Exploring green infrastructure program scenarios through ... · Exploring green infrastructure...

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Exploring green infrastructure program scenarios through stakeholder informed agent-based simulations Presented by: FA Montalto

Transcript of Exploring green infrastructure program scenarios through ... · Exploring green infrastructure...

Page 1: Exploring green infrastructure program scenarios through ... · Exploring green infrastructure program scenarios through stakeholder informed agent-based simulations Presented by:

Exploring green infrastructure programscenarios through stakeholder informed agent-based simulationsPresented by: FA Montalto

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Funding:

Partners:

Bartrand, T; Geldi, J; Loomis, C; McAfee, C; Montalto, FA; Riggal, G; Travaline, K; and A Waldman

Disclaimer: Research findings do not necessarily reflect any official position of PWD

Acknowledgements

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Managerial question:• What GI program strategies will result in meaningful

quantities of runoff being controlled on “x” % of an urban watershed within “y” yrs?

Confounding factors:• Different land typologies• Different types of GI • Complex community dynamics (+ and -)• Hodge podge of physical, social, regulatory, legal and fiscal

factors that vary time and in space

Generalized Goals:

Addresses watershed goalsAddresses

regulatory timeframe

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Managerial question:• What GI program strategies will result in meaningful

quantities of runoff being controlled on “x” % of an urban watershed within “y” yrs?

Confounding factors:• Different land typologies• Different types of GI • Complex community dynamics (+ and -)• Hodge podge of physical, social, regulatory, legal and fiscal

factors that vary time and in space

Generalized Project Goals:

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A family of computational models, typically custom built, that simulate the “bottom up” actions and interactions of autonomous “agents” in a network environment

Can be used to develop insights into how agent behavior and multi-domain interactions affect system performance

Methods:agent-based modeling

Netlogo: A free multi-agent programmable modeling environment developed at the Center for Connected Learning (CCL)at Northwestern University

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Study Site:Point Breeze Neighborhood – Philadelphia, PA

Neighborhood Statistics:Area: ~ 175 hectares10,363 lots18.5% of lots are vacant75% of lots are residential82% of surface imperviousPop: 21,20035% below poverty line82% Af. Am. 10% Asian

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Planimetric data• Tax lot boundaries• Building footprints• Impervious areas• Canopy cover• Lengths and widths of streets, sidewalks,

alleys

Planning data• Property status (owner occupied, rented,

vacant, tax delinquent)• Designated land uses/zones• Required setbacks• Street classification (one way, two way)• Parking status (one side, both sides)• Special features

• Previously implemented pilot projects• Bus/Metro Stops • Play street designation

Demographic data• Owner type (private or public)• Owner name & address• Tenure status (owner occupied or rented• Mean household income• Last sale date

Georeferenced windshield survey data

• Downspout status (to front or back of house)• Vehicular and pedestrian traffic counts• Community assets

Other• Street reconstruction/ repaving schedule• Philadelphia real estate market conditions

Derived• Connectedness status

• Value for management of routed runoff

Physical Model Components (GIS):Spatial features & their attributes

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Interfacing Netlogo with GISCustomized dashboard for advanced spatial analysis

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Example #1:Rain gardens possible with setback = 10ft

Green: private lots w/ rain gardens

Brown: Raingardens not feasible

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Example #2: Rain gardens possible with setback = 10ft, green roofs with minimum building footprint (roof area) = 1000 sq. ft.

Green: private lots w/ rain gardens

Brown: Raingardens not feasible

Blue: meet green roof minimum area requirement

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Example #3: Rain gardens possible with setback = 10ft, connectivity between rain gardens

Green: private lots w/ rain gardens

Orange: public lots that can capture 1” rain on their own area, but also yellow lots

Yellow: cannot capture 1” on site, but can be hydraulically connected to orange lots through alleys.

Blue: meet green roof minimum area Requirement

Brown: GI not feasible

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Example #4: Rain gardens possible with setback = 10ft, connectivity between rain gardens, green roofs with minimum building footprint (roof area) = 1000 sq. ft., green streets (streets & sidewalks greened), social constraints = only connectivity to rain gardens on city owned properties

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Next step in model developmentAdd “agents” and rules governing their behavior

What is an “agent”?• An autonomous entity that can interact with its

environment

What are “agent types”?• Groups of agents with common sets of goals, percepts,

and possible actions

How do individuals differ from one another?• Take different actions based on unique attributes and

experiences

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Methods: Empirical methods for selecting agent types and behavioral rules in concert with stakeholders

Participant-observation 20-month period > 30 meetings & events Purposive Sampling

