Geospatial Meaning

I wrote a post about an INTEL initiative for innovative ideas to help poor nations. Now Google has released the 10¹⁰⁰ project. They offer $10 million to implement the best 100 ideas that can help the most number of people posible.

Some of the categories of evaluation are Energy, Enviroment, health, Education, Shelter and Communitiy. I think it is worth a look, and submit your idea in a total maximum of 1050 words. You will be evaluated with these criteria:

- Reach: How many people would this idea affect?
- Depth: How deeply are people impacted? How urgent is the need?
- Attainability: Can this idea be implemented within a year or two?
- Efficiency: How simple and cost-effective is your idea?
- Longevity: How long will the idea’s impact last?

A video explaining Google initiative:

Geospatial Semantic Web is gradually establishing as a Research proper field. Terra Cognita, a specialized Workshop in conjuntion with the 7th International Semantic Web Conference is going to be held in Karlsruhe, Germany.

A great chance to share experiences and ideas with geospatial and semantic web experts.

See you there.

The Intel’s INSPIRE-EMPOWER challenge offers $100000 for innovative projects that can help to better the education, healthcare, economic development, or the environment of people in poor nations.

Morro de Carapina, Governador Valadares, Minas Gerais, Brazil

It is worth a thought how can 1st world researchers cooperate with new ideas with these people with less geographic luck. Poverty belongs to certain countries, or regions, or neighbourhoods in some cities. This reality is extremely harsh in a very rich country as Brazil, where hundreds of thousands of people leave in the favelas, with no chance to improve their lives in their heavily conditioned context, spatial enviroment.

The early attempts to store geographic data in standard relational tables failed. This issue gave rise to spatial databases, which extent the relational model providing specific representations for geospatial data.

As the number of geographic features in a database grew, it became evident that it was imposible to store all the relationships between geographic entities in a large GIS. So distances, intersections, centroids, etc. are easyly computed with built-in functions. Questions like the depicted in the image could be seen as real-time spatial inferencing.

Is featureA within a 5km distance from the border of any water body feature?

When developing spatial reasoning support for Semantic Web we find the same problems. Description logic standards as OWL don’t provide specific spatial built-ins to enable real-time spatial inferencing or querying.

So we need the preprocessing of all spatial-related statements using spatial specific algorithms (or GIS) before adding them to the semantic KB or we need the adding of some kind of spatial layer that combines spatial queries with semantic queries to extent DL expressivity.

We use both approaches in our spatial model for CINeSPACE project:

• A preprocessing of fuzzy spatial relationships such as mereology or neighbourhood, that are added to the KB. This is an feasible approach while the number of spatial elements remains low (hundreds of elements for each city) and the spatial extent of polygons remains constant (non-monotonicity will be reviewed in a future post).
• A spatial layer used for dynamic queries, such as “Retrieve me Geoconcepts I am looking at”, where geoconcept is used to name any entity with an inherently or indirectly associated spatial dimension. SPARQL queries are used over the spatial results to refine the answer.

The need of extra spatial computation limits the success of Geospatial Semantic Web in terms of GIS professionals, who see Semantic Web as a complex world with not great benefits for dealing with space. A step forward spatial integration with DL logics. We are looking for researchers and people interested in this integration. Feel free to contact!!!

We attended at the 5th Geographic Information Systems conference at Istanbul the 2-5 July, where we were introducing our Geoconcepts architecture. One of the main themes of the conference was Geographic Information Science Education. Robert S. Bednarz and Sarah W. Bednarz introduced us some key points in spatial thinking skills and explicit spatial training. They consider three main scenarios in spatial thinking:

• Thinking IN space. This happens in our daily activities, when we move from one place to another, or when we arrange things in space, as these big amount of unread papers in my desk
• Thinking ABOUT space. This is more related to geographic science, and to the study of how reality is organized spatially. Maps and 3D models are convinient ways to think about space.
• Thinking WITH space. Sometimes we use spatial representations for abstract or complex concepts or theories. An example could be a hierarchy diagram of an ontology classes. Also graphs, concept maps etc. are powerful tools to think with space.

These three contexts overlap so often. For instance, in Geospatial ontologies modeling, we use space (WITH space) to model how we think spatially (IN space), and try to approach our traditional ways to explain space (ABOUT space).