Using high resolution optical imagery to detect earthquake-induced liquefaction: the 2011 Christchurch earthquake

Using automated supervised methods with satellite and aerial imageries for liquefaction mapping is a promising step in providing detailed and region-scale maps of liquefaction extent immediately after an earthquake. The accuracy of these methods depends on the quantity and quality of training samples and the number of available spectral bands. Digitizing a large number of high-quality training samples from an event may not be feasible in the desired timeframe for rapid response as the training pixels for each class should be typical and accurately represent the spectral diversity of that specific class. To perform automated classification for liquefaction detection, we need to understand how to build the optimal and accurate training dataset. Using multispectral optical imagery from the 22 February, 2011 Christchurch earthquake, we investigate the effects of quantity of high-quality training pixel samples as well as the number of spectral bands on the performance of a pixel-based parametric supervised maximum likelihood classifier for liquefaction detection. We find that the liquefaction surface effects are bimodal in terms of spectral signature and therefore, should be classified as either wet liquefaction or dry liquefaction. This is due to the difference in water content between these two modes. Using 5-fold cross-validation method, we evaluate performance of the classifier on datasets with different pixel sizes of 50, 100, 500, 2000, and 4000. Also, the effect of adding spectral information was investigated by adding once only the near infrared (NIR) band to the visible red, green, and blue (RGB) bands and the other time using all available 8 spectral bands of the World-View 2 satellite imagery. We find that the classifier has high accuracies (75%–95%) when using the 2000 pixels-size dataset that includes the RGB+NIR spectral bands and therefore, increasing to 4000 pixels-size dataset and/or eight spectral bands may not be worth the required time and cost. We also investigate accuracies of the classifier when using aerial imagery with same number of training pixels and either RGB or RGB+NIR bands and find that the classifier accuracies are higher when using satellite imagery with same number of training pixels and spectral information. The classifier identifies dry liquefaction with higher user accuracy than wet liquefaction across all evaluated scenarios. To improve classification performance for wet liquefaction detection, we also investigate adding geospatial information of building footprints to improve classification performance. We find that using a building footprint mask to remove them from the classification process, increases wet liquefaction user accuracy by roughly 10%.Published versio

Detecting demolished buildings after a natural hazard using high resolution RGB satellite imagery and modified U-Net convolutional neural networks

Collapsed buildings are usually linked with the highest number of human casualties reported after a natural disaster; therefore, quickly finding collapsed buildings can expedite rescue operations and save human lives. Recently, many researchers and agencies have tried to integrate satellite imagery into rapid response. The U.S. Defense Innovation Unit Experimental (DIUx) and National Geospatial Intelligence Agency (NGA) have recently released a ready-to-use dataset known as xView that contains thousands of labeled VHR RGB satellite imagery scenes with 30-cm spatial and 8-bit radiometric resolutions, respectively. Two of the labeled classes represent demolished buildings with 1067 instances and intact buildings with more than 300,000 instances, and both classes are associated with building footprints. In this study, we are using the xView imagery, with building labels (demolished and intact) to create a deep learning framework for classifying buildings as demolished or intact after a natural hazard event. We have used a modified U-Net style fully convolutional neural network (CNN). The results show that the proposed framework has 78% and 95% sensitivity in detecting the demolished and intact buildings, respectively, within the xView dataset. We have also tested the transferability and performance of the trained network on an independent dataset from the 19 September 2017 M 7.1 Pueblo earthquake in central Mexico using Google Earth imagery. To this end, we tested the network on 97 buildings including 10 demolished ones by feeding imagery and building footprints into the trained algorithm. The sensitivity for intact and demolished buildings was 89% and 60%, respectively.Published versio

Factors Associated With Unhealthy Snacks Consumption Among Adolescents in Iran’s Schools