Interviews 13 Semi-structured Countless Unstructured Purposive Sampling

Community Street Fair attended by > 40 local

residents

Questionnaires 70 residents local preferences,

concerns, and adoption of GI

Policy Official Outreach 5 local agencies 1 local councilmember 1 state representative 2 state departments 1 regional agency

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Local NGOs & informal associations

Tax lots, Streets, Alleys, Sidewalks, Census blocks, & GI facilities

PWD

Issue-driven organizations & other govt

agencies

Non-resident owners &

speculators

Local institutions (churches, schoolsChew, etc)

Global agent Local agent set Reactive set

Our “Agents”

Residents, Resident Owners, Non-resident owners

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1. PWD establishes a block-scale GI Program Establish goals: Amount and timing of expenditures, spatial %

green goals, adaptive or static program Action #1: Prioritizing blocks for greening Action #2: Prioritizing incentives for each block

2. Property owners decide whether or not to adopt GI

3. Information is transmitted through the agent network

4. The sequence repeats

What happens in the model?

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PWD selects streetblock based on implementation

strategy

PWD allocates funds for extant GI O&M

While budget remains, PWD funds/incentivizes GI

Private property

Streets

Schools & parks

Vacant lots

Quarter = Quarter + 1

30-year simulation complete?

GI adoption decisions made

PWD Selects GI implementation strategy, sets

solution parameters

Stopno yes

All streetblocks assessed?

Block and neighborhood attributes updated

no

yes

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All blocks assessed

?

Budget exhausted

?

Among streetblocks not

yet at green target, select

streetblock with highest potential private GI green

fraction

PWD incentivizes

private GI for all eligible private

property owners

Private property owners decide

whether to adopt GI (rain garden, green

roof, flow-through planter)

Adopt GI?PWD installs GI,

updates block properties

Incentive offered to all private property owners?

Street paving?

PWD installs ETPs, porous

pavement Non-greened school , park?

PWD greens school or

parkBlock

organized ?

Covert vacant lots to

GI

Allow GI adoption on other blocks, no incentive offered

Advance to next quarter

yes

noyes

no

no

yes

yes

no

yes

no

yes

no

no

yes

Sample GI implementation strategy(prioritizing private property GI)

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Collect information

Social network

Local conditions

Assess information

Values

Trust

Decide (stochastic)

Index

Constraints

Inform

Social network

Physical environment

Decision-making process:Property owners

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Social network Local conditions

Block environment

Taxlot environment

PWD incentives

Global and local market conditions

Non-resident owner class (City,

Organization or Landlord

Neighbors (each with a separate social

network)

Renter (with a separate social

network)

Organizations with which non-resident owner is affiliated

Owner assesses

information, makes

decision

Owner experience

with GI

GI adoption decisions:tax lot owners

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P(GI adoption) = fs * fe * fk

fs = binary spatial feasibility factor, based on lot configuration [0,1]

fe = baseline probability of adoption, modeled with a scaled logistic distribution function based on initial incentive ($108.75) as a fraction of monthly household income (MHH)

Sample adoption algorithm

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108.75 Incentive includes: 1-time incentive to adopt: $100 monthly SW fee (perm.): $8.75

(based on Portland, Oregon downspout disconnect model)

Monthly Household Income derived for each lot based on

2000 US Census Tract data

50%

99%

gowner = $108.75 / (monthly household income)ownerLookup function

Scaled logistic distribution function used in model fe

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P(GI adoption) = fs * fe * fk

fk = knowledge factor modeled w/ scaled logistic distribution function based on property owner▪ Exposure / experience with GI▪ Number of investment properties on block▪ Property owner membership in GI advocacy groups

Sample adoption algorithm

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r = tenure status of property0 if owner occupied, 1 if rented

m = membership status of owner1 if member of NGO, 0 if not

nGI/np = fraction of block properties already retrofit w/ GI

nro/np = fraction of owners who reside on block

xlot = 1 – rlot + 2mowner + (nGI/np)block+(nro/np)block

3%

92%

Lookup function

Scaled logistic function used to represent fk

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ResultsDynamic adoption of GI in Point Breeze

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Ongoing work

1. Refining PWD behavioral rules and property owner adoption algorithms through more stakeholder interactions

2. Exploration of effectiveness of different GI policies, budgets, budget phasing, etc (current model exhausts budget because water utility assumed to pay for O&M)

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Preliminary Conclusions

1. ABM▪ Useful tool for water utilities struggling to predict the spatiotemporal

extent of GI that may emerge in complex urban watersheds

2. Potential uses: ▪ Advanced spatial analysis of intricate GI opportunities▪ Consideration of dynamic interactions between stakeholders▪ Stimulating productive dialogue…emphasizing process over

outcome…