Better Routing in Developing Regions:Weather and Satellite-Informed Road Speed Prediction

Inaccurate digital road networks significantly complicate the use of analytics in developing, data scarce, environments. For routing purposes, the most important characteristic of a digital road network is the information about travel times/speeds of roads. In developing regions, these are often unknown, and heavily dependent on the weather (e.g., rainfall). This may, for instance, cause vehicles to experience longer travel times than expected. Current methods to predict the travel speeds are designed for the short upcoming period (minutes or hours). They make use of data about the position of the vehicle, the average speed on a given road (section), or patterns of trafic flow in certain periods, which are typically not available in more developing regions. This paper presents a novel deep learning method that predicts the travel speeds for all roads in a data scarce environment using GPS trajectory data and open-source satellite imagery. The method is capable of predicting speeds for previously unobserved roads and incorporates specific circumstances, which are characterized by the time of the day and the rainfall during the last hour. In collaboration with the organization PemPem, we perform a case study in which we show that our proposed procedure predicts the average travel speed of roads in the area (that may not exist in the GPS trajectory data) with an average RMSE of 8.5 km/h

Enhancing Digital Road Networks for Better Operations in Developing Countries

Data scarcity in developing countries often significantly complicates the use of analytics to address development challenges. One of the most fundamental data structures needed in operations management is digitized road data; e.g., a poorly digitized road network significantly reduces our ability to optimize trade of micro-enterprises (SDG 8) and placement of hospitals (SDG 3). Unfortunately, current methods to extend or create digital road networks are not well-adapted to regions with sparse geospatial data and, as a result, road networks are often poorly represented digitally in less-developed regions such as rural areas of developing countries. To address this, we propose a novel method to create digital road networks in regions with sparse geospatial data, by adapting existing methods to ensure they extract as much information as possible from the limited available data. Our proposed method combines projection-based incremental insertion methods that incrementally add new information to existing road networks when it becomes available, with a simple edge adjustment procedure that allows edge geometries to be improved when more information becomes available. This method is well-suited to either incrementally adjust a large existing road network (e.g., OSM) or combine multiple sources of road networks in regions with sparse data (e.g., OSM and eStrada, a dataset provided by the World Bank). Our method significantly improves the digital road network for smallholder farmers in Indonesia, where only 40% of the origin-destination pairs in our dataset were previously digitized. In a case study of optimizing geospatial accessibility to healthcare in Timor-Leste, we find that the improved road network detects an additional 5% of people to be in the vicinity of a hospital

The hanta hunting study: Underdiagnosis of puumala hantavirus infections in symptomatic non-travelling leptospirosis-suspected patients in the Netherlands, in 2010 and April to November 2011

Leptospirosis and haemorrhagic fever with renal syndrome (HFRS) are hard to distinguish clinically since these two important rodent-borne zoonoses share hallmark symptoms such as renal failure and haemorrhage. Leptospirosis is caused by infection with a spirochete while HFRS is the result of an infection with certain hantaviruses. Both diseases are relatively rare in the Netherlands. Increased incidence of HFRS has been observed since 2007 in countries that border the Netherlands. Since a similar rise in incidence has not been registered in the Netherlands, we hypothesise that due to overlapping clinical manifestations, hantavirus infections may be confused with leptospirosis, leading to underdiagnosis. Therefore, we tested a cohort of non-travelling Dutch patients with symptoms compatible with leptospirosis, but with a negative diagnosis, during 2010 and from April to November 2011. Sera were screened with pan-hantavirus IgG and IgM enzyme-linked immunosorbent assays (ELISAs). Sera with IgM reactivity were tested by immunofluorescence assay (IFA). ELISA (IgM positive) and IFA results were confirmed using focus reduction neutralisation tests (FRNTs). We found hantavirus-specific IgG and/or IgM antibodies in 4.3% (11/255) of samples taken in 2010 and in 4.1% (6/146) of the samples during the 2011 period. After FRNT confirmation, seven patients were classed as having acute Puumala virus infections. A review of hantavirus diagnostic requests revealed that at least three of the seven confirmed acute cases as well as seven probable acute cases of hantavirus infection were missed in the Netherlands during the study period

