MAPC study areaMAPC study area

2030: Alternate Futures2030: Alternate Futures

WOC envisions the results WOC envisions the results of more comprehensive of more comprehensive regional planningregional planning

Result of community Result of community workshops—what do workshops—what do participants want their participants want their town to look like in 2030?town to look like in 2030?

Looked at housing, Looked at housing, demographic trends, demographic trends, employment, land use, etc.employment, land use, etc.

“Let It Be”

“Winds Of Change”

2030: Alternate Futures2030: Alternate Futures

LIB predicts much LIB predicts much greater loss of open greater loss of open spacespace LIB: 152,000 acres lostLIB: 152,000 acres lost WOC: 48,000 acres lostWOC: 48,000 acres lost

Water shortageWater shortage LIB: 50 systems exceed LIB: 50 systems exceed

permit limitspermit limits WOC: Only 8 exceed WOC: Only 8 exceed

limitslimits

Housing TypesHousing Types

Housing TypesHousing Types

-8 housing types

-3 land use categories: BNW, Residential, Commercial/Industrial,

-For “Buildable Non-Wetland” landuse, two different model projections

Grid data: VMTGrid data: VMT

Grid data: VMTGrid data: VMT

Can do interesting Can do interesting things with grid things with grid data- map data- map algebra, focal sum algebra, focal sum grids, etc.grids, etc.

How to combine How to combine with TAZ-level with TAZ-level data on housing?data on housing?

Task at hand: Housing AllocationTask at hand: Housing Allocation

Why look at housing below the level of the TAZ?Why look at housing below the level of the TAZ?

-Accessibility affects where housing will (or -Accessibility affects where housing will (or should) goshould) go

-Smaller scale -Smaller scale more engaging? more engaging?

-May be able to predict environmental -May be able to predict environmental impact—how VMT responds to developmentimpact—how VMT responds to development

-A “check” on the MetroFuture model-A “check” on the MetroFuture model

-Lofty goal: an algorithm to dump any data -Lofty goal: an algorithm to dump any data intointo

Case study: BrooklineCase study: Brookline

Intermediate between Intermediate between urban and rural—some urban and rural—some open space, but mostly open space, but mostly residential and residential and industrialindustrial

Case study: BrooklineCase study: Brookline

Intermediate between Intermediate between urban and rural—some urban and rural—some open space, but mostly open space, but mostly residential and residential and industrialindustrial

Case study: BrooklineCase study: Brookline

Intermediate between Intermediate between urban and rural—some urban and rural—some open space, but mostly open space, but mostly residential and residential and industrialindustrial

Case study: WrenthamCase study: Wrentham

Rural—Rural—lots of lots of BNW landBNW land

Case study: WrenthamCase study: Wrentham

Rural areaRural area TAZs are very large TAZs are very large

compared to compared to intermediate/urban intermediate/urban townstowns

Effect of breaking Effect of breaking down housing to down housing to smaller scale even smaller scale even more apparentmore apparent

Allocation: What is it for?Allocation: What is it for?

Assumptions are built into the allocation Assumptions are built into the allocation algorithmalgorithm

This has implications for how you talk This has implications for how you talk about allocation at the local levelabout allocation at the local level Is the allocation a goal?Is the allocation a goal? Is it a prediction?Is it a prediction?

Allocation StrategiesAllocation Strategies

Shan: Housing demand and accessibilityShan: Housing demand and accessibility Paul: Two algorithms: Low VMT and Paul: Two algorithms: Low VMT and

RandomRandom Masa: Case study: Wrentham Masa: Case study: Wrentham Abner: Case study: Brookline Abner: Case study: Brookline Wanli and Yi: Neighborhood EmphasisWanli and Yi: Neighborhood Emphasis