Background: Well-informed interventions are needed if school-based health promotion is to be effective. Among other aims, the Iranian Health Promoting School (IHPS) program that was launched in 2011, has an important aim of promoting dietary behaviors of adolescents. The present study, therefore, aimed to investigate the factors affecting unhealthy snacking of adolescents and provide evidence for a more effective IHPS program. Methods: In a cross-sectional study design, 1320 students from 40 schools in Kerman city were selected using a proportional stratified random sampling method. A modified qualitative Food Frequency Questionnaire (FFQ) was used to gather data about unhealthy snacking behavior. Data about intrapersonal and environmental factors were obtained using a validated and reliable questionnaire. A mixed-effects negative-binomial regression model was used to analyze the data. Results: Taste and sensory perception (prevalence rate ratio [PRR]=1.18; 95% CI: 1.09-1.27), being a male (PRR=1.20; 95% CI: 1.05-1.38) and lower nutritional knowledge (PRR=0.96; 95% CI: 0.91-0.99) were associated with higher weekly unhealthy snaking. Perceived self-efficacy (PRR=0.95; 95% CI: 0.91-1.00) negatively influenced the frequency of unhealthy snaking, with this approaching significance (P<.06). In case of environmental factors, high socio-economic status (SES) level (PRR=1.45; 95% CI: 1.26-1.67), single-parent family (PRR=1.14; 95% CI: 1.01-1.30), more social norms pressure (PRR=1.08; 95% CI: 1.01-1.17), pocket money allowance (PRR=1.21; 95% CI: 1.09-1.34), easy accessibility (PRR=1.06; 95% CI:1.01-1.11), and less perceived parental control (PRR=0.96; 95% CI: 0.92-0.99) all had a role in higher consumption of unhealthy snacks. Interestingly, larger school size was associated with less unhealthy snacking (PRR=0.79; 95% CI: 0.68-0.92). Conclusion: Unhealthy snacking behavior is influenced by individual, socio-cultural and physical-environmental influences, namely by factors relating to poor parenting practices, high SES level, family characteristics, improper social norms pressure, and less knowledge and self-efficacy of students. This evidence can be used to inform a more evidencebased IHPS program through focusing on supportive strategies at the home, school, and local community level

Geographical disparities in the health of iranian women: Health outcomes, behaviors, and health-care access indicators

Background: Women's health is a key factor affecting the health of the whole population. Tackling inequality in determinants of health is recognized as the main path toward reducing the inequality in health outcomes. This study aimed to analyze the provincial inequality in determinants of women's health and health care in Iran. Methods: Using the Moss's model (2002) as a comprehensive framework of determinants of women's health, including “geopolitical environment,” “culture, norms, sanctions,” “women's roles in reproduction and production,” “health-related mediators,” and “health outcome” categories, we chose 13 indicators. Afterward, using data sources including the Iranian Multiple Indicators of Demographics and Health Survey, the National Organization for Civil Registration, and Statistics Centre of Iran, we analyzed provincial inequality in these indicators in Iran (2011). Gini coefficient and Lorenz curve were used for measuring inequality. Results: Gini coefficients calculated as follows; life satisfaction level (0.027), literate women (0.398), women with proper knowledge about HIV/AIDS prevention (0.483), unemployed women (0.380), women without an income (0.384), women who use at least one type of mass media (0.389), women who used computer or internet (0.467), women who had received pregnancy care from a skill birth attendant (SBA) (0.420), women who had delivered with the help of an SBA (0.426), women who currently smoke cigarettes (0.603), women who currently consume hookah (0.561), women with at least one chronic disease (0.438), and women's deaths in 2010 and 2011 (0.393 and 0.359, respectively). Conclusions: We found large provincial disparities in determinants of women's health in Iran. Determinants such as lifestyle, health behavior, health knowledge, and health-care services availability should be considered by health policymakers in addressing the inequality in women's health at a provincial level

Factors Associated With Unhealthy Snacks Consumption Among Adolescents in Iran’s Schools

