So as our first speaker this morning, I'd like to introduce Matthew and

Vishall from AECOM.

And they're going to talk to you a little bit today about sustainability and

an application they've developed to support design and

sustainability at kind of the regional neighbourhood scale.

>> All right, good morning.

I'm Vishall Bhargava, I'm a urban designer sustainable land planner with AECOM.

And Matt Palavido, he's a senior GIS specialist with AECOM as well.

I'm going to talk a little bit about something that relates to what

Jack talked about yesterday, evaluating alternative futures.

And understanding how we can make the most out of the planning process and

design process using tools as decision support.

[COUGH] One of the questions we often get troubled with is,

what makes one plan or alternative more sustainable than the other?

And answers are often intuitive or

the designer's guess or teams make suggestions.

But what we've found is urban form is the single largest driver for

a reduction, or the determinant for greenhouse gas levels in a plant.

If you take greenhouse gases to be sort of a benchmark for sustainability.

But the idea for

the tool we've been working on is to move away from intuition from the designer, and

try and develop a framework that actually evaluates objectively the different plan

alternatives and spits out a result that we can all hang our hat on, hopefully.

And we call it the SSIM system,

which is a sustainable system integration methodology.

So what does SSIM do?

And you'll see there are three stages to this process,

which we'll describe today in our presentation.

But stage one, what does that really answer?

It answers the two key questions that I talked about.

One, which scenario has the least adverse impact to the environment?

And secondly, which has the greatest potential for sustainability?

There are a number of systems that get involved into this SSIM framework,

and you'll see them, but water, transportation, building energy,

the ecology, the social systems that go into large communities,

all get evaluated within one consolidated framework.

So why do we really need such a plan?

First of all, [COUGH] we try to move away from the idea of intuition and

be more quantitative or objective about it.

Secondly, it's a comparison of performance at various levels,

one at a spatial level and B, metrics of performance that

go into things like the level of water that a plan uses,

the level of building energy a plan uses.

And we'll talk to the more.

But another key feature for this system that we feel is required is being able to

convey complex information like that effectively to a broad range of audiences.

Whether it's in a community outreach session or

through experts in the field like yourselves.

Or to legislators with conveying that information through maps and

tools effectively is another key criteria for us.

Now the performance indicators that the SSIM system evaluates or

the stage one evauluates, break down into a series of categories that go

from the statistics that go into a plan.

So simple things like the total population, the density,

what is the cost of the development, the projected cost.

Or urban design matrix like the FAR, the area that we have for

possible rooftop recapture or the block size that we're working for

depending on the context, the urban context at which we're developing.

Then we have spatial.

And this is where the GIS component gets very deeply entrenched in the process,

where we try and evaluate accessibility.

To key things like parks, amenities, transit, employment and do it in

a way that it's easy to communicate, and objective going from one plan to the next.

And then we have ecological performances and more performance oriented

level of resource use, waste outputs and things like that.

But without taking much more time, let's jump into the methodology.

And Matt's going to talk through the methodology here So,

as Vishall mentioned, there's a spacial portion of this that's actually very GIS

intensive this whole process.

My job was to make this easy for

the planners to quickly come up with their land use plans.

Make it easy for the GIS analyst to evaluate these plans.

And then also make it easy to

represent the results in a manner that stakeholders could easily understand.

One of the things that I will say with our GIS 10,

the new editing templates, have made it really easy for

us to implement and allow our planners to come up with plans quickly.

We can build in a lot of assumptions to the various land uses, and

they can quickly sketch out a plan, and we can feed it into our system.

This is just kind of a quick overview of the granularity of our system.

We start out at the building level and build blocks from those buildings.

And we can take that up to a district and up to a neighborhood.

All of this information gets aggregated up, so

we can get totals for the entire plan.

So all the way down at the building level,

we actually work with our building physics team, and then we also use publicly

available databases to gather information and model a number of different buildings.

Then we can put those into blocks.

Say we're using a mixed use development block.

We figure out typical building mix that would be in that block and

we have that available to us.

Then the planner can go ahead and design their districts, and

then we can aggregate that up to the entire plan as a whole.

As part of that process, when we're building these blocks,

we have the 3D library of the different building types we have, so

we can rapidly develop a 3D model that people can look at and

get a little better feel for how the plan's being setup.

And on the back end, this is just the attribute table of the database back end.

And how the nested hierarchy acts.

So we have our building definition that gets fed into our blocks and so

on and so forth.

This is just a tabular example of that.

So our spatial analysis methodology.

A lot of examples I've seen, when we're looking at service areas,

we want to know what a five minute service area is for a park.

That's great to know, but we like to go into a little more detail than that.

