scholarly journals Citizen Sensing: An Action-Orientated Framework for Citizen Science

2021 ◽  
Vol 6 ◽  
Author(s):  
Saskia Coulson ◽  
Mel Woods ◽  

Citizen Sensing, a correlative of Citizen Science, employs low-cost sensors to evidence local environmental issues and empowers citizens to use the data they collect. Whilst motivations for participation can vary, communities affected by pollution frequently have changemaking as their goal. Social innovation is closely aligned with citizen sensing, however the process of co-creating practices and solutions with citizens who wish to shape their world can be highly complex to design. Therefore, our research articulates an action-orientated framework which emerges from a 2-year pan European project by which follow-on communities may replicate sensing initiatives more easily. The authors examine five studies and explore the cross-cutting principles, phases, stakeholders, methods, and challenges which form this framework. The authors argue that whilst data collection and data awareness are crucial to the citizen sensing process, there are precursory and subsequent stages which are necessary to equip citizens to address complex environmental challenges and take action on them. Therefore, this paper focuses on the stages and methods which are distinctive to citizen sensing. It concludes with recommendations for future practice for citizen sensing and citizen science.

2021 ◽  
Author(s):  
Christopher Getschmann ◽  
Florian Echtler

Data acquisition is a central task in research and one of the largest opportunities for citizen science. Especially in urban surveys investigating traffic and people flows, extensive manual labor is required, occasionally augmented by smartphones. We present DesPat, an app designed to turn a wide range of low-cost Android phones into a privacy-respecting camera-based pedestrian tracking tool to automatize data collection. This data can then be used to analyze pedestrian traffic patterns in general, and identify crowd hotspots and bottlenecks, which are particularly relevant in light of the recent COVID-19 pandemic.All image analysis is done locally on the device through a convolutional neural network, thereby avoiding any privacy concerns or legal issues regarding video surveillance. We show example heatmap visualizations from deployments of our prototype in urban areas and compare performance data for a variety of phones to discuss suitability of on-device object detection for our usecase of pedestrian data collection.


i-com ◽  
2021 ◽  
Vol 20 (2) ◽  
pp. 125-139
Author(s):  
Christopher Getschmann ◽  
Florian Echtler

Abstract Data acquisition is a central task in research and one of the largest opportunities for citizen science. Especially in urban surveys investigating traffic and people flows, extensive manual labor is required, occasionally augmented by smartphones. We present DesPat, an app designed to turn a wide range of low-cost Android phones into a privacy-respecting camera-based pedestrian tracking tool to automatize data collection. This data can then be used to analyze pedestrian traffic patterns in general, and identify crowd hotspots and bottlenecks, which are particularly relevant in light of the recent COVID-19 pandemic. All image analysis is done locally on the device through a convolutional neural network, thereby avoiding any privacy concerns or legal issues regarding video surveillance. We show example heatmap visualizations from deployments of our prototype in urban areas and compare performance data for a variety of phones to discuss suitability of on-device object detection for our usecase of pedestrian data collection.


2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
D Godoi Bernardes Da Silva ◽  
R Dias Santos ◽  
M Sommer Bittencourt ◽  
J.A.M Carvalho ◽  
M Franken ◽  
...  