Enhancing Digital Road Networks for Better Operations in Developing Countries

Data scarcity in developing countries often significantly complicates the use of analytics to address development challenges. One of the most fundamental data structures needed in operations management is digitized road data; e.g., a poorly digitized road network significantly reduces our ability to optimize trade of micro-enterprises (SDG 8) and placement of hospitals (SDG 3). Unfortunately, current methods to extend or create digital road networks are not well-adapted to regions with sparse geospatial data and, as a result, road networks are often poorly represented digitally in less-developed regions such as rural areas of developing countries. To address this, we propose a novel method to create digital road networks in regions with sparse geospatial data, by adapting existing methods to ensure they extract as much information as possible from the limited available data. Our proposed method combines projection-based incremental insertion methods that incrementally add new information to existing road networks when it becomes available, with a simple edge adjustment procedure that allows edge geometries to be improved when more information becomes available. This method is well-suited to either incrementally adjust a large existing road network (e.g., OSM) or combine multiple sources of road networks in regions with sparse data (e.g., OSM and eStrada, a dataset provided by the World Bank). Our method significantly improves the digital road network for smallholder farmers in Indonesia, where only 40% of the origin-destination pairs in our dataset were previously digitized. In a case study of optimizing geospatial accessibility to healthcare in Timor-Leste, we find that the improved road network detects an additional 5% of people to be in the vicinity of a hospital

Kennismanagement : de LAT-relatie van organisatie, mens en ICT

De hype rondom kennismanagement lijkt voorbij. Veel organisaties hebben met wisselend succes initiatieven ontplooid. Gericht op het vergroten van het innovatieve vermogen van organisaties, blijkt het in de praktijk niet eenvoudig de effecten zichtbaar en meetbaar te krijgen. Daarbij blijken investering-effectanalyses nog geen gemeengoed te zijn. Kennismanagement is bij uitstek een multidisciplinaire aangelegenheid. Waar in het begin een zwaar accent werd gelegd op ICT, zien we de laatste jaren dat initiatieven zich meer richten op cultuur, motivatie en organisatie. ICT wordt hierbij slechts als ondersteunend middel gezien. Onderzoek van TNO toont aan dat een belangrijk deel van de beoogde effecten juist door de verkeerde inzet van ICT teniet wordt gedaan. In deze notitie wordt inzichtelijk gemaakt wat de rol en positie van kennis binnen de bedrijfsvoering kan zijn en wordt aangegeven hoe ICT zodanig ingericht kan worden dat de bedrijfsvoering en de natuurlijke ontwikkeling van organisaties integraal worden ondersteund

Optical-model potential for electron and positron elastic scattering by atoms

An optical-model potential for systematic calculations of elastic scattering of electrons and positrons by atoms and positive ions is proposed. The electrostatic interaction is determined from the Dirac-Hartree-Fock self-consistent atomic electron density. In the case of electron projectiles, the exchange interaction is described by means of the local-approximation of Furness and McCarthy. The correlation-polarization potential is obtained by combining the correlation potential derived from the local density approximation with a long-range polarization interaction, which is represented by means of a Buckingham potential with an empirical energy-dependent cutoff parameter. The absorption potential is obtained from the local-density approximation, using the Born-Ochkur approximation and the Lindhard dielectric function to describe the binary collisions with a free-electron gas. The strength of the absorption potential is adjusted by means of an empirical parameter, which has been determined by fitting available absolute elastic differential cross-section data for noble gases and mercury. The Dirac partial-wave analysis with this optical-model potential provides a realistic description of elastic scattering of electrons and positrons with energies in the range from ~100 eV up to ~5 keV. At higher energies, correlation-polarization and absorption corrections are small and the usual static-exchange approximation is sufficiently accurate for most practical purposes