The Experts below are selected from a list of 2145 Experts worldwide ranked by ideXlab platform
J. R. Lori - One of the best experts on this subject based on the ideXlab platform.
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Protocol for geolocating rural villages of women in Liberia utilizing a maternity waiting home
BMC research notes, 2019Co-Authors: K. H. James, J. E. Perosky, K. Mclean, A. Nyanplu, C. A. Moyer, J. R. LoriAbstract:Objective Geospatial data are used by health systems and researchers to understand disease burdens, trace outbreaks, and allocate resources, however, there are few well-documented protocols for collecting and analyzing geographic information systems data in rural areas of low- and middle-income countries. Even with the proliferation of spatial technologies such as open street map and Google maps, basic geographic data—such as village locations—are not widely available in many countries in sub-Saharan Africa. The purpose of this paper is to report a step-wise protocol, using geographic information system techniques and tools, developed to collect and analyze the type of spatial data necessary to calculate the distance between rural villages and maternity waiting homes located near rural primary healthcare facilities in Bong County, Liberia.
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Protocol for geolocating rural villages of women in Liberia utilizing a maternity waiting home
BMC, 2019Co-Authors: K. H. James, J. E. Perosky, K. Mclean, A. Nyanplu, C. A. Moyer, J. R. LoriAbstract:Abstract Objective Geospatial data are used by health systems and researchers to understand disease burdens, trace outbreaks, and allocate resources, however, there are few well-documented protocols for collecting and analyzing geographic information systems data in rural areas of low- and middle-income countries. Even with the proliferation of spatial technologies such as open street map and Google maps, basic geographic data—such as village locations—are not widely available in many countries in sub-Saharan Africa. The purpose of this paper is to report a step-wise protocol, using geographic information system techniques and tools, developed to collect and analyze the type of spatial data necessary to calculate the distance between rural villages and maternity waiting homes located near rural primary healthcare facilities in Bong County, Liberia. Results Using a step-wise approach incorporating local healthcare provider knowledge, intensive field work, and spatial technologies such as open street map and Google maps for village geospatial data collection and verification, we identified village locations of 93.7% of the women who accessed the five maternity waiting homes in our study from 2012 to 2016
Tommaso Di Noia - One of the best experts on this subject based on the ideXlab platform.
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exposing open street map in the linked data cloud
International Conference Industrial Engineering & Other Applications Applied Intelligent Systems, 2016Co-Authors: Vito Walter Anelli, Andrea Calì, Tommaso Di Noia, Matteo Palmonari, Azzurra RagoneAbstract:After the mobile revolution, geographical knowledge has getting more and more importance in many location-aware application scenarios. Its popularity influenced also the production and publication of dedicated datasets in the Linked Data (LD) cloud. In fact, its most recent representation shows Geonames competing with DBpedia as the largest and most linked knowledge graph available in the Web. Among the various projects related to the collection and publication of geographical information, as of today, open street map (OSM) is for sure one of the most complete and mature one exposing a huge amount of data which is continually updated in a crowdsourced fashion. In order to make all this knowledge available as Linked Data, we developed LOSM: a SPARQL endpoint able to query the data available in OSM by an on-line translation form syntax to a sequence of calls to the OSM overpass API. The endpoint makes also possible an on-the-fly integration among open street map information and the one contained in external knowledge graphs such as DBpedia, Freebase or Wikidata.
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IEA/AIE - Exposing open street map in the linked data cloud
Trends in Applied Knowledge-Based Systems and Data Science, 2016Co-Authors: Vito Walter Anelli, Andrea Calì, Tommaso Di Noia, Matteo Palmonari, Azzurra RagoneAbstract:After the mobile revolution, geographical knowledge has getting more and more importance in many location-aware application scenarios. Its popularity influenced also the production and publication of dedicated datasets in the Linked Data (LD) cloud. In fact, its most recent representation shows Geonames competing with DBpedia as the largest and most linked knowledge graph available in the Web. Among the various projects related to the collection and publication of geographical information, as of today, open street map (OSM) is for sure one of the most complete and mature one exposing a huge amount of data which is continually updated in a crowdsourced fashion. In order to make all this knowledge available as Linked Data, we developed LOSM: a SPARQL endpoint able to query the data available in OSM by an on-line translation form syntax to a sequence of calls to the OSM overpass API. The endpoint makes also possible an on-the-fly integration among open street map information and the one contained in external knowledge graphs such as DBpedia, Freebase or Wikidata.
