Karibu: A Flexible, Highly-Available, and Scalable Architecture for Urban Data Collection

2014 ◽  
Author(s):  
Henrik Christensen ◽  
Henrik Blunck ◽  
Niels Bouvin ◽  
Robert Brewer ◽  
Markus Wüstenberg
Keyword(s):  
Data Collection ◽  
Scalable Architecture ◽  
Urban Data

Community-Centered Urban Sensing

2019 ◽  
Vol 8 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Andrew Mondschein ◽  
Zihao Zhang ◽  
Mona El Khafif
Keyword(s):  
Data Collection ◽  
Smart Cities ◽  
Urban Systems ◽  
University Of Virginia ◽  
Planning And Design ◽  
Data Collection And Analysis ◽  
Urban Planning And Design ◽  
Urban Sensing ◽  
The University ◽  
Urban Data

The authors examine the problem of integrating urban sensing into engaged planning. The authors ask whether enhanced urban data and analysis can enhance resident engagement in planning and design, rather than hinder it, even when current urban planning and design practices are dysfunctional. The authors assess the outcomes of a planning and design effort in Charlottesville, Virginia, USA. Community-Centered Urban Sensing is a participatory urban sensing initiative developed by urban planners and designers, architects, landscape architects, and technologists at the University of Virginia to address the need for actionable information on the urban environment through community-engaged urban data collection and analysis. These findings address how technological urbanism moves from data to action, as well as its potential for marginalization. Finally, the authors discuss a conceptualization of smart and engaged planning that accounts for urban dysfunction. The smart cities paradigm should encompass modes and methods that function even when local urban systems are dysfunctional.

Cellular Census: Explorations in Urban Data Collection

2007 ◽  
Vol 6 (3) ◽  
pp. 30-38 ◽  
Author(s):  
Jonathan Reades ◽  
Francesco Calabrese ◽  
Andres Sevtsuk ◽  
Carlo Ratti
Keyword(s):  
Data Collection ◽  
Urban Data

scholarly journals Urban IoT: Advances, Challenges, and Opportunities for Mass Data Collection, Analysis, and Visualization

2021 ◽  
pp. 701-719
Author(s):  
Andrew Hudson-Smith ◽  
Duncan Wilson ◽  
Steven Gray ◽  
Oliver Dawkins
Keyword(s):  
Data Collection ◽  
Internet Of Things ◽  
Smart City ◽  
Urban Areas ◽  
Urban Modeling ◽  
Urban Fabric ◽  
Mass Data ◽  
Form Part ◽  
Challenges And Opportunities ◽  
Urban Data

AbstractUrban Internet of Things (IoT) is in an early speculative phase. Often linked to the smart city movement, it provides a way of sensing and collecting data—environmental, societal, and transitional—both automatically, remotely, and with increasing levels of spatial and temporal detail. From city-wide data collection down to the scale of individual buildings and rooms, this chapter details the technology behind the rise of IoT in urban areas and explores the challenges (societal and technical) behind city-wide deployments. Drawing from a series of deployments at the Queen Elizabeth Olympic Park, London, it details the challenges and opportunities for mass data collection. Widening out the view, it looks at what is becoming known as “the humble lamp post” in Urban IoT fields to detail the potential of Urban IoT with the objects that already form part of the urban fabric. Finally, it examines the potential of Urban IoT for input into urban modeling and how we are on the edge of a shift in the collection, analysis, and communication of urban data.

A Planning Model for Cognitive Cities

2019 ◽  
pp. 287-303
Author(s):  
Sarmada Madhulika Kone
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Spatial Cognition ◽  
Performance Monitoring ◽  
Urban System ◽  
Participatory Planning ◽  
Urban Systems ◽  
Rapid Urbanization ◽  
Data Collection And Analysis ◽  
Urban Data

A city is a real-time function with constantly changing variables. Rapid urbanization of the cities and increase in a number of mega cities has made the entire urban management complex. With many parameters involved in it, urban data has started to resemble the characteristics of big data. The nexus between spatial cognition and the frequency of data collection of an urban system explains the role of big data analysis in performance monitoring of the urban systems. Urban data collection and analysis can be possible through participatory planning and participatory citizens. This chapter focuses on understanding the correlation between spatial cognition and participatory planning.

Density Adaptive Urban Data Collection in Vehicular Sensor Networks

2014 ◽  
Vol 9 (8) ◽  
Author(s):  
Zongjian He ◽  
Huijuan Zhang
Keyword(s):  
Sensor Networks ◽  
Data Collection ◽  
Urban Data

Urban data collection by airborne sensor

1969 ◽  
Vol 35 (1) ◽  
pp. 35-43 ◽  
Author(s):  
Eric G. More ◽  
Barry S. Wellar
Keyword(s):  
Data Collection ◽  
Urban Data

Electron Diffraction of Wet Biological Membranes

1974 ◽  
Vol 32 ◽  
pp. 158-159
Author(s):  
S.W. Hui ◽  
D.F. Parsons
Keyword(s):  
Electron Diffraction ◽  
Data Collection ◽  
Biological Membranes ◽  
Small Areas ◽  
Electron Diffraction Technique ◽  
Electron Microscopes ◽  
Collection Time ◽  
Camera Length

The development of the hydration stages for electron microscopes has opened up the application of electron diffraction in the study of biological membranes. Membrane specimen can now be observed without the artifacts introduced during drying, fixation and staining. The advantages of the electron diffraction technique, such as the abilities to observe small areas and thin specimens, to image and to screen impurities, to vary the camera length, and to reduce data collection time are fully utilized. Here we report our pioneering work in this area.

