SPIRIT (Spatially-Aware Information Retrieval on the Internet) is a research project engaged in the design and implementation of a search engine to find documents and datasets on the web relating to places or regions referred to in a query.

The project has been working on the creation of software tools and techniques that will be used to produce search engines and websites that display intelligence in the recognition of geographical terminology. In order to demonstrate and evaluate the project outcomes, a prototype spatially-aware search engine has been built and is serving as the platform for testing and evaluation of new techniques in geographical information retrieval.

Tags: information retrieval

Somewherenear is the UK’s leading Geographic Search Engine, a guide for leisure or business travellers looking for places to visit, food, accommodation and more. “Somewherenear” helps you to find places of interest in England, Wales or Scotland.

Tags: information retrieval, Location Based Services

C. B. Jones, R. Purves, A. Ruas, M. Sanderson, M. Sester, M. van Kreveld, and R. Weibel. Spatial information retrieval and geographical ontologies: An overview of the spirit project. In Proceedings of the SIGIR’02, Tampere, Finland, August 11-15 2002. ACM. [url]

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A large proportion of the resources available on the world-wideweb refer to information that may be regarded as geographically located. Thus most activities and enterprises take place in one ormore places on the Earth’s surface and there is a wealth of survey data, images, maps and reports that relate to specific places or regions.

Despite the prevalence of geographical context, existing web search facilities are poorly adapted to help people find information that relates to a particular location. When the name of a place is typed into a typical search engine, web pages thatinclude that name in their text will be retrieved, but it is likely thatmany resources that are also associated with the place may not beretrieved. Thus resources relating to places that are inside the specified place may not be found, nor may be places that are nearby or that are equivalent but referred to by another name. Specification of geographical context frequently requires the use of spatial relationships concerning distance or containment for example, yet such terminology cannot be understood by existing search engines.

Tags: information retrieval, spatial clustering

I spent some time today reflecting on how to cluster the messages of the STAMPS system. So I started back from the main goal, which is to support navigation and information retrieval. Therefore the goal is to find messages that have some similarities, that either describe the same resource in a different place or either that characterize the same place with content that is related.

In the system we can expect different degrees and kinds of similarities. We will have direct similarity when a search keyword will match directly to one of the keyword of the message. Else, a bit indirect will be to have a search keyword that matches one of the stemming alternative of the messages keywords.

Then a variety of degrees of relevance can be visualized on the messages that are connected by threads to the original messages.

A different case is a search string that contains multiple keywords, in which case we can imagine a plain match in a single message but also a match on different message. In this last case also, this search set the ground for a new connection between the keywords. The same thing can goes for messages that have different keywords but that are in physical proximity. In this case is the spatial proxemics that sets the relevance between the messages.

I still have in mind that an external referrer can be used to add semantics to the playground.

Another concept keep bouncing in my mind: is it the case to have a kind of permanence of this keywords concentration in a certain spot? Can we use this information to define landmarks that characterize a certain cluster? But then the point is that a cluster needs dimensions and I cannot figure out a proper way to find these parameters along the way.

Tags: spatial clustering

The TreeTagger is a tool for annotating text with part-of-speech and lemma information which has been developed within the TC project at the Institute for Computational Linguistics of the University of Stuttgart. The TreeTagger has been successfully used to tag German, English, French, Italian, Spanish, Greek and old French texts and is easily adaptable to other languages if a lexicon and a manually tagged training corpus are available.

Sample output:

word    lemma     

The      DT       

TreeTagger      NP

is          VBZ – be

easy      JJ

to          TO

use      VB

Tags: text data mining

The Nolli Web Site presents the 1748 Nolli map of Rome as a dynamic, interactive, hands-on tool. The public now has access to cataloged information about the map in both written and graphical form. The map not only provides rich information, but it has the ability to be updated with new data over time to embrace expanding knowledge.

The interactive Nolli Map Web Site intends to:

1) resurrect the integrity of the original Nolli map, ?2) greatly enhance the quality and flexibility of its visual display ?3) grant easy access for scholars and students alike to a vast body of information.

Tags: maps

Now we start seeing the effects of adding social/local information to a map. People can start playing with social rating and clubs, for instance. Here we see as Google Maps provide the perfect infrastructure to support this kind of communication.

The service is called Boston Online, a listing of stores and restaurants that are open around the clock.

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Tags: google, hack, maps

Today I had a meeting with my advisor on the clustering algorithm, and I came out with this refined version of the procedure I am working on. The goal of this is still finding the similarities between messages attached to a shared map. Such kind of data are non-spatial, or textual. Therefore lots of different semantic techniques can be used to the end of clustering.

Of course, these messages can also be treated as geographical entities, for which another sets of methodologies are more acquainted. Still, I cannot find a reasonable way to merge together the semantic and the geographical techniques.

The method described by Osaragi 2002 is a geographical method, while the method described by Ng 1994 is based on non-geographical features.

One one end of a continuum we have a similarities of keywords of messages that have no geographical meaning. On the other end we have distances of messages that are properly geographical. The goal is to understand how to use these two dimensions.

One of the idea we came out is this concept of running a geographical clustering several times, depending on a semantic threshold parameter, trying to minimize the clouds of different outcomes. The mathematical dimension of this is still fuzzy.

Tags: map algorithms, spatial clustering, statistics

Noah Vawter  wrote an hack of google maps to extract big sized-maps from google maps

Copyright notice: the present content was taken from the following URL, the copyrights are reserved by the respective author/s.

Tags: google, hack, maps

These guys managed to make what we were planning since a long time, a datalogger to collect the positions of the GSM antennas and offer them, free of charge to the wide public.

The also offer the logger to volunteers that have a bluetooth GPS device. Please contribute.

Tags: CellID, maps, Symbian