1. David Peterson UP206a – GIS (Estrada) December 6, 2010 Source: Ecotality

2.  Mayor wants LA to be #1 city for EVs  EVSE can influence adoption rates  Where will public investment in EVSEs generate highest benefit?

3.  EVs require a completely new infrastructure to connect to electricity grid  Single-family residential: not an issue

5.  Focus on Problem Areas:  Multi-family residential  Employment Centers  Commercial Centers

7.  Goal:  Anticipate concentrations of EV ownership  Use 2008 Hybrid ownership data as proxy for EV ownership  Origins: Multifamily residential problem  Destinations: Making sure they can charge at destination

9.  Absolute number of vehicles?  Percent capture of total vehicles in LA?  Percent of total vehicles within zip code?

10. Zip Code 90501: #1 for vehicles: 2,537 #8 In terms of Percent of Local Zip Code Zip Code 90001: #2 for Penetration of Local Zip Code Only has 2 vehicles! What’s the best measurement?

13.  41,079 hybrids in LA  12,948 in the top 10 (32%)

14.  In top 10, what is percent housing type?  Data Problem: don’t know housing type by hybrid vehicle ownership.  Assumption: Hybrid owners reflect zip code housing type distribution

16.  90071: no housing, but ranks 5 th (1,070) by total hybrids – must be government/business/etc.  Multifamily charging is an issue:  Mix of SFR and MFR  Range 15% to 73%  Mean: 41%

18.  Index that combines vehicle ownership and multifamily housing  Greater weight on more vehicles (1-4)  Greater weight on more MFR (1-4)  Combine to create Investment Prioritization Index Hotspot Analysis  Index=[vehicles_weighted]+[MFR_weighted]

23.  Data Problem: don’t know exactly where hybrid owners commute  Assumption: use zip code trip distribution  Methodology:  Weight % allocation of trips by actual number of hybrid vehicles in origin zip codes  Aggregate for a complete picture of destinations

24.  Is destination charging a real concern?  Average Commute range: 40 miles (r/t)  Battery Range: 80-100 Miles  Not a real concern given current travel behavior, but people might travel differently with EVs

31.  Use Index to Allocate Funds to Origin Zip Codes that will benefit the most.  Know the top 10 destinations for these origins.  Not imperative to invest in public charging given vehicle range  Need to monitor/track travel behavior  Providing EVSEs at these stations could induce greater adoption (but is it best use of public funds?

32.  Appendix A: Models  Appendix B: Original Map Layer  Appendix C: Metadata  Appendix D: Map with 7 Layers  Appendix E: Skills

33.  Model for Rasterizing Layers for index/hotspot analysis inputs

34.  Model for 50-mile buffer

35.  Model for Clipping Buffer to Land Contours

39.  Slide 8  Inset Map  Geoprocessing: clipped California zip code files  Slide 10  tables  Slides 11/12:  Attribute sub-set selection based on number of hybrid vehicles

40.  Slides 13/15  Tables  Slide 17:  Sub-set selection; Pie charts  Slides 19/20/21  Rasterization of data layers using a model  Creation of Index for Hotspot Analysis using a model  Use of Spatial Analyst

41.  Slide 22:  Table  Slide 25:  Used model to create distance buffer from top 10 zip code centroids  Slide 26:  Use of Network Analyst to generate database file and OD Cost matrix  Slide 27:  Table

42.  Slide 28/29:  Attribute sub-set selection  Slide 30:  Table  Slides 33-35:  Models  Slide 36  Original Map Layer  Slide 37:  Creation of Metadata  x