Abstract Introduction The Finnish Diabetes Risk Score (FINDRISC) was developed in Europe to predict type 2 diabetes mellitus (T2DM) risk without need of laboratory tests. Small cross-sectional studies analyzed the association between RF with metabolic syndrome (MS) or hepatic steatosis (HS). Our objective was to test the association of FINDRISC with MS or HS, in a transversal and longitudinal way. Methods In 41,668 individuals (age 41.9±9.7 years; 30.8% women) who underwent health evaluation between 2008 and 2016 in a single centre in Brazil, we tested the transversal association between FINDRISC and MS or HS, in multivariate models. The same analyzes were performed longitudinally in non-diabetic subgroups, followed for 5±3 years, to test the predictive value of FINDRISC and the incidental risk of MS (n=10,075 individuals) or HS (n=7,097 individuals), using logistic regression. Models were adjusted for confounders such as sex, use of medications for dyslipidemia, smoking, and baseline plasma levels of glucose, creatinine and lipids. A receiver operating characteristic (ROC) curve was used to evaluate the discriminative and predictive values of FINDRISC for MS and HS. Results In the cross-sectional analysis, 2,252 (5%) individuals had MS and 14,176 (34%) HS. In the longitudinal analysis, there were 302 cases of incidental MS (2%) and 1,096 cases of HS (15%). FINDRISC was independently associated with MS and HS in the cross-sectional analysis (respectively, OR 1.27, 95% CI: 1.25–1.28, P<0.001; and OR 1.21, 95% CI: 1.20–1.22, P<0.001, per FINDRISC unit) and in longitudinal analysis (respectively, OR of 1.18, 95% CI: 1.15–1.21, P<0.001; and OR of 1.10, 95% CI: 1.08–1.11, P<0.001, per FINDRISC unit). In comparison with individuals with low FINDRISC, those with moderate, high and very high values showed significant and proportional increases of the 12 to 77 fold in the chance of current SM (P<0.001) and 3 to 10 fold in the chance of HS (P<0.001). During follow-up, these increases were 3 to 10 fold in the chance of incidental MS (P<0.001) and 1 to 3 fold in the chance of HS (P<0.001). The AUC from cross-sectional analysis for MS and HS were respectively 0.82 (95% CI 0.81–0.83) and 0.76 (95% CI 0.75–0.76), and in longitudinal analysis 0.73 (95% CI 0.70–0.76) and 0.63 (95% CI 0.61–0.65), respectively. Conclusion FINDRISC was associated with the presence and onset of MS and HS, but it predicted better metabolic syndrome risk than hepatic steatosis. Therefore, this simple, practical and low-cost score can be useful for population screening and identification of subgroups of individuals at higher risk future metabolic diseases. Funding Acknowledgement Type of funding source: None


2015 ◽  
Vol 162 (4) ◽  
pp. 365-373 ◽  
Author(s):  
Fabrizio Buldrini ◽  
Antinisca Simoncelli ◽  
Stefania Accordi ◽  
Giovanna Pezzi ◽  
Daniele Dallai

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Budi Istiyanto, Lailatan Nugroho

AbstractThis study aimed to determine the effect of variable brand image, price, quality of product to decision of purchasing a car LCGC (Low Cost Green Car), either partial or jointly and to find among variables brand image, price, and quality of products which are larger role in influencing purchasing decisions LCGC car. Data collection techniques researchers did by observation and questionnaires directly by visiting the object of the research is to consumers who have made a purchase decision, especially type Agya LCGC car, AYLA, and Karimun Wagon R in Surakarta which would then be sampled. The data have been collected and tabulated and analyzed using multiple regression analysis.The results showed that the variables that significantly affect purchasing decisions is price and quality of products. While the variable Brand Image does not affect significantly. While the variables that affect predominantly variable price.Keyword: Brand Image, Price, Quality Product, Purchase Decision AbstrakPenelitian ini bertujuan untuk mengetahui pengaruh antara variable brand image, harga, dan kualitas produk terhadap keputusan pembelian mobil LCGC (Low Cost Green Car) baik secara partial maupun secara bersama-sama dan untuk mengetahui diantara variable brand image, harga, dan kualitas produk mana yang lebih berperan dalam mempengaruhi keputusan pembelian mobil LCGC. Teknik pengumpulan data peneliti lakukan dengan cara observasi dan penyebaran kuisioner secara langsung dengan cara mendatangi obyek penelitian yaitu kepada konsumen yang telah melakukan keputusan pembelian mobil LCGC terutama type AGYA,AYLA, dan Karimun Wagon R di wilayah Surakarta yang selanjutnya akan dijadikan sampel. Data yang telah terkumpul kemudian ditabulasi dan diolah dengan menggunakan analisis regresi berganda.Hasil penelitian menunjukkan bahwa variabel yang mempengaruhi secara signifikan keputusan pembelian adalah harga dan kualitas produk. Sedangkan variabel Brand Image tidak mempengaruhi secara signifikan. Sedangkan variabel yang berpengaruh secara dominan adalah variabel harga.Kata kunci: Brand Image, Harga, Kualitas Produk, Keputusan Pembelian


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