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losm a sparql endpoint to query open street map
International Semantic Web Conference, 2015Co-Authors: Tommaso Di NoiaAbstract:Geographical data is gaining momentum in scientific and industrial communities thanks to the high level and quality of information and knowledge it encodes. The most recent representation of the Linked open Data cloud shows GeoNames competing with DBpedia as the largest and most linked dataset available in the Web. In the “normal” Web, open street map (OSM) has reached, in the last years, a maturity stage thus allowing the users to exploit its data for a daily use. We developed LOSM (Linked open street map), a SPARQL endpoint able to query the data available in OSM by an on-line translation form SPARQL syntax to a sequence of calls to the overpassAPI. The endpoint comes together with a Web interface useful to guide the user during the formulation of a query.
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International Semantic Web Conference (Posters & Demos) - LOSM: a SPARQL Endpoint to Query open street map.
2015Co-Authors: Tommaso Di NoiaAbstract:Geographical data is gaining momentum in scientific and industrial communities thanks to the high level and quality of information and knowledge it encodes. The most recent representation of the Linked open Data cloud shows GeoNames competing with DBpedia as the largest and most linked dataset available in the Web. In the “normal” Web, open street map (OSM) has reached, in the last years, a maturity stage thus allowing the users to exploit its data for a daily use. We developed LOSM (Linked open street map), a SPARQL endpoint able to query the data available in OSM by an on-line translation form SPARQL syntax to a sequence of calls to the overpassAPI. The endpoint comes together with a Web interface useful to guide the user during the formulation of a query.
K. H. James - One of the best experts on this subject based on the ideXlab platform.
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Protocol for geolocating rural villages of women in Liberia utilizing a maternity waiting home
BMC research notes, 2019Co-Authors: K. H. James, J. E. Perosky, K. Mclean, A. Nyanplu, C. A. Moyer, J. R. LoriAbstract:Objective Geospatial data are used by health systems and researchers to understand disease burdens, trace outbreaks, and allocate resources, however, there are few well-documented protocols for collecting and analyzing geographic information systems data in rural areas of low- and middle-income countries. Even with the proliferation of spatial technologies such as open street map and Google maps, basic geographic data—such as village locations—are not widely available in many countries in sub-Saharan Africa. The purpose of this paper is to report a step-wise protocol, using geographic information system techniques and tools, developed to collect and analyze the type of spatial data necessary to calculate the distance between rural villages and maternity waiting homes located near rural primary healthcare facilities in Bong County, Liberia.
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Protocol for geolocating rural villages of women in Liberia utilizing a maternity waiting home
BMC, 2019Co-Authors: K. H. James, J. E. Perosky, K. Mclean, A. Nyanplu, C. A. Moyer, J. R. LoriAbstract:Abstract Objective Geospatial data are used by health systems and researchers to understand disease burdens, trace outbreaks, and allocate resources, however, there are few well-documented protocols for collecting and analyzing geographic information systems data in rural areas of low- and middle-income countries. Even with the proliferation of spatial technologies such as open street map and Google maps, basic geographic data—such as village locations—are not widely available in many countries in sub-Saharan Africa. The purpose of this paper is to report a step-wise protocol, using geographic information system techniques and tools, developed to collect and analyze the type of spatial data necessary to calculate the distance between rural villages and maternity waiting homes located near rural primary healthcare facilities in Bong County, Liberia. Results Using a step-wise approach incorporating local healthcare provider knowledge, intensive field work, and spatial technologies such as open street map and Google maps for village geospatial data collection and verification, we identified village locations of 93.7% of the women who accessed the five maternity waiting homes in our study from 2012 to 2016
Felix Bachofer - One of the best experts on this subject based on the ideXlab platform.