The way we look at it, if we have in this example,

say this is a five minute service area.

We want to know the population that's within that five minute service area,

the size of the amenity that that service area is serving,

the number of accessible amenities.

That is to say, if you have two amenities close to each other and

their service areas overlap.

We want to know in that space that you have access to two separate amenities.

And then we also look at the relative value of the amenity.

The relative value of the amenity we base loosely on Maslow's hierarchy of needs.

So, in this example we're looking at service types.

So, for neighborhood retail, that would have the highest weighting in our system

just because that's a basic need that everybody needs to live.

They need to be able to get to retail to get food, clothing, and so on.

And you can see the rating kind of goes down as the need goes down,

similar to the Maslow's hierarchy on the left.

So, as I mentioned neighborhood retail would get the highest.

Something like a church, art center, community garden, great to have,

but not necessary to actually live, so it gets a lower weighting.

So this is an example of one we did in Northern California for

a transit-oriented development,

Fairfield, where we were looking at access to services.

And you can see that we have over here on the left hand side our ranks for

the different types of services, and

then we're showing the percentage that has access to those services.

So for each individual block or parcel anywhere on this plan,

we're calculating a service accessibility score,

which is based on that methodology that I showed before.

But then we also have a score for the plan as a total.

So, we can see the plan as a total has an accessibility score as well.

The legend got cut off of here.

I apologize for that.

That's really bad photography on my part.

But, the colors represent, the darker colors have greater

population density, and possibly access to more amenities.

So, it's just a good graphical representation

that people can easily understand.

I've got a quick video here of the tool that we developed.

I apologize if it's little blurry,

my screen at home isn't this big so Let's see.

So, our goal was just to try to make it as easy and

flexible as possible for the GIS analysts.

And we can configure our land use in many different ways because we know data comes

in to us from a number of different ways depending on the planner, the GIS analyst,

and we've also made it so all of your settings can be exported and imported.

So, if you've modeled building energy for a site close by you can re-import those.

You can do mixed use, single use.

And we've got a couple of different accessibility methods that we can use.

But, the key is that it's rapid.

Traditionally, it was kind of a manual process,

took a long time to get feedback to the planners.

In this case, the planner can draw their plan, we can feed it into the tool,

we can run it, and we can have our results displayed immediately, which is great.

So just add some information into ArcMap.

Our feature class has attribute table that tracks

the number of features that are available from any given point on the plan,

the weight, which is that relative value, the residential population,

non-residential population, transient or

visitor population, service population, which is combination of all of them.

And then we have our percentage, so

84% of the residential population has access, so on for each of those.

And then we have our overall plan score.

Let's see.

So, again back to our example in Fairfield.

These were our land use plans we were evaluating, the top left hand side

here is what we're calling our business as usual or our base case.

So we made our best estimate based on the surrounding area,

existing practices, what if development went on as it was currently planned,

what it would look like in this area.

And that's what we kind of test against.

We come up with different scenarios or

different alternatives here that have different land uses than this.

So we're trying to increase density closer to the train station, and

get a more mixed use development.

So again, here's just examples where when we run the tool on each of the scenarios,

we can compare against this baseline and compare the plans between each other.

So we can see as we move densities around, we get different scores,

different levels of access to services in this case.

This is just another example, this is bus routes, and

then the train station is down here.

So this one is access to transit.

And again, the colors indicate, the darker the color,

the better the plan is operating there.

And another example, access to parks,

the exact same methodology, just with parks instead of transit.

This is interesting one,

going back to that granularity I spoke about before with the building energy,

so the building energy we're able to aggregate that up to the block level and

estimate the kilowatt hours per capita per year.

So we can compare the plans and see per capita

which ones are consuming less energy, or are projected to consume less energy.

This is a tabular representation of the graphics we were just looking at.

Again, it gives you the ability to compare the four different plans and

see how they're operating against each other.

We give them a total accessibility score, so,

you can see in this case, alternative one has the best accessibility score.

We're also tracking carbon per service population.

That's important especially in California with all of the mandates that we have and

we have to meet.

And all of this information eventually ends up in what we call our evaluation

matrix.

And a lot of times we'll use this in a collaborative setting

with our clients where we can kind of

make adjustments real time to see how the plans are operating against each other.

So, up here it tells you it ranks the plans based on the scores.

But you also have the ability over here to weight certain characteristics.

So, for instance, if you are primarily interested in access and

mobility, you can bump that weighting up and this will adjust and

tell you which plan is giving you the best access and mobility.

Or, if you were interested in planned demographic mobility,

you could change the weighting so that's higher and

see which plan performs better in that scenario.

