resilience.io Technical Briefing for UB City – Meeting – Rembrandt Koppelaar - 11th June 2015

Resilience.io Platform
Technical Brief on Model
Architecture & Decision Support
Ulaanbaatar - Mongolia
11 June 2015
Rembrandt Koppelaar – Senior Researcher
Institute for Integrated Economic Research (IIER)

Resilience.io
Technical development team
IIER team:
• Hannes Kunz, May Sule, Alfeo Ceresa, Zoltan Kis,
Nikul Pandya, Eleanor Watkins, Stavros Pyrotis
Imperial College team:
• James Keirstead, Nilay Shah, Koen van Dam,
Charalampos Triantafyllidis
Foundations in SynCity and SmartCity models developed at
Imperial College London*
Keirstead, J., Shah, N., Fisk, D., 2013. Urban Energy Systems: an integrated
approach. Routledge

Overview
• Simulation and optimization modelling
• Resilience.io model components
• Building a UB local model
• Decision insight use

Agent-based modelling (simulation)
Structure example of algorithm:
At time t = t + h {
for (A in agentSet) {
for (x,y,z in conditionSet) {
If condition X,Y,Z….
updateState (Aa to Ab)
}
}
}
Approach:
To model behaviour of an ‘object’ in a
flexible way using language decision logic
based algorithms
Use in resilience.io:
• Activities of people
• Transportation
• Well-being indicators
• Factory operation
• Educational decisions
Software use:
• Java coding
• Repast symphony libraries
• http://repast.sourceforge.net/
• BSD License

Agent Decision Socio-Economics
People
Institutions
(Regulatory, Planning,
Soft Policies, Culture)
Government
Decisions
Markets
Outcomes
(Production, Investment, Activities,
Well-being as happiness and health, etc.)
Demographics
Firmographics
Companies
Households
Labour
Supply &
Demand
Supply &
Demand
Shape
Shape
Shape
Make
Make
Make
Influence
Influence
External
World
Regulate
Supply & Demand

Agent factory
Population
Data
General
Rules
Synthetic population with n agents
Generates
Agent ID: xxxxx1
Individual
rules
Individual
data
System level behaviour
(emergence)
Generates
Validation
Scenario analysis
(transport, energy, etc.)
Generates
Spatial
Socio-
demographic
Technical
parameters
External
data
Scenario definition
(van Dam and Bustos-Turu, in print)

Optimization modelling
Approach:
To calculate an outcome by finding the
minimal or maximal value of particular
mathematical functions, including a set of
constraints.
Use in resilience.io:
• Technology/Process
operation
• Service network flows
• Market equilibrium
Software use:
• Java coding
• GLPK solver
• http://www.gnu.org/software/glpk/
• GNU GPL License

Хувьцаа
- Эх үүсвэр
- Материал
- Бараа
Яаж – компьютерын хялбаршуулсан ертөнч
"Хүний экосистем" орон зайн загвар’
Нарны аймгийн хөдөлгөөн, “Байгаль”
- -
Цаг хугацаа, орон зай
Урт хугацааны хүчин зүйл
- Хүн ам зүй
- Хөдөлмөр эрхлэлт
- Боловсрол
Дэд бүтэц
- Барилга
- Үйлчилгээний сүлжээ
- Аж үйлдвэр
Хэрэглэгчийн оролцоо
Технологийн хөрөнгө
оруулалт
Зах зээлийн бодлого Төлөвлөлтийн журам
Гадаадын
- импортын
- экспортын
- шилжих
хөдөлгөөн
- хөрөнгө оруулалт
Оролцогч
- Хэрэглэгчид
- Ажилчид
- Эзэмшигчид
- Худалдаачид
Явцын урсгал
- Технологийн мэдээлэл
- Эрчим хүч, олон нийтийн тэнцэл
- Хөдөлмөрийн хувь нэмэр

