ABSTRACT
Half of the world's population lives in cities, where usually there are few little green space and there are also high levels of air pollution. Moreover, the traditional urbanization of cities contributes to climate change, promotes the loss of global biodiversity and induces serious health problems for citizens. Both climate change and the loss of biodiversity affect negatively to the ecosystems and therefore human health, as they are responsible for providing clean air, food, fresh water, medicines, renewable resources. . . This deterioration increases significantly the risk of human-borne infectious diseases such as coronavirus or HIV. The ability we have to re-naturalize anthropogenic spaces and learn to generate spaces for coexistence will be key for the future of our society. The research presented in this paper aims to do a step forward to achieve that ability by working in three schools of the city of Barcelona and their surroundings. Among other actions, in this project, a diagnosis of neighborhood has been carried out. The diagnosis includes the identification and quantification of relevant indicators regarding neighborhood's biodiversity and also the quality of daily life and the analysis of pollutants (NO2 and PM10) near the schools during the 2021-2022 school year. All these information has been merged in a single geographic data base and relevant hotspots where to act have been identified. The information has been shared with city council and citizens. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda.
ABSTRACT
The worldwide health crisis is caused by the widespread of the Covid-19 virus. The virus is transmitted through droplet infection and it causes the common cold, coughing, sneezing, and also respiratory distress in the infected person and sometimes becomes fatal causing death. As the world battles against covid-19, the proposed approach can help to contain the clustering of covid hotspot areas for the treatment of over a million affected patients. Drones/ Unmanned Aerial Vehicles (UAVs) offer a great deal of support in this pandemic. As suggested in this research, they can also be used to get to remote places more quickly and efficiently than with conventional means. In the hospital's control room, there would be a person in command of the ambulance drone. For hotspot area detection, the drone would be equipped with FLIR camera and for detection and recognition of face the video transmission is used by raspberry pi camera. The detection of face is done by Haar cascade Classifier and recognition of the face with LBPH algorithm. This is used for identify the each individual's medical history or can be verified by Aadhar Card. Face recognition between still and video photos was compared, and the average accuracy of still and video images was 99.8 percent and 99.57 percent, respectively. To find the hotspot area is to use the CNN Crowd counting algorithm. If the threshold value is less than equal to 0.5 than it is hotspot area , if it is greater than 0.5 and less than equal to 0.75 than it is semi-normal area , if it is greater than 0.75 and less than equal to 1 than it is normal area. © 2022 IEEE.
ABSTRACT
Kernel density visualization (KDV) has been widely used in many geospatial analysis tasks, including traffic accident hotspot detection, crime hotspot detection, and disease outbreak detection. Although KDV can be supported by many scientific, geographical, and visualization software tools, none of these tools can support high-resolution KDV with large-scale datasets. Therefore, we develop the first versatile programming library, called LIBKDV, based on the set of our complexity-optimized algorithms. Given the high efficiency of these algorithms, LIBKDV not only accelerates the KDV computation but also enriches KDV-based geospatial analytics, including bandwidth-tuning analysis and spatiotemporal analysis, which cannot be natively and feasibly supported by existing software tools. In this demonstration, participants will be invited to use our programming library to explore interesting hotspot patterns on large-scale traffic accident, crime, and COVID-19 datasets. © 2022, VLDB Endowment. All rights reserved.
ABSTRACT
Recently, the COVID-19 pandemic created a worldwide emergency as it is estimated that such a large number of infections are due to humanto-human transmission of the COVID-19. As a necessity, there is a need to track users who came in contact with users having travel history, asymptomatic and not yet symptomatic, but they can be in the future. To solve this problem, the present work proposes a solution for contact tracing based on assisted GPS and cloud computing technologies. An application is developed to collect each user’s assisted GPS coordinates once all the users install this application. This application periodically sends assisted GPS data (coordinates) to the cloud. To determine which devices are within the permissible limit of 5 m (tunable parameter), we perform clustering over assisted GPS coordinates and track the clusters for about t mins (tunable parameter) to allow the measure of spread. We assume that it takes around 3–5 mins to get the virus from an infected object. For clustering, the proposed M-way like tree data structure stores the assisted GPS coordinates in degree, minute, and second (DMS) format. Thus, every user is mapped to a leaf node of the tree. The crux of the solution lies at the leaf node. We split the “seconds” part of the assisted GPS location into m equal parts (a tunable parameter), which amount to d meter in latitude/longitude. Hence, two users who are within d meter range will map to the same leaf node. Thus, by mapping assisted GPS locations every t mins (usually t = 2:5 mins), we can find out how many users came in contact with a particular user for at least t mins. Our work’s salient feature is that it runs in linear time O(n) for n users in the static case, i.e., when users are not moving. We also propose a variant of our solution to handle the dynamic case, that is, when users are moving. Besides, the proposed solution offers potential hotspot detection and safe-route recommendation as an additional feature, and proof-of-concept is presented through experiments on simulated data of 2/4/6/8/10M users. © 2021, Springer Nature Switzerland AG.