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Project log

Alexander Zipf
added 4 research items
Background: SARS-CoV-2, the virus causing coronavirus disease 2019 (COVID-19), is rapidly spreading across sub-Saharan Africa (SSA). Hospital-based care for COVID-19 is particularly often needed among older adults. However, a key barrier to accessing hospital care in SSA is travel time. To inform the geographic targeting of additional healthcare resources, this study aimed to determine the estimated travel time at a 1km x 1km resolution to the nearest hospital and to the nearest healthcare facility of any type for adults aged 60 years and older in SSA. Methods: We assembled a unique dataset on healthcare facilities' geolocation, separately for hospitals and any type of healthcare facility (including primary care facilities) and including both private- and public-sector facilities, using data from the OpenStreetMap project and the KEMRI Wellcome Trust Programme. Population data at a 1km x 1km resolution was obtained from WorldPop. We estimated travel time to the nearest healthcare facility for each 1km x 1km raster using a cost-distance algorithm. Findings: 9.6% (95% CI: 5.2% - 16.9%) of adults aged 60 and older years had an estimated travel time to the nearest hospital of longer than six hours, varying from 0.0% (95% CI: 0.0% - 3.7%) in Burundi and The Gambia, to 40.9% (95% CI: 31.8% - 50.7%) in Sudan. 11.2% (95% CI: 6.4% - 18.9%) of adults aged 60 years and older had an estimated travel time to the nearest healthcare facility of any type (whether primary or secondary/tertiary care) of longer than three hours, with a range of 0.1% (95% CI: 0.0% - 3.8%) in Burundi to 55.5% (95% CI: 52.8% - 64.9%) in Sudan. Most countries in SSA contained populated areas in which adults aged 60 years and older had a travel time to the nearest hospital of more than 12 hours and to the nearest healthcare facility of any type of more than six hours. The median travel time to the nearest hospital for the fifth of adults aged 60 and older years with the longest travel times was 348 minutes (IQR: 240 - 576 minutes) for the entire SSA population, ranging from 41 minutes (IQR: 34 - 54 minutes) in Burundi to 1,655 minutes (IQR: 1065 - 2440 minutes) in Gabon. Interpretation: Our high-resolution maps of estimated travel times to both hospitals and healthcare facilities of any type can be used by policymakers and non-governmental organizations to help target additional healthcare resources, such as new make-shift hospitals or transport programs to existing healthcare facilities, to older adults with the least physical access to care. In addition, this analysis shows precisely where population groups are located that are particularly likely to under-report COVID-19 symptoms because of low physical access to healthcare facilities. Beyond the COVID-19 response, this study can inform countries' efforts to improve care for conditions that are common among older adults, such as chronic non-communicable diseases.
Accurate and detailed geographical information digitizing human activity patterns plays an essential role in response to natural disasters. Volunteered geographical information, in particular OpenStreetMap (OSM), shows great potential in providing the knowledge of human settlements to support humanitarian aid, while the availability and quality of OSM remains a major concern. The majority of existing works in assessing OSM data quality focus on either extrinsic or intrinsic analysis, which is insufficient to fulfill the humanitarian mapping scenario to a certain degree. This paper aims to explore OSM missing built-up areas from an integrative perspective of social sensing and remote sensing. First, applying hierarchical DBSCAN clustering algorithm, the clusters of geo-tagged tweets are generated as proxies of human active regions. Then a deep learning based model fine-tuned on existing OSM data is proposed to further map the missing built-up areas. Hit by Cyclone Idai and Kenneth in 2019, the Republic of Mozambique is selected as the study area to evaluate the proposed method at a national scale. As a result, 13 OSM missing built-up areas are identified and mapped with an over 90% overall accuracy, being competitive compared to state-of-the-art products, which confirms the effectiveness of the proposed method.
Alexander Zipf
added 45 research items
Geographic information crowdsourcing is an increasingly popular approach to derive geographic data about human settlements from remotely sensed imagery. However, crowdsourcing approaches are frequently associated with uncertainty about the quality of the information produced. Although previous studies have found acceptable quality of crowdsourced information in some application domains, there is still lack of research about the quality of information produced with mobile crowdsourcing tools. This paper aims to contribute towards filling this gap by presenting an initial analysis of the contributions of two crowdsourcing projects based on the MapSwipe mobile app, in Madagascar and South Sudan. Our results show, that there is substantial agreement amongst volunteers thus suggesting that mobile crowdsourcing is a viable approach to support the mapping of human settlements. Nevertheless, this study also identifies several factors that may cause disagreement between volunteers (e.g. bad imagery, dependence on individual users) and thus reduce the reliability of the information they produce.
The identification of elements at risk is an essential part in hazard risk assessment. Especially for recurring natural hazards like floods, an updated database with information about elements exposed to such hazards is fundamental to support crisis preparedness and response activities. However, acquiring and maintaining an up-to-date database with elements at risk requires both detailed local and hazard-specific knowledge, being often a challenge for local communities and risk management bodies. We present a new approach for leveraging Volunteered Geographic Information to identify elements at risk from the free and open-source mapping project OpenStreetMap. We present initial results from a case study in the city of Cologne, Germany, to validate our approach in the case of flood- hazard. Our results show that the identification of elements at flood risk from OpenStreetMap is a suitable and cost- effective alternative for supporting local governments and communities in risk assessment and emergency planning.
Alexander Zipf
added a project goal