using fme to solve spatial and non-spatial problems
TRANSCRIPT
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Phil Tivel & Rich SeidlitzGreat-Circle Technologies
Spatial and Non-Spatial uses of FME
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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.
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No Puzzle is a Single Piece
Data are diverse and plentiful – how do we normalize them for analysis?
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No Puzzle is a Single Piece
Data are diverse and plentiful – how do we normalize them for analysis?
How can we automate normalization?
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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
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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
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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.
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Raw DRG Data
Bonus: The DRG data has marginalia that overlaps each other and is projected in UTMs.
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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?
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The FME Model
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The Results• The Data is Downloaded• Re-projected to GCS WGS1984• Then the Marginalia is clipped off
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The Results
Then the data is mosaicked to the chosen state leveland output as a single raster (.tif)
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The Results
The mosaicked state raster is then clipped into a rasterfor each county within the state
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The FME model is versatile. It is easy to incorporateother types of raster datasets or even vector datasets
Versatility
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Non-Spatial Example
Geospatial data without context is a map. But with context, it becomes a story.
Text documents can provide that context.
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The Original Data
Text that can consist of multiple languages and emoticons
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The Results
FME model creates seven more fields in the data. Each containing new linguistic information about the data.
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The Results
A closer look
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The Results
A closer look
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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.
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The FME Model
Regular Expression
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Linguistic information can be derived because of theCharacter Code Extractor Transformer
Custom Transformer
The FME Model
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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
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Thank You!
Questions?
For more information contact:
Phil Tivel
Richard Seidlitz