Foundation components of model

Activity simulation of the population
Activities examples
Leisure, work, food, travel,
personal care, home care, religious
practice, sleep
Aim: Simulate activities by socio-
economic groups of people in time
and space
Activity transition rulesets:
APi= {(ACTj, MDTj, SDj, PDj)}
ACTj : Activity j
MDTj : Mean departure time
SDj : Standard deviation
PDj : Probability of departure
* Keirstead J, Sivakumar A, 2012, Using Activity-
Based Modeling to Simulate Urban Resource
Demands at High Spatial and Temporal
Resolutions, Journal of Industrial Ecology, Vol:16,
ISSN:1088-1980, Pages:889-900

1) Population/household group
characteristics per spatial area
- Population, household numbers
- household types
- Population gender, age distribution
- Employment,
- Educational enrollment
2) Activity time data from surveys to
establish and validate activity transition
rulesets
Activity data needs:
Population characteristics and activity dataset

Service consumption from activities
Model output examples
• Spatial maps of use
• Changes in space over time as a
video (sequence of maps)
• Electricity use profiles
Aim: Simulate consumption of
services caused by population
activities
Calculation example:
• Calculate the total population in
each area based on density
• Calculate occupancy % in
buildings for each area per period
(based on activities profile)
• Calculate electricity demand from
occupancy based on use rates,
building size, base electricity use,
peak electricity
* Keirstead J, Sivakumar A, 2012, Using Activity-Based Modeling to Simulate Urban Resource Demands at High
Spatial and Temporal Resolutions, Journal of Industrial Ecology, Vol:16, ISSN:1088-1980, Pages:889-900

Processes as the means for resource
accounting of the economy

Operation of Process/Technology Networks
Underlying functions
• Simple input-output factors
• Linear equations
• Dynamic models
C
CHP
HDH HX
WH
CO2
E

By identification of Infrastructure
• Site type: commercial, industrial,
agricultural, residential etc.
• Spatial location
• Outputs produced
• Infrastructure/technology type
Add ‘process blocks’ from the IIER
process database
• Mass inputs and outputs
• Energy inputs and outputs
• Labour inputs
• Input to output relationships
Process data needs:
Spatial identification of sites/outputs
Distribution centre
Meat process factory
Football stadium
Hospital
Residences

Market exchange for goods and services

Demand and supply in other markets/services
Aim:
Simulate influence of long term 5+
year changes in societies on
outcomes
Markets/services to include:
• Change in occupations and jobs
from labour markets.
• Change in physical capital from
investment decisions
• Change in Human Capital from
Education and Labour as well as
Health Markets.
Transactions
of Goods &
Services
Markets
Investment &
Property
Markets
Agents as
1) Consumers
2) Processors
3) Owners
4) Traders
Health
services
Labour
Markets
Educational
services

Population demographics development
Data input:
Birthrates, death rates, fertility
rates, migration rates, migratory
events, household types,
relationships change
Aim: Create scenarios for change in
population numbers and households
Example of calculation:
• Population births and deaths
based on a rate per household
type (births) and age (deaths)
• Households can transition
between types (sole-person, one-
parent, couples, couplies with
kids, students, etc.)
• Household transitions dependent
on relationship change,
employment, births, deaths,
education

Creating linkages between the Economy,
Ecosystems functioning, and Human well-being

Human and ecotoxicity impact assessments
Aim: To incorporate indicators for
assessment of implications of
environmental flow outputs
Calculations:
The model generates flow data of
solids, liquids, and gasses into the
atmosphere, surface, soils.
These can used to assess effects
using toxicity indicators (as per LCA)
and dose-response functions from
toxicological research*
*Ritz, C. 2010. Towards a Unified
Approach to Dose-Response Models in
Ecotoxicology. 29(1). pp. 220-229
Concentration
Emission
Dose
Probability of effect
Severity of effect

Human and ecotoxicity impact assessments
EPA Eco-Health Relationship browser
http://enviroatlas.epa.gov/enviroatlas/tools/Eco
Health_RelationshipBrowser/index.html
Aim: Simulate changes in human health
and subsequent linkages on society,
service needs, employment, quality of life
Example for human health:
HSj,t+1= {(HSj,t, MDj,t, SDj.t, PEj)}
HSj : Health status j
MDj,t : Mean dose over time
SDj,t : Standard deviation
PEj : Probability of effect
SIj,t= {(HSj,t, SDj,t, PSj,t)}
Sij,t : Sickness from work status j
SDj,t : Standard deviation
PSj : Probability of sickness

