Science paper or big data? Assessing invasion dynamics using observational data

Non-native species are spreading at an unprecedented rate over large spatial scales, with global environmental change and growth in commerce providing novel opportunities for range expansion. Assessing the pattern and rate of spread is key to the development of strategies for safeguarding against future invasions and efficiently managing existing ones. Such assessments often depend on spatial distribution data from online repositories, which can be spatially biased, imprecise, and lacking in quantity. Here, the influence of disparities between occurrence records from online data repositories and what is known of the invasion history from peer-reviewed published literature on non-native species range expansion was evaluated using 6693 records of the Pacific oyster, Magallana gigas (Thunberg, 1793), spanning 56 years of its invasion in Europe. Two measures of spread were calculated: maximum rate of spread (distance from introduction site over time) and accumulated area (spatial expansion). Results suggest that despite discrepancies between online and peer-reviewed data sources, including a paucity of records from the early invasion history in online repositories, the use of either source does not result in significantly different estimates of spread. Our study significantly improves our understanding of the European distribution of M. gigas and suggests that a combination of short- and long-range dispersal drives range expansions. More widely, our approach provides a framework for comparison of online occurrence records and invasion histories as documented in the peer-reviewed literature, allowing critical evaluation of both data sources and improving our understanding of invasion dynamics significantly.

Assessing the potential impact of glacial lake outburst floods on individual objects using a high-performance hydrodynamic model and open-source data

Glacial lake outburst floods (GLOFs) are one of the major natural hazards in certain populated mountainous areas, e.g., the Himalayan region, which may lead to catastrophic consequences including substantial loss of lives. Evaluating the potential socio-economic impact of GLOFs is essential for risk mitigation and enhancing community resilience. Yet in most of the cases, this is confronted with the challenges of limited availability of data and inaccessibility to most of the glacial lakes in the high-altitude areas. This study aims to exploit open data from different sources and high-performance hydrodynamic modelling to develop a new framework for GLOF exposure and impact assessment. In the new framework, different GLOF scenarios are created using a simple dam breach model. A high-performance hydrodynamic model is then adopted to simulate the resulting flood hydrodynamics. Necessary socio-economic information is collected and processed from multiple sources including OpenStreetMap, Google Earth, and global data products to support exposure analysis. Established depth-damage curves are used to assess the GLOF damage extents to different exposed objects and an existing fatality estimating procedure is adopted to assess the potential loss of lives. The evaluation framework is applied to the Tsho Rolpa glacial lake in Nepal. From the results, the worst GLOF scenario as considered can potentially inundate 1647 buildings, impact 5038 people and hit 123 key facilities including schools, hospitals, airports, hydropower plants, etc. It may substantially damage 900 buildings, 10.63 km² of agricultural land and 50.9 km roads and may potentially lead to 45 deaths even if warning is available.

Innovating at the human-technology interface in disasters and disease outbreaks

Disasters and disease outbreaks have long been a catalyst for innovative applications of emerging technologies. The urgent need to respond to an emergency leads to resourceful uses of the technologies at hand. However, the best and most cost-effective use of new technologies is to prevent disease and improve resilience. In this paper, the authors present a range of approaches through which both opportunities can be grasped. Global connectedness enables more data to be collected and processed in emergencies, especially with the rise of open-source data, including social media. In general, the poorest and most remote populations are most vulnerable to disaster. However, with smaller, faster, smarter, cheaper and more connected technology, reliable, efficient, and targeted response and recovery can be provided. Initially, crowdsourcing was used to find people, map affected areas, and determine resource allocation. This led to the generation of an overwhelming amount of data, and the need to extract valuable information from that data in a timely manner. As technology evolved, organisations started outsourcing many tasks, first to other people, then to machines. Since the volume of data generated outpaces human capacity, data analysis is being automated using artificial intelligence and machine learning, which furthers our abilities in predictive analytics. As we move towards prevention rather than remediation, information collection and processing must become faster and more efficient while maintaining accuracy. Moreover, these new strategies and technologies can help us to move forwards, by integrating layers of human, veterinary, public, and environmental health data for a One Health approach.

Modeling crash risk of horizontal curves using large-scale auto-extracted roadway geometry data

Highway horizontal curves (H-curves) provide a smooth transition between two tangent sections of roadways. They allow vehicles to adjust their travel directions gradually. However, the geometry changes of the highway sections with H-curves often raise safety concerns. In order to deploy effective safety countermeasures, a critical task is to understand the risk factors associated with H-curves. Existing studies have made efforts to probe the safety issues associated with H-curves, whereas they were limited to relatively small-scale examinations because of the challenges in identifying H-curves from large road networks. In addition, due to the lack of well-archived traffic and roadway information, gathering other data associated with the H-curves is also difficult. Regarding to these gaps, this study aims to leverage open-source data to analyze the crash risk of highway sections with H-curves. In particular, the present study highlights itself from two main aspects: (i) a H-curve extraction tool was developed to facilitate large-scale curve data collection through the analytics of different open source data; and (ii) a crash modeling framework was developed to quantify H-curve crash risk. A case study based on a statewide road network was performed to test the developed crash risk models with the collected curve data. The results show the opportunities of using the developed tool for large-scale data collection and analyze the safety impacts of H-curve geometric properties, elevation change, traffic exposure, among others. Findings of this study provide insights into the improvement of H-curve data collection and safety evaluation.

