Ammatzia Peled

Air pollution and congenital heart defects.

Environmental factors such as ambient air pollution have been associated with congenital heart defects. The aim of this study was to investigate the association between gestational exposure to air pollution and the risk of congenital heart defects. We conducted a registry-based cohort study with a total of 135,527 live- and still-births in the Tel-Aviv region during 2000-2006. We used a Geographic Information System-based spatiotemporal approach with weekly inverse distance weighting modeling to evaluate associations between gestational exposure to ambient air pollution during weeks 3-8 of pregnancy and the risk for congenital heart defects. The following pollutants were studied: carbon monoxide, nitrogen-dioxide, ozone, sulfur-dioxide and particulate matter with aerodynamic diameter smaller than 10 μm and 2.5 μm (PM10, PM2.5 respectively). Logistic models, adjusted for socio-demographic covariates were used to evaluate the associations. We found that maternal exposure to increased concentrations of PM10 was associated with multiple congenital heart defects (adjusted OR 1.05, 95% CI: 1.01 to 1.10 for 10 μg/m(3) increment). An inverse association was observed between concentrations of PM2.5 and isolated patent ductus arteriosus (adjusted OR 0.78, 95% CI: 0.68 to 0.91 for 5 µg/m(3) increment). Sensitivity analyses showed that results were consistent. Generally there were no evidence for an association between gaseous air pollutants and congenital heart defects.Our results for PM10 and congenital heart defects confirm results from previous studies. The results for PM2.5 need further investigations.

Green spaces and adverse pregnancy outcomes

Objective The objective of this study was to evaluate the associations between proximity to green spaces and surrounding greenness and pregnancy outcomes, such as birth weight, low birth weight (LBW), very LBW (VLBW), gestational age, preterm deliveries (PTD) and very PTD (VPTD). Methods This study was based on 39 132 singleton live births from a registry birth cohort in Tel Aviv, Israel, during 2000–2006. Surrounding greenness was defined as the average of satellite-based Normalised Difference Vegetation Index (NDVI) in 250 m buffers and proximity to major green spaces was defined as residence within a buffer of 300 m from boundaries of a major green space (5000 m2), based on data constructed from OpenStreetMap. Linear regression (for birth weight and gestational age) and logistic regressions models (for LBW, VLBW, PTD and VPTD) were used with adjustment for relevant covariates. Results An increase in 1 interquartile range greenness was associated with a statistically significant increase in birth weight (19.2 g 95% CI 13.3 to 25.1) and decreased risk of LBW (OR 0.84, 95% CI 0.78 to 0.90). Results for VLBW were in the same direction but were not statistically significant. In general, no associations were found for gestational age, PTD and VPTD. The findings were consistent with different buffer and green space sizes and stronger associations were observed among those of lower socioeconomic status. Conclusions This study confirms the results of a few previous studies demonstrating an association between maternal proximity to green spaces and birth weight. Further investigation is needed into the associations with VLBW and VPTD, which has never been studied before.

Identifying and Tracking Major Events Using Geo-Social Networks

In recent years, several technological advancements have changed the lives of millions throughout the globe. These include broadband Internet wireless access, advanced mobile platforms and smartphones including accurate global positioning system capabilities, and the introduction of social networks. The fusion of these technological advances led to the massive adoption of mobile platform-operated social networking applications and unleashed new real-time and on-site social information. The ability to generate content anywhere and anytime leads to a detectable projection of real-life events on geo-social networks (GSN). For example, in preparation for a rally, the geo-social activity may precede the actual event, allowing predictive capabilities. Alternatively, in a natural event such as a wildfire, early content generated in the proximity of the event may allow early identification of the event and the assessment of its physical boundaries. In this article, we propose to use the massive and rapidly accumulating information communicated within GSN to identify and track major events and present a proof of concept. We discuss means and methods to retrieve relevant information from the networks, through a set of adequate spatial, temporal, and textual filters. Our preliminary empirical results corroborate our assumptions and show that major events may have detectable “abnormal” impact on GSN activities, which allows prompt identification and real-time tracking. Our approach is expected to pave the way to the development of real-time systems and algorithms for early identification and geographical tracking of major events.

Periodicity and time trends in the prevalence of total births and conceptions with congenital malformations among Jews and Muslims in Israel, 1999‐2006: A time series study of 823,966 births

BACKGROUND Congenital malformations (CMs) are a leading cause of infant disability. Geophysical patterns such as 2-year, yearly, half-year, 3-month, and lunar cycles regulate much of the temporal biology of all life on Earth and may affect birth and birth outcomes in humans. Therefore, the aim of this study was to evaluate and compare trends and periodicity in total births and CM conceptions in two Israeli populations. METHODS Poisson nonlinear models (polynomial) were applied to study and compare trends and geophysical periodicity cycles of weekly births and weekly prevalence rate of CM (CMPR), in a time-series design of conception date within and between Jews and Muslims. The population included all live births and stillbirths (n = 823,966) and CM (three anatomic systems, eight CM groups [n = 2193]) in Israel during 2000 to 2006. Data were obtained from the Ministry of Health. RESULTS We describe the trend and periodicity cycles for total birth conceptions. Of eight groups of CM, periodicity cycles were statistically significant in four CM groups for either Jews or Muslims. Lunar month and biennial periodicity cycles not previously investigated in the literature were found to be statistically significant. Biennial cycle was significant in total births (Jews and Muslims) and syndactyly (Muslims), whereas lunar month cycle was significant in total births (Muslims) and atresia of small intestine (Jews). CONCLUSION We encourage others to use the method we describe as an important tool to investigate the effects of different geophysical cycles on human health and pregnancy outcomes, especially CM, and to compare between populations.