Background Well-informed interventions are needed if school-based health promotion is to be effective. Amongother aims, the Iranian Health Promoting School (IHPS) program that was launched in 2011, has an important aimof promoting dietary behaviors of adolescents. The present study, therefore, aimed to investigate the factors affectingunhealthy snacking of adolescents and provide evidence for a more effective IHPS program. Methods In a cross-sectional study design, 1320 students from 40 schools in Kerman city were selected using aproportional stratified random sampling method. A modified qualitative Food Frequency Questionnaire (FFQ) wasused to gather data about unhealthy snacking behavior. Data about intrapersonal and environmental factors wereobtained using a validated and reliable questionnaire. A mixed-effects negative-binomial regression model was usedto analyze the data. Results Taste and sensory perception (prevalence rate ratio [PRR] = 1.18; 95% CI: 1.09-1.27), being a male (PRR = 1.20;95% CI: 1.05-1.38) and lower nutritional knowledge (PRR = 0.96; 95% CI: 0.91-0.99) were associated with higher weeklyunhealthy snaking. Perceived self-efficacy (PRR = 0.95; 95% CI: 0.91-1.00) negatively influenced the frequency ofunhealthy snaking, with this approaching significance (P< .06). In case of environmental factors, high socio-economicstatus (SES) level (PRR = 1.45; 95% CI: 1.26-1.67), single-parent family (PRR = 1.14; 95% CI: 1.01-1.30), more socialnorms pressure (PRR = 1.08; 95% CI: 1.01-1.17), pocket money allowance (PRR = 1.21; 95% CI: 1.09-1.34), easyaccessibility (PRR = 1.06; 95% CI:1.01-1.11), and less perceived parental control (PRR= 0.96; 95% CI: 0.92-0.99) all hada role in higher consumption of unhealthy snacks. Interestingly, larger school size was associated with less unhealthysnacking (PRR = 0.79; 95% CI: 0.68-0.92). Conclusion Unhealthy snacking behavior is influenced by individual, socio-cultural and physical-environmentalinfluences, namely by factors relating to poor parenting practices, high SES level, family characteristics, improper socialnorms pressure, and less knowledge and self-efficacy of students. This evidence can be used to inform a more evidencebased IHPS program through focusing on supportive strategies at the home, school, and local community levels

Prevalence and intensity of catastrophic health care expenditures in Iran from 2008 to 2015: a study on Iranian household income and expenditure survey

Abstract Background Households exposure to catastrophic health expenditure is a valuable measure to monitor financial protection in health sector payments. The present study had two aims: first, to estimate the prevalence and intensity of catastrophic health expenditures (CHE) in Iran. Second, to investigate main factors that influence the probability of CHE. Methods CHE is defined as an occasion in which a household’s out-of-pocket (OOP) spending exceeds 40% of the total income that remains after subtraction of living expenses. This study used the data from eight national repeated cross-sectional surveys on households’ income and expenditure. The proportion of households facing CHE, as a prevalence measure, was estimated for rural and urban areas. The intensity of CHE was also calculated using overshoot and mean positive overshoot (MPO) measures. The factors affecting the CHE were also analyzed using logistic random effects regression model. We also used ArcMap 10.1 to display visually disparities across the country. Results An increasing number of Iranians has been subject to catastrophic health care costs over the study period in both rural and urban areas (CHE = 2.57% in 2008 and 3.25% in 2015). In the same period, the overshoot of CHE and the mean positive overshoot ranged from 0.26% to 0.65% and from 12.26% to 20.86%, respectively. The average absolute monetary value of OOP spending per month has been low in rural areas over the years, but the prevalence of CHE has been higher than urban areas. Generally put, rural settlement, higher income, receiving inpatient and outpatient services, and existence of elderly people in the household led to increase in CHE prevalence (p < 0.05). Interestingly, provinces with more limited geographical and cultural accessibility had the lowest CHE. Conclusions According to the findings, Iran’s healthcare system has failed to realize the aim of five-year national development plan regarding CHE prevalence (1% CHE prevalence according to the plan). Therefore, revision of financial health care protection policies focusing on pre-payments seems mandatory. For instance, these policies should extend the interventions that target low-income populations particularly in rural areas, provide more coverage for catastrophic medical services in basic benefit packages, and develop supplementary health insurance