Coupling Distinct Models to Inform Integrated Urban Development

Policy Decisions: International Applications

Harutyun Shahumyan1, Brendan Williams1, Gerrit Knaap2, Eda Ustaoglu3, Rolf Moeckel4

1 School of Architecture, Planning & Environmental Policy, University College Dublin, Ireland

harutyun.shahumyan@ucd.ie, brendan.williams@ucd.ie

2 National Center for Smart Growth, University of Maryland, College Park, MD, USA

gknaap@umd.edu

3 Independent researcher, completed PhD at UCD

edaustaoglu@gmail.com

4 Department of Civil, Geo & Environmental Engineering Technical University Munich, Germany

rolf.moeckel@tum.de

Abstract. Confronted with challenges of managing complex urban and environmental systems in an

uncertain environment, decision makers need adequate tools to better understand and evaluate the

effects of policy interventions in urban regions. Such pressure led to the development of numerous geo-

spatial models covering different discipline-specific areas. The interconnected character of human and

natural systems requires an integrated approach in both decision making and modeling. This paper

provides examples of recent researches on integrated geo-spatial modelling informing regional and

urban development policy decisions and applications of such models to priority policy areas. The

suggested model coupling approach is especially efficient when the individual models are developed in

different programming languages, their source codes are not available, or the licensing restrictions make

other coupling approaches impractical. The case studies of the Baltimore-Washington Region in US

and the Greater Dublin Region in Ireland are discussed.

Keywords: model coupling, integrated modelling, scenario analysis, spatial decision support system.

1. Introduction

In many jurisdictions policy makers are facing severe challenges of managing complex urban and

environmental systems in an uncertain economic and financial environment influenced by

changing global factors. These factors include population growth, migration, economic recessions

and recoveries, climate change along with actions by local actors such as parties or companies

who direct the development according to their own vested interest. One of the key challenges for

a sustainable future is to manage those factors and resulting development trends with minimal

adverse impacts on both the environment and society.

Regional and urban development policy systems have evolved as development led in many

jurisdictions internationally (Oxley 2009). A development led planning system can evolve in

practice towards a legal adversarial system with competing local groups, whose sectoral and

vested interests dominate decision making. This localised emphasis can be at the expense of the

wider public interest and longer term planning objectives. Competencies to evaluate alternative

policy options are limited and recent attempts to develop comprehensive or integrated planning

models have been met with only limited success (Borning et al. 2008). This occurs as the more

integrated approaches using system models require extensive and effective cooperation, decision

making and data sharing across layers of government and public and private sectors including the

research community.

The resulting rapid and often dispersed urbanization and accompanying land use transitions can

substantially impact on all aspects of the environment including water and air quality in a region.

Agricultural activities may degrade water quality through excessive soil loss or export of

fertilizers and pesticides (Carpenter 2008). Urbanization can increase the impervious area

resulting in higher flows and more contaminants that have a negative effect on stream water

quality (Kennen et al. 2010). It also increases the number of cars and traffic flow resulting in more

emissions and higher air pollution.

Confronted with such complexity and often under critical time pressure, decision makers need

adequate tools to better understand and evaluate the effects of policy interventions in urban

regions. Research has led to the development of numerous models covering different discipline-

specific areas such as transport demand and air quality. The increasingly interconnected character

of human and natural systems requires integrated approaches in both decision making and

modelling, often within tight budget and time constraints. This paper presents case studies

illustrating recent research on Integrated Geo-Spatial Modelling Informing Regional and Urban

Development Policy Decisions and applications of such models to priority policy areas.

The examination of individual case studies or urban models at a single local level can provide

evidence on challenges of coupling specific models or on factors such as the key local drivers of

land use change. Each case can however be highly specific in policy contexts, processes, and

scale. By including the experience of two case studies this research allows for the development of

conclusions and insights which potentially can be generalised and transferred to other locations.