Exploring the Impact of a Spatial Data Infrastructure on Value-Added Resellers and Vice Versa

A spatial data infrastructure (SDI) is an evolving concept for facilitating, coordinating and monitoring the exchange and sharing of geospatial data and services. In earlier work, we developed a formal model for an SDI from the Enterprise, Information and Computational Viewpoints of the Reference Model for Open Distributed Processing. Within the Enterprise Viewpoint, we identified six stakeholders, including a Value-added Reseller (VAR), a stakeholder who adds value to an existing product or group of products, and then makes it available as a new product. A VAR is particularly important because they extend the usefulness of SDI products: high quality and useful VAR products help ensure continued funding by governments of publicly provided data. We engaged with various types of VAR around the world, to understand what encourages or inhibits VARs in an SDI, and the contributions VARs can make to an SDI. The results are described here.

Geo-Based Statistical Models for Vulnerability Prediction of Highway Network Segments

This study describes four statistical models—Poisson; Negative Binomial; Zero-Inflated Poisson; and Zero-Inflated Negative Binomial—which were devised in order to examine traffic accidents and estimate the best probability estimating model in terms of future risk assessment at interurban road sections. The study was conducted on four sets of fixed-length sections of the road network: 500, 750, 1000, and 1500 m. The contribution of transportation and spatial parameters as predictors of road accident rates was evaluated for all four data sets separately. In addition, the Empirical Bayes method was applied. This method uses historical accidents information, allowing regression to the mean phenomenon so as to improve model results. The study was performed using Geographic Information System (GIS) software. Other analyses, such as statistical analyses combined with spatial parameters, interactions, and examination of other geographical areas, were also performed. The results showed that the short road sections data sets of 500 and 750 m yielded the most stable models. This allows focused treatment on short sections of the road network as a way to save resources (enforcement; education and information; finance) and potentially gain maximum benefit at minimum investment. It was found that the significant parameters affecting accident rates are: curvature of the road section; the region and traffic volume. An interaction between the region and traffic volume was also found.

Real‐Time Major Events Monitoring and Alert System through Social Networks

The volume of information generated by social and cellular networks has significantly increased in recent years. Automated collection of these data and its rapid analyses allow for better and faster detection of major (in terms of National impact) ‘real life’ events. This study uses data obtained from social networks such as Twitter and Google+. It proposes a mechanism for detecting major events and a system to alert on their manifestation. The article describes the considerations and needed algorithms required to develop and establish such a system. The methodology presented here is based on linking major events that occurred in Israel during the years 2011–2014, with information extracted from social networks. Results indicate that alerts were received shortly after the event occurred for most of major events. Such are large fires, earthquakes and terror attacks. However, attempts to achieve alerts for ‘local’ secondary events failed. This as their impact on the social network is low. Furthermore, it was found that the volume of false alerts depends on the type of domain and keywords.

Detection of discrepancies in existing land-use classification using IKONOS satellite data

Geoinformation systems (GIS) and other spatial databases containing land-use data are usually subjected to intensive change processes that impact the quality of their inherent classification and diminish its relevance. Consequently, with time, these databases accumulate various types of erroneous information (discrepancies) that inconsistent with reality. The aim of the study was to investigate how well the discrepancies in land-use classification could be detected using high-resolution optical IKONOS satellite data based on iterative discriminant analyses (IDA). The approach proposed in the research is intended to update an existing land-use classification with one derived from the IDA classifier. Seven land-use classes were extracted from outdated Israeli National GIS spatial database for the area which was also the extent of recent IKONOS satellite image. In order to detect the discrepancies in the land-use classification, IDA algorithm was applied, utilizing the spectral properties of the land-use polygons, acquired from the image. As result IDA has changed, the classification of discrepant polygons which was found inconsistent with the up-to-date spectral data. The polygons with spectral properties that were found consistent with the original classification have remained assigned to initial land-use classes. The overall fraction of the polygons that were correctly classified by IDA was estimated as 92.6% and the fraction of polygons correctly detected as discrepant was estimated as 84.1%. The main advantage of the proposed detection of discrepancies by the IDA is its analytical simplicity that allows for straightforward employment of original bands and ratio (indices) bands in the classification process. The continuous revision of land-use classification databases by IDA may assist the overcoming of interpreter’s errors and the misclassifications caused by changes in land-use.