Michael Hagenlocher

Spatial-Explicit Modeling of Social Vulnerability to Malaria in East Africa.

BackgroundDespite efforts in eradication and control, malaria remains a global challenge, particularly affecting vulnerable groups. Despite the recession in malaria cases, previously malaria free areas are increasingly confronted with epidemics as a result of changing environmental and socioeconomic conditions. Next to modeling transmission intensities and probabilities, integrated spatial methods targeting the complex interplay of factors that contribute to social vulnerability are required to effectively reduce malaria burden. We propose an integrative method for mapping relative levels of social vulnerability in a spatially explicit manner to support the identification of intervention measures.MethodsBased on a literature review, a holistic risk and vulnerability framework has been developed to guide the assessment of social vulnerability to water-related vector-borne diseases (VBDs) in the context of changing environmental and societal conditions. Building on the framework, this paper applies spatially explicit modeling for delineating homogeneous regions of social vulnerability to malaria in eastern Africa, while taking into account expert knowledge for weighting the single vulnerability indicators. To assess the influence of the selected indicators on the final index a local sensitivity analysis is carried out.ResultsResults indicate that high levels of malaria vulnerability are concentrated in the highlands, where immunity within the population is currently low. Additionally, regions with a lack of access to education and health services aggravate vulnerability. Lower values can be found in regions with relatively low poverty, low population pressure, low conflict density and reduced contributions from the biological susceptibility domain. Overall, the factors characterizing vulnerability vary spatially in the region. The vulnerability index reveals a high level of robustness in regard to the final choice of input datasets, with the exception of the immunity indicator which has a marked impact on the composite vulnerability index.ConclusionsWe introduce a conceptual framework for modeling risk and vulnerability to VBDs. Drawing on the framework we modeled social vulnerability to malaria in the context of global change using a spatially explicit approach. The results provide decision makers with place-specific options for targeting interventions that aim at reducing the burden of the disease amongst the different vulnerable population groups.

A spatial model of socioeconomic and environmental determinants of dengue fever in Cali, Colombia

Dengue fever has gradually re-emerged across the global South, particularly affecting urban areas of the tropics and sub-tropics. The dynamics of dengue fever transmission are sensitive to changes in environmental conditions, as well as local demographic and socioeconomic factors. In 2010, the municipality of Cali, Colombia, experienced one of its worst outbreaks, however the outbreak was not spatially homogeneous across the city. In this paper, we evaluate the role of socioeconomic and environmental factors associated with this outbreak at the neighborhood level, using a Geographically Weighted Regression model. Key socioeconomic factors include population density and socioeconomic stratum, whereas environmental factors are proximity to both tire shops and plant nurseries and the presence of a sewage system (R2=0.64). The strength of the association between these factors and the incidence of dengue fever is spatially heterogeneous at the neighborhood level. The findings provide evidence to support public health strategies in allocating resources locally, which will enable a better detection of high risk areas, a reduction of the risk of infection and to strengthen the resilience of the population.

Mapping malaria risk and vulnerability in the United Republic of Tanzania: a spatial explicit model.

BackgroundOutbreaks of vector-borne diseases (VBDs) impose a heavy burden on vulnerable populations. Despite recent progress in eradication and control, malaria remains the most prevalent VBD. Integrative approaches that take into account environmental, socioeconomic, demographic, biological, cultural, and political factors contributing to malaria risk and vulnerability are needed to effectively reduce malaria burden. Although the focus on malaria risk has increasingly gained ground, little emphasis has been given to develop quantitative methods for assessing malaria risk including malaria vulnerability in a spatial explicit manner.MethodsBuilding on a conceptual risk and vulnerability framework, we propose a spatial explicit approach for modeling relative levels of malaria risk - as a function of hazard, exposure, and vulnerability - in the United Republic of Tanzania. A logistic regression model was employed to identify a final set of risk factors and their contribution to malaria endemicity based on multidisciplinary geospatial information. We utilized a Geographic Information System for the construction and visualization of a malaria vulnerability index and its integration into a spatially explicit malaria risk map.ResultsThe spatial pattern of malaria risk was very heterogeneous across the country. Malaria risk was higher in Mainland areas than in Zanzibar, which is a result of differences in both malaria entomological inoculation rate and prevailing vulnerabilities. Areas of high malaria risk were identified in the southeastern part of the country, as well as in two distinct “hotspots” in the northwestern part of the country bordering Lake Victoria, while concentrations of high malaria vulnerability seem to occur in the northwestern, western, and southeastern parts of the mainland. Results were visualized using both 10×10 km2 grids and subnational administrative units.ConclusionsThe presented approach makes an important contribution toward a decision support tool. By decomposing malaria risk into its components, the approach offers evidence on which factors could be targeted for reducing malaria risk and vulnerability to the disease. Ultimately, results offer relevant information for place-based intervention planning and more effective spatial allocation of resources.

