Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

June 12, 2011

clusterPy: Library of spatially constrained
clustering algorithms

Filed under: Clustering,Geo Analytics,Geographic Data,Geographic Information Retrieval — Patrick Durusau @ 4:13 pm

clusterPy: Library of spatially constrained clustering algorithms

From the webpage:

Analytical regionalization (also known as spatially constrained clustering) is a scientific way to decide how to group a large number of geographic areas or points into a smaller number of regions based on similarities in one or more variables (i.e., income, ethnicity, environmental condition, etc.) that the researcher believes are important for the topic at hand. Conventional conceptions of how areas should be grouped into regions may either not be relevant to the information one is trying to illustrate (i.e., using political regions to map air pollution) or may actually be designed in ways to bias aggregated results.

May 29, 2011

May 19, 2011

How to map connections with great circles

Filed under: Geographic Data,Mapping,R — Patrick Durusau @ 3:26 pm

How to map connections with great circles

From the post:

There are various ways to visualize connections, but one of the most intuitive and straightforward ways is to actually connect entities or objects with lines. And when it comes to geographic connections, great circles are a nice way to do this.

This is a very nice R tutorial on using great circles to visualize airline connections.

The same techniques could map “connections” of tweets, phone calls, emails, any type of data that can be associated with a geographic location.

March 25, 2011

Open-source Data Science Toolkit

Filed under: Dataset,Geographic Data,Geographic Information Retrieval,Software — Patrick Durusau @ 4:32 pm

Open-source Data Science Toolkit

From Flowingdata.com:

Pete Warden does the data community a solid and wraps up a collection of open-source tools in the Data Science Toolkit to parse, geocode, and process data.

Mostly geographic material but some other interesting tools, such as extracting the “main” story from a document. (It has never encountered one of my longer email exchanges with Newcomb. 😉 )

It is interesting to me that so many tools and data sets related to geography appear so regularly.

GIS (geographic information systems) can be very hard but perhaps they are easier than the semantic challenges of say medical or legal literature.

That is it is easier to say here you are with regard to a geographic system than to locate a subject in a conceptual space which has been partially captured by a document.

Suspect the difference in hardness could only be illustrated by example and not by some test. Will have to give that some thought.

March 9, 2011

Neo4j Spatial, Part 1: Finding things close to other things

Filed under: Geographic Data,Geographic Information Retrieval,Neo4j — Patrick Durusau @ 4:25 pm

Neo4j Spatial, Part 1: Finding things close to other things

Start of a great series of posts on geographic information processing.

Topic maps for travel, military, disaster and other applications will face this type of issue.

Not to mention needing to map across different systems with different approaches to resolving these issues.

February 22, 2011

T I G E R – Topologically Integrated Geographic Encoding and Referencing system

Filed under: Geographic Data,Mapping,Maps — Patrick Durusau @ 1:28 pm

T I G E R – Topologically Integrated Geographic Encoding and Referencing system

From the US Census Bureau.

From the website:

Latest TIGER/Line® Shapefile Release

  • TIGER/Line®Shapefiles are spatial extracts from the Census Bureau’s MAF/TIGER database, containing features such as roads, railroads, rivers, as well as legal and statistical geographic areas.
  • They are made available to the public for no charge and are typically used to provide the digital map base for a Geographic Information System or for mapping software.
  • They are designed for use with geographic information system (GIS) software. The TIGER/Line®Shapefiles do not include demographic data, but they contain geographic entity codes that can be linked to the Census Bureau’s demographic data, available on American FactFinder

2010 TIGER/Line® Shapefiles Main Page — Released on a rolling basis beginning November 30, 2010.

and,

TIGER®-Related Products

Great source of geographic and other data.

Can use it for mashups or, you can push beyond mashups to creating topic maps.

For example, plotting all the crime in an area is a mashup.

Interesting I suppose for real estate agents pushing housing in better neighborhoods.

Having the crime reported in an area and the location of crimes committed by the same person (based on arrest reports) and known associates of that person, that is starting to sound like a topic map. Then add in real time observations and conversations of officers working the area.

Enhancing traditional law enforcement, the most effective way to prevent terrorism.

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