So, the examples I was showing were pretty basic.

We were using as the crow flies buffers just cause it was real quick, easy to do.

But on some of our other projects we've had to get a little more complex.

We've had to introduce network based success ability analysis.

So, service areas using a network,

which take longer to analyze, but

then we also had to figure out how to incorporate counting those overlaps,

doing the population analysis and all that information.

Effective slope on walking distance, that was an interesting one.

At multiple levels of infrastructure, and

I have some examples of that we'll show in a minute.

And then a new one for

us was consideration given to vertical land use components.

So a recent project that we just completed in Singapore is

this area called Jurong Lake.

And in this particular project these are the three new ideas that we implemented.

The multiple levels of infrastructure, the network based accessibility analysis, and

vertical land use components.

The vertical land use components was necessary

because this was a very dense urban area.

And any building they were putting in they required 100% greenery replacement.

But they also didn't have room to put new parks in.

So I don't know how many of you are familiar with Singapore or

have seen pictures, but

they actually have sky parks, green spaces that they put on the roofs of buildings.

So we had to, when we did our park accessibility analysis,

had to take into consideration the vertical nature of it.

This is just an example of three different land use plans and

the different conceptual ideas for this particular project.

So this is an interesting example where we were doing access to multiple venues.

So for this Singapore project, it was kind of centered around this lake.

And then there was a central business area over here that was really dense.

But there were venues around this lake which were theme parks,

museums, different attractions that they wanted people to have access to.

So in this case we were comparing apples to apples.

It was the same land use plan but different infrastructure on each different

pedestrian and transit infrastructure on each plan.

So we wanted to see how that would affect accessibility.

So in this kind of baseline case here we just have the at-grade pedestrian

crossings, the street networks, sidewalks, just your typical infrastructure.

And we can see that we only had five percent of the population had access to

three venues, which was the client's goal.

By adding a open-air tramway and some second level pedestrian

crossings we were able to increase that to 18%.

And by running the tool we can quickly see where better access is.

The next level of that wasn't much of a change.

The only change the client made in that case was to change

the open air tram to a fixed guide way tram.

Bumped up the percentages a little because they figured it was more attractive,

more people would use it, it's a little faster.

A recent example we did in Australia was for a Greenfield Development.

In this particular case, it was a hilly area and

the client wanted us to incorporate the effect of slope on walking distance.

We had some Akom colleagues in the UK that did a pretty

extensive study on how slope affects walking distance.

And we were able to apply that into the network data set and

run that through the tool to determine our service areas.

And this was kind of an interesting example.

I think it was Micheal Goodchild that was mentioning once you

get to the big d, working with stakeholders,

differences of opinions, and how that all feeds into the planning process.

In this example, we were looking at the location

of the town center in a Greenfield Development.

The, Western one-third of this project was all one land holder.

And then the other two-thirds of the project was between 200 and

300 private landholders.

So the government thought the town center should

go in this area because it's more central, serves the district a little better.

The private landholder, obviously,

wanted the town center located on their piece of property for economic reasons.

But the government's argument was, well this doesn't centrally serve this area.

So one of the challenges we had was to demonstrate that empirically.

So we were able to run the analysis and

show the percentages that have access on the two different scenarios.

But we came up with a third scenario and ran that,

which kind of appeased both parties, was to locate the town center here,

which is still on that private landholder's property.

But it's still serves a little more centrally,

almost the same amount of people as the plan up here.

Ultimately, they didn't end up going with that because they're going to

end up putting up a very large highway through here, which will cut off access.

So, but it was a good process to go through.

going to pass it back to Vishal here.

>> Thanks Matt.

So I want to talk a little bit about what do we do beyond stage one.

Stage one gives us a very quick and

objective evaluation of multiple alternatives in a plan.

And once we get to a preferred plan, what stage two, three, and four for

the tool allow you to do is to improve and refine the performance of the plan itself,

to achieve a set of benchmarks that we establish for the plan.

Let me explain that some more.

So once you've got a plan, we will take that plan and

take different components within the plan, transportation, building energy,

water, the level of carbon sequestration, the public realm energy component.

And fine-tune those based on a set of metrics we've established for

ourselves based on cost, based on ease of implementability,

and come up with packages that achieve different levels.

And what stage three does is take those different,

and let's just call them baseline, good, better, and best.

Take those four levels of improvement for each of those components, and

allow you to gain them.

And see what the cost implications are and see what the benefits are from

a common standpoint or from a building energy standpoint, and that's stage three.

And the final stage lets you, when the project gets built out and

implemented, sort of evaluate the performance and monitor that.

[COUGH] So, the inputs into the next level is the master plan itself.

