85 research outputs found
Assessing the riverine flood forecast skill of GloFAS with streamflow observations and impact data: a case study for Mali
No full textGeospatial Health: achievements, innovations, priorities
No full text: An expert panel discussion on achievements, current areas of rapid scientific progress, prospects, and critical gaps in geospatial health was organized as part of the 16thsymposium of the global network of public health and earth scientists dedicated to the development of geospatial health (GnosisGIS), held at the Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente in The Netherlands in November 2023. The symposium consisted of a three-day scientific event that brought together an interdisciplinary group of researchers and health professionals from across the globe. The aim of the panel session was threefold: firstly, to reflect on the main achievements of the scientific discipline of geospatial health in the past decade; secondly, to identify key innovation areas where rapid scientific progress is currently made and thirdly, to identify critical gaps and associated research and education priorities to move the discipline forward. [...]
Characterizing data ecosystems to support official statistics with open mapping data for reporting on sustainable development goals
No full textReporting on the Sustainable Development Goals (SDGs) is complex given the wide variety of governmental and NGO actors involved in development projects as well as the increased number of targets and indicators. However, data on the wide variety of indicators must be collected regularly, in a robust manner, comparable across but also within countries and at different administrative and disaggregated levels for adequate decision making to take place. Traditional census and household survey data is not enough. The increase in Small and Big Data streams have the potential to complement official statistics. The purpose of this research is to develop and evaluate a framework to characterize a data ecosystem in a developing country in its totality and to show how this can be used to identify data, outside the official statistics realm, that enriches the reporting on SDG indicators. Our method consisted of a literature study and an interpretative case study (two workshops with 60 and 35 participants and including two questionnaires, over 20 consultations and desk research). We focused on SDG 6.1.1. (Proportion of population using safely managed drinking water services) in rural Malawi. We propose a framework with five dimensions (actors, data supply, data infrastructure, data demand and data ecosystem governance). Results showed that many governmental and NGO actors are involved in water supply projects with different funding sources and little overall governance. There is a large variety of geospatial data sharing platforms and online accessible information management systems with however a low adoption due to limited internet connectivity and low data literacy. Lots of data is still not open. All this results in an immature data ecosystem. The characterization of the data ecosystem using the framework proves useful as it unveils gaps in data at geographical level and in terms of dimensionality (attributes per water point) as well as collaboration gaps. The data supply dimension of the framework allows identification of those datasets that have the right quality and lowest cost of data extraction to enrich official statistics. Overall, our analysis of the Malawian case study illustrated the complexities involved in achieving self-regulation through interaction, feedback and networked relationships. Additional complexities, typical for developing countries, include fragmentation, divide between governmental and non-governmental data activities, complex funding relationships and a data poor context.Information and Communication Technolog
Understanding Population Movement Patterns after a Major Disaster: A case study of the effects of Hurricane Matthew in Haiti in 2016
No full textCall Detail Record (CDR) data enables the analysis of human behaviour on a large scale and the information that it contains can be promising. Not only does it allow us to track the movements of many individuals throughout time, it uncovers patterns in a persons decision making process that potentially tell us a lot about the effects of different interventions. The opportunity of finding new information on human behaviour has been noticed in several research fields, but every researcher eventually finds the same blockade: privacy. The data represents a detailed track of individuals and therefore these individuals must give approval (almost certainly lowering the amount of data that can be collected), or the data must be aggregated to the point that user anonymity is guaranteed. As a consequence of aggregated data, potentially important information could be lost. Especially in the case that both the dimension of location and time are aggregated, as these two could be considered as the essence of the CDR data. There are however techniques that increase the aggregation level, by de-aggregating the data. Naive Bayes classification has shown to be a functioning method within Machine Learning to de-aggregate a dataset that has incorporated information on at least one of the two essential dimensions; location in this case. By using the same variables to describe the administrative areas within the country that were used to describe the rows within the data, Naive Bayes classification can find the area that is most likely to fit the row. Matching the variables of the areas to the variables within the displacement dataset represents the backbone of the process, as the de-aggregation is driven by the closeness of datapoints between the two datasets.Engineering and Policy Analysi
The role of data and information sharing when slow-onset natural disasters and conflict collide
No full textThe frequency and severity of natural disasters is increasing worldwide, leading to a growing number of people struggling to survive. While climate related natural disasters affect large portions of the world, communities who are already struggling to survive due to conflict, insecurity or poverty are hit the most. In fragile states, slowly unfolding natural disasters are getting more and more intertwined with conflict. In these areas, humanitarian and peacekeeping organizations have increasingly overlapping goals and scarce resources. Sharing information between humanitarian and peacekeeping organizations can improve the effectiveness and efficiency of both humanitarian response operations and peacekeeping missions, which may result in not only saving time and money but most importantly saving lives and reducing human suffering. Nevertheless, the process of information sharing between humanitarian and peacekeeping organizations is not common practice. This is a comprehensive study on the complexities of information sharing between humanitarian and peacekeeping organizations in fragile areas. It includes desk research, interviewing, modeling approaches and a qualitative case study on Mopti, Mali where the Red Cross Movement is actively fighting food insecurity and Dutch peacekeepers are contributing to the UN peacekeeping mission called MINUSMA.Master project reportEngineering and Policy Analysi