Modeling Hotspots of Climate Change in the Sahel Using Object-Based Regionalization of Multidimensional Gridded Datasets

The population of subsaharan Africa, and particularly of the countries of the Sahel and western Africa, is one of the most vulnerable to climate change and climate-related extreme events. To provide updated information for targeted climate change adaptation measures, we modeled hotspots of climate change and related extreme events in an integrative manner. This was achieved by constructing a spatial composite indicator of cumulative climate change impact, which integrates four climate- and hazard-related subindicators: seasonal temperature trends, seasonal precipitation trends, drought occurrences, and major flood events. The analysis is based on time-series of freely available continuous, gridded geo-spatial datasets, including remote sensing data. The aggregation of the four subindicators was performed by making use of a regionalization approach, based on segmentation techniques widely used in the remote sensing community in the field of object-based image analysis. Following the approach presented in this paper, 19 hotspots with most severe climatic changes were identified, evaluated, and mapped. The method enables not only the prioritization of intervention areas, but also allows decomposing the identified hotspots into their underlying subindicators, and thus additional information for effective climate change adaptation measures can be provided.

Geons – domain-specific regionalization of space

The design of methods and tools to build adequate representations of complex geographical phenomena in a way that spatial patterns are emphasized is one of the core objectives of GIScience. In this paper, we build on the concept of geons as a strategy to represent and analyze latent spatial phenomena across different geographical scales (local, national, regional) incorporating domain-specific expert knowledge. Focusing on two types, we illustrate and exemplify how geons are generated and explored. So-called composite geons represent functional land-use classes, required for regional planning purposes. They are created via class modeling to translate interpretation schemes from mapping keys. Integrated geons, on the other hand, address abstract, yet policy-relevant phenomena such as societal vulnerability to hazards. They are delineated by regionalizing continuous geospatial data sets representing relevant indicators in a multidimensional variable space. Using the geon approach, we create spatially exhaustive sets of units, scalable to the level of policy intervention, homogenous in their domain-specific response, and independent from any predefined boundaries. From a GIScience perspective, we discuss either type of geons in a semantic hierarchy of geographic information constructs. Despite its validity for decision-making and its transferability across scales and application fields, the delineation of geons requires further methodological research to assess their statistical and conceptual robustness.

A review of vulnerability indicators for deltaic social–ecological systems

The sustainability of deltas worldwide is under threat due to the consequences of global environmental change (including climate change) and human interventions in deltaic landscapes. Understanding these systems is becoming increasingly important to assess threats to and opportunities for long-term sustainable development. Here, we propose a simplified, yet inclusive social–ecological system (SES)-centered risk and vulnerability framework and a list of indicators proven to be useful in past delta assessments. In total, 236 indicators were identified through a structured review of peer-reviewed literature performed for three globally relevant deltas—the Mekong, the Ganges–Brahmaputra–Meghna and the Amazon. These are meant to serve as a preliminary “library” of potential indicators to be used for future vulnerability assessments. Based on the reviewed studies, we identified disparities in the availability of indicators to populate some of the vulnerability domains of the proposed framework, as comprehensive social–ecological assessments were seldom implemented in the past. Even in assessments explicitly aiming to capture both the social and the ecological system, there were many more indicators for social susceptibility and coping/adaptive capacities as compared to those relevant for characterizing ecosystem susceptibility or robustness. Moreover, there is a lack of multi-hazard approaches accounting for the specific vulnerability profile of sub-delta areas. We advocate for more comprehensive, truly social–ecological assessments which respond to multi-hazard settings and recognize within-delta differences in vulnerability and risk. Such assessments could make use of the proposed framework and list of indicators as a starting point and amend it with new indicators that would allow capturing the complexity as well as the multi-hazard exposure in a typical delta SES.

