lecture: How Linked Open Data finds the bar near you
Within the GIS community we became very fond of our web map servers and feature request possibilities to share and access data. Sharing data is relevant and applicable to other fields and communities. This led to the rise of the semantic web and to web 3.0. Clearly defined relationships between objects make it possible to interlink them and allow to search for relationships themselves. In this presentation I will demonstrate a web application that uses different techniques to access linked open data and show how the individual results can be used as input for the next search request.
An open innovation platform on linked data was started in the Netherlands. One of their results was to open a server to store and access linked open data. I have used this data warehouse as a starting point for a demonstration in a geo web application. The application is based solely on open source frameworks (OpenLayers, proj4js, jQuery, and pure). The user enters a zipcode and house number, and the application uses linked data techniques to retrieve the location. This first search result connects to the next open dataset to obtain statistical information about the area. One of these statistics is the average number of bars within a 1 km radius. But where exactly are these bars? Using yet another open dataset (OpenStreetMap with Overpass API) we can pinpoint the location of bars and pubs.
Links to project: https://github.com/borrob/LOD_example