  
Space Station ◽  
Similarity Matching ◽  
Social Media Data ◽  
International Space ◽  
Sensing Data ◽  
Fine Resolution ◽  
Population Mapping ◽  
Media Data

Previous studies have attempted to disaggregate census data into fine resolution with multisource remote sensing data considering the importance of fine-resolution population distribution in urban planning, environmental protection, resource allocation, and social economy. However, the lack of direct human activity information invariably restricts the accuracy of population mapping and reduces the credibility of the mapping process even when external facility distribution information is adopted. To address these problems, the present study proposed a novel population mapping method by combining International Space Station (ISS) photography nighttime light data, point of interest (POI) data, and location-based social media data. A similarity matching model, consisting of semantic and distance matching models, was established to integrate POI and social media data. Effective information was extracted from the integrated data through principal component analysis and then used along with road density information to train the random forest (RF) model. A comparison with WordPop data proved that our method can generate fine-resolution population distribution with higher accuracy ( R 2 = 0.91 ) than those of previous studies ( R 2 = 0.55 ). To illustrate the advantages of our method, we highlighted the limitations of previous methods that ignore social media data in handling residential regions with similar light intensity. We also discussed the performance of our method in adopting social media data, considering their characteristics, with different volumes and acquisition times. Results showed that social media data acquired between 19:00 and 8:00 with a volume of approximately 300,000 will help our method realize high accuracy with low computation burden. This study showed the great potential of combining social sensing data for disaggregating fine-resolution population.

scholarly journals Design of a Small-Scale Experimental Model of the International Space Station Crew Quarters for a PIV Flow Field Study

2019 ◽  
Vol 111 ◽  
pp. 01045
Author(s):  
Matei-Razvan Georgescu ◽  
Ilinca Nastase ◽  
Amina Meslem ◽  
Mihnea Sandu ◽  
Florin Bode
Keyword(s):  
International Space Station ◽  
Numerical Models ◽  
Space Station ◽  
Scale Model ◽  
Microgravity Environment ◽  
Small Scale ◽  
Piv Measurements ◽  
International Space ◽  
The Subject ◽  
Small Scale Model

An attempt at improving the ventilation solution for the crew quarters aboard the International Space Station requires a thorough understanding of the flow dynamics in a microgravity environment. An experimental study is required in order to validate the numerical models. As part of this process, a small-scale model was proposed for a detailed study of the velocity field. PIV measurements in water offer high quality results and were chosen for the subject. Following certain similitude criteria, an equivalence can be found between the results of these measurements and the real ventilation scenario. This paper describes the development process of this small-scale model as well as its performance in the initial test runs. Details regarding the advantages and weaknesses of this first model are the core of the paper, with the intention of aiding researchers in their design of similar models. The conclusion presents future steps and proposed improvements to the model.

Human factors assessment of International Space Station (ISS) medical equipment packs

2005 ◽  
Author(s):  
Danielle Paige Smith ◽  
Vicky E. Byrne ◽  
Cynthia Hudy ◽  
Mihriban Whitmore
Keyword(s):  
Human Factors ◽  
International Space Station ◽  
Medical Equipment ◽  
Space Station ◽  
International Space

Dose Tracker Application for Collecting Medication Use Data from International Space Station Crew

2020 ◽  
Vol 91 (1) ◽  
pp. 41-45 ◽  
Author(s):  
Virginia. E. Wotring ◽  
LaRona K. Smith
Keyword(s):  
Data Collection ◽  
International Space Station ◽  
Sleep Disturbances ◽  
Space Station ◽  
Medication Use ◽  
International Space ◽  
Flight Data ◽  
Open Questions ◽  
Share Data ◽  
Usability Feedback

INTRODUCTION: There are knowledge gaps in spaceflight pharmacology with insufficient in-flight data to inform future planning. This effort directly addressed in-mission medication use and also informed open questions regarding spaceflight-associated changes in pharmacokinetics (PK) and/or pharmacodynamics (PD).METHODS: An iOS application was designed to collect medication use information relevant for research from volunteer astronaut crewmembers: medication name, dose, dosing frequency, indication, perceived efficacy, and side effects. Leveraging the limited medication choices aboard allowed a streamlined questionnaire. There were 24 subjects approved for participation.RESULTS: Six crewmembers completed flight data collection and five completed ground data collection before NASA’s early study discontinuation. There were 5766 medication use entries, averaging 20.6 ± 8.4 entries per subject per flight week. Types of medications and their indications were similar to previous reports, with sleep disturbances and muscle/joint pain as primary drivers. Two subjects treated prolonged skin problems. Subjects also used the application in unanticipated ways: to note drug tolerance testing or medication holiday per research protocols, and to share data with flight surgeons. Subjects also provided usability feedback on application design and implementation.DISCUSSION: The volume of data collected (20.6 ± 8.4 entries per subject per flight week) is much greater than was collected previously (<12 per person per entire mission), despite user criticisms regarding app usability. It seems likely that improvements in a software-based questionnaire application could result in a robust data collection tool that astronauts find more acceptable, while simultaneously providing researchers and clinicians with useful data.Wotring VE, Smith LK. Dose tracker application for collecting medication use data from International Space Station crew. Aerosp Med Hum Perform. 2020; 91(1):41–45.

The «Obstanovka» experiment aboard the International Space Station

2004 ◽  
Vol 10 (2-3) ◽  
pp. 81-86 ◽  
Author(s):  
S.I. Klimov ◽  
◽  
V.Ye. Korepanov ◽  
Keyword(s):  
International Space Station ◽  
Space Station ◽  
International Space
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