1. MAPC study area

2. 2030: Alternate Futures  WOC envisions the results of more comprehensive regional planning  Result of community workshops—what do participants want their town to look like in 2030?  Looked at housing, demographic trends, employment, land use, etc. “Let It Be” “Winds Of Change”

3. 2030: Alternate Futures  LIB predicts much greater loss of open space  LIB: 152,000 acres lost  WOC: 48,000 acres lost  Water shortage  LIB: 50 systems exceed permit limits  WOC: Only 8 exceed limits

4. Housing Types

5. - 8 housing types -3 land use categories: BNW, Residential, Commercial/Industrial, -For “Buildable Non- Wetland” landuse, two different model projections

6. Grid data: VMT

7.  Can do interesting things with grid data- map algebra, focal sum grids, etc.  How to combine with TAZ-level data on housing?

8. Task at hand: Housing Allocation  Why look at housing below the level of the TAZ? -Accessibility affects where housing will (or should) go -Smaller scale  more engaging? -May be able to predict environmental impact—how VMT responds to development -A “check” on the MetroFuture model -Lofty goal: an algorithm to dump any data into

9. Case study: Brookline  Intermediate between urban and rural—some open space, but mostly residential and industrial

10. Case study: Brookline  Intermediate between urban and rural—some open space, but mostly residential and industrial

11. Case study: Brookline  Intermediate between urban and rural—some open space, but mostly residential and industrial

12. Case study: Wrentham  Rural— lots of BNW land

13. Case study: Wrentham  Rural area  TAZs are very large compared to intermediate/urban towns  Effect of breaking down housing to smaller scale even more apparent

14. Allocation: What is it for?  Assumptions are built into the allocation algorithm  This has implications for how you talk about allocation at the local level  Is the allocation a goal?  Is it a prediction?

15. Allocation Strategies  Shan: Housing demand and accessibility  Paul: Two algorithms: Low VMT and Random  Masa: Case study: Wrentham  Abner: Case study: Brookline  Wanli and Yi: Neighborhood Emphasis