2. Model Integration

Developing and validating a new comprehensive model integrating various disciplines would be

a costly and time-consuming process. Instead, the model coupling approach discussed below

allows the effective linking of existing discipline-specific tools and independent models to devise

a quick and effective solution to generate integrated models for different urban regions. This

approach aims to make integration of existing models easier, overcoming challenges such as

differences in programming languages, unavailability of the source codes or licensing restrictions.

The primary goal of model coupling is making originally independent modelling processes

interact to produce scientifically valid results. For environmental models, coupling hierarchy

defined by Brandmeyer and Karimi (2000) includes manual data transfer, loose coupling, shared

coupling, joined coupling and tool coupling. Each of these approaches has its benefits and

limitations; and the selection of the most relevant one depends on many factors including the

requirements of the existing models, data flow directions and frequencies, research goals,

resources (Shahumyan and Moeckel 2016). Here, we focus on the type of models which are

developed independently without any built-in mechanism for integration with other models. Such

models can be developed in different programming languages, can use various data formats and

licenses. The main requirement is that the output data of one model can be used as an input data

for another. However, even if the models are comparable in terms of data being exchanged, it is

rare that one model output identically matches the input format of the other model (Droppo et al.

2010). In such cases, specific data processing tools can be developed to organise necessary data

transformations before passing an output from one model to the next.

Taking into account the characteristics of the involved models for the case study regions the loose

model coupling approach was adapted. In case of loose coupling, the data exchange between

models is automated, but the models still work independently and the user can interact with each

model separately (Brandmeyer and Karimi 2000). Loose coupling has low initial cost, requires

minimal changes to existing codes, and the models can be developed independently, which fits

the requirements of this research. To implement this for a set of models, a specially structured

Python script was developed to wrap existing independent models and provide a user-friendly

interface through ArcGIS Model Builder for data exchange with other tools or models. As each

model has specific run parameters, input and output files, the script is fine-tuned for each model

explicitly (Shahumyan and Moeckel 2016). The suggested approach is especially efficient when

the models are developed in different programming languages, their source codes are not

available, or the licensing restrictions make other coupling approaches impractical.

The approach was successfully tested and applied at two distinct case study regions:

1. Baltimore-Washington Region, USA - coupling five independently developed models

including land use, transportation, building and mobile emissions and land cover;

2. Greater Dublin Region, Ireland - coupling independently developed land use and water

quality models.

3. Applications

3.1 Baltimore-Washington Region

The loose coupling approach was applied for the Baltimore-Washington Region (Fig. 1) coupling

five independently developed models described below (Shahumyan and Moeckel 2016).

The Maryland Statewide Transportation Model (MSTM) is an advanced trip-based model

designed to estimate the impacts of transportation investments, changes to land use development,

and impacts from factors beyond state boundaries, particularly freight (FHWA 2014). The model

input data include population and employment by model zones, highway and public transit

networks, and data on travel behaviour. The model outputs report traffic impacts on the overall

system, corridors or individual links.

Simple Integrated Land Use Orchestrator (SILO) is a microscopic discrete choice land use

model which micro-simulates household relocation, demographic changes and developers who

add, upgrade or demolish dwellings (Moeckel 2011). Thus, every household, person and dwelling

is treated as an individual object. Spatial decisions, such as relocation, development of new

dwellings, etc., are modelled with Logit models. Other decisions, such as getting married, giving

birth to a child, etc., are modelled by Markov models that apply transition probabilities. SILO

uses the Public Use Microdata Sample to create individual households and their dwellings. The

MSTM provides the zone-to-zone travel time by auto and public transit. SILO generates a

synthetic population with households, persons, dwellings and jobs for the base year 2000 and

incrementally updates these dataset in one-year increments through 2040. Every year the MSTM

runs, SILO provides updated socio-demographic data.

