a discussion session co-sponserd by affordable …...mafraq 100,071 127,681 1% total: 56.5% urban...

Korea

Green Growth

Trust Fund

A Discussion Session Co-sponserd by Affordable Housing KSB, Land TG,

and Safe and Inclusive Cities KSB, Smart Cities KSB, and Urbanscapes KSB

February 14, 2018

Part I Introduction and Findings

▪ How would Jordanian cities look like in 2030?

▪ How would today’s policy and investment decisions lead to

alternative futures?

▪ How do spatial patterns/land use changes affect infrastructure

costs, municipal service costs, GHG emissions, energy and

water consumptions, etcetera?

City PPL (2015) Projected PPL (2030)(27.59% increase)

%of total ppl in Jordan

Amman 3,423,389 4,367,902 36%

Irbid 815,815 1,040,898 8.6%

Zarqa 588,232 750,584 6.2%

Russeifa 451,315 575,832 4.7%

Mafraq 100,071 127,681 1%

Total: 56.5%

Urban Growth Scenarios for Five Jordanian Cities

AM

MA

NB

AU

MO

DER

ATE

VIS

ION

Municipal services costsEnergy

Infrastructure costs

GHG emissions

Landconsumption

41.44

24.19

0.00

4,323

4,312

3,695

1,308

1,304

1,120

231.67

135.23

16.55

58

55

49

km2

km2

km2

kWh/capita/annum

kWh/capita/annum

kWh/capita/annum

kgCO2eq/capita/annum



Millions of JD

Millions of JD

Millions of JD

JD / capita

JD / capita

JD / capita

CO

MP

AC

T G

RO

WTH

0.00km2

3,719 1,128 16.99 50

kWh/capita/annum kgCO2eq/capita/annum Millions of JD JD / capita

Moderate Compact Growth

BAU Vision

Results in Different Scenarios for Amman

Population in Base Year (2015): 3,423,389Population in Horizon Year (2030): 4,367,902

AM

MA

NB

AU

MO

DER

ATE

VIS

ION



GHG emissions

Landconsumption

41.44

24.19

0.00

4,323

4,312

3,695

1,308

1,304

1,120

231.67

135.23

16.55

58

55

49

km2

km2

km2

kWh/capita/annum

kWh/capita/annum

kWh/capita/annum




Millions of JD

Millions of JD

Millions of JD

JD / capita

JD / capita

JD / capita

CO

MP

AC

T G

RO

WTH

0.00km2

3,719 1,128 16.99 50



BAU Vision

BAU vs Compact / Vision

Land Use Changes in Amman:

AM

MA

N4

,36

7,9

02

inh

abit

ants

by

20

30 BA

UM

OD

ERA

TEV

ISIO

N



GHG emissions

Landconsumption

41.44

24.19

0.00

4,323

4,312

3,695

1,308

1,304

1,120

231.67

135.23

16.55

58

55

49

km2

km2

km2

kWh/capita/annum

kWh/capita/annum

kWh/capita/annum




Millions of JD

Millions of JD

Millions of JD

JD / capita

JD / capita

JD / capita

CO

MP

AC

T G

RO

WTH

0.00km2

3,719 1,128 16.99 50



BAU Vision 500 million JD saved in costs of municipal services between 2015 and 2030

AM

MA

N4

,36

7,9

02

inh

abit

ants

by

20

30 BA

UM

OD

ERA

TEV

ISIO

N



GHG emissions

Landconsumption

41.44

24.19

0.00

4,323

4,312

3,695

1,308

1,304

1,120

231.67

135.23

16.55

58

55

49

km2

km2

km2

kWh/capita/annum

kWh/capita/annum

kWh/capita/annum




Millions of JD

Millions of JD

Millions of JD

JD / capita

JD / capita

JD / capita

CO

MP

AC

T G

RO

WTH

0.00km2

3,719 1,128 16.99 50



BAU Vision

• urban growth scenarios = projecting possible future conditions of cities

• Assess the possible outcomes of implementing different projects, instruments or policies

