Environmental Noise Sensing Approach Based on Volunteered Geographic Information and Spatio-Temporal Analysis with Machine Learning

2016 ◽  
pp. 95-110 ◽  
Author(s):  
Miguel Torres-Ruiz ◽  
Juan H. Juárez-Hipólito ◽  
Miltiadis Demetrios Lytras ◽  
Marco Moreno-Ibarra
Keyword(s):  
Machine Learning ◽  
Volunteered Geographic Information ◽  
Temporal Analysis ◽  
Environmental Noise ◽  
Geographic Information ◽  
Spatio Temporal

scholarly journals Dimensions of the Use of Volunteered Geographic Information in Mass Crisis Events

2015 ◽  
Author(s):  
◽  
Xiannian Chen ◽  
Keyword(s):  
Rural Communities ◽  
Real Time ◽  
Disaster Management ◽  
Volunteered Geographic Information ◽  
Temporal Analysis ◽  
Geographic Information ◽  
Web Gis ◽  
Kent State University ◽  
Spatio Temporal ◽  
Mass Evacuation

Recent studies have suggested that catastrophic events that trigger mass evacuation require surrounding communities to be well-prepared to act as ingress or pass-through areas for potential evacuees; however surrounding rural communities may have insufficient disaster-related logistical resources. In the response phase of disaster management, officials must be able to deploy resources to demand locations in types and quantities based on real-time requirements. Effective cross-jurisdictional disaster management needs real-time information, which is usually unavailable from official, authoritative sources. Conversely, VGI (volunteered geographic information) has the capability to provide real-time and local information in disaster management. This study investigates the possibility of utilizing real-time or near real-time VGI in mass evacuation scenarios. The study identifies a potential VGI data source, Tweets from Twitter and how to search for, discover and select relevant Tweets. The dissertation proposes research methods for harvesting, managing live Tweets and saving them to a distributed geodatabase for further spatio-temporal analysis and dissemination to users, such as responders and evacuees.;The study implements a Web GIS application, which includes a tweets discovery component, a geo-tagged tweets mapping component, and an online geo-tagged tweets operation component. The major research goals include designing an application programing interface (API) to harvest relevant Tweets and implement a distributed geodatabase system for storage, analysis, and display of the harvested Tweets so that vital information can be distributed in near real-time. Two case studies, based on Super Storm Sandy in 2012 and a shooting at Kent State University in 2014, were used to evaluate the pros and cons of Tweets from Twitter for response in emergency management and offered prototypes for the development of the final on-line Web GIS.

scholarly journals Public health monitoring using Geographic Information Systems: case viral hepatitis E

2021 ◽  
Vol 319 ◽  
pp. 01083
Author(s):  
El Omari Hajar ◽  
Abdelkader Chahlaoui ◽  
Ouarrak Khadija ◽  
Adel Kharroubi
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
High Risk ◽  
Viral Hepatitis ◽  
Temporal Analysis ◽  
Hepatitis E ◽  
Geographic Information ◽  
Average Risk ◽  
Health Events ◽  
Spatio Temporal

Among the major parasitic diseases having major health and socio-economic impacts in the world and in Morocco, are viral hepatitis. These are acute inflammations of the liver caused by a virus. The 3 most frequently encountered viruses are viruses A, B, C. The objective of this study is to map health events, in our case the incidence of viral hepatitis E in the different prefectures of the region of Meknes-Fez by creating a database containing geographic and health parameters in geographic information system (GIS). This database was then used to create the risk map which identifies the high-risk prefectures. This study shows that the average incidence of viral hepatitis H is higher in the prefecture of Meknes during all the years of the study, with a high risk compared to other prefectures and provinces which have an average risk. Indeed, the mapping of health events is a descriptive tool implemented to evaluate the spatial disparities of incidence, which allowed us to perform a spatio-temporal analysis of the epidemic. Spatial technologies, such as geographic information systems (GIS), offer a new option for disease prevention, predicting risk locations based on factors favoring the emergence or re-emergence of the epidemic.

scholarly journals Forecasting Dengue, Chikungunya and Zika cases in Recife, Brazil: a spatio-temporal approach based on climate conditions, health notifications and machine learning

2021 ◽  
Vol 10 (12) ◽  
pp. e452101220804
Author(s):  
Cecilia Cordeiro da Silva ◽  
Clarisse Lins de Lima ◽  
Ana Clara Gomes da Silva ◽  
Giselle Machado Magalhães Moreno ◽  
Anwar Musah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Aedes Aegypti ◽  
Temporal Analysis ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Monitoring And Control ◽  
Linear Regression Models ◽  
Vector Machines ◽  
Spatio Temporal

