An Evaluation of Open Source Geographic Information Systems Routing Tools in Vaccine Delivery in Kano State, Northern Nigeria

The recent proliferation of online/desktop/mobile open source geographic information systems (GIS)routing tools such as qgis road graph plugin (QRG), open street routing machine (OSRM), google maps engine (GME), graphhopper (GH), and Osm And has led to the need to provide a method for comparatively evaluating the strength and weakness of these routing tools. This is crucial in view of its implication on the prospect and otherwise of routing related projects such as supply chain logistics, supply/delivery operations, and emergency services, among others. In this paper, comparative evaluation of these tools has been carried out using drive test survey and desktop routing estimation with respect to routine vaccine delivery in Kano, Nigeria. Kano state being one of the states in Nigeria with huge burden of health challenges with records of 3062 maternal death between 2005 – 2010(Ibrahim, 2014) . Thus vaccine delivery is one of such healthcare delivery programmes used to addressing some of these health challenges. The primary objective of this paper is to demonstrate comparative advantage of using open sourceGIS routing tools to optimize vaccine delivery process such that there would be significant reduction in logistics, manpower and cost associated with routine vaccine delivery. The capacity of the selected open source GIS routing tools was evaluated against this backdrop. Hence drive test survey was used to define the benchmark for determine the best rank among these desktop routing tools. The drive test survey was carried on selected number of delivery routes and the results were compared with values derived from desktop routing estimation using these tools. Two rounds of drive test survey were carried out for the delivery routes and an average was considered in order to minimum possible error associated with possible inconsistent in driving behavior. Significant discrepancies were observed in the outputs derived between desktop (QRG), online (OSRM, GME, GH) and mobile (Osm And) routing platforms. OSM vector base map was used across all the routing tools except GME.The overall outcome indicated QRG had the highest cumulative error margin of 67.52km while the lowest was reported for GraphHoper (46.17km) using same OSM base map. This is an indication that the routing algorithm used is not the same. When compared with GME that uses different base map, the cumulative error margin is very close (QRG – 67.52, GME – 55.99), an indication that similar routing algorithm has been used. Drive test outcome may not be sufficient to determine best or otherwise routing tool, it may be appropriate to consider other valuable criteria for the purpose of ranking these tools. Hence, those criteria were not limited to drive test/routing output error margin, others include capacity for multiple routing, base map completeness/content, support for traffic input, routing platform, and alternative routing option. With these considerations, QRG was ranked 1st. while OsmAnd (5) was least ranked. GME and GH had same ranking (2). QRG was ranked above other OSM based routing tools because it uses desktop platform and a capacity to integrate traffic input. It was ranked above GME majorly because of its robust OSM base map compared to google base map

Modelling Cyclists Route Choice Using Strava and OSMnx : A Case Study of the City of Glasgow

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Peer reviewedPublisher PD

Land Use Spatial Optimization Using Accessibility Maps to Integrate Land Use and Transport in Urban Areas

The scarcity of urban land resources requires a well-organized spatial layout of land use to better accommodate human activities, however, as a widely accepted concept, the integration of land use and transport is not given due consideration in land use spatial optimization (LUSO). This paper aims to integrate land use and transport in LUSO to support urban land use planning. Maximizing accessibility fitness, which follows the underlying logic between land use types and transport characteristics, is introduced into multi-objective land use spatial optimization (MOLUSO) modelling to address transport considerations, together with widely-used objectives such as maximizing compactness, compatibility, and suitability. The transport characteristics, in this study, are identified by driving accessibility, cycling accessibility, and walking accessibility. Accessibility maps, which quantify and visualize the spatial variances in accessibility fitness for different land use types, are developed based on the empirical results of the relationship between land use types and transport characteristics for LUSO and addressing policy issues. The 4-objective LUSO model and a corresponding non-dominated sorting genetic algorithm (NSGA-II) based optimization method constitute a prototype decision support system (DSS) for urban land use planning. Decision-makers (e.g., planning departments) can choose an ideal solution to accommodate urban development needs from a set of Pareto-optimal alternatives generated by the DSS. The approaches to creating accessibility maps and MOLUSO modelling are demonstrated by the case study of Eindhoven, the Netherlands. This study advocates limited changes to the current land use pattern in urban planning, and the LUSO emphasizes urban renewal and upgrading rather than new town planning.</p

Enrichment of OpenStreetMap data completeness with sidewalk geometries using data mining techniques

Tailored routing and navigation services utilized by wheelchair users require certain information about sidewalk geometries and their attributes to execute efficiently. Except some minor regions/cities, such detailed information is not present in current versions of crowdsourced mapping databases including OpenStreetMap. CAP4Access European project aimed to use (and enrich) OpenStreetMap for making it fit to the purpose of wheelchair routing. In this respect, this study presents a modified methodology based on data mining techniques for constructing sidewalk geometries using multiple GPS traces collected by wheelchair users during an urban travel experiment. The derived sidewalk geometries can be used to enrich OpenStreetMap to support wheelchair routing. The proposed method was applied to a case study in Heidelberg, Germany. The constructed sidewalk geometries were compared to an official reference dataset ("ground truth dataset"). The case study shows that the constructed sidewalk network overlays with 96% of the official reference dataset. Furthermore, in terms of positional accuracy, a low Root Mean Square Error (RMSE) value (0.93 m) is achieved. The article presents our discussion on the results as well as the conclusion and future research directions

How Good Is Open Bicycle Infrastructure Data? A Countrywide Case Study of Denmark

Cycling is a key ingredient for a sustainability shift of Denmark's transportation system. To increase cycling rates, a better nationwide network of bicycle infrastructure is required. Planning such a network requires high-quality infrastructure data, however, the quality of bicycle infrastructure data is severely understudied. Here, we compare Denmark's two largest open data sets on dedicated bicycle infrastructure, OpenStreetMap (OSM) and GeoDanmark, in a countrywide data quality assessment, asking whether data is good enough for network-based analysis of cycling conditions. We find that neither of the data sets is of sufficient quality, and that data set conflation is necessary to obtain a complete dataset. Our analysis of the spatial variation of data quality suggests that rural areas are more likely to suffer from problems with data completeness. We demonstrate that the prevalent method of using infrastructure density as a proxy for data completeness is not suitable for bicycle infrastructure data, and that matching of corresponding features thus is necessary to assess data completeness. Based on our data quality assessment we recommend strategic mapping efforts towards data completeness, consistent standards to support comparability between different data sources, and increased focus on data topology to ensure high-quality bicycle network data