Fig. 1 Baltimore - Washington Region

The Mobile Emissions Model (MEM) estimates transportation emissions by applying emission

of the MOVES 2010 EPA model to MSTM-generated traffic flows (Welch 2013). The MEM

input data includes road network, vehicle trips, temperatures by month and hour for each county

in the study area, humidity, average speed distribution, the vehicle miles of travel (VMT) on

varied road types, fuel formulation and supply. MEM runs every time the MSTM has run.

The Building Energy Consumption & Emissions Model (BEM) estimates CO2 emissions and

energy consumption from the built environment within Maryland (Welch 2013). It uses the

building, location and climate variables of each property to determine whether the structure is

likely to combust fossil fuels on site. If the probability is greater than 50%, then the model

calculates CO2 emissions from local combustion based on a set of related multipliers derived from

a regression of the data from US Energy Information Administration’s residential (RECS) and

commercial building (CBECS) energy consumption surveys. SILO provides the building stock

for BEM. CO2 emissions and energy consumption are the primary outputs of the model.

Chesapeake Bay Land Change Model (CBLCM) was developed by the USGS within the

Chesapeake Bay Program. It uses a stochastic methodology to emulate residential urban land use

development in Maryland over a series of predefined time segments. It is as an independent

cellular automata model that translates exogenous county-level projections of population and

employment to estimates of urban land demand and then spatially allocates that onto 30m-

resolution raster cells. The locations of future growth are informed by data on protected lands,

zoning, slopes, land cover, proximity to urban centres, and proximity to locations of recent job

and housing growth. The model calculates a probability surface for growth locations, and allocates

households and dwellings provided by SILO (Shahumyan et al. 2016). CBLCM generates fine-

grained patterns of residential urban growth across the study area.

Fig. 2 presents the data flow between the models integrated for the Baltimore-Washington Region.

The figure also demonstrates the diversity of the programming languages of the source codes of

those models. With the use of Python wrappers, the implementation of the coupler is separated

from the models’ source codes and links them under a single user interface within ArcGIS Model

Builder without changing their original source codes.

The outputs from this integrated modelling suite include several useful socio-economic indicators,

covering: population and employment, transport flow, land use, building and mobile emissions,

and other factors.

Fig. 2. Links and data flow in the Baltimore-Washington Region integrated modelling suite.

Four exploratory development scenarios of 2040 were developed for the Baltimore-Washington

Region and simulated with this integrated modelling suite. The scenarios were based on different

assumptions regarding economic growth, the uptake of transportation technologies, energy prices,

and more. The initial results suggest that differences in critical driving forces could have important

implications for the sustainability of the region. Rapid growth, autonomous cars and low fuel

prices, for example, could escalate urban sprawl, vehicle miles travelled, greenhouse gas

emissions and water quality degradation. The rapid adoption of electric vehicles, however, in a

high growth and fuel price environment, could encourage more compact growth, transit ridership,

reductions in GHGs and improved water quality. Alternative assumptions, reflected in the other

two scenarios yield alternative results.

3.2 Greater Dublin Region

For the Greater Dublin Region (Fig. 3), the approach was applied to couple land use change model

MOLAND with the Source Load Apportionment Model SLAM to estimate annual nutrient

losses in case of different regional development scenarios.

Fig. 3. Greater Dublin Region.

The MOLAND land use model generates alternative future scenarios informing urban planners

and policy makers on the possible implications of their decisions in terms of land use change

(Engelen et al. 2007). It comprises two dynamic sub-models with a common temporal increment

of one year but working at different scales. At the macro scale, the model allocates regional

population and jobs among the sub-regions. At micro scale, the provision for population and jobs

is translated into demand for various land types using constrained cellular automaton (CA), which

for the GDR consists of (i) a land use raster grid with 200m cell size and 23 classes, (ii) a set of

factors influencing the direction of land use change such as suitability, zoning, accessibility, and

neighbouring land uses, and (iii) transition rules determining the attraction and repulsion between

land uses. The model also includes a stochastic parameter which insures the generation of realistic

land use patterns.

