Phil Tivel & Rich SeidlitzGreat-Circle Technologies

Spatial and Non-Spatial uses of FME

Great-Circle Technologies is a Big Data analytics small business focusing on multi-INT predictive analytics and visualization.

We bring a holistic approach to create solutions for hard problems.

GeoCyber, multi-lingual SMA, and semantic, geospatial, and behavioral analytics.

Our answer isn’t more bodies, it’s more innovation.

No Puzzle is a Single Piece

Data are diverse and plentiful – how do we normalize them for analysis?

No Puzzle is a Single Piece

Data are diverse and plentiful – how do we normalize them for analysis?

How can we automate normalization?

No Puzzle is a Single Piece

Data are diverse and plentiful – how do we normalize them for analysis?

How can we automate normalization?

What if our data are distinctly different?

Geospatial, text, various INT data

Problem: Raster collection and processing can be difficult and time consuming. It often needs to be collected, managed, clipped, and mosaicked.

Solution: Let FME do it all!

Spatial Example

DRG (Digital Raster Graphic) index for the USA

Represents 89,111 DRGs! Each DRG is 1:24,000 topographic map of that grids area and has a weird name like o36075g8.

Raw DRG Data

Bonus: The DRG data has marginalia that overlaps each other and is projected in UTMs.

Raw DRG Data

For the state of Virginia (727 DRGs) How long would it takeyou to download, re-project, clip off marginalia, mosaic together,and clip again to the county? ….Hours?...Days?...Weeks?

The FME Model

The Results• The Data is Downloaded• Re-projected to GCS WGS1984• Then the Marginalia is clipped off

The Results

Then the data is mosaicked to the chosen state leveland output as a single raster (.tif)

The Results

The mosaicked state raster is then clipped into a rasterfor each county within the state

The FME model is versatile. It is easy to incorporateother types of raster datasets or even vector datasets

Versatility

Non-Spatial Example

Geospatial data without context is a map. But with context, it becomes a story.

Text documents can provide that context.

The Original Data

Text that can consist of multiple languages and emoticons

The Results

FME model creates seven more fields in the data. Each containing new linguistic information about the data.

The Results

A closer look

The Results

A closer look

The FME Model

Python plays a large role in this model. The model starts by usingpython to create a regular expression to find all known emoticonsin the input Excel file.

The FME Model

Regular Expression

Linguistic information can be derived because of theCharacter Code Extractor Transformer

Custom Transformer

The FME Model

The data can be easily separated out at this point based on different language statistics:

If it contains an emoticon or possible emoticon

If it has single language or multi language content

If the content has like languages

What can be done now

Thank You!

Questions?

For more information contact:

Phil Tivel

ptivel@great-circletech.com

Richard Seidlitz

rseidlitz@great-circletech.com