Gender-based vulnerability: combining Pareto ranking and spatial statistics to model gender-based vulnerability in Rohingya refugee settlements in Bangladesh

BACKGROUND

The Rohingya refugee crisis in Bangladesh continues to outstrip humanitarian resources and undermine the health and security of over 900,000 people. Spatial, sector-specific information is required to better understand the needs of vulnerable populations, such as women and girls, and to target interventions with improved efficiency and effectiveness. This study aimed to create a gender-based vulnerability index and explore the geospatial and thematic variations in gender-based vulnerability of Rohingya refugees residing in Bangladesh by utilizing pre-existing, open source data.

METHODS

Data sources included remotely-sensed REACH data on humanitarian infrastructure, United Nations Population Fund resource availability data, and the Needs and Population Monitoring Survey conducted by the International Organization for Migration in October 2017. Data gaps were addressed through probabilistic interpolation. A vulnerability index was designed through a process of literature review, variable selection and thematic grouping, normalization, and scorecard creation, and Pareto ranking was employed to rank sites based on vulnerability scoring. Spatial autocorrelation of vulnerability was analyzed with the Global and Anselin Local Moran's I applied to both combined vulnerability index rank and disaggregated thematic ranking.

RESULTS

Of the settlements, 24.1% were ranked as 'most vulnerable,' with 30 highly vulnerable clusters identified predominantly in the northwest region of metropolitan Cox's Bazar. Five settlements in Dhokkin, Somitapara, and Pahartoli were categorized as less vulnerable outliers amongst highly vulnerable neighboring sites. Security- and health-related variables appear to be the most significant drivers of gender-specific vulnerability in Cox's Bazar. Clusters of low security and education vulnerability measures are shown near Kutupalong.

CONCLUSION

The humanitarian sector produces tremendous amounts of data that can be analyzed with spatial statistics to improve research targeting and programmatic intervention. The critical utilization of these data and the validation of vulnerability indexes are required to improve the international response to the global refugee crisis. This study presents a novel methodology that can be utilized to not only spatially characterize gender-based vulnerability in refugee populations, but can also be calibrated to identify and serve other vulnerable populations during crises.

The validity of open-source data when assessing jail suicides

BACKGROUND

The Bureau of Justice Statistics' Deaths in Custody Reporting Program is the primary source for jail suicide research, though the data is restricted from general dissemination. This study is the first to examine whether jail suicide data obtained from publicly available sources can help inform our understanding of this serious public health problem.

METHODS

Of the 304 suicides that were reported through the DCRP in 2009, roughly 56 percent (N = 170) of those suicides were identified through the open-source search protocol. Each of the sources was assessed based on how much information was collected on the incident and the types of variables available. A descriptive analysis was then conducted on the variables that were present in both data sources. The four variables present in each data source were: (1) demographic characteristics of the victim, (2) the location of occurrence within the facility, (3) the location of occurrence by state, and (4) the size of the facility.

RESULTS

Findings demonstrate that the prevalence and correlates of jail suicides are extremely similar in both open-source and official data. However, for almost every variable measured, open-source data captured as much information as official data did, if not more. Further, variables not found in official data were identified in the open-source database, thus allowing researchers to have a more nuanced understanding of the situational characteristics of the event.

CONCLUSIONS

This research provides support for the argument in favor of including open-source data in jail suicide research as it illustrates how open-source data can be used to provide additional information not originally found in official data. In sum, this research is vital in terms of possible suicide prevention, which may be directly linked to being able to manipulate environmental factors.

Assessment of ambient background concentrations of elements in soil using combined survey and open-source data

Understanding ambient background concentrations in soil, at a local scale, is an essential part of environmental risk assessment. Where high resolution geochemical soil surveys have not been undertaken, soil data from alternative sources, such as environmental site assessment reports, can be used to support an understanding of ambient background conditions. Concentrations of metals/metalloids (As, Mn, Ni, Pb and Zn) were extracted from open-source environmental site assessment reports, for soils derived from the Newer Volcanics basalt, of Melbourne, Victoria, Australia. A manual screening method was applied to remove samples that were indicated to be contaminated by point sources and hence not representative of ambient background conditions. The manual screening approach was validated by comparison to data from a targeted background soil survey. Statistical methods for exclusion of contaminated samples from background soil datasets were compared to the manual screening method. The statistical methods tested included the Median plus Two Median Absolute Deviations, the upper whisker of a normal and log transformed Tukey boxplot, the point of inflection on a cumulative frequency plot and the 95th percentile. We have demonstrated that where anomalous sample results cannot be screened using site information, the Median plus Two Median Absolute Deviations is a conservative method for derivation of ambient background upper concentration limits (i.e. expected maximums). The upper whisker of a boxplot and the point of inflection on a cumulative frequency plot, were also considered adequate methods for deriving ambient background upper concentration limits, where the percentage of contaminated samples is <25%. Median ambient background concentrations of metals/metalloids in the Newer Volcanic soils of Melbourne were comparable to ambient background concentrations in Europe and the United States, except for Ni, which was naturally enriched in the basalt-derived soils of Melbourne.