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Towards a Large-Scale 3D Modeling of the Built Environment—Joint Analysis of TanDEM-X, Sentinel-2 and open street map Data
Remote Sensing, 2020Co-Authors: Thomas Esch, Julian Zeidler, Daniela Palacios-lopez, Mattia Marconcini, Achim Roth, Milena Mönks, Benjamin Leutner, Elisabeth Brzoska, Annekatrin Metz-marconcini, Felix BachoferAbstract:Continental to global scale mapping of the human settlement extent based on earth observation satellite data has made considerable progress. Nevertheless, the current approaches only provide a two-dimensional representation of the built environment. Therewith, a full characterization is restricted in terms of the urban morphology and built-up density, which can only be gained by a detailed examination of the vertical settlement extent. This paper introduces a methodology for the extraction of three-dimensional (3D) information on human settlements by analyzing the digital elevation and radar intensity data collected by the German TanDEM-X satellite mission in combination with multispectral Sentinel-2 imagery and data from the open street map initiative and the Global Urban Footprint human settlement mask. The first module of the underlying processor generates a normalized digital surface model from the TanDEM-X digital elevation model for all regions marked as a built-up area by the Global Urban Footprint. The second module generates a building mask based on a joint processing of open street map, TanDEM-X/TerraSAR-X radar images, the calculated normalized digital surface model and Sentinel-2 imagery. Finally, a third module allocates the local relative heights of the normalized digital surface model to the building structures provided by the building mask. The outcome of the procedure is a 3D map of the built environment showing the estimated local height for all identified vertical building structures at 12 m spatial resolution. The results of a first validation campaign based on reference data collected for the seven cities of Amsterdam (NL), Indianapolis (US), Kigali (RW), Munich (DE), New York (US), Vienna (AT), and Washington (US) indicate the potential of the proposed methodology to accurately estimate the distribution of building heights within the built-up area.
J. E. Perosky - One of the best experts on this subject based on the ideXlab platform.
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Protocol for geolocating rural villages of women in Liberia utilizing a maternity waiting home
BMC research notes, 2019Co-Authors: K. H. James, J. E. Perosky, K. Mclean, A. Nyanplu, C. A. Moyer, J. R. LoriAbstract:Objective Geospatial data are used by health systems and researchers to understand disease burdens, trace outbreaks, and allocate resources, however, there are few well-documented protocols for collecting and analyzing geographic information systems data in rural areas of low- and middle-income countries. Even with the proliferation of spatial technologies such as open street map and Google maps, basic geographic data—such as village locations—are not widely available in many countries in sub-Saharan Africa. The purpose of this paper is to report a step-wise protocol, using geographic information system techniques and tools, developed to collect and analyze the type of spatial data necessary to calculate the distance between rural villages and maternity waiting homes located near rural primary healthcare facilities in Bong County, Liberia.
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Protocol for geolocating rural villages of women in Liberia utilizing a maternity waiting home
BMC, 2019Co-Authors: K. H. James, J. E. Perosky, K. Mclean, A. Nyanplu, C. A. Moyer, J. R. LoriAbstract:Abstract Objective Geospatial data are used by health systems and researchers to understand disease burdens, trace outbreaks, and allocate resources, however, there are few well-documented protocols for collecting and analyzing geographic information systems data in rural areas of low- and middle-income countries. Even with the proliferation of spatial technologies such as open street map and Google maps, basic geographic data—such as village locations—are not widely available in many countries in sub-Saharan Africa. The purpose of this paper is to report a step-wise protocol, using geographic information system techniques and tools, developed to collect and analyze the type of spatial data necessary to calculate the distance between rural villages and maternity waiting homes located near rural primary healthcare facilities in Bong County, Liberia. Results Using a step-wise approach incorporating local healthcare provider knowledge, intensive field work, and spatial technologies such as open street map and Google maps for village geospatial data collection and verification, we identified village locations of 93.7% of the women who accessed the five maternity waiting homes in our study from 2012 to 2016