Assessing the forecast skill of agricultural drought forecast from satellite-derived products in the Lower Shire River Basin
No full textIn 2008 the Red Cross Red Crescent (RCRC) started with Forecast-based Financing pilots to improve existing Early-Warning Early Action systems. Forecast-based financing is a new methodology to prepare, deliver and respond in a more effective and efficient manner, based on hazard forecasts. Actions are triggered when a forecast exceeds a danger level in a vulnerable intervention area. Forecast-based financing consists of several implementation steps, of which the first three aim at impact-based forecasting. Therefore, In this study we investigate how forecast skill of agricultural drought forecasts can be achieved. More specifically, the aim is to identify the contribution of machine learning and satellite-derived products in early warning early action systems improving the forecast skill of agricultural drought forecasts. We explore this through a machine learning model for a case-study area of the Lower Shire River Basin in Malawi. Several experiments with different sets of predictors and predictands are conducted to test which data adds to the skill and at what spatial detail. As predictors, the following agro-climatic indices are used: cumulative precipitation, soil moisture anomalies,mland surface temperature anomalies, El Niño Southern Oscillation in July and four different dry spell categories within the growing season (0-2 days dry spell, 3-4 day dry spell, 5-10 day dry spell and larger than 10 day dry spell). As drought predictand, the normalized difference vegetation index (NDVI) and the vegetation optical depth(VOD) in March are used, the latter obtained from satellite data company VanderSat. The final set of predictors and predictands is narrowed down based on which data is available and with which quality (timeliness, reliability, accuracy). Initial results, show higher accuracy and weighted accuracy values for the models including soil moisture data compared to the ones without soil moisture, expect for the last month in the growing season, where it give opposite results. The outcome of the model can support humanitarian organisations to increase the lead time necessary to act upon a drought trigger and reduce the impact of such event.IPACE-MalawiNERC-SHEARWater Management | Hydrolog
Information diffusion in complex emergencies: A model-based evaluation of information sharing strategies
No full textIn an emergency, humanitarian organisations share information to prevent redundant data collection and avoid gaps and overlap in the relief activities that they undertake. An analysis of hygiene kit distribution in the Bangladesh-Myanmar displacement crisis and consultation of both literature and humanitarian professionals led to the construction of a model on information diffusion in complex emergencies. This model proved to be able to evaluate strategies that have a level of complexity that could not be apprehended by existing models. Experimentation with this model leads to the conclusion that a locally sourced team, with an outward focused organisation that produces near real-time information products, is most effective in diffusing information.Master project reportEngineering and Policy Analysi
Coordination and Information Management in the Haiyan Response: Observations from the Field
No full textAbstractThe response to the Level 3 disaster of Typhoon Haiyan in the Philippines involved a large number of organizations providing assistance and support. Coordination structures between a large variety of international and national organizations, the government and the military were established at the national, provincial and local levels. These coordination efforts were accompanied by a significant information management effort, including the needs assessment of the affected population and monitoring and evaluation regarding the response and assistance provided. This paper presents preliminary findings from a research field trip conducted in the aftermath of the Typhoon response by the authors. Interviews were conducted with a broad range of decision makers in various functions in the disaster response organizations and with varying responsibilities. These interviews were complemented with in-field observations and secondary data collection. Preliminary findings show a decreasing complexity and rigidity of coordination structures from the headquarters to the (deep) field, and a corresponding decreasing sophistication of information management. While information management at the headquarters seemed to be targeted in large part towards international advocacy and policy, information management in the field focused on very concrete response actions
Improving the reliability of an impact-based forecasting model: A case study for typhoons and landslides in the Philippines
No full textAnticipatory action requires models that can accurately and reliably predict the impact of natural hazards. However, impact forecasts are often underestimated when consecutive hazards are not considered. In the Bicol region in the Philippines, typhoons trigger 90% of landslides, causing a lot of fatalities and damage to infrastructure and agriculture. The lack of information on past landslide events has hampered the construction of landslide forecasting models. Currently, a machine learning (ML) impact-based forecasting (IBF) model for typhoons is operational in the Philippines. The model was developed by 510, an initiative of the Netherlands Red Cross. The model predicts impact due to the high wind speeds associated with typhoons and includes the possible impact due to landslides only via a static landslide susceptibility map. Hence, this study focused on extending the 510 typhoon model via hybrid modeling into a multi-hazard forecasting model for both typhoons and landslides to improve the forecast by considering impact from typhoon-induced landslides. The implementation of the hybrid multi-hazard impact-based forecasting model was tested on two typhoon events in the Bicol region. A hydrometeorological landslide IBF model was successfully created, even with the limited data on landslide occurrences and rainfall available. The newly established regional event duration threshold for Bicol was applied on the case study events with an increased impact boundary of 300 km compared to the typhoon impact boundary of 100 km. The results of the hybrid multi-hazard model showed an improved impact forecast -compared to the model considering solely static input of landslides, which underestimated impact- in both location extent of the impact forecast and in accuracy: the True Positives doubled, whereas the False Negatives reduced by half. The separate landslide IBF model as an extension of the existing ML typhoon model provided additional benefits as these models can be decoupled to optimize the performance and reliability of both. This study resulted in the prototype of an impact-based multi-hazard model for typhoons and landslides for the Philippines and demonstrated the importance of considering impact from consecutive hazards.Civil Engineering | Environmental Engineerin
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