And these are the components that they evaluate.

But the key point we want to make here is it's really an economics driven approach.

because what it does is once you've got these benchmarks and

sets of strategies established for achieving the good or the better or

the best, we do a cost benefit analysis.

What is the cost of implementing those sets of strategies and

how much is the payback?

I mean, how long does it take for you to recover your capital cost you've put in?

Is it three years, five years, for this set of strategy?

And that helps you in the gaming process, and

developing what would be an optimal solution for a particular context.

This is an example of the water model.

This is the interface for the water model.

And this will track, for example,

as you change your sets of strategies, how much rooftop do we have?

How much condensate are we generating through this plan?

How much black water are we generating that we can recycle back into the plant

for secondary irrigation?

And at the same time, what it tracks for you is total potable water used.

What is the cost per square foot for

the development based on these sets of water strategies?

And what some clients have found useful is we also started to track

based on these sets of water strategies, how many lead points would

you be able to get if you were interested in going that route?

So, we have a lead calculator that’s built-in.

Staying with the water example,

once you've got your sets of strategies sort of identified,

what we do is the establishment of three or four levels of benchmarking.

The baseline good, better and best.

The baseline would be business as usual or what we do, and the good, better and

best could be a 5% reduction in potable water demand.

The better could be a 10% reduction in potable water demand and

the best could be 15%.

And then the model will allow you to develop sets of strategies that would

achieve those levels of reduction.

And obviously, as you go more aggressive with your levels of reduction,

you get incremental costs and the model tracks those costs as well.

So here is an example of where those alternatives get developed,

the baseline, the good, the better and the best.

This is just a water gameboard.

And here you can actually choose different sets of water related strategies and

come up with what you think is the optimal way of achieving the 5%,

the 10% and the 15% reduction.

And this is just an example, but what we do is we have similar gameboards and

benchmarks established for multiple systems.

So one of the key strengths for this approach is it allows you to incorporate

building energy, transportation, water, all into one combined matrix.

And that's what we'll see in stage three, which is our same gameboard for

the final optimization program selection.

So you got building energy, water, transportation, ecosystems,

green building, all of those.

And what decision makers, planners, almost anyone can go in and do is go and select.

Do you want to do good here?

Do you want to do better here?

Do you want to do baseline here?

For different strategies on the plan itself that are transportation related,

that are water related.

And the moment you make your complete set of selections,

it spits out the performance of the plan.

What is the level of reduction you're achieving?

You can even granular look at it.

What is the building energy that we're using?

What is the water we're using?

Or fold that back into, what is the common footprint for the plan?

What is the CO2 equivalence per capita that we're achieving on this plan here?

So it has sort of this legislative implication where you can evaluate this

based on the mandates we're getting in California or

you could look at it from a cost center perspective.

What does the builder need to do?

What does the master developer on this project need to do to achieve a given set

of goals?

And the simplicity of this is compelling, is that once you've done this and

established those sets of metrics for the good, better and best,

almost anyone can gain infinite number of scenarios and see what the impact is.

Another interesting route we've taken with this approach is not just for

greenfield or redevelopment projects, but let's look at it at a citywide scale.

For a project, sort of a very similar approach, but let's extrapolate that out

and see if we can do this for a much larger geographic scale.

For a project we're doing here in Southern California,

some of you may recognize this is the City of Ventura.

We start by tracking some of these parameters at a parcel level.

Just like Matt had described using the building, because we understand

the building really well and the metrics that go into the performance of

a building, we started tracking building energy use, water use at a building level.

We even calibrate that based on age of building or

the level of performance based on utility data that we've got.

And once you've got that base information,

we can quickly aggregate those parcels based on zoning or

place types or districts into larger chunks within the city.

So how does a residential single-family detached neighborhood perform

based on those aggregations at a parcel level?

How does a mixed-used district or a business complex perform?

And then we take that and we develop a baseline greenhouse gas inventory for

the entire city.

And the reason we use the greenhouse gas inventory here as our metric for

evaluation is because a lot of times we found, at least in the work we've done so

far, is that a citywide scale, really the carbon footprint of the GHG emission level

is the metric the cities are interested in,

because of the mandates that are coming from AB32 and things like that.

And we're able to track and evaluate the total performance at a parcel level,

and at a city level.

And then what we're able to do is because we have the granularity in the data and

the spatial component, we're able to select strategies and

apply them to specific districts within the city, certain areas within the city

and see what the impact is on the citywide performance.

And again, take the same gameboard approach and apply it here.

So what you're seeing here is building energy, transportation.

Those really are the two biggest factors that affect your CO2 equivalence levels.

