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Online Spatial Analysis for Spending Equity Mapping
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Online Spatial Analysis for Spending Equity Mapping

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Azavea is working with OMB Watch and Esri to develop a new online software tool that supports mapping of socioeconomic need against federal stimulus spending. To perform these calculations online ...

Azavea is working with OMB Watch and Esri to develop a new online software tool that supports mapping of socioeconomic need against federal stimulus spending. To perform these calculations online requires significant performance improvements over existing geoprocessing tools. Azavea has developed a high performance distributed processing system, DecisionTree, to support highly scalable raster processing on the web. Presented at the 2011 Esri Federal User Conference.

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  • So, these days people do this kind of work using desktop GIS systems. You are looking at the ArcMap application from ESRI
  • So, these days people do this kind of work using desktop GIS systems. You are looking at the ArcMap application from ESRI
  • So this story starts with my wife and I looking for a house and being frustrated by the type of information we had access to. We didn't know where to start. Each of the real estate agents we met knew a particular part of the city really well, so they tended to steer us toward those houses.
  • So, these days people do this kind of work using desktop GIS systems. You are looking at the ArcMap application from ESRI

Online Spatial Analysis for Spending Equity Mapping Online Spatial Analysis for Spending Equity Mapping Presentation Transcript

  • Robert Cheetham, Azavea [email_address] Online Spatial Analysis for Spending Equity Mapping Esri Federal User Conference 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? Searching for a house, for instance… Walk to Grocery Store  Biking Distance to Work  Near Restaurants  Near Library  vital very important somewhat important nice to have
  • Weighted Overlay + + + = x 5 x 2 x 3 x 1
  • 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 This cell based approach enables us to combine layers using a process called map algebra 1
  • 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
  • + + + + + + + + =
  •  
  • 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, Azavea [email_address] Online Spatial Analysis for Open Data Esri Federal User Conference 20 January 2011