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Real-Time Flickr Carolina Libardi Carolina Miro Erina Filipovska

The Possible Interface



The idea of the project is to analyze cities/countries throughout what is being posted about them in the virtual world. To demonstrate the concept, we chose the Flickr website as the data source, and started mapping the tags that were related to the images. As an example, we chose the word “Park� and filtered the last 500 pictures that included that specific tag. After creating a table with the relevant information about those selected images, we geolocated each image and overlapped that information with a map. In that way, it is possible to visualize in witch cities the word is most recurrent. It is also possible to link the map with the data source website, so that the user can click on the chosen city and access the image related to the word he is searching for. The workspace, as it is now, gives us two different possibilities for a final presentation. The first is to predefine a specific country, allowing the user to input only the word he’s interested in researching. The other option is to allow the user to choose freely the word and the country. The down point of the second option is the zoom. Although we connected a scroll to the map, every time you change the zoom, the data has to be reloaded, and that can be very time consuming. Besides that, the user would have to find in the map the chosen country by himself. The first option is more user friendly, considering we can chose a center point for the predefined country, like we did in the shown example. In this workspace we defined Madrid as the center of the map, so whenever the user zooms in or out, the country in the center of the map is always Spain.

Step 1:

load the FlickrImageSearch API and define the word or sentence to be searched (we used “Parque” in the example). After that, define the number of photos that the API will return (50, in this case). Step 2: insert a TableVisualizator to visualize the data from the searched photos. Step 3: insert the GetElementFromList operator to select an element in the list originated by your search given it’s position in the recently created table. Step 4: load the FlickrGeoLocator API to get the coordinates where each of the searched photos were taken through the flickr photo id.

Step 5:

insert a GoogleMapVisor visualizator and a Polygon2DSimpleVisualizator2. Make sure both of them have the same size by activating the 10X10 magnetic grid (in this case, the rectangles have 400X400). Step 6: connect the “Rectangle” output of the GoogleMapVisor to the “Rectangle” output of the FlickrImageSearch we loaded on Step 1. Step 7: overlap both visualizors making sure they are in the very same position.

Step 8: To download the searched images from Flickr insert a GetElementFromList operator and connect it to the FlickrImageSearch API that we inserted on step 1. Insert a MultiImageLoader controler, witch receives a list of images URL and downloads the files to return the data as a Bitmap List. Connect the “StringList” output to the “Object” output of the previous operator. Step 9: insert a GetElementFromList operator and connect it to the “BitmapDataList” output of the previous element. Step 10: insert a BitmapDataSimpleVisualizator to view the selected images. If desired, insert a TableVisualizator to view the list the the infirmation about the searched images. Step 11: To link the downloaded photos with the map, insert a FlickInformationMultiLoader and connect it to the map through a GetElementFromList operator.

Step 11: To project any geometric data structure containing Geo data insert the UniversalProjectionOnTransformationGeo operator. Connect it to the FlickrGeoLocator API inserted on Step 4. Also connect its “Object” output to the Polygon2D input of the Polygon2DSimpleVisualizator2 inserted on Step 5 . Step 12: To create a new TransformatioGeo, from a geo projection type, fixing a point and a horizontal value, insert the CreatesTransformationGeo operator. Connect it to the previous element and define “Mercator” as the GeoProjection name. Step 13: Insert another UniversalProjectionOnTransformationGeo operator. Connect it to the CreatesTransformationGeo operator and to both visualizators inserted on Step 5.

Step 12:

To determine the central coordinate of the map, insert the GeocoderGoogleMaps and type the address you want to be found (in this case “Spain”). Step 13: insert the GetElementFromTable operator. In this case, the information we need is in the first line and first column of the table we created with the geocodes API. In that case, type 0 for number of List, and 0 for number of Row. Step 14: connect that element to the “Point” input of the GoogleMapVisor. Step 15: To determine the Zoom of the Map, install a SimpleScroll control and connect it to to both visualizators inserted on Step 5.


Link to the Workspace:


Step-by-Step of the final project for Impure.

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