Post on 12-Jan-2015
description
Robert Cheetham, Azaveacheetham@azavea.com
Esri Federal User Conference
Online Spatial Analysis for Spending Equity Mapping
20 January 2011
About Azavea
Founded in 2000 26 people Web & Mobile apps Spatial Analysis R&D B Corporation
Projects with Social Value Pro Bono Program Donate at least 2% of profits 10% Research Program
Employee-focused Culture
EGAP Application
What were we aiming to do?
Map Indicators Map Spending Enable users to:
– Select their own definition of need
– Weight the inputs– Calculate the results
on-the-fly
Transform maps on-the-fly
EGAP
EGAP
EGAP
EGAP
EGAP
EGAP
EGAP
EGAP
EGAP
EGAP
EGAP
ArcGIS Server
Flex, Silverlight and JS APIs
Publish tasks and models
Caching
Optimized MSD files
Technical Challenge
30 sec – 60 sec calculation time
Multiple simultaneous users …
… who are impatient
Where did this come from?
Classic Spatial Analysis Scenario
How do you identify an area that matches your priorities?
Walk to Grocery Store Biking Distance to Work
Near Restaurants Near Library
vitalvery importantsomewhat importantnice to have
Searching for a house, for instance…
Weighted Overlay
x 5 x 2x 3x 1
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=
Desktop GIS
How does it work?
City of Philadelphia
How does it work?
Broken into a grid of cells
City of Philadelphia
City of Philadelphia
Broken into a grid of cells
Each cell has a value for any given layer of information
City of Philadelphia
Broken into a grid of cells
Each cell has a value for any given layer of information
City of Philadelphia
Broken into a grid of cells
Each cell has a value for any given layer of information
City of Philadelphia
Broken into a grid of cells
Each cell has a value for any given layer of information
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This cell based approach enables us to combine layers using a process called map algebra
Proximity to Transit Lines
High Per Capita Income
High Density of College Grads
High Density of Home Sales
In An Economic Incentive Zone
Business siting is about making a choice based on the composite of several location based decision
variables
Proximity to Transit Lines
High Per Capita Income
High Density of College Grads
High Density of Home Sales
In An Economic Incentive Zone
Map Layers
Proximity to Transit Lines
High Per Capita Income
High Density of College Grads
High Density of Home Sales
In An Economic Incentive Zone
Map Layers
Proximity to Transit Lines
High Per Capita Income
High Density of College Grads
High Density of Home Sales
In An Economic Incentive Zone
Map Layers
Proximity to Transit Lines
High Per Capita Income
High Density of College Grads
High Density of Home Sales
In An Economic Incentive Zone
Map Layers
Proximity to Transit Lines
High Per Capita Income
High Density of College Grads
High Density of Home Sales
In An Economic Incentive Zone
Map Layers
Proximity to Transit Lines
High Per Capita Income
High Density of College Grads
High Density of Home Sales
In An Economic Incentive Zone
x 2
x 4
x 5
x 2
x -2+
Output Decision Raster
Map Layers
Proximity to Transit Lines x 2
High Per Capita Income x 2
Density of College Grads x 4
Density of Home Sales x 5
Economic Incentive Zone -2
Generate Output Heat Map
What we did
Specific Optimization Goals
Faster file format
Distribute computation across:– Threads– Cores– CPU’s– Machines
Cache data
New technology
Distributed Processing
Next generation calculation engine
Reduced calculation time to
~40ms
Walkability: Walkshed.org
Walkability: Walkshed.org
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Land Conservation
Elections
Elections
Sea Level Rise
GPU geoprocessing research
National Science Foundation funded
OpenCL based Cross-platform (ATI, Nvidia)
15 – 100+ times faster than CPU
But wait, there’s more…
Many Thanks!© Photo used with permission from Alphafish, via Flickr.com
Robert Cheetham, Azaveacheetham@azavea.com
Online Spatial Analysis for Open Data
Esri Federal User Conference 20 January 2011