Ecological model linkages
Examples of data linkages:
• Spatial dispersion of pollution
in the air
• Scenarios for flow rates in Tuul
river in coming decades
Aim: To create links to ecological
models to better understand
ecosystem impacts
Model examples:
• Hydrological models of Tuul River
Basin and underground aquifers
• Wind dispersion models of
pollution entering the air
• Ecosystem / species models of
Bogd Khan mountain
* Tuul River flow model in Altansukh, O., 2008. Water
quality Assessment and Modelling Study in the Tuul
River, Ulaanbaatar city, Mongolia. ITC
Source of figure: Emerton, L., N. Erdenesaikhan, B. De Veen, D. Tsogoo, L. Janchivdorj, P. Suvd, B. Enkhtsetseg, G.
Gandolgor, Ch. Dorisuren, D. Sainbayar, and A. Enkhbaatar. 2009. The Economic Value of the Upper Tuul Ecosystem.
Mongolia Discussion Papers, East Asia and Pacific Sustainable Development Department. Washington, D.C.: World Bank.

Simulation of human well-being indicators
Existing and emerging metrics:
• Gallup world poll’s well-being
index
• OECD “Better Life Index”
• ISO31720 indicators for city
services and quality of life
• EU/Eurostat “quality of life
indicators” under development
• WHO framework under
development
Aim: Simulate well-being of the
population based on modelled
relationships and outcomes
Indicator examples:
• Health status of agents / health
service access
• Services standard of living indicators
• Employment and educational
development
• Quality of the city environment
• Recreational possibilities
• Fire and emergency infrastructure

Building an integrated data map of UB
People and household data
•Numbers per khoroolol
•Demographics changes
•Workforce, employment, education records
•Time spent on activities per day
•Health records and happiness surveys
•Transportation records
•….
Physical Infrastructure
•Buildings and roads
•Electricity, heat, water, service networks
•Forests, farms, parks, grasslands
•Site records: factories, warehouses, processing plants,
recreational sites, schools

Building an integrated data map of UB
Regulations and market data
• Land use planning data
• Market tariffs / fees / prices for services
• Building regulations
• Property investment data
Ecological data
• Soil, air, and water quality
• Biomass / Ecosystem productivity
• Climate records
• Factories, warehouses, recreational sites
Resource flow data
• Consumption of water, goods, energy, food
• Production of minerals, materials, goods, wastes
• Imports and exports of materials and goods
• Estimated losses in networks
• ….

Data development influenced by priority areas
Ecosystems (Terrestrial, Aquatic)
Construction
Energy Generation
Transportation
Human and
animal Services
Mineral
Extraction
Physical
manufacturing
Chemical
manufacturing
Recycling, disposal,
remanufacturing
Water Supply and
Sanitation
Agriculture &
Seafood
2016 2017 2018
Forestry
Agri-Food
processing
Biological
processing
Human
consumption

Building and adjusting rulesets so that they work for the
local context
• Influence of extreme temperature
changes on behaviour/technologies.
• Culture and planning of activities.
• Behaviour to market prices and
people’s investments
• Responses to policy changes in
adjusting behaviour

Testing and validation using local data
• Uncertainty analysis – feed in
range for parameters of agent
properties and decisions, and
assess whether outcomes
change.
• Plausibility of results analysis –
do the results make sense based
on historic and current data +
fundamental knowledge.
• Accounting assessment – test if
physical input to output values
match up over space and time.
Run model
Improve
rulesets
Simulation
Results
Inputs
Calculations
Outputs
Comparison
Historic data
Result range
Logic

Overview
• Simulation and optimization modelling
• Resilience.io model components
• Creating a UB local model
• Decision insight use

• Resilience.io is not a predictive
modelling platform which seeks to
describe the future.
• Resilience.io is normative as the
aim is to create insights in how to
shape the future.
• Its value is the ability to simulate
investment, planning, and policy
decisions.
• And giving users visibility on
decision impact in economic, social,
and environmental dimensions.
Decision Support for Regional Design
Model
Regional
Design
Simulation
Results
Investment
Planning
Policies
Visibility
Resilience
Performance
State of society