Dengue has become a challenge for many countries. Arboviruses transmitted by Aedes aegypti spread rapidly over the last decades. The emergence chikungunya fever and zika in South America poses new challenges to vector monitoring and control. This situation got worse from 2015 and 2016, with the rapid spread of chikungunya, causing fever and muscle weakness, and Zika virus, related to cases of microcephaly in newborns and the occurrence of Guillain-Barret syndrome, an autoimmune disease that affects the nervous system. The objective of this work was to construct a tool to forecast the distribution of arboviruses transmitted by the mosquito Aedes aegypti by implementing dengue, zika and chikungunya transmission predictors based on machine learning, focused on multilayer perceptrons neural networks, support vector machines and linear regression models. As a case study, we investigated forecasting models to predict the spatio-temporal distribution of cases from primary health notification data and climate variables (wind velocity, temperature and pluviometry) from Recife, Brazil, from 2013 to 2016, including 2015’s outbreak. The use of spatio-temporal analysis over multilayer perceptrons and support vector machines results proved to be very effective in predicting the distribution of arbovirus cases. The models indicate that the southern and western regions of Recife were very susceptible to outbreaks in the period under investigation. The proposed approach could be useful to support health managers and epidemiologists to prevent outbreaks of arboviruses transmitted by Aedes aegypti and promote public policies for health promotion and sanitation.

scholarly journals Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning

2020 ◽  
Vol 9 (12) ◽  
pp. 752
Author(s):  
Anna Kovacs-Györi ◽  
Alina Ristea ◽  
Clemens Havas ◽  
Michael Mehaffy ◽  
Hartwig H. Hochmair ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Science ◽  
Temporal Analysis ◽  
Data Sources ◽  
Urban Systems ◽  
Urban Dynamics ◽  
Analysis Methods ◽  
Data Driven Approach ◽  
Spatio Temporal

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement.

scholarly journals Using textual volunteered geographic information to model nature-based activities: A case study from Aotearoa New Zealand

2021 ◽  
pp. 25-63
Author(s):  
Ekaterina Egorova
Keyword(s):  
Volunteered Geographic Information ◽  
Online Reviews ◽  
Temporal Variations ◽  
Geographic Information ◽  
Training Dataset ◽  
Environment Interaction ◽  
Human Environment ◽  
Urban Settings ◽  
Fine Grained ◽  
Spatio Temporal

A boom in volunteered geographic information has led to extensive data-driven exploration and modeling of places. While many studies have used such data to explore human-environment interaction in urban settings, few have investigated natural, non-urban settings. To address this gap, this study systematically explores the content of online reviews of nature-based recreation activities, and develops a fine-grained hierarchical model that includes 28 aspects grouped into three main domains: activity, settings, and emotions/cognition. It further demonstrates how the model can be used to explore the variation in recreation experiences across activities, setting the stage for the analysis of the spatio-temporal variations in recreation experiences in the future. Importantly, the study provides an annotated corpus that can be used as a training dataset for developing methods to automatically capture aspects of recreation experiences in texts.

scholarly journals Spatio-temporal analysis of Pertussis using geographic information system among Iranian population during 2012-2018

2020 ◽  
Author(s):  
Yousef Alimohamadi ◽  
Seyed Mohsen Zahraei ◽  
Manoochehr Karami ◽  
Mehdi Yaseri ◽  
Mojtaba Lotfizad ◽  
...  
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Temporal Analysis ◽  
Geographic Information ◽  
Iranian Population ◽  
Spatio Temporal

scholarly journals Integrating GEOBIA, Machine Learning, and Volunteered Geographic Information to Map Vegetation over Rooftops

2018 ◽  
Vol 7 (12) ◽  
pp. 462 ◽  
Author(s):  
David Griffith ◽  
Geoffrey Hay
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Vegetation Index ◽  
Residential Buildings ◽  
Accuracy Assessment ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Processing Load ◽  
Image Object

The objective of this study is to evaluate operational methods for creating a particular type of urban vegetation map—one focused on vegetation over rooftops (VOR), specifically trees that extend over urban residential buildings. A key constraint was the use of passive remote sensing data only. To achieve this, we (1) conduct a review of the urban remote sensing vegetation classification literature, and we then (2) discuss methods to derive a detailed map of VOR for a study area in Calgary, Alberta, Canada from a late season, high-resolution airborne orthomosaic based on an integration of Geographic Object-Based Image Analysis (GEOBIA), pre-classification filtering of image-objects using Volunteered Geographic Information (VGI), and a machine learning classifier. Pre-classification filtering lowered the computational burden of classification by reducing the number of input objects by 14%. Accuracy assessment results show that, despite the presence of senescing vegetation with low vegetation index values and deep shadows, classification using a small number of image-object spectral attributes as classification features (n = 9) had similar overall accuracy (88.5%) to a much more complex classification (91.8%) comprising a comprehensive set of spectral, texture, and spatial attributes as classification features (n = 86). This research provides an example of the very specific questions answerable about precise urban locations using a combination of high-resolution passive imagery and freely available VGI data. It highlights the benefits of pre-classification filtering and the judicious selection of features from image-object attributes to reduce processing load without sacrificing classification accuracy.

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