Localizing Local Vulnerabilities in Urban Areas Using Crowdsourced Visual Data from Participatory Sensing

Adequate nighttime lighting of city streets is necessary for safe vehicle and pedestrian movement, deterrent of crime, improvement of the citizens’ perceptions of safety, and so on. However, monitoring and mapping of illumination levels in city streets during the nighttime is a tedious activity that is usually based on manual inspection reports. The advancement in smartphone technology comes up with a better way to monitor city illumination using a rich set of smartphone-equipped inexpensive but powerful sensors (e.g., light sensor, GPS, etc). In this context, the main objective of this work is to use the power of smartphone sensors and IoT-cloud-based framework to collect, store, and analyze nighttime illumination data from citizens to generate high granular city illumination map. The development of high granular illumination map is an effective way of visualizing and assessing the illumination of city streets during nighttime. In this article, an illumination mapping algorithm called Street Illumination Mapping is proposed that works on participatory sensing-based illumination data collected using smartphones as IoT devices to generate city illumination map. The proposed method is evaluated on a real-world illumination dataset collected by participants in two different urban areas of city Kolkata. The results are also compared with the baseline mapping techniques, namely, Spatial k-Nearest Neighbors, Inverse Distance Weighting, Random Forest Regressor, Support Vector Regressor, and Artificial Neural Network.

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COMPARISON BETWEEN RGB AND RGB-D CAMERAS FOR SUPPORTING LOW-COST GNSS URBAN NAVIGATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-991-2018 ◽

2018 ◽

Vol XLII-2 ◽

pp. 991-998 ◽

Cited By ~ 1

Author(s):

L. Rossi ◽

C. I. De Gaetani ◽

D. Pagliari ◽

E. Realini ◽

M. Reguzzoni ◽

...

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Urban Areas ◽

Ad Hoc ◽

Focal Length ◽

Low Cost ◽

Experimental Tests ◽

Microsoft Kinect ◽

Visual Data ◽

Data Accessibility

A pure GNSS navigation is often unreliable in urban areas because of the presence of obstructions, thus preventing a correct reception of the satellite signal. The bridging between GNSS outages, as well as the vehicle attitude reconstruction, can be recovered by using complementary information, such as visual data acquired by RGB-D or RGB cameras. In this work, the possibility of integrating low-cost GNSS and visual data by means of an extended Kalman filter has been investigated. The focus is on the comparison between the use of RGB-D or RGB cameras. In particular, a Microsoft Kinect device (second generation) and a mirrorless Canon EOS M RGB camera have been compared. The former is an interesting RGB-D camera because of its low-cost, easiness of use and raw data accessibility. The latter has been selected for the high-quality of the acquired images and for the possibility of mounting fixed focal length lenses with a lower weight and cost with respect to a reflex camera. The designed extended Kalman filter takes as input the GNSS-only trajectory and the relative orientation between subsequent pairs of images. Depending on the visual data acquisition system, the filter is different because RGB-D cameras acquire both RGB and depth data, allowing to solve the scale problem, which is instead typical of image-only solutions. The two systems and filtering approaches were assessed by ad-hoc experimental tests, showing that the use of a Kinect device for supporting a u-blox low-cost receiver led to a trajectory with a decimeter accuracy, that is 15&thinsp;% better than the one obtained when using the Canon EOS M camera.

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Management of minor ailments in primary schoolchildren in rural and urban areas

Child Care Health and Development ◽

10.1046/j.1365-2214.1996.787787.x ◽

1996 ◽

Vol 22 (3) ◽

pp. 167-174

Author(s):

J A Cantrill ◽

B Johannesson ◽

M Nicholson ◽

P R Noyce

Keyword(s):

Urban Areas ◽

Rural And Urban Areas ◽

Rural And Urban ◽

Primary Schoolchildren ◽

Minor Ailments

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Cannabis use in Switzerland: The role of attribution of drug use to friends, urbanization and repression 1The study was supported by the Swiss Federal Office of Public Health (Bundesamt für Gesundheit BAG) Grant 316.96.5980.

Swiss Journal of Psychology ◽

10.1024//1421-0185.60.2.99 ◽

2001 ◽

Vol 60 (2) ◽

pp. 99-107 ◽

Cited By ~ 7

Author(s):

Holger Schmid

Keyword(s):

Drug Use ◽

Urban Areas ◽

Hierarchical Linear Model ◽

Cannabis Use ◽

School Aged Children ◽

Structural Variables ◽

Hbsc Study ◽

Nationally Representative ◽

Highly Correlated ◽

Individual Use

Cannabis use does not show homogeneous patterns in a country. In particular, urbanization appears to influence prevalence rates, with higher rates in urban areas. A hierarchical linear model (HLM) was employed to analyze these structural influences on individuals in Switzerland. Data for this analysis were taken from the Switzerland survey of Health Behavior in School-Aged Children (HBSC) Study, the most recent survey to assess drug use in a nationally representative sample of 3473 15-year-olds. A total of 1487 male and 1620 female students indicated their cannabis use and their attributions of drug use to friends. As second level variables we included address density in the 26 Swiss Cantons as an indicator of urbanization and officially recorded offences of cannabis use in the Cantons as an indicator of repressive policy. Attribution of drug use to friends is highly correlated with cannabis use. The correlation is even more pronounced in urban Cantons. However, no association between recorded offences and cannabis use was found. The results suggest that structural variables influence individuals. Living in an urban area effects the attribution of drug use to friends. On the other hand repressive policy does not affect individual use.

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