The Source Load Apportionment Model (SLAM) is a source-orientated load apportionment

model which estimates of the relative contribution of sources of nitrogen (N) and phosphorus (P)

to surface waters in catchments without in-stream monitoring data (Mockler 2016). It incorporates

multiple national spatial datasets relating to nutrient emissions to surface water, including land

use and physical characteristics of the sub-catchments. The agriculture (pasture & arable) and

septic tank systems modules use spatial outputs from the Catchment Characterisation Tool (CCT)

and SANICOSE models, respectively. The diffuse nutrient emissions from forestry, peatlands and

urban areas were modelled based on export coefficients from the land cover classes.

Fig. 4. The links and data flow within the Greater Dublin Region integrated modelling suite.

The MOLAND model was used extensively by the UCD research group in a series of projects for

the Dublin Regional Authority and Local Authorities. These uses included the preparation of

scenarios for the review of Dublin Regional Plans and in projects analysing present and future

urban economic trends for the region. The MOLAND model was specifically used to simulate

four alternative scenarios illustrating the effects of future policy directions on the GDR up to 2026

which formed an essential element of the Review of the Regional Planning Guidelines (D&MERA

2010). The scenarios were developed using five driving forces, namely: population, economic

trends, urbanization, transport and overall trends (Brennan et al. 2009, Petrov et al. 2011). The

base year of the MOLAND simulation was 2006, and 2026 was set as the final year, facilitating

the use of and aligning with official population projections (CSO 2008). The MOLAND outputs

for those scenarios were used to feed the SLAM model and to explore likely nutrient losses and

relevant effect on the water quality in the region by 2026 (Fig. 4).

The land use patterns from those scenarios vary significantly (Brennan et al. 2009, Brennan et al.

2010). In particular, the dispersed settlement pattern and merger of formerly separate urban areas

are observed in the Business as Usual scenario. While, in the case of the Compact Development

scenario, the urban development is focused into the Dublin metropolitan footprint and a few

growth centres along major transport routes. In the case of the Managed Dispersed scenario, the

urban development is consolidated into several growth centres. Finally, the Recession scenario

shows dispersed development similar to the Business as Usual scenario with essentially less

overall urban growth. The average net impact of land cover change as represented by these four

scenarios results in a net reduction in nutrient emissions. This is due to the replacement of

agricultural land cover with its relatively high nutrient emissions with urban development

(Shahumyan et al. forthcoming).

The MOLAND scenario outputs were also used for the environmental sustainability analysis of

the impacts of planned rail infrastructure investments on the urban form and development in the

Greater Dublin Region. Dublin’s Metro North project1 was assessed through application of a cost-

benefit analysis (CBA) approach that is based on a straight net present value calculation. For the

scenario-based evaluation of the transport-land use impacts of the Metro North investment, the

Business as Usual scenario incorporating a continuation dispersed pattern of urban development

with limited transport infrastructure investment is compared to the Compact Development

scenario where high densities and polycentric urban agglomerations are associated with the

provision of Metro North and other transport investments.

The CBA findings from this study have confirmed that the expected benefits from an integration

of public transportation and multi-centred development in the Greater Dublin Region could

probably be achieved in the longer-term implying a time over 40 years or more. The high

infrastructure costs of the Metro North investment were identified as the main factor extending

the time for receiving benefits from this process. The existing urban structure and incomplete

urban transformation processes in the Greater Dublin Region (Murphy 2004) from a traditional

compact city into a polycentric urban structure is also significant. This transformation implies a

more complicated trip pattern with an increase in the number of trips, trip distances and travel

time between a number of sub-centres and a strong central business district. Followed by the CBA

results, a combined sensitivity testing was also carried out by setting some specific parameters

which simultaneously influence the appraisal outcomes.