Outcome as a trajectory of key performance
indicators
Each scenario simulation provides an outcome range of
indicators (via numerous model runs, as opposed to a
“predictive” optimal outcome)
--------------------------> Time

Using the model as a ‘test-bed’ to add
evidence and ideas to planning for decisions

Investment, Policy, Planning,
Impacts visible at multiple levels
Level 3 :
Underlying Indicator
system relation details
Level 4:
Quantitative & Qualitative
Variable and Parameter values
Level 1:
Sector Key Performance
Indicators & Spatial
variation
Level 2:
City wide information
(long-term trends)

Meta overview of scoping of indicators for
complete model
Indicator Category Description
Economic Development
The sum of resource flows related to an economy (or sector) in material input/outputs, energy input/outputs, and the total of
resulting goods and/or services produced. Values are expressed in physical quantities, quality adjusted labour hour currency
(QLH), including the reproduction of a QLH based GDP figure from quantity and market transactions.
Employment The simulated number of people in the workforce and in employment (for inclusion in phase 1a at sector level).
Environmental quality and services
A set of indicators related to biomass productivity, air quality from gaseous emissions, and water quality of local water bodies
and flows
Human health The access to health services of the population and their life expectancy and the impact of health on productivity.
Income inequality The distribution of household income in QLH following from employment.
Quality of the living environment A set of indicators related to the amount of greenspace, recreational area, and access to luxury services.
Production efficiency
The conversion efficiency of materials and energy to produce goods and services and consume them in the city-region based
on losses of materials and heat for different types of work.
Resource access
The availability of basic services related to population livelihood including access to water, energy, and transportation
services.
Stability of resource availability
Indicators which relate to the overall physical availability of resources as extracted in the supply hinterland such as from a
mine-site or a forest, and through imports from the outside world.
Waste and pollution flows
The generation of solid, liquid, and gaseous wastes through production and consumption across the spatial landscape.
Inclusive of information on the final end-point as a non-harmful waste, as a pollutant such as GHG emissions, or as being
reintroduced into production by recycling, re-manufacturing, or re-use.
Well-being and happiness
An index indicator initially to be based on a weighting of variables such as simulated household income, employment status,
productivity ratio of work to leisure time, human health, access to utility services, and proximity to pollution. The index can
be adjusted over time as the comprehensiveness of the model develops.

Economic Instruments
Legislative & Public Instruments
Taxes and tax
concessions
Purchasing
Tradable
Permits
Educational
programmes
Standards and
Penalties
Covenants
Accreditation
systems
Licensing
Subsidies and
grants
Public service
provision
Simulating Policy Decisions
• The model is being built with a library of policy options (put policies into effect
and vary their degree).
• Policy effects are simulated based on changes in market operation and decisions
of the population and company agents.
• Impacts become visible through changes in outcomes (production, consumption,
activities) and indicators (social, economic, environmental) in space and time.

• Users can as “central planner” choose their own investment
ideas (e.g. new drainage infrastructure, water treatment
plant).
• Investments options are then simulated are based on a three-
step procedure, first: technology choice, second: selection of
plausible efficient spatial options, third: cost-benefit type
analyses.
• Analyse investment condition impacts by adjusting
parameters requirements (NPV, ROI, Time Horizon), value
inclusion (Economic, Social, Environmental).
• Aim for long term model expansion is for investment
decisions to also be taken within internal model logic (by
simulated companies/government)
Simulating Technology Investment Decisions

Simulating Planning Decisions
• At baseline for a region the local
spatial planning map is
reconstructed in the model.
• The platform users can adjust
planning rules as a “planning
permission authority” about land
use, construction, building
standards, demolition etc.
• Any investment or policy decision
generated in the simulation will
then be evaluated and accepted,
adjusted, or rejected based on user
set planning rules.
Built
environment
change
Planning
Investment
Planning consideration
Simulated Planning
Application
Acceptance/Rejection
based on user rules

Koppelaar@iier.ch
Resilience.io
Technical Brief on Model Architecture
& Decision Support

resilience.io Technical Briefing for UB City – Meeting – Rembrandt Koppelaar - 11th June 2015

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