And then we've got the sets of strategies.

And we've got our baseline, our good, better and best, already pre-calibrated.

We didn't show you examples of that,

because it looks very similar to what we described for stage two.

And we can go in.

What you're seeing on the right is the 1990 level,

which is what the goal is oftentimes for CO2 equivalence level.

And the top of the wedge is where you're at right now.

As you change some of these to a more aggressive set of strategies,

of course with increased cost, you can see that

now you're achieving something closer to your 1990 goal at a citywide scale.

You applied even more aggressive set of strategies and

you're able to achieve what your goal is.

And what the cities can do with this is take these sets of strategies and

create a policy that would help incentivize these sets of strategies so

they're more implementable.

Or encapsulate that into a climate action plan and see how this goes forward.

So I'm going to talk about one more interesting sort of implication of this

process.

Oftentimes, conventionally, as it has been, is that we've got the land planning

process which we develop alternatives, evaluate them, create a final plan.

And then we go into an EIR process where we start to see what are the impacts of

the plan that we've decided to pick.

And then create mitigation strategies and understand the costs.

And oftentimes, plans don't get built out because those costs are too high or

the level of implementability of the plan might be difficult.

Using an approach like this where we start to evaluate multiple alternatives

at the plan development stage, as well as in the plan refinement stage.

Allows us in some ways to develop a self-mitigating plan,

that might be a little aggressive but closer to a self-mitigating plan.

And it's kind of obvious if you think about it because

you develop your set of conceptual alternatives.

Go through a stage one evaluation in your transportation fees and

come up with a preferred set of plans that have strategies,

that are built in that reduce the VMT.

Reduce the building energy.

Have the optimal mix of land uses distributed in a fairly compelling

framework or distribution within a plant, the jungle field, the plant itself.

Then take that preferred plan,

which you already brought the mitigation levels required for that plan.

Down some and then put that into stage two evaluation,

where you start to refine the performance of the plan itself.

Based on the multiple components we looked at and the final plan that then gets

fed into the environmental impact review process, might have much fewer impacts.

So we've started to test this approach on a couple of projects and

we've had some success.

So this is kind of where it all start to tie together and

when it drives to point back to, is a very detail spacial level analysis.

And decision support for

the planning process, usually helps elevate the quality of planning and

evaluation that goes through over a twelve or eighteen month period for a plan.

So just to summarize some of the benefits for this GeoDesign based approach.

A moving away from the design institution.

And I think that somewhat seems like the best plan, to a more qualitative set

of metrics and quantitative set of metrics that help evaluate that objectively.

Another thing is as you've through some of the graphic representation on this plan,

that it is a little bit more compelling in terms of representing it on 3D or

maps that you can see.

You know where the hotspots are for building energy or

where the hotspots are for carbon.

And then applying new set of strategies and

again in real-time evaluating what the impacts has been.

Another is, and this is really where I think the most important factor is,

it allows you to incorporate multiple systems into one platform and

look at them in one go.

So that we thought, was quite compelling.

And then of course apply strategies that are not just

the highest return on investment from a economics perspective, but

also fine tune them from a spatial perspective.

And let me explain, if you have a city and

you have areas where there is a old industrial district and

there are areas where there is a new single-family detached district.

Applying building energy measures to the old

industrial districts sometimes gives you much higher levels of energy reduction

than compared to homes that are built two years ago.

So being able to do that geographically within a city has advantages.

So you get maximum return on investment.

And lastly we're hoping that it creates and we don't know for sure but

high performance plans.

So this is the final slide and it's kind of tying everything that we've talked

about in the last half an hour together.

So you can sketch the plans, you can test the feedback, you can refine the plan and

make adjustments real-time, you can go back and re-evaluate this.

Somewhat just below the sign of the process,

the seven steps of evaluations and then decide your group's course of action,

justify it,

and hopefully communicate it effectively to the audience.

So that's our structured presentation.

We're happy to take questions.

>> [APPLAUSE] >> So I do have one question for

the two of you.

You showed us a lot of dashboards,

game boards with a lot of very kind of intense information summarized from GIS.

I'm just curious if you'd be willing to share what that's built on,

what application that's built on?

>> Sure we can.

Go ahead Matt.

>> Well, the GIS tool itself was built using ArcObjects, but

then that gets fed into Excel.

>> Okay, yeah.

>> [LAUGH] So all that, all those amazing dashboards, all the summaries,

all those building to do with that gaming.

That's all Excel, a very simple tool but powerful tool.

>> Yeah.

>> That's what I think is so amazing.

>> Thank you very much.

Guest Lecture 2: Week 5

Geodesign: Change Your World

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