Conceptual Framework of Urban Growth Scenarios

Input variablesIndicators

Public t ransit network

Employment density

Populat ion density

Pedestrian infrastructure

Land value

Urban form

Urban development plans


Water demand

Energy consumpt ion

Infrastructure cost

Public space proximity

Water consumpt ion

Land consumpt ion

Job proximity

GHG emissions

Public t ransport proximity

SCENARIO

AM

MA

N

BA

UM

OD

ERA

TEV

ISIO

N

MasterPlan

Compactgrowth

The city expands according to historicalgrowth patterns. No efficiency measures orpolicy levers are considered.

Planned bus & BRT lines

16 MW solar farm

100% LED

Planned bus & BRT linesProposed BRT line in East Amman

16 MW solar farm

GBC in 14% of newdwellings

Two additional transfer stations

GBC in 90% of newdwellings

100% LED

Two additional transfer stations

Building Scenarios with Policy Levers

Population in Base Year (2015):3,423,389 inhabitants

Population in Horizon Year (2030):4,367,902 inhabitants

meetingsworkshops

Urban concerns

Policy leversSource: CAPSUS photo archive 2017.

10months

15

Urban Concerns and Policy Levers are Context-SpecificTechnical staff of the five municipalities, LTRC, DOS, DLS, MoMA and MoPIC were involved.

On-line visualization tool:

http://jordan.capitalsustenta

ble.com.mx

Capacity building,

knowledge transfer, and

ownership:

Part II Methodology and Engagement Process

▪ How to Conduct Urban Growth Scenarios Modelling?

▪ Model Specification, Data Collection and Cleaning, Indicator

Identification, and Scenario Development

▪ Client Engagement and Capacity Building

Urban concerns & potential solutions

Data gathering & indicators selection

APRIL MAY JUNE JULY AUG. SEPT. OCT. NOV. DEC.

Methodsdevelopment

Policy leversdefinition

Scenariodevelopment

Resultsdissemination

JAN.

Preliminary activities

Introduction &first contact

Meetings with stakeholders

Dissemination meetings

National Workshop

Follow up meetings

Meetings with decision makers

FEB.

Spatial DataAt the heart of the Urban Growth Scenario Modelling.

Geospatial data is used to:

1) Predict urban expansion

2) Model population distribution

3) Calculate indicators

Geodatabase in PostreSQL using pgAdmin and QGIS as interfaces

1) Urban Expansion ModelModelling land use change with machine learning algorithms.

Three machine learning algorithms were used to model land use changes:

• Random forests

• Extratrees

• Logistic regressions with regularization

They analyze land use changes in three moments in time (1990, 2000 and 2014) and

their explanatory variables to ”train” to predict possible changes in future land use.

Historic growth patterns

Urban footprint in 1990

Urban footprint 2000

Urban footprint 2014

Urban Expansion Model

Areas most likely to

become urban by 2030

Extratrees prediction for 2030

Urban Expansion Model

Data Source Temporality Format

Built-up Grid Global Human Settlements

Layer (GHSL) by EU

1990, 2000 and 2014 Raster data (250x250 m pixel

resolution)

Population Grid GHSL 1990, 2000 and 2015 Raster data (250x250 m pixel

resolution)

Digital Elevation

Model

U.S. Geological Survey’s Center

for Earther Resources

Observation and Science

(EROS)

1996 Raster data (1000x1000 m

pixel resolution)

GDP Distribution National Oceanic and

Atmospheric Administration

(NOAA)

1995, 2000 and 2013 Raster data (1000x1000 m

pixel resolution)

Highways Open Street Maps Starting from 2008 Lines geometry

Geolocations (airports, schools,

universities, worship

places, hospitals)

Open Street Maps Starting from 2008 Points geometry

Water Bodies Esri Data and Maps 2017 Polygons geometry

Explanatory Variables of the Urban Expansion ModelGlobal data sources with similar temporality to compare results among cities.

