“…Boundaries of informally developed neighborhoods were estimated from the crowd-sourced platform OpenStreetMap using Thiessen polygons. Population counts were obtained from previously published satellite-derived estimates and aggregated to the neighborhood level. …”
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“…This paper is based on the city’s multi-source large data set including 2018 AutoNavi POI (Point of Interests), OSM (OpenStreetMap) road network data and LandScan population data set, and evaluates the current status of Shanghai’s 15min-CLC through the fusion of kernel density estimation, service area analysis and other statistical models and proposes relevant optimization suggestions. …”
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“…This study analyzed a medium-sized city, Jiaozuo, as an example, with the help of point of interest (POI) data, OpenStreetMap road network density data and WorldPop population data. …”
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“…Freely available data from online geocoding services such as OpenStreetMap (OSM) can be used to determine the spatial distribution of these obesogenic factors. …”
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“…METHODS: We used a participatory approach to map all the paths, residential areas, buildings and rice fields on OpenStreetMap (OSM). We estimated shortest routes from every household in the District to the nearest primary health care center (PHC) and community health site (CHS) with the Open Source Routing Machine (OSMR) tool. …”
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“…METHODOLOGY: Between 2018 and 2020, these countries implemented the system design approach, involving four key steps: (1) advocate and introduce to engage stakeholders and prioritize identification of modeling scenarios; (2) collect data and plan analysis through document review and key informant interviews; (3) analyze system design scenarios using computer software modeling tools (LLamasoft’s Supply Chain Guru and AnyLogic's AnyLogistix) for optimization and simulation modeling as well as further analysis with Excel, Google maps, and OpenStreetMap; and (4) build consensus on optimized model and implementation roadmap using the Traffic Light Analysis tool and building on stakeholder input. …”
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“…METHODS: Data collection was undertaken based on geospatial open-source technologies and the collaborative mapping platform OpenStreetMap using a systematic approach involving three phases: remote mapping, ground-truthing, and food-outlet survey. …”
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“…Roads were a fusion of OpenStreetMap and data manually delineated from satellite imagery. …”
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“…We estimated travel times to emergency-equipped hospitals from non-equipped facilities and relative to population distributions using geocoded facility locations and an established accessibility mapping approach using global road network datasets from OpenStreetMap and Google. RESULTS: Four (3.5, 95% CI: 1.3–8.9) of 116 Malawi hospitals were emergency-equipped. …”
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“…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. …”
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“…Publicly accessible databases, Google Maps and Open Street Map, were used to obtain geographic information system data on distance to neighbourhood amenities, including lifestyle (cafés, libraries, movie theatres, parks), daily life (post offices, convenience stores), healthcare (hospitals, pharmacies) and percentages of local green and blue spaces within 400 and 800 m of participants’ residences. …”
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“…A road network grid was built from OpenStreetMap and satellite data and modified using UN Yemen Logistics Cluster data and other datasets to account for lines of conflict and road accessibility. …”
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“…In addition, we estimated the population within two-hours travel time of an EmONC facility by incorporating data on terrain from Open Street Map. Using these data sources, we calculated and compared the value of the indicator after incorporating data on facility performance and functionality while varying the reference population used. …”
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“…METHODS: Openly accessible land-cover, land-use, population and OpenStreetMap data were employed to spatially model Plasmodium falciparum parasite rate standardized to the age group 2–10 years (PfPR(2–10)) in the two cities through the use of a Random Forest (RF) regressor. …”
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“…METHODS: We assembled a dataset on the geolocation of health-care facilities, separately for hospitals and any type of health-care facility and including both private-sector and public-sector facilities, using data from the OpenStreetMap project and the Kenya Medical Research Institute–Wellcome Trust Programme. …”
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“…The data for analysis is obtained from following data sources: (1) Acxiom Deutschland GmbH (2020) provided a test dataset for the locations of general practitioners (GPs); (2) OpenStreetMap (OSM) data is utilized for road networks; and (3) the Statistische Ämter des Bundes und der Länder (German official census 2011) provided a population distribution dataset stemming from the 2011 Census. …”
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“…The approach consists of building a predictive model of vehicle speeds and steering angles based on collected data related to driver–vehicle interactions and other aggregated data intrinsic to the traffic environment, such as roundabout geometry and the number of lanes obtained from Open-Street-Maps and offline video processing. The study systematically generates rules of action regarding the vehicle speed and steering angle required for autonomous vehicles to achieve complete roundabout maneuvers. …”
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“…This research proposes a novel approach based on LiDAR point clouds and OpenStreetMaps (OSM) via a constrained particle filter, which significantly improves the vehicle localization accuracy. …”
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“…To incorporate high-level instructions (i.e., turn right vs. turn left at intersections), we compare various representations provided by lightweight and open-source OpenStreetMaps (OSM) and formulate a conditional generative model strategy to explicitly capture the multimodal characteristics of urban driving. …”
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“…Crowd-based sources of data such as OpenStreetMaps (OSM) have created a wealth of worldwide geographic information including on transportation systems (e.g., road networks). …”
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