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Brewing the Ultimate Data Fusion
35

Brewing the Ultimate Data Fusion

Apr 08, 2017

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Page 1: Brewing the Ultimate Data Fusion

Brewing the Ultimate Data Fusion

Page 2: Brewing the Ultimate Data Fusion

1. Choose your Potion Ingredients ●  Spatial (GIS, CAD, BIM, raster, etc.)

●  Web

○  FTP ○  Web Services - OGC, AWS, Dropbox, etc. ○  APIs ○  HTML webpages ○  JSON, XML, RSS feeds

●  So many more!

Page 3: Brewing the Ultimate Data Fusion

2. Choose a Cauldron NoSQL

Relational database

File-based database

Visualization/BI tool like Tableau, Qlikmaps

Metadata catalog

Page 4: Brewing the Ultimate Data Fusion

Master Database vs. Metadata Catalog

Page 5: Brewing the Ultimate Data Fusion

Data Fusion # 1

Page 6: Brewing the Ultimate Data Fusion

Awesome Vector Tile World Backgrounds

Ana García de Vicuña Pablo Martínez

Page 7: Brewing the Ultimate Data Fusion

Vector Tiles Format in FME

Geographic information stored in GeoJSON format into an SQLite database. Different layers for each zoom level.

Page 8: Brewing the Ultimate Data Fusion

Raster Tiles •  Static PNG

•  Each collection only one style •  Large storage cost •  Tile generation takes time •  Painful update of data

Vector Tiles •  GeoJSON

– Dynamic PNG on the fly – Style defined in XML file

•  Each collection multiple styles •  Less storage cost •  Less generation time •  Easy to update

Raster Tiles vs. Vector Tiles

Page 9: Brewing the Ultimate Data Fusion

Multilevel Tile Cache Generation Engine

Vector data text labels

Vector Tile Mapnik XML Symbology

PNG from Mapnik in the Server

Tile Render

Page 10: Brewing the Ultimate Data Fusion

Vector and Raster Tile Composition Merge Raster Tiles (Orthoimage or Shaded Relief) with stylized Vector Tiles to get another background.

Page 11: Brewing the Ultimate Data Fusion

FME Workbench

Input Workbench Output

tiles

View

Get the whole story at the

Page 12: Brewing the Ultimate Data Fusion

Data Fusion # 2

Page 13: Brewing the Ultimate Data Fusion

YVR & the Indoor Mapping Experience

Carlos Silva GIS Specialist, Engineering Services

Vancouver International Airport

Page 14: Brewing the Ultimate Data Fusion

YVR: 4 yrs ago (pre-GIS)

CAD, 3 – 6 months post-construction

Graphic design pamphlets, PDF

Page 15: Brewing the Ultimate Data Fusion

How the Mapping Process Works @YVR

1st Stage "Internal", FME

2nd Stage "Public", FME

3rd Stage "Uploads" via FME

4th Stage, "Apple AVF" Hideyuki KAMO

Page 16: Brewing the Ultimate Data Fusion

ArcMap: Map Production; Data Verification & Edits; Map & Feature Services (Internal Only)

Page 17: Brewing the Ultimate Data Fusion

YVR Digital Gateway - JSON

Page 18: Brewing the Ultimate Data Fusion

Where is YVR now?

Page 19: Brewing the Ultimate Data Fusion

1) Public Terminal Maps (PDFs, Hard Copy Pamphlet, Website Maps (Leaflet with GeoJSON)

2) Internally: CAD 2 GIS via FME, accessible on

an internal GIS application

3) YVR App & Website: Data Delivery via YVR Digital Gateway

Where is YVR now?

Page 20: Brewing the Ultimate Data Fusion

Typical Issues/Solutions - timeliness

- spatial errors/topology - additional data points (sources) changing

- software & DB updates - I.T. updates

KEY: Automation, standards, and API access

Get the whole story at the

FMEUC

Page 21: Brewing the Ultimate Data Fusion

Data Fusion # 3

Page 22: Brewing the Ultimate Data Fusion

DRONES! Drones Everywhere!

Renato Salvaleon Alabama Power

Page 23: Brewing the Ultimate Data Fusion

SCS Milestones in UAS Program

•  UAS Policy in place (FAA compliance)

•  UAS certification training of 60

•  Southern Company is leading the electric utility industry in UAS

PlantFarleyUASInspec2on:NuclearIndustryfirst

UASCer2fica2onTraining

PlantBranch,GA-1000feetabovegroundlevel

Page 24: Brewing the Ultimate Data Fusion

Geospatial data in the UAS workflow

Fly

ReviewDelivery(Option)

Plan

ArcGISPla+orm

Page 25: Brewing the Ultimate Data Fusion

From Excel and File Structure

…is there a better way to visualize these missions and data products?

Page 26: Brewing the Ultimate Data Fusion

Transform missions from rows to points

… project & data locations of each mission are stored in an Excel or HTML report created by FME and stored as an

attachment on each mission point feature

Page 27: Brewing the Ultimate Data Fusion

From Excel into a Dashboard in Portal

Page 28: Brewing the Ultimate Data Fusion

Every Mission delivered as feature layer in Portal

… mission data delivered as a feature layer by FME rendered identically using FME Python Caller and standard ESRI layer file. A web map is created in Portal,

every picture is a point feature on the map and is hyperlinked for review purposes

Page 29: Brewing the Ultimate Data Fusion

UAS Data Products that can be processed

NADIR

Oblique

Video

Metadata

Log files

MEANINGFUL, RELIABLE and TIMELY DATA

Page 30: Brewing the Ultimate Data Fusion

Data Fusion # 4

Page 31: Brewing the Ultimate Data Fusion

P. Veenstra, TRC

Pipelines and the AttributeCreator:

Crunchy Magic for Accessing Previous and Next Rows

Page 32: Brewing the Ultimate Data Fusion

How do I… without programming? Challenge 1:

Access the values in the previous or subsequent rows in a data set without scripting or programming?

•  Read data •  Load data into a record-set or in-memory data set (array, list, dictionary) •  Access data •  Process data

Challenge 2:

Do this in a workspace that takes a set of points in the field (X,Y,Distance) and create a M-aware polyline with cumulative measure set for each vertex along the line?

•  Use the ‘AttributeCreator’

Page 33: Brewing the Ultimate Data Fusion

The Workspace •  CSV file containing ID, X, Y, and Distance •  Transform into M-Aware (measure) points and a polyline route

•  Each point feature will have a M value assigned to the vertex •  Each polyline feature will have M values assigned to each

vertex in the line AND will store the begin distance = 0 and the cumulative total distance = ?

•  Utilize AttributeCreator to access previous points in data set to calculate cumulative distance

•  Utilize MeasureSetter to make geometry M-Aware •  PointConnector preserves the M values of the points and applies

them to the polylines

Page 34: Brewing the Ultimate Data Fusion

The Workspace

Page 35: Brewing the Ultimate Data Fusion

Other magical artefacts …

•  Calculate true rhum (geodesic) distance between points … •  Calculate simple 3D pythagorem distance between points … •  Calculate the angle left or right from one vector to another … •  Other than the expression editor in FME, it makes math for points on

a line easy …