As a priority, two different economic conditions were represented through the parameters

including economic growth (as in the cases of business as usual and compact development

scenarios) and recessionary development. From 2007 to 2013, following the property market

collapse in Ireland, governments and policy processes deferred major infrastructure investment

spending while addressing the economic, banking and financial collapses which resulted from the

property crash (Williams and Nedovic-Budic 2016). By 2017 economic recovery was underway

and major infrastructure and transport investments were once again under consideration.

From sensitivity analysis, the probable outcomes in urban development shown using the Compact

Development scenario and the Recession scenario have indicated differences in appraisal

outcomes from the CBA model. Unexpected fluctuations in external factors influence urban

structure and development and affect the benefits and costs received by the society.

The identification and prioritisation of these different land development scenarios can allow the

CBA process be used as a policy support tool in discussions of alternative development and

investment decisions such as compact and dispersed developments in the Dublin Region. The

research underlines the importance of flexible planning tools and policies as well as co-ordination

and integration in infrastructure, economic development and land use planning activities to

coincide with uncertainties in future developments.

4. Conclusion

The main goal of model coupling is making originally independent models interact to produce

scientifically valid results. In this research, an approach of linking such models to exchange data

in a single modelling platform is analysed. The benefits of the presented coupling approach

include of being open source and easy to implement, allowing to run models developed in different

1 Metro North is a proposed metro system to run from Dublin city centre to Dublin Airport and onwards to Swords.

environments, visualisation and mapping capabilities through integration with ArcGIS and most

importantly that it does not require the models’ source codes modification. The approach has also

limitations such as running model processes independently from one another, not supporting

parallel model runs and dynamic data exchange. These limitations are mainly caused by the

constraints of licensing and bounds of changing the source codes of the models.

The initial outcomes of these two case studies and their comparisons are promising. The

independently developed models smoothly exchange data in a single modelling platform,

allowing non-technical users to focus on analysis and results rather than the technical processes

involved allowing decision makers to be involved in the modelling process directly. Moreover,

this approach allows adding new models relatively easily; and the works are currently under

progress to enhance the Baltimore-Washington Region modelling suite with water quality and

habitat models.

This research has resulted in interesting potential future development scenarios being investigated

which otherwise would be debated in an evidence free manner. For example, in Baltimore the

rapid adoption of electric vehicles, in a high growth and fuel price environment was shown could

encourage more compact growth, transit ridership, reductions in GHGs and improved air quality.

In the Dublin region, an important and often neglected issue of estimating annual nutrient losses

in case of different regional development scenarios was analysed. This research clearly

established estimates of annual nutrient losses in case of different regional development scenarios

and their impact on water quality throughout the region was carried out based on the four scenarios

included in the official regional planning guidelines.

Public policy making and Planning both in Ireland and internationally has to develop a strategic

approach to managing and planning the state’s economic development and environmental

management in a period of major transition. This research supports strategic research, policy

support and education in this important policy area by building on and linking existing urban and

environmental models to create new enhanced platforms for policy decisions and analysis. Such

analysis contributes to a successful managed planning and development process which is essential

for ongoing sustainable development. From a scientific perspective, the work is set to significantly

contribute to the understanding of human activity and environmental linkages in urban areas,

enabling improved policy development and decision-making focused on ensuring urban

sustainability. The adaptation of the integrated models to specific policy priorities in each of the

study areas provides an opportunity to test the models using real data and urban policy problems.

Acknowledgements

This research was supported by a Marie Curie International Outgoing Fellowship (GeoSInPo)

within the 7th European Community Framework Program, the Urban Environment Project

sponsored by the Irish Environmental Protection Agency as part of the ERTDI programme, and

by the National Socio-Environmental Synthesis Center (SESYNC) under funding received from

the US National Science Foundation DBI-1052875. The work presented in this paper benefited

from many discussions with Uri Avin, Peter Claggett, Frederick Ducca, Daniel Engelberg, Sevgi

Erdogan, Eva Mockler, Timothy Welch and Di Yang.

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