Environmental change and Rift Valley fever in eastern Africa: projecting beyond HEALTHY FUTURES

Outbreaks of Rift Valley fever (RVF), a relatively recently emerged zoonosis endemic to large parts of sub-Saharan Africa that has the potential to spread beyond the continent, have profound health and socio-economic impacts, particularly in communities where resilience is already low. Here output from a new, dynamic disease model [the Liverpool RVF (LRVF) model], driven by downscaled, bias-corrected climate change data from an ensemble of global circulation models from the Inter-Sectoral Impact Model Intercomparison Project run according to two radiative forcing scenarios [representative concentration pathway (RCP)4.5 and RCP8.5], is combined with results of a spatial assessment of social vulnerability to the disease in eastern Africa. The combined approach allowed for analyses of spatial and temporal variations in the risk of RVF to the end of the current century. Results for both scenarios highlight the high-risk of future RVF outbreaks, including in parts of eastern Africa to date unaffected by the disease. The results also highlight the risk of spread from/to countries adjacent to the study area, and possibly farther afield, and the value of considering the geography of future projections of disease risk. Based on the results, there is a clear need to remain vigilant and to invest not only in surveillance and early warning systems, but also in addressing the socio-economic factors that underpin social vulnerability in order to mitigate, effectively, future impacts.

Vulnerability and risk of deltaic social-ecological systems exposed to multiple hazards

Coastal river deltas are hotspots of global change impacts. Sustainable delta futures are increasingly threatened due to rising hazard exposure combined with high vulnerabilities of deltaic social-ecological systems. While the need for integrated multi-hazard approaches has been clearly articulated, studies on vulnerability and risk in deltas either focus on local case studies or single hazards and do not apply a social-ecological systems perspective. As a result, vulnerabilities and risks in areas with strong social and ecological coupling, such as coastal deltas, are not fully understood and the identification of risk reduction and adaptation strategies are often based on incomplete assumptions. To overcome these limitations, we propose an innovative modular indicator library-based approach for the assessment of multi-hazard risk of social-ecological systems across and within coastal deltas globally, and apply it to the Amazon, Ganges-Brahmaputra-Meghna (GBM), and Mekong deltas. Results show that multi-hazard risk is highest in the GBM delta and lowest in the Amazon delta. The analysis reveals major differences between social and environmental vulnerability across the three deltas, notably in the Mekong and the GBM deltas where environmental vulnerability is significantly higher than social vulnerability. Hotspots and drivers of risk vary spatially, thus calling for spatially targeted risk reduction and adaptation strategies within the deltas. Ecosystems have been identified as both an important element at risk as well as an entry point for risk reduction and adaptation strategies.

Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?

Stochastic weather generators can generate very long time series of weather patterns, which are indispensable in earth sciences, ecology and climate research. Yet, both their potential and limitations remain largely unclear because past research has typically focused on eclectic case studies at small spatial scales in temperate climates. In addition, stochastic multi-site algorithms are usually not publicly available, making the reproducibility of results difficult. To overcome these limitations, we investigated the performance of the reduced-complexity multi-site precipitation generator TripleM across three different climatic regions in the United States. By resampling observations, we investigated for the first time the performance of a multi-site precipitation generator as a function of the extent of the gauge network and the network density. The definition of the role of the network density provides new insights into the applicability in data-poor contexts. The performance was assessed using nine different statistical metrics with main focus on the inter-annual variability of precipitation and the lengths of dry and wet spells. Among our study regions, our results indicate a more accurate performance in wet temperate climates compared to drier climates. Performance deficits are more marked at larger spatial scales due to the increasing heterogeneity of climatic conditions.

The most recent view of vulnerability

Disaster risk is determined by the combination of physical hazards and the vulnerabilities of exposed elements. Vulnerability relates to the susceptibility of assets such as objects, systems (or part thereof) and populations exposed to disturbances, stressors or shocks as well as to the lack of capacity to cope with and to adapt to these adverse conditions. Vulnerability is dynamic, multifaceted and composed of various dimensions, all of which have to be considered within a holistic vulnerability assessment.Conceptually, there are two types of storm in meteorology: (1) the hazardous weather phenomena themselves (such as windstorms, rainstorms, snowstorms, hailstorms, thunderstorms and ice storms (freezing rain)), and (2) the meteorological features in the atmosphere — the ‘storm systems’ — that can be said to be responsible for this adverse weather (notably tropical cyclones, extra-tropical cyclones and convective systems). These storm systems, which are a focal point in the following discussion, can be distinguished from one another by their mechanism of development (growth), their structure, their geographic location, their spatial scale and their typical lifetime. Other types of storm system do exist, but these can be considered subtypes of the three systems listed above.