BEGINPop to allocate= projected populationIncrement between the base year and the horizon year

Vacant housingDistribute population

in existing vacant housing units

LEVERReduce vacant housing rate is

ON

LEVERSettlement of

new population

For each square:Population = Population +(I * max_hu-hu) * hu_size

For each square, if max_hu = NULL:Population = Population +(assumed_hu-hu) * hu_size

STEP 1Distribute population in polygon 1




STEP 5JobFootprintexpansion

STEP 1JobTransitFootprint base

STEP 2JobFootprint base

STEP 3TransitFootprint base

STEP 4Footprint base

STEP 6TransitFootprintexpansion

STEP 7Footprintexpansion

STEP 8Politicalboundary

END

AreaUpdate the urban footprint areaand calculate the infill area

Footprint = existing + new areas

Infill area = sum of area where population had a 2 fold increase

NO

EXIT

max_hu

I = 80%

hu

hu

assumed_hu


Footprintexpansion

2) Population Settlement Model

0- Population settles in expansion areas (BAU)

1- Prioritize served areas (Vision / Compact Growth)

2- Settlement in zoned expansion areas (Master Plan / Moderate)

3) Calculating indicators for JordanData collected from the municipalities was adapted and complemented to create indicators.

Main data collected:

• Population projections

• Population distribution

• Land use and permitted housing

• Public transport routes and stops

• Landmarks (amenities)

• Job densities

Data limitations:

• Disaggregation level

• Incomplete / partial data

• Incompatible geo-codes

• Different projections

Data Sources and Indicators

country_code city_code square_id longitude latitude pop_lever population area location pob15_64

52 55 3101300010061001 -89.522891 21.0791353 2 29 3.21 POINT(-89.522891055921.0791353056) 15

52 55 3101300010061002 -89.521324 21.0789368 2 89 3.25 POINT(-89.521323710121.078936754) 59

52 55 3101300010061003 -89.521506 21.0774544 2 72 3.1 POINT(-89.521505622521.0774543836) 49

52 55 3101300010061004 -89.523061 21.0776538 2 44 2.86 POINT(-89.52306084121.0776538179) 33

52 55 3101300010061005 -89.524656 21.0778438 2 46 2.75 POINT(-89.52465639321.0778437987) 39

52 55 3101300010061006 -89.52361 21.0826073 2 10 3.53 POINT(-89.523609747221.0826073492) 10

52 55 3101300010061007 -89.521155 21.0803561 2 61 3.58 POINT(-89.521155381821.0803560717) 39

52 55 3101300010061008 -89.519573 21.0801917 2 61 3.88 POINT(-89.519573293721.0801917252) 39

52 55 3101300010061009 -89.519755 21.0787294 2 97 3.57 POINT(-89.519754575621.0787293529) 71

52 55 3101300010061010 -89.519938 21.0772663 2 99 3.36 POINT(-89.519938235221.0772662844) 70

52 55 3101300010061011 -89.520289 21.0749864 2 13 3.08 POINT(-89.520288539421.0749863962) 10

52 55 3101300010061012 -89.521688 21.0759851 2 121 2.78 POINT(-89.521688374321.0759851279) 88

52 55 3101300010061013 -89.523239 21.0761782 2 124 2.64 POINT(-89.523238657821.0761781914) 92

27

Population that lives 700 meters or less awayfrom a school

Total population

28

Coverage area

School proximity indicator

Public space proximity indicator for Russeifa

GHG emissions indicatorCalculation method

Infrastructure costs indicatorCalculation method

Thank you!

For more information:

Bank:

● Yuan Xiao [email protected]

● Ellen Hamilton [email protected]

CAPSUS:

● Carmen Valdez [email protected]

mailto:[email protected]



a discussion session co-sponserd by affordable …...mafraq 100,071 127,681 1% total: 56.5% urban...

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