Automated electron tomography: from data collection to image processing

1995 ◽  
Vol 53 ◽  
pp. 26-27
Author(s):  
Weiping Liu ◽  
Jennifer Fung ◽  
W.J. de Ruijter ◽  
Hans Chen ◽  
John W. Sedat ◽  
...  
Keyword(s):  
Image Processing ◽  
Data Collection ◽  
Structural Information ◽  
Imaging Technique ◽  
Electron Tomography ◽  
Ccd Camera ◽  
Automated Data Collection ◽  
Full Control ◽  
Transmission Electron ◽  
Amorphous Structures

Electron tomography is a technique where many projections of an object are collected from the transmission electron microscope (TEM), and are then used to reconstruct the object in its entirety, allowing internal structure to be viewed. As vital as is the 3-D structural information and with no other 3-D imaging technique to compete in its resolution range, electron tomography of amorphous structures has been exercised only sporadically over the last ten years. Its general lack of popularity can be attributed to the tediousness of the entire process starting from the data collection, image processing for reconstruction, and extending to the 3-D image analysis. We have been investing effort to automate all aspects of electron tomography. Our systems of data collection and tomographic image processing will be briefly described.To date, we have developed a second generation automated data collection system based on an SGI workstation (Fig. 1) (The previous version used a micro VAX). The computer takes full control of the microscope operations with its graphical menu driven environment. This is made possible by the direct digital recording of images using the CCD camera.

Productivity of Emerging Word Combinations in Toddlers With Specific Expressive Language Impairment

1997 ◽  
Vol 6 (4) ◽  
pp. 34-47 ◽  
Author(s):  
Steven H. Long ◽  
Lesley B. Olswang ◽  
Julianne Brian ◽  
Philip S. Dale
Keyword(s):  
Data Collection ◽  
Young Children ◽  
Language Impairment ◽  
Treatment Approach ◽  
Expressive Language ◽  
Semantic Consistency ◽  
Session Analysis

This study investigated whether young children with specific expressive language impairment (SELI) learn to combine words according to general positional rules or specific, grammatic relation rules. The language of 20 children with SELI (4 females, 16 males, mean age of 33 months, mean MLU of 1.34) was sampled weekly for 9 weeks. Sixteen of these children also received treatment for two-word combinations (agent+action or possessor+possession). Two different metrics were used to determine the productivity of combinatorial utterances. One metric assessed productivity based on positional consistency alone; another assessed productivity based on positional and semantic consistency. Data were analyzed session-by-session as well as cumulatively. The results suggest that these children learned to combine words according to grammatic relation rules. Results of the session-by-session analysis were less informative than those of the cumulative analysis. For children with SELI ready to make the transition to multiword utterances, these findings support a cumulative method of data collection and a treatment approach that targets specific grammatic relation rules rather than general word combinations.

Enhancing Clinical Supervision Using High-Definition Real-Time Digital Recording, Annotation, and Bluetooth Technology

2019 ◽  
Vol 4 (2) ◽  
pp. 356-362
Author(s):  
Jennifer W. Means ◽  
Casey McCaffrey
Keyword(s):  
Graduate Students ◽  
Data Collection ◽  
Real Time ◽  
Clinical Supervision ◽  
Video Recording ◽  
Advanced Technology ◽  
High Definition ◽  
Recording Technology ◽  
Self Confidence ◽  
Additional Data Collection

Purpose The use of real-time recording technology for clinical instruction allows student clinicians to more easily collect data, self-reflect, and move toward independence as supervisors continue to provide continuation of supportive methods. This article discusses how the use of high-definition real-time recording, Bluetooth technology, and embedded annotation may enhance the supervisory process. It also reports results of graduate students' perception of the benefits and satisfaction with the types of technology used. Method Survey data were collected from graduate students about their use and perceived benefits of advanced technology to support supervision during their 1st clinical experience. Results Survey results indicate that students found the use of their video recordings useful for self-evaluation, data collection, and therapy preparation. The students also perceived an increase in self-confidence through the use of the Bluetooth headsets as their supervisors could provide guidance and encouragement without interrupting the flow of their therapy sessions by entering the room to redirect them. Conclusions The use of video recording technology can provide opportunities for students to review: videos of prospective clients they will be treating, their treatment videos for self-assessment purposes, and for additional data collection. Bluetooth technology provides immediate communication between the clinical educator and the student. Students reported that the result of that communication can improve their self-confidence, perceived performance, and subsequent shift toward independence.

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